Historical Review of Learning Strategies Research: Strategies for the Whole Learner—A Tribute to Claire Ellen Weinstein and Early Researchers of This Topic

Abstract

This selective historical review summarizes research on learning strategies conducted in the past 50 years and summarizes how the field has evolved. Two goals guide the review: (1) update the literature on the origins of the learning strategies research “movement” and (2) highlight in the supplement the work of one of the early contributors, Claire Ellen Weinstein, whose pioneering work endures up to now. This review fills the gap of other recent reviews by including research on learning strategies began in the 1960s and 1970s that received significant funding from military sources but remained largely hidden in technical reports and hard to find academic documents. The outcomes of this review reveal that the field is thriving, with two major unifying themes. First, there is a focus on metacognition and second, there is a focus on the whole learner and interventions that address cognitive, metacognitive, affective, physical, cultural, and social needs. The research of Dr. Weinstein (who passed June 23, 2016) has framed past and current learning strategies research agendas and includes her development, validation, and implementation of the Learning and Study Strategies Inventory in traditional and online learning contexts. This review and Supplementary Material section’s personal stories include many of the early learning strategies research findings, definitions, and interventions that remain in use across the nation and world today. Future research issues and areas needing more focused attention in the years ahead given our increasing complex, digital, and diverse world are summarized in final sections of this review.

Historical Background of Learning Strategies as a Research Area

It is nearly impossible to be right in tracing where the term “learning strategies” actually originated given the flurry of studies in the last century focused on moving from behaviorism to cognitive theories of learning (cf. Piaget, 1926; Ryle, 1949; Cronbach, 1951, 1957, 1975; Cronbach and Meehl, 1955; Bloom, 1956; Inhelder and Piaget, 1958; Rogers, 1961; Flavell, 1963, 1971, 1976; McLuhan, 1967; Newell and Simon, 1972). Some researchers (e.g., Vygotsky, 1962, 1978; Zimmerman and Schunk, 2001, 2003) trace the origins to Dewey (1910) or Thorndike (1912) but what I know from my own experience in the late 1960s is that the term “learning strategies” was derived from research on “study skills and memory strategies” (e.g., Hare, 1963; Atkinson and Shiffrin, 1968; Hagen and Kingsley, 1968; Belmont and Butterfield, 1969; Corsini, 1971; Wittrock, 1974a,b). It also derived from new cognitive theories such as Ausubel’s (Ausubel, 1960, 1963, 1968; Ausubel and Fitzgerald, 1962; Ausubel and Youssef, 1963) research on the value of advanced organizers for student learning—reactions in large part to Skinner’s (Skinner, 1953) behaviorism. It is within this context that my graduate research training and team of professors and fellow graduate students at Florida State University’s (FSU) Computer-Assisted Instruction (CAI) Center were encouraged to pursue dissertation topics of high interest to military training (e.g., Leherissey, 1971; Leherissey et al., 1971).

A few researchers working in various military research organizations preferred to refer to “learning strategies” that could support student success in self-paced and/or computer-based military training environments (e.g., McCombs et al., 1973b; Judd et al., 1979). As a graduate student at FSU’s CAI Center from 1969 through late 1971, I joined the team in conducting military research on the effects of various forms of computer-managed instructional (CMI) and CAI systems. The era was marked with excitement and healthy competition within our own and other universities studying strategies for computer-based learning. Joining as new researchers, Claire Ellen Weinstein at the University of Texas in Austin and I were the only two female graduate students in a group of about 40 noted researchers and research center directors with military funding to do learning strategies research (Leherissey, 1971; Leherissey et al., 1971; O’Neil et al., 1972; McCombs et al., 1973a, 1977, 1986a,b; Weinstein, 1978; McKeachie, 1986, 1988, 1990; Weinstein and Mayer, 1986; Weinstein et al., 2000).

Throughout this early period of learning strategies research, different areas of interest emerged and solidified over time. There continued to be those who focused on the area of reading comprehension strategies. Zhang’s (Zhang, 1993) literature review highlighted much of this research, including that of Carroll (1977), Anderson (1977), Don Dansereau (Holley et al., 1979), and others working to improve students’ reading comprehension and memory (e.g., Smith, 1967; Beck et al., 1982; Blanton and Wood, 1984; Afflerbach, 1990; Bell, 1991). Researchers were then recommending four categories of reading strategies: cognitive strategies, compensation strategies, memory strategies, and test-taking strategies (Paivio, 1986; Zhang, 1993).

Other significant research was being published by learning strategies researcher, Wittrock (1978, 1986a,b, 1989a,b, 1990, 1991, 1992) and Wittrock and Alesandrini (1990). Wittrock (1974a,b) assumed that learners link new with old ideas to gain a better conceptual understanding, but his major contribution was to acknowledge (a) metacognition as a higher order process learners could demonstrate, (b) motivational processes that impact memory processes and information processing, (c) neurological brain functions involved in learning, and (d) constructivism as a better way of understanding learning than prior cognitive views. Wittrock (1978, 1980, 1992) was also a visionary in recognizing the links between neurophysiology and cognition. As interest surged in the brain sciences in the 1970s and 1980s, Botkin (1980) reported research connecting brain research to issues in education such as creativity, imagination, learning disabilities, gender differences in brain functioning, and art education. What this research revealed was that students could be taught strategies for enhancing their methods of processing information, solving problems, and comprehending or remembering what they were learning. As this research evolved, educators, policymakers, and researchers began to envision a newly emerging concept of holistic education (Bull and Wittrock, 1973; Wittrock, 1981, 1986a,b; Wittrock and Alesandrini, 1990).

Applying Wittrock’s generative learning theory in military training inspired many of us doing early learning strategies research to explore new paradigms for schools and training settings. We were a fairly tight-knit group of researchers who organized conferences, symposia at national conferences, and who shared our research findings on personal and professional levels. Funding agency representatives from civilian and military organizations were present, and lively debates were part of the agenda. Given that many current researchers may be unaware of this early research, it is important to highlight Wittrock’s (Wittrock, 1989a,b) applications of cognitive psychology concepts to the analysis of military language in decision-making. His research led to other military research examining the role of background knowledge in military communication, the effects of context on meaning, the relevance of syntactic and semantic analysis for military language use, and the usefulness of inferential and domain-specific processing.

Similarly, Bob Seidel (associated with early military research related to learning strategies and new computer-based systems for education and training) foresaw the kinds of learning models and contexts that would better serve the needs of students and their instructors (Seidel, 1969, 1971, 1973; Seidel et al., 2005). Others influential in the research community in these early days were Sigmund Tobias, Thomas Duffy, and Dexter Fletcher—all of whom contributed to our understanding of the role of prior knowledge and a host of other learner, context, and system variables to learner performance (e.g., Tobias and Duffy, 2009; Tobias, 2016; Tobias et al., 2016).

Early Findings and Research Directions

New research questions were posed, presentations of current research projects were given ample time for discussion, and side meetings to identify new research projects were held with interested funding agencies that included the Office of Naval Research, Army Research Institute, National Science Foundation, Naval Personnel Research and Development Center, US Department of Education, Human Resources Research Organization, Defense Advanced Research Projects Agency, and University of Pittsburg’s Learning Research & Development Center (cf. Glaser, 1963; Atkinson, 1968; Atkinson and Shiffrin, 1968; Seidel, 1969, 1971; Newell and Simon, 1972; Suppes, 1972, 1973, 1974, 2002; Wesche, 1975; Collins, 1978; Dansereau, 1978; Chipman et al., 1985; McKeachie et al., 1985; McKeachie, 1986, 1988, 1990; Sternberg, 1997; Tobias, 2016; Tobias et al., 2016). Government, military, and industry agencies research began research programs that have continued in various forms until now. The following summarizes these early efforts and leaders in the field.

The Use of Technology for Individualized and Self-Directed Learning

Constructivism was the dominant learning theory of the 1960s and 1970s and led the way for new uses of technology in military and non-military settings. A recent online paper by Allsop (2016) describes how early work by Piaget and others began to influence those leading learning strategies research projects. Missing in this account, however, was research by those of us involved in initial military research projects. This was because much of this research ended up in technical reports and only a few academic journals. It was not until the 1980s and 1990s that much of this work came into the spotlight (O’Neil et al., 1972; McCombs et al., 1973a,b; Paris and Lindauer, 1976; Paris et al., 1977; Judd et al., 1979; McCombs, 1982a,b, 1986a,b,c, 1988; McCombs and Marzano, 1989, 1990; Weinstein and McCombs, 1998; Paris and Paris, 2001).

Most of the studies done during the late 1970s and early 1980s on using technology to individualize learning revolved around how strategy training could enhance problem solving and comprehension of the material while reading in various content areas (Lefcourt, 1976; Brown et al., 1983a,b; Chipman et al., 1985; Bransford et al., 2000). Important findings during this time led to the conclusion that learning strategies could be incorporated within an information processing model that also looked at how metacognitive, cognitive, and social affective strategies could assist students in acquiring higher levels of second language learning (cf. O’Malley et al., 1985; Chamot et al., 2004). For example, Slavin (1980) combined cooperative learning with reading comprehension strategies and demonstrated enhanced performance for students receiving both types of training.

Others were also exploring such combinations, notably Dansereau et al. (1983) and others using an affective component (Rubin, 1975, 1981; Naiman et al., 1978; Rubin and Thompson, 1982). At the same time, Brown and Palincsar (1982) recognized that ideal training packages would consist of practice in the use of task-appropriate strategies, instruction concerning the significance of those activities, and instruction concerning the monitoring and control of strategy use. These researchers separated cognitive strategies (those more concerned with individual tasks and requiring the material to be manipulated or transformed to enhance understanding) from the metacognitive strategies (concerned with the planning for the learning, monitoring of understanding, and evaluation of one’s own learning) to maximize students’ learning potential (Brown et al., 1983a,b).

Influences from Developmental Psychologists in the Early Years

Of the many constructs that emerged during the 1970s, metacognition as described by Livingston (1997) was a large part of the cognitive theory revolution. The origin of the term is credited to Flavell (1979) and later in 1987 distinguished between metacognitive knowledge and metacognitive experiences or regulation. The metacognitive knowledge component was defined as acquired knowledge about cognitive processes that can be used to control cognitive processes. Metacognitive knowledge was further divided by Flavell (1976) into three categories: knowledge of person variables, task variables, and strategy variables. Earlier Flavell (1971) had used the term metamemory to refer to an individual’s ability to manage and monitor the input, storage, search, and retrieval of the contents of his or her own memory. The academic community was invited to engage in additional metamemory research, and this theme of metacognitive research continued more than 30 years later. Flavell (1963, 1971) also implied that metacognition is intentional, conscious, foresighted, purposeful, and directed at accomplishing a goal or outcome. In subsequent research, these implications have been carefully scrutinized; and Kentridge et al. (2004) argued that metacognitive processes needed not to operate in a person’s conscious awareness.

Flavell (1976) recognized that metacognition consisted of both monitoring and regulation aspects. In the context of information storage and retrieval, Flavell (1976) defined three “metas” that children gradually acquire: (a) to identify situations in which intentional, conscious storage of certain information may be useful at some time in the future; (b) to keep current any information that may be related to active problem-solving and have it ready to retrieve as needed; and (c) to make deliberate systematic searches for information that may be helpful in solving a problem, even when the need for it has not been foreseen. Later Flavell (1981, 1987, 2004) proposed that the emergence of awareness of the flow of time—awareness of a future time—could support the ability to form metacognitive goals. Most importantly for the field of learning strategies research, he emphasized the sense of the self as an active agent in one’s own experiences emerged during childhood development. He also began in 1987 to actively encourage the development of children’s metacognition given that school settings provide many opportunities for students to develop metacognitive knowledge about persons, tasks, and strategies. His visionary research paved a big path in the learning strategies research agenda.

Connecting Cognitive and Metacognitive Strategies in the 1980s and 1990s

The field advanced at that point by defining knowledge as metacognitive when actively used in a strategic manner to ensure that a goal is met—by providing direct instruction in learning strategies so that teachers can help improve the self-confidence and achievement of their students especially the educationally disadvantaged (cf. Weinstein, 1978). A metacognitive strategy would then consider a person variable, a task variable, and a strategy variable. The following example describes a student who uses his or her knowledge in planning how to approach a math exam: “I know that I (person variable) have difficulty with word problems (task variable), so I will answer the computational problems first and save the word problems for last (strategy variable).” As Livingston (1997) explains, simply knowing one’s cognitive strengths or weaknesses and the nature of the task without actively using this information to regulate, monitor, or oversee learning is not metacognitive.

The 1980s and 1990s Debate: Do Learning Strategies Enhance Skill and Will to Learn Across Developmental Stages and Content Areas?

Further elaborations of the learning strategies that proved most effective for a variety of learners were provided by Borkowski et al. (1987), Brown (1978, 1990), Entwistle and Hounsell (1975), Pressley and Harris (1990), and Carr et al. (1989). Most of the studies focused on learning strategies while reading, with some emphasis on motivation and metacognitive strategies for enhancing comprehension of what was read (Palincsar, 1986; Palinscar and Brown, 1986; Brown, 1992). Scott Paris, however, refocused attention on differences between reading comprehension skills and the will to read (cf. Paris and Lindauer, 1976; Paris et al., 1977, 1983, 1984, 1986; Paris and Cross, 1983; Paris, 1998). Early collaborations began between me and Barbara Lindauer who worked for/with me at McDonnell Douglas and the University of Denver and these soon led to collaborations with Scott Paris and Claire Ellen Weinstein around the need for learning strategies that combined will, skill, and strategic thinking (e.g., Weinstein, 1978, McCombs, 1982a, 1986a,b, 1988, 1989; McCombs and Marzano, 1989, 1990; Zimmerman, 1989, 1990, 2000, 2001; Paris et al., 1991; Weinstein and McCombs, 1998).

Looking at the Skill Component

Most researchers in the early years looked for general classes of learning strategies that could enhance learning in training and educational settings. A stimulus for much of the early military research on learning strategies was Don Norman (Norman, 1969, 1976, 1977; Lindsay and Norman, 1972, 1977). What Norman (1969) began to identify was the need for students to think about their own mental processes, their short-term memory limitations, and how they could “chunk” related concepts to improve their memory short-term. He was among the first to find that learning strategies could be generalized across diverse content areas for young children through adults. More importantly, Norman (Norman and Rumelhart, 1975; Norman, 1977) identified holistic learning strategies that college students could be taught to improve their academic success.

Some learning strategies researchers were concerned more specifically with memory and reading comprehension (e.g., Rothkopf, 1970; Anderson and Biddle, 1975; Paris and Lindauer, 1976; Pressley, 1976, 1977; Paris et al., 1977, 1983, 1986; Brown, 1978, 1990, 1992; Palinscar and Brown, 1986; Nolan, 1991). Bloom (1980, 1985) later built on some of these ideas in creating his own taxonomy of learning strategies and approaches for children of different ages and stages of development. Others applied cognitive psychology to helping students learn strategies for remembering, learning, and understanding (cf. Bransford and Heldmeyer, 1983).

Looking at the Will Component

Weinstein and McCombs (1998) and McCombs and Marzano (1989, 1990) put forth a conceptual framework that defined the will component as including (a) affective and motivational strategies and (b) cognitive and metacognitive strategies identified through research to be part of the learner’s tool kit for success across content areas. At the same time, the specific strategies needed for success in domain-specific areas such as reading and mathematics were described by Weinstein (1978), Derry (1990), Paris (1991), Paris and Winograd (1990), and Zimmerman (1989).

The practical application of this integration of learning strategies interventions was on my research agenda during these years and resulted in a book series for the American Psychological Association (APA Books) entitled Psychology in the Classroom. More than 14 books were commissioned by editors Dr. Sharon McNeely and myself over nearly 10 years as a project for APA’s Division 15, Educational Psychology (McCombs and McNeely, 1994). We each worked with an elementary, middle, and high school practicing teacher with the objective to produce practical guidelines and strategies for classroom implementation. For example, one booklet with a middle school mathematics teacher was published on the topic of “motivating hard to reach students” (McCombs and Pope, 1994) another with a high school English teacher on the topic of “stimulating self-regulated learning” (Ridley, 1991; Ridley et al., 1994). The series continues to be relevant today and is sold to teachers across the US and world.

More recently the APA’s Education Division invited a group of experts on topics facing teachers for which they needed professional development training. We met initially in 2004 and worked collaboratively to develop a series of online modules for teacher certification through 2011, after which time our online modules were programmed for teacher use. My module (McCombs, 2012) on Developing Responsible and Autonomous Learners: A Key to Motivating Students can be accessed at http://www.apa.org/education/k12/learners.aspx. This module takes into consideration the holistic nature of individual student learning and the most effective practices for helping them develop into autonomous and responsible learners. Addressing the whole learner in developmentally appropriate ways includes establishing positive student relationships and listening to each learner’s voice in creating productive learning climates.

Further Updates of Learning Strategies Research Reviews

In addition to the research reported above, there have been only a few major learning strategies research reviews that update the field from 2009 through the present. Findings from these reviews are briefly summarized, twenty-first century research leaders are identified, and research themes are identified. Highlights from pioneer researcher and innovator, Claire Ellen Weinstein, are presented. The section ends with a view of how the field has evolved to the present.

Reviews of Learning Strategies Research from the 1970s through 1990

One of the last major reviews of research in the learning strategies area was done by Nambiar (2009). The focus of this review was to capture what had been the origins of the learning strategies research area as well as significant findings. This was an important paper for those who were just beginning to explore various content-specific and more general strategies for helping students learn more effectively from early school years into adulthood. In his review, the origins were traced to the field of cognitive psychology from 1970 to 1990, after which the research on learning strategies became more diverse and more revealing in its findings. He acknowledged among the earliest contributors Dansereau (1978), Rigney (1978), Wesche (1975), and Weinstein (1978).

What Nambiar (2009) does not report is that during this same time period, Weinstein and colleagues (Weinstein et al., 1987; Weinstein and Palmer, 1990) were validating her Learning and Study Strategies Inventory (LASSI). The LASSI has been revalidated and revised several times since and has been used in international studies with college students, recently by Magno (2010, 2011) with 755 college students from different university in the Philippines. It was Weinstein and Mayer (1986) who believed that information processing could help us understand the role of learning strategies in the learning process in a four-stage encoding process involving selection, acquisition, construction, and integration. They suggested that the process of selection and acquisition focuses on the gathering of knowledge while construction and integration focuses on what knowledge is acquired and how it is organized.

Also missing were Weinstein and Mayer’s (Weinstein and Mayer, 1986) findings that learning strategies are used intentionally by learners to facilitate their learning, suggesting that learning strategies affect learners’ motivational or affective state—the way a learner selects, acquires, organizes, or integrates new knowledge. This was a major step forward for the field, and helped researchers focus on the role of metacognitive, motivational, and affective processes in enhancing student learning. In my own research, these findings had also emerged and were defining what we now refer to as “learner-centered” approaches addressing whole learners across major domains that also included the social and emotional needs of learners at different developmental stages (cf. McCombs, 1986a, 1988, 1989; McCombs and Marzano, 1989, 1990; McCombs and Whisler, 1989).

Other Learning Strategies Research Reviews

Strategic learning was found by Ertmer and Newby (1996) to be a characteristic of expert learning wherein learners can clearly realize their individual advantages and disadvantages regarding all aspects of strategies to enable them to better manage their learning. However, in online learning environments, it is often more time and effort consuming for students to decompose a task into a sequence of subtasks in order to plan and manage their own online learning. In addition, choosing meaningful information from the Internet and integrating it into learning domains can present another challenge for all online learners. Online environments also challenged students in learning to learn skills such as articulation and reflection, planning skills, study skills, finding and applying relevant examples, and self-evaluation. The technological aspects of Internet-based learning environments were unfamiliar to particularly disadvantaged or developmentally challenged students. As a result, modifying the construct of strategic learning of Weinstein (1978) and Weinstein and McCombs (1998) became necessary and provided an impetus for the latest version of the LASSI (Weinstein et al., 2016) described in a later section.

Additional reviews were reported by Oxford (1990, 1996), with the suggestion that there is a system of strategies that support each other in categories of direct and indirect learning strategies. In this system, direct strategies include memory, cognitive, and compensation strategies while indirect strategies include social, affective, and metacognitive strategies. In all, there are further divisions in 19 sets of strategies that cover 62 behaviors that help explain how learners learn. Nambiar (2009) pointed out that this is problematic because (a) many of the behaviors are overlapping and make it difficult to identify which strategies and behaviors are most important to learning and (b) the behaviors cannot be attributed to any particular theory of learning. Nonetheless, Oxford (2001) reported that the system provided the foundation for the Strategy Inventory for Language Learning used in major studies around the world.

Using Learning Strategies Research in 1980s and 1990s Classroom Interventions

In more applied research reviews, Seifert (1993) described how learning strategies can be used in the classroom. He acknowledged that much research had been conducted on domain-specific problem solving and other learning strategies but focused his discussion on generalizable strategies that were well-researched and had been demonstrated to enhance memory while also generalizing across content domains and a wide age range from grade three through university undergraduates. These studies altered student behavior using direct instruction, self-instruction, and reciprocal instruction. Maximum learning gains were realized when students spontaneously engaged in appropriate strategy use, leading Seifert (1993) to suggested that teachers not only need to teach students various strategies for enhancing learning but also need to explain to students why and when these strategies are most effective.

Much of the work done in the 1980s in learning strategy research was in helping to identify good learning strategies and ultimately compile a list of such strategies. Cohen (1998) argued that a close look at the parallels between the work done in cognitive psychology and learning strategies shows that some of the work done with learning strategies in the area of language learning also has some theoretical base in cognitive theory. Cohen (1998) concludes despite research in the early 1980s, the vast research conducted on identifying strategies and compiling lists of characteristics of good language learners found a need to examine any similarities or differences in these characteristics in the non-English-speaking world.

Finally, a big movement through the 1980s was research on social and emotional intelligence. As reported recently by one of the early leaders in this field, Goleman (2016) laid out steps for enhancing emotional intelligence (e.g., asking students if they were motivated to put in the time and effort and really cared, getting very honest feedback from trusted people about their strengths and opportunities for growth using a 360-degree systematic assessment instrument, developing a learning plan to begin practicing competencies such as controlling negative emotions, and finding naturally occurring opportunities to practice skills until they become the preferred neurological pathway in your brain). These skills comprised a more comprehensive definition of metacognition that included knowledge of one’s own cognitive and affective processes and ability to consciously monitor and regulate those processes.

Researching how well programs for teaching these general skills work has shown highly successful results even as much as 7 years later according to Harvard researchers who tracked people longitudinally and found the skills retained their strength as reported by others with whom they now work (Weissberg and Greenberg, 1998; Zins et al., 2000, 2004; Goleman et al., 2002; Boyatzis, 2008; Goleman, 2016). We see in the next section that indeed newer research continues these successful results.

Trends in Learning Strategies Research from 2009 through the Present

In recent years, several trends are worth noting in both theoretical and empirical or applied research. From my vantage point, one of the most important trends is formulating a strong theoretical foundation based on a whole person approach to basic and applied learning strategies research. In my own research, it has been essential to define the perspective of the self in learning to learn more effectively in a lifespan that covers preschool through adult years (cf. McCombs, 1986a, 1988, 1989, 1991a,b,c, 2001, 2008, 2013a,b, 2014; McCombs and Marzano, 1989, 1990; McCombs and Whisler, 1989). Much of my research has focused on the motivational, affective, and relational strategies that students can employ to help generate the will to learn when they feel or believe they have lost their love of learning in schools. This trend is also revealed in research selectively reported here since the mid-2000s.

Constructivism and Social Constructivism as Major Theories Grounding Learning Strategies Research

Throughout the 1990s, constructivism and social constructivism were conceptual frameworks guiding and shaping new instructional approaches that emphasized the social and cultural context of cognition (Duffy and Cunningham, 1996). For Jonassen (1991, 2001), social interaction was crucial in the learning process and should lead to collaboration. He advocated this specific approach to learning and instruction in designing computer-based learning environments. For others (Weinstein and Mayer, 1986; Zimmerman and Martinez-Pons 1986, 1988, 1990; Pintrich, 1989), students’ motivational orientations and learning strategies were said to help students regulate their cognition and effort, and when combined with critical thinking, helped learners analyze, synthesize, understand, and remember information. Ames (1992), Pintrich (1989), and Pintrich et al. (1993) suggested that the learning context is critical to fostering motivation and cognitive engagement, along with active learner participation and responsibility, which fosters a motivational orientation toward deep-level cognitive processing, persistence, and effort and significantly effects students’ motivational beliefs.

Within the social constructivist learning theory, Driscoll (2002) also suggested that learning is enhanced when students are actively involved in the learning and when critical thinking is promoted through applied and reflective activities. Collaborative problem-based learning was recommended to help students develop skills such as teamwork, collaboration, and cooperation along with critical thinking through the analysis, synthesis, evaluation, and reflection while solving authentic problems in interactive and cooperative forms of learning, which encourage students to develop team skills, such as peer interaction and help. Driscoll (2002) suggested that students’ perceptions of online collaborative learning be assessed about group discussions, critical thinking and problem solving activities, peer learning, and help provided. Others have recently added that students should assess their preferences regarding an “ideal” learning environment (Nauert, 2016; Rubin, 2016), laying the groundwork for learner-centered principles and practices.

Addressing Social, Emotional, and Motivational Strategies for Learning

Anderman (2010) undertook the task of reviewing research supporting not only the important roles of cognition, prior knowledge, transfer, and generation in human learning but also how Wittrock’s (Wittrock, 1974b) Generative Model of Learning relates to the social, emotional, and cognitive aspects of academic motivation. Anderman contended that Wittrock’s model may have led educational psychologists to seriously consider motivation variables and affective issues that had largely been ignored prior to the 1970s. In particular, Anderman (2010) described how motivation theories drastically changed after the mid-1970s with an emphasis on social-cognitive theories of motivation. Added to these was the importance of prior knowledge as reflected in Eccles and Wigfield’s expectancy-value theory (Wigfield and Eccles, 1992, 2002) and Bandura’s notion of self-efficacy in his social cognitive theory (Bandura, 1986, 1993, 1997), which acknowledged that motivation to engage in future behavior is intricately tied to prior knowledge and experiences in particular domains. In addition, Wittrock’s (Wittrock, 1974b) model suggested a paradigm shift in the study of learning with attention being paid to the role of the learner’s mind in creating meaning out of novel situations and the role of the self in the field of motivation.

In a tribute summarizing Wittrock’s contributions to educational psychology, Tobias (2010) characterized Wittrock’s generative learning theory as “remarkably prescient” in setting the stage for the later paradigm shift from cognitive to constructivist approaches to instruction, including constructivist learning strategies. Other researchers studying motivation from different theoretical orientations focused on linking student motivation and self-regulated learning strategies at the college level (e.g., Pintrich and Zusho, 2002). Pintrich and Zusho (2002) addressed the persistent problem of college student motivation at all levels of the postsecondary system, including that students do not seem to care about their work, seem more interested in the course content, only care about their grades but not learning, procrastinate, and try to study for an exam at the last minute, or try to write a paper the day before it is due. Pintrich and Zusho (2002) provided an overview of current research on college student motivation and self-regulated learning, along with insights and suggestions for learning strategies interventions such as helping students be more organized and exerting more effort when they do not perform very well.

Around the same time, international researchers Zhu et al. (2008, 2009) examined cultural gaps in student perceptions of online collaborative learning, and the changes over time of student perceptions, motivation, and learning strategies due to the actual involvement in a collaborative e-learning environment. Parallel e-learning environments for first year, Flemish and Chinese students were implemented, and student perceptions of the online collaborative learning environment and their motivation and learning strategies were measured before and after the e-learning experience were measured. The findings showed that the Flemish group perceived the online collaborative learning environment more positively compared to the Chinese group. Chinese students’ motivation and learning strategies, however, changed significantly in ways more in line with a social constructivist learning approach after the online collaborative learning experience. Zhu et al. (2008, 2009) are among many who now use culturally responsive research to help instructors become aware of and more supportive of different student perceptions of online collaborative learning environments.

The Rise of Social and Emotional Learning (SEL) Approaches

The study of how children over time develop social and emotional skills was a topic of recent ongoing 8-year study by the Organisation for Economic Co-operation and Development (OECD, 2016). The focus was on children living in cities and aimed to better understand how teachers, parents, and communities “drive” their children’s social and emotional development and how the development of these skills can help them later in life to have success in education and the world of work. This longitudinal study also sought to (a) identify future outcomes, including educational attainment, labor market, health status, relationships, and civic engagement; (b) understand how investments made by families, schools, and communities influence the development of skills; and (c) develop recommendations and measurement tools for policymakers and practitioners to better monitor and enhance social and emotional skills. Cities studied are members and non-members of OECD, and the populations studied are children in grades 1–7 of the approximate ages of 6–12. This study will follow the lives of a large number of children starting from grades 1 and 7 until early adulthood by collecting information on social–emotional skills, learning contexts, and future outcomes.

Other current research on social and emotional skills is reported in a new book by Elias et al. (2016), which seeks to better serve the whole learner by looking at non-academic outcomes such as character development (CD) and SEL. They need to be reported so that parents and others concerned with their child’s education can see SEL and CD outcomes as part of any school- or district-wide grading system. Their research products include guided exercises for analyzing existing report cards, samples and suggested report card designs, tips on improving communication with parents, and case studies highlighting common challenges. There are testimonials from teachers and students reflecting all of the important characteristics of an educational system geared to student success in developing the skills they need for the future. The key role played by SEL/CD in each student’s development challenges the tradition of putting them at the back of the report card.

Greenberg (2017) has recently described emotion-focused therapy (EFT) and the adaptive role of emotion in human functioning. Research shows that the EFT approach leads to enduring change in effective emotional well-being. For those suffering from anxiety disorders, this theory and its constructs demonstrate one way in which early attempts to reduce anxiety through learning strategies interventions have evolved (e.g., Spielberger, 1972, 1977; McCombs, 1982a,b). These efforts began to change the way educators and policymakers viewed the function and purpose of schooling and the term “personalized learning” began to be the buzzword of the 1990s and early 2000s up to the present.

Personalized Learning Evolves to Meet Whole Learner Needs

A recent critical look at how personalized learning has evolved and is likely to change in the future was undertaken by Bushweller (2016). Bushweller claims that personalized learning has not made the impact expected in the 1990s and early 2000s. Schools that have adopted a personalized learning approach still look like traditional schools did 5–10 years ago when digital tools were available but were not extensively used to individualize or tailor instruction to the strengths and weaknesses of individual students. Bushweller states this is due in part to educational and technological challenges of designing rigorous curricula and assessments around individual student interests. At the same time, however, Bushweller (2016) describes the current push to identify and design teaching and learning strategies around individual student’s academic needs and personal interests—a trend that has entered and expanded into the K–12 mainstream.

In a similar vein, Kaplan (2016) recently examined how research continues to find few relationships between motivation and students’ achievement. She claims this is due to an “infatuation” researchers have with particular concepts (e.g., goal orientations and self-efficacy) to the point they lose sight of the overall phenomena involved in how achievement is produced. This has led, Kaplan argues, to an under-determination of the role students’ motivation actually plays in achievement—which itself is often a generalized contextual variable that lacks criterion validity. The result is the definition of what constitutes a quality education is narrowed, and the power of outcomes such as purposeful and meaningful learning, personal growth, creativity, self-exploration, citizenship, and collaborative orientation are overlooked. Kaplan (2016) argues that research on the role of motivation in student achievement has become political, highlighting the need to design studies: (a) capturing the complex contextual and dynamic nature of this phenomena and (b) using rigorous methodologies grounded in validated theoretical assumptions that give the research a higher ideological or ethical foundation.

Others who have moved their focus on holistic learning strategies into the digital age include Don Norman (Norman, 2014; Norman and Stappers, 2016). These researchers are now exploring complex human-centered sociotechnical systems, including education, healthcare, transportation, governmental policy, and environmental protection. They concluded that the major challenges stem not from trying to understand or address the issues but arise during implementation, when political, economic, cultural, organizational, and structural problems overwhelm all else. It is suggested that designers play an active implementation role and develop solutions with small, incremental steps to reduce political, social, and cultural disruptions. This “muddling through” requires tolerance for existing constraints and tradeoffs, and a modularity that allows measures that do not compromise the whole. Others, myself included, have argued that rather than trying to make and measure incremental change, it is more promising to optimize the design with learner-centered principles and practices (cf. APA Task Force on Psychology in Education, 1993; APA Work Group of the Board of Educational Affairs, 1997; McCombs, 1998, 2000, 2012, 2013a,b, 2014; Scharmer, 2011; Senge, 2011, 2012; Scott, 2016).

Learning Strategies for the Whole Learner

For the overall field of learning strategies, it is clear that within a whole learner perspective, learners of all ages and backgrounds seek to find meaning in what they are learning and personally generate their own meaning when needed or when effort is required (cf. McCombs, 2012). Like the influence of Frankl (1984), in my own research in the 1980s I was influenced by philosophies that acknowledged learner’s epistemic curiosity and search for personal meaning in what they were learning. The role of the self was emerging as a growing area of interest during the 1970s and 1980s as discussed, especially by those researchers interested in self-regulated or self-directed learning (e.g., Rothkopf, 1970; Wittrock, 1974b; Entwistle and Hounsell, 1975; Knowles, 1975; Norman, 1976; Paris and Lindauer, 1976; Pressley, 1976; Bandura, 1977; Weinstein, 1978; Kopp, 1982; Brown et al., 1983a,b; Chipman et al., 1985; Good and Brophy, 1986; Palinscar and Brown, 1986; Shavelson et al., 1986; Vygotsky, 1986; Weinstein and Mayer, 1986; Perkins and Salomon, 1987, 1992; McCombs and Whisler, 1989; Schunk, 1989, 1994; Zimmerman, 1990, 2001; Zimmerman and Martinez-Pons, 1990; Salomon, 1993).

The current ongoing interest in self-assessments can be seen in a recent paper from the Educational Testing Service by Witherspoon et al. (2016). This paper demonstrates the interest in innovative ways to assess the teaching practice of leading classroom discussion (LCD) in its National Observational Teaching Examination assessment series. In this assessment, candidates interact with a small class of virtual students represented by avatars in a computer-based, simulated classroom. Five avatars are enacted by a single simulation specialist who has been trained and certified on an elementary English language arts or mathematics task. The construct of LCD is defined and a review of the research and scholarly literature provided that supports the importance of this self-assessment practice for effective teaching. Other studies of similar innovative approaches to studying the whole learner with twenty-first century technology continue to surface daily, making them too numerous to bring to this already lengthy review.

Leading the Way to Learner-Centered Educational Systems

Looking at how learning strategies research has evolved into the affective and motivational realms, I continue to be a fan of self-determination theory (Deci, 1975, 1980; Deci and Ryan, 1985, 2000, 2002, 2006; d’Ailly, 2003, 2004) and the innate health/health realization model of Roger Mills (cf. McCombs, 1986a, 1991a,b; Mills, 1991). These theoretical orientations place the person at the center of the learning paradigm but more importantly emphasize the importance of innate psychological needs (competence, control, and agency) and working from an inside-out perspective when facilitating learning. Placing the responsibility for learning on the learner while at the same time, understanding that to be motivated by a will to learn, the context must attend to how much learner control is present, whether relationships are caring and supportive, and whether opportunities are present to develop competence in areas that matter to the learner. These practices are based on foundational principles of learning.

International work begun two decades ago with a wave of student voice research surfaced in the 1990s and early 2000s (e.g., Fielding, 1997, 2007; Rudduck, 1998; Rudduck, 2006). At the same time, many US researchers were providing theoretical and applied self-theories and theories of self-regulated learning (e.g., Deci and Ryan, 1985, 2000; Zimmerman and Schunk, 1989, 2001, 2003; Schunk and Zimmerman, 1998, 2007; Ryan and Deci, 2000) that mirrored the importance of student control and participation in their own learning processes. Expanding the learning strategies agenda to include “student voice,” Michael Fielding (Fielding and Kirby, 2009;Fielding, 2011, 2015a,b) at the University of Cambridge in the UK, recently, updated his more than 20 years of research demonstrating significant gains in broadly defined student out comes when students are given significant voice and control over their own school learning (Fielding, 2015a,b). These outcomes included increases in student creativity, teamwork, collaboration, problem solving, and academic achievement.

Other influential researchers from the UK such as Sir Ken Robinson¹ lobby for educational systems that are learner centered and accept the assumptions of innate curiosity, love of learning, and need for autonomy and control in the learning process. These researchers are making popular the concept of a major education paradigm shift, as are some of our US researchers, including David Berliner (Berliner, 2000, 2009; Tobias et al., 2016), Alfie Kohn,² and Charlie Reigeluth (cf. Reigeluth, 1994; Reigeluth et al., 2017). In communications and collaborations with these researchers, I have accepted the legitimacy and importance of taking the applied research results from learner-centered educational paradigms to the public, aiming to influence policy and practice. In my immediate circle of professional friends and colleagues, well-recognized academic researcher Harter (2006, 2012, 2016), whose lifelong study of the developing self, has been a major contributor to my own thinking and research on the role of the self in self-regulated earning strategies.

Reigeluth et al. (2017) have looked thoughtfully at what others leading movements toward a learner-centered paradigm of education include in their models. They also address and update what instructional design theories and models can contribute to our understanding of what constitutes a personalized integrated educational system. Reigeluth’s (Reigeluth et al., 2017) chapter on how to design technology interventions to provide the supports for the truly learner-centered instruction outlined in the first chapter of this edited book. The four major functions required to support students include recordkeeping for student learning, planning for student learning, instruction for student learning, and assessment for/of student learning and three secondary functions include communication and collaboration, system administration, and improvement. If developed fully, Reigeluth maintains that this platform can support the implementation of all five learner-centered principles: attainment-based instruction, task-centered instruction, personalized instruction, changed roles, and changed curriculum.

Taking these integrated, personalized learning system views to another level, there are a number of researchers in the private and public sectors arguing for the globalization of education and the use of advanced artificial intelligence, virtual reality, and robotic technologies (e.g., Senge et al., 2000; Calvert, 2016; Davis, 2016; Latham et al., 2016; Norman and Stappers, 2016; Scott, 2016; Vander Ark, 2016a,b). The basic argument is that these systems will be more efficient and effective, reducing teacher workloads and allowing students to take increasing responsibility and control over their own learning any time and any place. For example, Vander Ark (2016a,b) presents a case for robotic teachers who focus on relationships but do not get tired. Despite this case, teachers and human relationships still matter—as they did in the 1980s as part of our studies for the military (cf. McCombs, 1982a,b, 1984a,b, 1985, 1986a,b,c, 1987; McCombs and Lockhart, 1984; McCombs et al., 1986a,b, 1987).

How the Field of Learning Strategies Research Has Evolved

The evolution of learning strategies research in basic and applied areas is a complex one that has branched into what are now fairly well-defined specialties. A concern with strategies to support mindfulness began with Langer’s (Langer, 1989) initial definition of this construct as one that involves deliberate effortful abstraction and a search for connections. More recent research on “mindfulness” by Oaklander (2016) described “The Mindful Classroom” in an article for Time magazine. A fifth-grade classroom in Louisville, KY, USA is described where students practice twice weekly peaceful activities such as relaxation exercises that focus them for 45 min on the present moment. Children have been noted to be highly anxious and stressed out, having trouble paying attention, and worried about bullying. A follow-up Time magazine article by Schrobsdorff (2016) reinforced this finding and focused on American teens and the often debilitating anxiety facing them in today’s world and times. “Mindfulness” advice for teens and adults who care for them, however, is often a non-scientific or “Buddhist-type” soft approach to calming children and the adults around them is often criticized—distracting schools from their fundamental responsibility of educating students in rigorous curriculum standards or common core goals (Briggs, 2015).

Another huge shift in how twenty-first century strategic interventions are defined is exemplified by recent efforts promoted by UCLA’s National Center for Research on Evaluation, Standards and Student Testing (CRESST) at their 2016 conference on September 21–22. The conference featured thought leaders in technology, academia, education, and policy leading discussions on the latest evidence-based global trends and opportunities in education. Speakers included Li Cai, CRESST director, and UCLA professor of education and psychology; Pedro Noguera, UCLA distinguished professor of education; John Hattie, director, Melbourne Educational Research Institute, University of Melbourne; and Alan Kay, president, Viewpoints Research Institute. The main speakers were video-recorded, and these were posted to several YouTube locations, with examples accessible at: https://www.youtube.com/watch?v=-jPAgwjHp_c, https://www.youtube.com/watch?v=2rnGiJTUtL0, and https://www.youtube.com/watch?v=c_fI_z7K-dw.

In listening to these presentations, it becomes clear that the field of learning strategies research is evolving nationally and internationally in novel, dynamic, transformative, and innovative ways. How research data on individual student and contextual levels are being used to inform the science of learning is a major focus with a variety of principles and warnings about the role of high quality designs for learning systems and personalized educational interventions using technology. Using the body of knowledge, we already have about student learning and the strategies that best promote learning at deep levels was an organizing theme of this conference. A primary area of concern was how to refine our interventions for increasingly diverse students with more than cognitive learning needs—an exciting contribution of this gathering.

Similarly, Goodwin (2016) has identified research showing how Coleman’s (Coleman, 1966; Goleman, 1995) early work with over 4,000 schools across the US on overcoming the effects of poverty led to the conclusion that non-school factors such as teacher quality outweighed school characteristics such as size and resources. The most important finding in the huge 800-page report was that a single student attitude factor showed a stronger relationship to achievement than all the school factors combined. This factor was how strongly students believed they could control their own destinies and those impoverished minority students who did feel they could control their destinies had higher levels of achievement than white students who lacked these convictions. Later studies confirmed these findings (e.g., Ekstrom et al., 1986; Finn and Rock, 1997) with high school dropouts who were more likely to attribute school success to external factors such as luck [see early and ongoing attribution theory research of Weiner (2016), at https://www.researchgate.net/profile/Bernard_Weiner].

More recently, several studies reveal how combining feelings of control of one’s life with other motivational variables such as academic self-efficacy and goal orientation can account for more than 20% of the variance in university students’ academic grade point averages (e.g., Cadinu et al., 2006; Richardson et al., 2012). These finding have been replicated in my own work with San Antonio College over 5 years, 2006–2011 (McCombs, 2008, 2010, 2011a,b, 2012; McCombs and Price, 2008) along with lower dropout rates for students in learner-centered compared to non-learner-centered classrooms. Motivational variables that most predicted retention and academic grades included academic self-efficacy, achievement goal orientation, low effort avoidance strategies, and knowledge seeking curiosity.

Another specialization emerging today is how to engage learners in digital learning environments, including data-driven, information management, and dynamic learning systems. There has been a big push for at least two decades both in the US and globally for using technology and digital learning environments in ways that personalize what students learn. One of the most recent was presented in a special report in Education Week on how personalized learning addresses the next generation of learners (cf. Bushweller, 2016). This special report looked critically at how personalized learning has evolved and what its future looks like given it is not sweeping through schools given the “thin” nature of the research evidence for academic gains with comprehensive personalized learning systems. The biggest issue per Bushweller is that teachers are not eager to change the way they teach and develop new kinds of curricula and assessments with current demands for teaching to common core standards assessed by state and national standardized tests. What the research says is discussed by Harold (2016) who maintains that despite the millions spent privately and nearly half a billion publicly to support the movement to more personalized K–12 education, evaluations have provided little conclusive evidence of the benefits of such systems. Issues revolve around how personalized learning is defined, the contexts in which such systems are implemented, and the types of software systems that support teacher efforts to provide learning materials tailored to individual student needs within and across different content areas.

A sixth area is biological and neuroscience applications. In this area, Mayer (2001, 2003, 2005, 2011) has continued the research of his colleague, Merl Wittrock, and continued to explore how brain research can inform our approaches to learning and instruction. In his recent review, Mayer (2016) explores how neuroscience has the potential for improving educational practice if viewed as linking conceptually with cognitive science, educational psychology, and educational practice. This paper explores the potential of neuroscience for improving educational practice by describing the perspective of educational psychology as a linking science; providing historical context showing educational psychology’s 100-year search for an educationally relevant neuroscience; offering a conceptual framework for the connections among neuroscience, cognitive science, educational psychology, and educational practice; and laying out a research agenda for the emerging field of educational neuroscience.

Finally, adaptive or individualized educational systems for meeting the needs of an increasingly diverse student population (including those with developmental or socioemotional learning issues). One of the early pioneers of the learning strategies movement, Alexander (2016), was honored recently by the Benchmark Center for Empowered learning for her significant contributions to how curriculum and instruction is informing their professional development seminars. She has been involved since 2001 in informing teachers about twenty-first century student needs to be knowledge builders and use goal-driven thinking strategies to function effectively in today’s world. Alexander’s current research has helped teachers and their students confront the realities of twenty-first century cultural and information processing realities. As a cautionary note, Scott (2016) points out that although the promise of personalization is there with the right approach to technology interventions, gadgets in the classroom do not improve learning—addressing the needs of individual learners is a complex interaction of students, teachers, and technology tools.

In looking back at the roots of the adaptive learning systems that were part of my graduate school education at Florida State University in the late 1960s and early 1970s, the most influential work was being done by Benjamin Bloom (Bloom et al., 1956), Gagne (1971), and my major professor, Duncan Hansen (Leherissey et al., 1971) who studied under Suppes at Stanford University. We were ahead of the times in studying adaptive learning systems, systems and instructional design models, and strategies for enhancing learning in computer-based learning environments. A look where more than 50 years of military research has revealed about the myth of average is provided by Perez (2016) who argues that we now know there is no average learner and systems must adapt instruction to learner variability from the start.

Some of the latest findings from neuroscience and brain imaging studies have further challenged the idea of average in relation to how the brain learns. Perez (2016) also contends that despite existing evidence, the trend toward personalized education is being resisted by all but innovative educators. Schools continue to design education around an average learner in one-size-fits-all learning approaches. He argues for a universal design system that uses the latest developments in technology to make the implementation of adaptive instructional strategies easier for educators to adopt. Similarly, Scott (2016) warns that big data and learning management systems may help in the implementation of personalized learning but they can also interfere with the human touch needed from teachers, peers, and others that connect with digital natives in our twenty-first century schools and prevent the shallow learning that may occur.

Current Research Directions and Further Questions

My own work over more than 25 years, aimed at examining learning strategies through the lens of learner-centered principles and practices, has led to an advocacy for ecologically sound systems that use 360-degree evaluation methodologies (cf. McCombs, 2013a,b, 2014). This review has provided another lens through which to examine the research directions and major findings emerging in educational psychology as the field. What is evident from basic and applied learning strategies research is that the concepts, contexts, and communities of practice have grown, debated, and changed directions. But overall, this research area has become more well integrated into the national and international dialog, research partnerships, and collaborations with culturally diverse researchers, practitioners, and policymakers. It is clear from this selective historical review of theory, research, and practice with a growing yet simplified list of interacting and overlapping learner variables that the field and concept of learning strategies has grown in importance and visibility.

Many ongoing studies using complex mixes of student populations, their teachers, and their families (or other mixed age, gender, and grade level groups) show that students thrive as whole persons when they perceive they are in learning environments with supportive on- or offline mentors and tools to become self-motivated, self-regulating learners in both traditional and progressive school contexts. We can confidently assert that learning strategies research will continue to evolve into a more coherent and robust field of study that is being joined by experts from cognitive science as well as related fields such as neuroscience, human development, sociology, health or medicine, economics, organizational psychology, business, and even anthropology. The question is where is the field now and where are we going?

Within the constructivist theoretical framework selected for defining instructor roles in 1980, the basic assumption in CMI systems captured what we know today: the student is responsible for his or her own learning. Given that this assumption had and continues to have implications for what instructors or teachers of students in all age groups are taught about their primary roles, instructors in this 1980 course learned that specifically students are expected to be responsible for attentive and motivated, making learning meaningful by the appropriate use of learning strategies and skills, initiating their own self-directed or self-paced learning, interacting effectively with both their peers and their instructors, and setting appropriate course and life goals (McCombs et al., 1983).

In our more recent work with online learning environments, my colleagues and I have focused on the extent that students having learning problems in synchronous or asynchronous learning environments or are unable to effectively exercise the above responsibilities (McCombs and Vakili, 2005; Hannum and McCombs, 2008; McCombs, 2008). In outlining the research-validated principles and practices that provide a foundation for online learning, we made sure the instructor or teacher guidelines included a thorough understanding of the set of learning strategies that will facilitate students’ increase in personal responsibility and learning confidence (Meece, 2002; McCombs, 2011a,b, 2012, 2013a,b, 2014). Thus, within the Learning Facilitator Instructor Role, a major training goal included familiarizing instructors with the kinds of cognitive, attentional, and motivational processes and strategies that are associated with effective, responsible, goal-oriented, and self-competent student learning.

Enduring Learning Strategies Research Concerns

What also continues to hold true is the need for teachers or instructors in training contexts to address both the function of learning management and facilitation of learning as defined early on by our research on instructor role training interventions in computer-based environments evaluated in Air Force, Army, Navy, and Marine Corp training environments (Carver et al., 1977; McCombs and Dobrovolny, 1980a,b, 1982; McCombs and McDaniel, 1981, 1983; McCombs et al., 1983, 1984; Back and McCombs, 1984, 1985; McCombs, 1984b, 1999, 2000, 2002; McCombs and Lockhart, 1984; McCombs and McNabb, 2001). Similarly, in public and private K–12 and college educational contexts, those advocating personalized learning argue that technology is a tool but not a substitute for good teachers and good teaching practices that include teaching critical thinking and other proven learning strategies (e.g., Harold, 2016).

An interesting set of commentaries has recently appeared in the literature that questions the research methods and federally required criteria for evaluating the effectiveness of replicable educational interventions. Some of the most recent (Editorial Projects in Education Research Center, 2011; Bill and Melinda Gates Foundation, 2014; Layton, 2015; Malouf and Taymans, 2016) have questioned whether findings of little or no impact from recent goals set forth in the No Child Left Behind, What Works Clearinghouse, or Race to the Top acts are a function of too much reliance on credible evidence-based methods and experimental approaches that may in fact mask real effects uncovered by more collaborative and qualitative findings (e.g., from case studies and classroom observations and survey research).

These arguments are not new and, in fact, surfaced early in the educational reform agenda and/or military training intervention research that relied on rigorous randomized studies with matched control groups and statistically significant outcome data or effect sizes that were at odds with the real achievement or performance goals of these interventions (Howard, 1986; Robson, 2002). For example, in the early days of individualized computer-based training interventions or self-paced instructional approaches, an over-reliance on linear modeling or factor analytic versus self-report assessments or observational research methods was a theme of methodologists (such as Glaser, 1963; Atkinson and Shiffrin, 1968; Wang, 1968, 1992, 1997; Resnick and Wang, 1969; Chu and Schramm, 1975; Cronbach, 1975; Snow, 1976, 1989; Cronbach and Snow, 1977; Snow et al., 1980; Perkins and Salomon, 1992; Cronbach and Shavelson, 2004).

During the 1980s and until the early 2000s, others were looking at whole school reform models such as Slavin’s comprehensive reading improvement model and Wang’s (Wang, 1992; Taylor and Wang, 1997) adaptive strategy-based model for addressing achievement gaps among school-age children in low performing schools. These school reform models led researchers to question the reliance of program evaluations on methodologies that put little value in non-randomized single case or correlational studies demonstrating larger effect sizes than those of large scale matched control studies (cf. Wang and Walberg, 1985; Branson, 1987; Cohen, 1990; McCombs, 1991c, 2009; Baker et al., 1994; Wang et al., 1994; McCombs and Quiat, 2002; McCombs and Vakili, 2005; Berliner, 2009; Slavin, 2011).

As I have heard many say over my 50-year professional career, “there is nothing new under the sun” and “we step on the toes of research leaders rather than stand on their shoulders.” It is gratifying albeit frustrating at times to realize that in the field of learning strategies I have seen both sayings come true. On the gratifying end, is how learning strategies are now defined.

Evolutions (Or Not) in Definitions of “Learning Strategies”

Weinstein and Mayer (1986) defined learning strategies broadly as “behaviors and thoughts that a learner engages in during learning” that are “intended to influence the learner’s encoding process” (p. 315). Mayer (1992, 1998, 2001) later specifically defined these strategies as behaviors of a learner that are intended to influence how the learner processes information. Self-regulation (Zimmerman, 1989, 2000) describes how individuals manage their personal learning process, especially how to plan, monitor, focus on, and evaluate their own learning. These early definitions from the educational literature reflect the roots of learning strategies in cognitive science, with its essential assumptions that human beings process information and that learning involves such information processing. Other researchers (e.g., Paris et al., 1984; Swartz and Perkins, 1989; Blakey and Spence, 1990; Barrell, 1995; Owens, 2016) claim that learning strategies are involved in all learning in and outside of school contexts, regardless of the content. Thus, a mix of learning strategies is recommended for use in the learning and teaching math, science, history, languages, and other subjects, in classroom or online learning settings and more informal learning environments.

This applied research focus continues through the present time and has emerged in recent studies in both traditional and digitally mediated contexts with results supporting a whole child, holistic view of what constitutes the ideal learning environment (cf. McCombs, 1993, 2010, 2011a, 2012, 2013a,b, 2014; Reigeluth and Garfinkle, 1994; Reigeluth et al., 2017).

A New Paradigm of Strategic Learning

The ongoing contribution of Weinstein and Mayer’s (Weinstein and Mayer, 1986) research for learning strategies research was recognized by Tsai (2009) whose Model of Strategic e-Learning was used to explain and evaluate student e-learning from metacognitive perspectives. An in-depth interview, pilot study and main study were used to construct the model and develop the Online Learning Strategies Scale (OLSS). The model framework has four dimensions of characteristics of e-learning environments and three core domains (perceived-skill, affection, and self-regulation) of student e-learning strategies. The OLSS instrument provides a diagnostic instrument for e-learning researchers, system designers, curriculum developers, and instructors to evaluate students’ e-learning strategies in their experiment, design, and development. Although instructors can accommodate students with different levels of metacognitive skills by selecting suitable teaching objectives and activities, Tsai (2009) pointed out that curriculum design and instruction are also needed to develop student metacognitive abilities and provide scaffolding for students to use holistic learning strategies for facilitating their learning achievement and motivation as suggested by Weinstein and her colleague’s research (Weinstein and Mayer, 1986; Ridley et al., 1994; Weinstein and McCombs, 1994, 1998).

Tsai (2009) concluded that Weinstein’s (Weinstein, 1978) concept of “strategic learning” explained student learning strategies based on metacognitive perspectives. Weinstein and her colleagues (Weinstein, 1978; Weinstein and Palmer, 1990) are further credited with the development of the “LASSI” to diagnose the strengths and weaknesses of students in relation to the above aspects of learning strategies. To elevate the empirical value of the LASSI for a wide range of young adults, Cano (2006) conducted an in the in-depth analysis to validate the LASSI, which involved conceptually grouping LASSI subscales into three categories: affective strategies, goal strategies, and comprehension monitoring strategies. These three main categories were then shown to be involved in what Weinstein (1978) called strategic learning that she validated in diverse learning contents and content areas. Thus, modifying the construct of strategic learning of Weinstein (1978) became necessary and provided an impetus for the latest version of the LASSI (Weinstein et al., 2016). This version of the LASSI and my personal tribute to Dr. Weinstein is included as Supplementary Material at the end of this article.

Summary and Conclusion

From this selective but broadly based historical review of learning strategies research over the past more than 50 years it is clear that the field is thriving. Research on “learning strategies” began in the 1960s and received significant funding from the Defense Advanced Research Projects Agency in the early 1970s. Invitational conferences were held that included well-known researchers and graduate students who were identifying various study and other learning strategies in their doctoral programs. This was a new direction and had its foundations in generative learning theory (Wittrock, 1974a), Bandura’s (Bandura, 1963, 1972) cognitive-behavioral theories, Glaser (1963), Glaser and Resnick (1972), Glaser and Strauss (1967), and Seidel’s (Seidel, 1969, 1971) adaptive learning theories, and individualized learning theories of Richard Snow (1974, 1977) and Cronbach (1951, 1957, 1975) and Suppes (1972, 1973). It led to exploring computer-based individualized instructional modeling based on complex empirical algorithms and heuristics predicting learning and training task performance (Parkhurst and McCombs, 1979). Those who began and continued researching learning strategies are now leaving research legacies to their graduate students, institutions, researchers, and practitioners at large.

This historical review revealed that a focus on metacognition is one unifying theme. Metacognitive strategies have continued to prove effective for diverse student populations and language learners, with findings that support both general learning strategies and task-specific strategies. A second unifying theme is a focus on the whole learner and on interventions that address cognitive, metacognitive, affective, physical, cultural, and social needs. Learner-centered principles and practices are becoming more widely used in both traditional and more innovative digital environments that recognize the value of close mentoring relationships and caring support as well as collaborative, culturally responsive, rigorous learning goals, and shared responsibility and accountability for student learning success.

Major theoretical orientations continue to be based in cognitive science but are increasingly being linked to other sciences such as motivational psychology, social and emotional intelligence, neuro-psychology, brain studies, and a variety of social and engineering sciences. Researchers looking at military training and psychological issues such as the growing number of post-traumatic stress disorders and suicides among enlisted and returning military personnel are also a direction where current learning strategies research holds promise. Many of the effective interventions for military personnel may also be of value in addressing growing stress and emotional disorders among adolescents, including the rising suicide rate in middle and high school population nationally and internationally. The field is likely to grow and expand into the future with ongoing needs to further refine and design learning strategies that meet the needs of learners in an increasing complex and diverse nation and world.

It is gratifying to know that the learning strategies research field has found favor and funding during my professional career and is still growing and expanding into new and exciting twenty-first century areas. It is even more gratifying know that a dear friend and colleague—Dr. Claire Ellen Weinstein—was one of the main contributors during her lifetime in the learning strategies research and practice arena.

References

• 1

  AfflerbachP. P. (1990). The influence of prior knowledge on expert readers’ main idea construction strategies. Read. Res. Q.25, 31–46.10.2307/747986

  • CrossRef
  • Google Scholar

• 2

  AlexanderP. A. (2016). Benchmark center for empowered learning: Dr. Patricia Alexander on 21^st century learning. Bebacnchmark Mag.2016, 10.

  • Google Scholar

• 3

  AllsopY. (2016). Does Technology Improve Learning? The Value of Constructivist Approaches to Technology-Based Learning. Available at: http://www.ictinpractice.com/does-technology-improve-learning-the-value-of-constructivist-approaches-to-technology-based-learning/

  • Google Scholar

• 4

  AmesC. (1992). Classrooms: goals, structures, and student motivation. J. Educ. Psychol.84, 261–271.10.1037/0022-0663.84.3.261

  • CrossRef
  • Google Scholar

• 5

  AndermanE. M. (2010). Reflections on Wittrock’s generative model of learning: a motivation perspective. Educ. Psychol.45, 55–60.10.1080/00461520903433620

  • CrossRef
  • Google Scholar

• 6

  AndersonR. C. (1977). Schema-Directed Processes in Language Comprehension (Tech. Report 50). Urbana-Champaign: Center for the Study of Reading, University of Illinois.

  • Google Scholar

• 7

  AndersonR. C.BiddleW. B. (1975). “On asking people questions about what they are reading,” in The Psychology of Learning and Motivation, ed. BowerG. H. (New York: Academic Press), 9.

  • Google Scholar

• 8

  APA Task Force on Psychology in Education. (1993). Learner-Centered Psychological Principles: Guidelines for School Redesign and Reform. Washington, DC: American Psychological Association and MidContinent Regional Educational Laboratory.

  • Google Scholar

• 9

  APA Work Group of the Board of Educational Affairs. (1997). Learner-Centered Psychological Principles: A Framework for School Reform and Redesign. Washington, DC: American Psychological Association.

  • Google Scholar

• 10

  AtkinsonR. C. (1968). Computerized instruction and the learning process. Am. Psychol.23, 225–239.10.1037/h0020791

  • CrossRef
  • Google Scholar

• 11

  AtkinsonR. C.ShiffrinR. M. (1968). “Chapter: human memory: a proposed system and its control processes,” in The Psychology of Learning and Motivation, Vol. 2, eds SpenceK. W.SpenceJ. T. (New York: Academic Press), 89–195.

  • Google Scholar

• 12

  AusubelD. (1963). The Psychology of Meaningful Verbal Learning. New York: Grune & Stratton.

  • Google Scholar

• 13

  AusubelD. P. (1960). The use of advance organizers in the learning and retention of meaningful verbal material. J. Educ. Psychol.51, 267–272.10.1037/h0046669

  • CrossRef
  • Google Scholar

• 14

  AusubelD. P. (1968). Educational Psychology: A Cognitive View. New York: Holt, Rinehart & Winston.

  • Google Scholar

• 15

  AusubelD. P.FitzgeraldD. (1962). Organizer, general background and antecedent learning variables in sequential verbal learning. J. Educ. Psychol.53, 243–249.10.1037/h0040210

  • CrossRef
  • Google Scholar

• 16

  AusubelD. P.YoussefM. (1963). Role of discriminability in meaningful parallel learning. J. Educ. Psychol.54, 331–336.10.1037/h0042767

  • CrossRef
  • Google Scholar

• 17

  BackS. M.McCombsB. L. (1984). Factors Critical to the Implementation of Self-Paced Instruction: A Background Review (AFHRL-TP-84-14). Lowry AFB, CO: Air Force Human Resources Laboratory, ID Division.

  • Google Scholar

• 18

  BackS. M.McCombsB. L. (1985). Field Test of the CAI Handbook: Volumes I and II (AFHRL Informal Rep). Lowry AFB, CO: Air Force Human Resources Laboratory, ID Division.

  • Google Scholar

• 19

  BakerE. T.WangM. C.WalbergH. J. (1994). The inclusive school: synthesis of research/the effects of inclusion on learning. Educ. Leadersh.52, 33–35.

  • Google Scholar

• 20

  BanduraA. (1963). Social Learning and Personality Development. New York: Holt, Rinehart, and Winston.

  • Google Scholar

• 21

  BanduraA. (1972). “Modeling theory: some traditions, trends, and disputes,” in Recent Trends in Social Learning Theory, ed. ParkeR. D. (New York: Academic Press, Inc), 38–62.

  • Google Scholar

• 22

  BanduraA. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychol. Rev.84, 191–215.10.1037/0033-295X.84.2.191

  • CrossRef
  • Google Scholar

• 23

  BanduraA. (1986). Social Foundations of Thought and Action: A Social Cognitive Theory. Englewood Cliffs, NJ: Prentice Hall.

  • Google Scholar

• 24

  BanduraA. (1993). Perceived self-efficacy in cognitive development and functioning. Educ. Psychol.28, 149–167.10.1207/s15326985ep2802_3

  • CrossRef
  • Google Scholar

• 25

  BanduraA. (1997). Self-Efficacy: The Exercise of Control. New York: Freeman.

  • Google Scholar

• 26

  BarrellJ. (1995). Teaching for Thoughtfulness: Classroom Strategies to Enhance Intellectual Development. White Plains, NY: Longman.

  • Google Scholar

• 27

  BeckI. L.OmansenR. C.MckeownM. G. (1982). An instructional redesign of reading lessons: effects on comprehension. Read. Res. Q.17, 462–681.10.2307/747566

  • CrossRef
  • Google Scholar

• 28

  BellN. (1991). Gestalt imagery: a critical factor in language comprehension. Ann. Dyslexia41, 246260.10.1007/BF02648089

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 29

  BelmontJ. M.ButterfieldE. C. (1969). “The relations of short-term memory to development and intelligence,” in Advances in Child Development and Behavior, Vol. 4, eds LipsittL. C.ReeseH. W. (New York: Academic), 29–82.

  • Google Scholar

• 30

  BerlinerD. C. (2000). “Research policy and practice: the great disconnect,” in Research Essentials: An Introduction to Design and Practice, eds LapanS. D.QuartaroliM. T. (San Francisco, CA: John Wiley/Jossey-Bass), 295–326.

  • Google Scholar

• 31

  BerlinerD. C. (2009). Introduction to special issue on research on teaching. Educ. Psychol.18, 125–126.10.1080/00461528309529269

  • CrossRef
  • Google Scholar

• 32

  Bill and Melinda Gates Foundation. (2014). Teachers Know Best: Teachers’ Views on Professional Development. Available at: http://k12education.gatesfoundation.org/wp-content/uploads/2015/04/Gates-PDMarketResearch-Dec5.pdf

  • Google Scholar

• 33

  BlakeyE.SpenceS. (1990). Developing Metacognition. Available at: http://eric.ed.gov/?id=ED327218

  • Google Scholar

• 34

  BlantonW. E.WoodK. D. (1984). Direct instruction in reading comprehension test-taking skill. Read World24, 10–19.10.1080/19388078409557797

  • CrossRef
  • Google Scholar

• 35

  BloomB. S. (1980). All Our Children Learning. New York: McGraw-Hill.

  • Google Scholar

• 36

  BloomB. S. (1985). Developing Talent in Young People. New York: Ballantine Books.

  • Google Scholar

• 37

  BloomB. S. (ed.) (1956). Taxonomy of Educational Objectives: Cognitive Domain. New York: David McKay Company, Inc.

  • Google Scholar

• 38

  BloomB. S.EngelhartM. D.FurstE. J.HillW. H.KrathwohlD. R. (1956). Taxonomy of Educational Objectives: The Classification of Educational Goals Handbook I: Cognitive Domain. New York: David McKay Company.

  • Google Scholar

• 39

  BorkowskiJ.CarrM.PresselyM. (1987). “Spontaneous” strategy use: perspectives from metacognitive theory. Intelligence11, 61–75.10.1016/0160-2896(87)90027-4

  • CrossRef
  • Google Scholar

• 40

  BotkinJ. N. (1980). Towards More Effective Teaching and Learning: What Can Research in the Brain Sciences Contribute? A Survey of Some Recent Research Efforts and Their Implications for Education. International Center for Integrative Studies, New York, NY. Washington, DC: Horace Mann Learning Center, U.S. Department of Education. Available at: http://files.eric.ed.gov/fulltext/ED200402.pdf

  • Google Scholar

• 41

  BoyatzisR. (2008). Becoming a Resonant Leader: Develop Your Emotional Intelligence, Renew Your Relationships, Sustain Your Effectiveness. Boston, MA: Harvard Business Press.

  • Google Scholar

• 42

  BransfordJ. D.BrownA. L.CockingR. R. (2000). How People Learn: Brain, Mind, Experience, and School, Expanded Edn. (Washington, DC: National Academy Press).

  • Google Scholar

• 43

  BransfordJ. D.HeldmeyerK. (1983). “Learning from children learning,” in Learning in Children: Progress in Cognitive Development Research, eds BisanzJ.BisanzG.KailR. (New York: Springer-Verlag), 171–190.

  • Google Scholar

• 44

  BransonR. K. (1987). Why schools can’t improve: the upper limit hypothesis. J. Instr. Dev.10, 15–26.10.1007/BF02905307

  • CrossRef
  • Google Scholar

• 45

  BriggsS. (2015). How Teaching Mindfulness Benefits Learning. Available at: http://www.opencolleges.edu.au/informed/features/how-teaching-mindfulness-benefits-learning/

  • Google Scholar

• 46

  BrownA. L. (1978). “Knowing when, where, and how to remember: a problem of metacognition,” in Advances in Instructional Psychology, 1, ed. GlaserR. (Hillsdale, NJ: Erlbaum), 77–165.

  • Google Scholar

• 47

  BrownA. L. (1990). Domain-specific principles affect learning and transfer in children. Cogn. Sci.14, 107–133.10.1016/0364-0213(90)90028-U

  • CrossRef
  • Google Scholar

• 48

  BrownA. L. (1992). Design experiments: theoretical and methodological challenges in creating complex interventions in classroom settings. J. Learn. Sci.2, 141–178.10.1207/s15327809jls0202_2

  • CrossRef
  • Google Scholar

• 49

  BrownA. L.BransfordJ. D.FerraraR. A.CampioneJ. C. (1983a). “Learning, remembering and understanding,” in Carmichel’s Manual of Child Psychology, Vol. 1, eds FlavellJ. H.MarkhamE. M. (New York: John Wiley), 14–21.

  • Google Scholar

• 50

  BrownA. L.BransfordJ. D.FerraraR. A.CampioneJ. C. (1983b). “Learning, remembering, and understanding,” in Handbook of Child Psychology: Cognitive Development, Vol. 3, eds FlavellJ. H.MarkmanE. M. (New York: Wiley), 77–166.

  • Google Scholar

• 51

  BrownA. L.PalincsarA. S. (1982). Inducing strategic learning from texts by means of informed, self-control training. Top. Learn. Learn. Disabil.2, 1–17.

  • Google Scholar

• 52

  BullS. L.WittrockM. C. (1973). Imagery in the learning of verbal definitions. Br. J. Educ. Psychol.43, 289–293.10.1111/j.2044-8279.1973.tb02269.x

  • CrossRef
  • Google Scholar

• 53

  BushwellerK. (2016). From the Editor: A Critical Look at the Evolution, and the Future, of Personalized Learning. Special Report – Personalized Learning: The Next Generation. Education Week. Available at: http://www.edweek.org/ew/articles/2016/10/19/from-the-editor-a-critical-look-at.html

  • Google Scholar

• 54

  CadinuM.MaassA.LombardtM.FrigerioS. (2006). Stereotype threat: the moderating role of locus of control beliefs. Eur. J. Soc. Psychol.36, 183–197.10.1002/ejsp.303

  • CrossRef
  • Google Scholar

• 55

  CalvertL. (2016). Moving from Compliance to Agency: What Teachers Need to Make Professional Learning Work. Oxford, OH: Learning Forward and NCTAF.

  • Google Scholar

• 56

  CanoF. (2006). An in-depth analysis of the learning and study strategies inventory (LASSI). Educ. Psychol. Meas.66, 1023–1038.10.1177/0013164406288167

  • CrossRef
  • Google Scholar

• 57

  CarrM.KurtzB. E.SchneiderW.TurnerL. A.BorkowskiJ. G. (1989). Strategy acquisition and transfer among U.S. and German children: environmental influences on metacognitive development. Dev. Psychol.25, 765–771.10.1037/0012-1649.25.5.765

  • CrossRef
  • Google Scholar

• 58

  CarrollJ. B. (1977). “Characteristics of successful second language learners,” in Viewpoints on English as a Second Language, eds BurtM.DulayH.FinocchiaroM. (New York: Regents), 1–7.

  • Google Scholar

• 59

  CarverD. W.JuddW. A.McCombsB. L.et al (1977). A Survey and Analysis of Military Computer-Based Training Systems: A Descriptive and Predictive Model for Evaluating Instructional Systems (MDC E1570). Arlington, VA: Defense Advanced Research Projects Agency.

  • Google Scholar

• 60

  ChamotA. U.MeloniC. F.BartosheskyA.KadahR.KeatleyC. (2004). Sailing with the 5 C’s of Learning Strategies: A Resource Guide for Secondary Foreign Language Educators. Georgetown University and the Center for Applied Linguistics. Washington, DC: National Capital Language Resource Center.

  • Google Scholar

• 61

  ChipmanS. F.SegalJ. W.GlaserR. (1985). Thinking and Learning Skills: Research and Open Questions, Vol. 2. Hillsdale: Erlbaum.

  • Google Scholar

• 62

  ChuG.SchrammW. (1975). Learning from Television. What the Research Says. Stanford, CA: Institute for Communication Research (ERIC No. ED 109 985).

  • Google Scholar

• 63

  CohenA. D. (1998). Strategies in Learning and Using a Second Language. New York: Longman.

  • Google Scholar

• 64

  CohenE. G. (1990). Teaching in multicultural heterogeneous classrooms: findings from a model program. McGill J. Educ.26, 7–23.

  • Google Scholar

• 65

  ColemanJ. S. (1966). Equality of Educational Opportunity Study. Washington, DC: U.S. Department of Health, Education, and Welfare.

  • Google Scholar

• 66

  CollinsA. (1978). Explicating the Tacit Knowledge in Teaching and Learning. (Tech. Rep. 3889). Cambridge, MA: Bolt, Beranek & Newman. (AlD A0587411).

  • Google Scholar

• 67

  CorsiniD. A. (1971). Memory: interaction of stimulus and organismic factors. Hum. Dev.14, 227–235.10.1159/000271217

  • CrossRef
  • Google Scholar

• 68

  CronbachL.SnowR. (1977). Aptitudes and Instructional Methods: A Handbook for Research on Interactions. New York: Irvington.

  • Google Scholar

• 69

  CronbachL. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika16, 297–334.10.1007/BF02310555

  • CrossRef
  • Google Scholar

• 70

  CronbachL. J. (1957). The two disciplines of scientific psychology. Am. Psychol.12, 671–684.10.1037/h0043943

  • CrossRef
  • Google Scholar

• 71

  CronbachL. J. (1975). Beyond the two disciplines of scientific psychology. Am. Psychol.30, 116–127.10.1037/h0076829

  • CrossRef
  • Google Scholar

• 72

  CronbachL. J.MeehlP. E. (1955). Construct validity in psychological tests. Psychol. Bull.52, 281–302.10.1037/h0040957

  • CrossRef
  • Google Scholar

• 73

  CronbachL. J.ShavelsonR. J. (2004). My current thoughts on coefficient alpha and successor procedures. Educ. Psychol. Meas.64, 391–418.10.1177/0013164404266386

  • CrossRef
  • Google Scholar

• 74

  d’AillyH. (2003). Children’s autonomy and perceived control in learning: a model of motivation and achievement in Taiwan. J. Educ. Psychol.95, 84–96.10.1037/0022-0663.95.1.84

  • CrossRef
  • Google Scholar

• 75

  d’AillyH. (2004). The role of choice in children’s learning: a distinctive cultural and gender difference in efficacy, interest, and effort. Can. J. Behav. Sci.36, 17–29.10.1037/h0087212

  • CrossRef
  • Google Scholar

• 76

  DansereauD. F. (1978). “The development of a learning strategies curriculum,” in Learning Strategies, ed. O’NeillH. F.Jr. (New York: Academic Press), 1–29.

  • Google Scholar

• 77

  DansereauD. F.LarsonC. O.SpurlinJ. E. (1983). Cooperative learning: impact on acquisition of knowledge and skills. Paper Presented at the Annual Meeting of the American Educational Research Association, Montreal.

  • Google Scholar

• 78

  DavisM. R. (2016). Checking up on personalized learning. Educ. Week36, 4. Available at: http://www.edweek.org/ew/articles/2016/10/19/checking-up-on-personalized-learning-pioneers.html

  • Google Scholar

• 79

  DeciE. L. (1975). Intrinsic Motivation. New York: Plenum Publishing Co. Japanese Edition, 1980. Tokyo: Seishin Shobo.

  • Google Scholar

• 80

  DeciE. L. (1980). The Psychology of Self-Determination. Lexington, MA: D. C. Heath (Lexington Books), 1985. Japanese Edition, Tokyo: Seishin Shobo.

  • Google Scholar

• 81

  DeciE. L.RyanR. M. (1985). Intrinsic Motivation and Self-Determination in Human Behavior. New York: Plenum.

  • Google Scholar

• 82

  DeciE. L.RyanR. M. (2000). The ‘what’ and ‘why’ of goal pursuits: human needs and the self-determination of behavior. Psychol. Inq.11, 227–268.10.1207/S15327965PLI1104_01

  • CrossRef
  • Google Scholar

• 83

  DeciE. L.RyanR. M. (2002). Overview of Self-Determination Theory: An Organismic Dialectical Perspective. Rochester, NY: University of Rochester Press.

  • Google Scholar

• 84

  DeciE. L.RyanR. M. (2006). The Handbook of Self-Determination Research. Rochester, NY: University of Rochester Press.

  • Google Scholar

• 85

  DerryS. J. (1990). “Learning strategies for acquiring useful knowledge,” in Dimensions of Thinking and Cognitive Instruction, eds JonesB. F.IdolL. (Hillsdale, NJ: Lawrence Erlbaum), 347–379.

  • Google Scholar

• 86

  DeweyJ. (1910). How We Think. New York: D. C. Heath & Co.

  • Google Scholar

• 87

  DobrovolnyJ. L.McCombsB. L.JuddW. A. (1979). Study Skills Package: Development and Evaluation (AFHRL-TR-79-43). Lowry AFB, CO: Air Force Human Resources Laboratory, Technical Training Division.

  • Google Scholar

• 88

  DriscollM. (2002). Web-Based Training: Creating E-Learning Experiences, 2nd Edn. San Francisco, CA: Jossey-Bass/Pfeiffer.

  • Google Scholar

• 89

  DuffyT. M.CunninghamD. J. (1996). “Constructivism: implications for the design and delivery of instruction,” in Educational Communications and Technology, ed. JonassenD. H. (New York: Simon & Schuster Macmillan), 170–199.

  • Google Scholar

• 90

  Editorial Projects in Education Research Center. (2011). Issues A-Z: no child left behind. Educ. Week. Available at: http://www.edweek.org/ew/issues/no-child-left-behind/

  • Google Scholar

• 91

  EkstromR. B.GoertzM. E.PollackJ. M.RockD. A. (1986). Who drops out of high school and why? Findings of a national study. Teach. Coll. Rec.87, 356–371.

  • Google Scholar

• 92

  EliasM. J.FerritoJ. J.MoceriD. C. (2016). The Other Side of the Report Card: Assessing Students’ Social, Emotional, and Character Development. Palo Alto, CA: Corwin.

  • Google Scholar

• 93

  EntwistleN.HounsellD. (1975). “How students learn,” in Institute for Research and Development in Post-Compulsory Education, University of Lancaster, Bailrigg, Lancaster, UK.

  • Google Scholar

• 94

  ErtmerP. A.NewbyT. J. (1996). The expert learner: strategic, self-regulated, and reflective. Instr. Sci.24, 1–24.10.1007/BF00156001

  • CrossRef
  • Google Scholar

• 95

  FieldingM. (1997). Beyond school effectiveness and school improvement: lighting the slow fuse of possibility. Curric. J.8, 7–27.10.1080/09585176.1997.11070759

  • CrossRef
  • Google Scholar

• 96

  FieldingM. (2007). Jean Rudduck (1937-2007) ‘Carving a new order of experience’: a preliminary appreciation of the work of Jean Rudduck in the field of student voice. Educ. Action Res.15, 323–336.10.1080/09650790701514234

  • CrossRef
  • Google Scholar

• 97

  FieldingM. (2011). ‘Rudduck, Jean (1937–2007)’ Oxford Dictionary of National Biography. Oxford: Oxford University Press. Available at: http://www.oxforddnb.com/view/article/98826

  • Google Scholar

• 98

  FieldingM. (2015a). “Student voice as deep democracy,” in The Connected School – A Design for Well-Being, ed. McLauglinC. (Pearson: National Children’s Bureau), 26–32. Available at: https://research.pearson.com/connectedschool

  • Google Scholar

• 99

  FieldingM. (2015b). Looking Back, Looking Forward. England, UK: Keynote address at the Student Voice Conference, University of Cambridge.

  • Google Scholar

• 100

  FieldingM.KirbyP. (2009). Developing student-led reviews: an exploration of innovative practice in primary, special and secondary schools. Paper Presented at the New Developments in Student Voice Conference, London, Institute of Education, November.

  • Google Scholar

• 101

  FinnJ. D.RockD. A. (1997). Academic success among students at risk for school failure. J. Appl. Psychol.82, 221–234.10.1037/0021-9010.82.2.221

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 102

  FlavellJ. H. (1963). The Developmental Psychology of Jean Piaget. New York: D. Van Nostrand.

  • Google Scholar

• 103

  FlavellJ. H. (1971). First discussant’s comments: what is memory development the development of?Hum. Dev.14, 272–278.10.1159/000271221

  • CrossRef
  • Google Scholar

• 104

  FlavellJ. H. (1976). “Metacognitive aspects of problem solving,” in The Nature of Intelligence, ed. ResnickL. B. (Hillsdale, NJ: Erlbaum), 231–236.

  • Google Scholar

• 105

  FlavellJ. H. (1979). Metacognition and cognitive monitoring: a new area of cognitive-developmental inquiry. Am. Psychol.34, 906–911.10.1037/0003-066X.34.10.906

  • CrossRef
  • Google Scholar

• 106

  FlavellJ. H. (1981). “Cognitive monitoring,” in Children’s Oral Communication Skills, ed. DicksonW. P. (New York: Academic Press), 35–60.

  • Google Scholar

• 107

  FlavellJ. H. (1987). “Speculation about the nature and development of metacognition,” in Metacognition, Motivation, and Understanding, eds WeinertF.KluweR. (Hillsdale, NJ: Lawrence Erlbaum), 21–29.

  • Google Scholar

• 108

  FlavellJ. H. (2004). Theory-of-mind development: retrospect and prospect. Merrill Palmer Q.50, 6.10.1353/mpq.2004.0018

  • CrossRef
  • Google Scholar

• 109

  FranklV. E. (1984). Man’s Search for Meaning, rev. Edn. New York: Washington Square Press, 86.

  • Google Scholar

• 110

  GagneR. (1971). Learning Hierarchies. Trenton, NJ: Prentice Hall, 63–84.

  • Google Scholar

• 111

  GlaserB. G.StraussA. L. (1967). The Discovery of Grounded Theory: Strategies for Qualitative Research. Available at: http://faculty.babson.edu/krollag/org_site/craft_articles/glaser_strauss.html

  • Google Scholar

• 112

  GlaserR. (1963). Instructional technology and the measurement of learning outcomes. Am. Psychol.18, 519–521.10.1037/h0049294

  • CrossRef
  • Google Scholar

• 113

  GlaserR.ResnickL. B. (1972). Instructional psychology. Annu. Rev. Psychol.23, 207–276.10.1146/annurev.ps.23.020172.001231

  • CrossRef
  • Google Scholar

• 114

  GolemanD. (1995). Emotional Intelligence. New York: Bantam Books.

  • Google Scholar

• 115

  GolemanD. (2016). Five Steps to Develop Emotional Intelligence – What Makes a Leader? Emotional and Social Intelligence. Available at: https://www.linkedin.com/pulse/five-steps-develop-emotional-intelligence-daniel-goleman

  • Google Scholar

• 116

  GolemanD.BoyatzisR. E.McKeeA. (2002). Primal Leadership: Realizing the Power of Emotional Intelligence. Boston: Harvard Business School Press.

  • Google Scholar

• 117

  GoodT. L.BrophyJ. (1986). “Teacher behaviour and student achievement,” in Handbook of Research on Teaching, 3rd Edn, ed. WittrockM. C. (New York, NY: Macmillan), 328–775.

  • Google Scholar

• 118

  GoodwinB. (2016). Flip the switch on fate control: research spotlights an invisible barrier to student success. Educ. Leadersh.74, 83–84.

  • Google Scholar

• 119

  GreenbergL. S. (2017). Emotion-Focused Therapy, Revised Edition. Washington, DC: American Psychological Association.

  • Google Scholar

• 120

  HagenJ. W.KingsleyP. R. (1968). Labeling effects in short-term memory. Child Dev.39, 113–121.10.2307/1127363

  • CrossRef
  • Google Scholar

• 121

  HannumW. H.McCombsB. L. (2008). Enhancing distance learning for today’s youth with learner-centered principles. Educ. Technol.48, 11–21.

  • Google Scholar

• 122

  HareR. M. (1963). Freedom and Reason. Oxford: Oxford United Press.

  • Google Scholar

• 123

  HaroldB. (2016). Personalized learning: what does the research say? Special report – personalized learning: the next generation. Educ. Week. Available at: http://www.edweek.org/ew/articles/2016/10/19/personalized-learning-what-does-the-research-say.html

  • Google Scholar

• 124

  HarterS. (2006). The Cognitive and Social Construction of the Developing Self. New York: Guilford Press.

  • Google Scholar

• 125

  HarterS. (2012). The Construction of the Self: Developmental and Sociocultural Foundations. New York: Guilford Press.

  • Google Scholar

• 126

  HarterS. (2016). The Cognitive and Social Construction of the Developing Self, 2nd Edn. New York: Guilford Press.

  • Google Scholar

• 127

  HolleyC.DansereauD. F.McdonaldA.GarlandJ. C.CollinsK. W. (1979). Evaluation of a hierarchical mapping technique as an aid to prose processing. Contemp. Educ. Psychol.4, 227–237.10.1016/0361-476X(79)90043-2

  • CrossRef
  • Google Scholar

• 128

  HowardG. S. (1986). Dare We Develop a Human Science?Notre Dame, IN: Academic Publications.

  • Google Scholar

• 129

  InhelderB.PiagetJ. (1958). The Growth of Logical Thinking from Childhood to Adolescence. New York: Basic Books.

  • Google Scholar

• 130

  JonassenD. H. (1991). Evaluating constructivistic learning. Educ. Technol.31, 28–33.

  • Google Scholar

• 131

  JonassenD. H. (2001). “Objectivism versus constructivism: do we need a new philosophical paradigm?” in Classic Writings on Instructional Technology, eds ElyD.PlompT. (Englewood: Libraries Unlimited), 53–65.

  • Google Scholar

• 132

  JuddW. A.McCombsB. L.DobrovolnyJ. L. (1979). “Time management as learning strategy for individualized instruction,” in Cognitive and Affective Learning Strategies, ed. O’NeilH. F.Jr. (New York: Academic Press), 133–175.

  • Google Scholar

• 133

  KaplanA. (2016). Research on motivation and achievement: infatuation with constructs and losing sight of the phenomenon. Paper Presented at the Biennial Meeting of the International Conference on Motivation, Thessaloniki, Greece. Available at: https://www.researchgate.net/publication/308104568

  • Google Scholar

• 134

  KentridgeR. W.ColeG. G.HeywoodC. A. (2004). The primacy of chromatic edge processing in normal and cerebrally achromatopsic subjects. Prog. Brain Res.144, 161–167.10.1016/S0079-6123(03)14411-1

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 135

  KnowlesM. (1975). Self-Directed Learning: A Guide for Learners and Teachers. Englewood Cliffs, NJ: Prentice Hall.

  • Google Scholar

• 136

  KoppC. B. (1982). Antecedents of self-regulation: a developmental perspective. Dev. Psychol.18, 199–214.10.1037/0012-1649.18.2.199

  • CrossRef
  • Google Scholar

• 137

  LangerE. J. (1989). Mindfulness. Reading, MA: Addison-Wesley.

  • Google Scholar

• 138

  LathamB.LenzB.Vander ArkT. (2016). Preparing Students for a Project-Based World. It’s a Project-Based World: A Thought Leadership Campaign. Available at: http://www.gettingsmart.com/its-a-project-based-world/

  • Google Scholar

• 139

  LaytonL. (2015). Study: Billions of Dollars in Annual Teacher Training Is Largely a Waste. The Washington Post. Available at: https://www.washingtonpost.com/local/education/study-billionsof-dollars-in-annual-teacher-training-is-largely-awaste/2015/08/03/c4e1f322-39ff-11e5-9c2d-ed991d848c48_story.html

  • Google Scholar

• 140

  LefcourtH. (1976). Locus of Control: Current Trends in Theory Research. Hillsdale, NJ: Erlbaum.

  • Google Scholar

• 141

  LeherisseyB. L. (1971). The Effects of Stimulating State Epistemic Curiosity on State Anxiety and Performance in a Complex Computer-Assisted Learning Task (Report No. 23). Tallahassee: Florida State University.

  • Google Scholar

• 142

  LeherisseyB. L.O’NeilH. F.Jr.HansenD. N. (1971). Effects of memory support on state anxiety and performance in computer-assisted learning. J. Educ. Psychol.62, 413–420.10.1037/h0031635

  • CrossRef
  • Google Scholar

• 143

  LindsayP. H.NormanD. A. (1972). Human Information Processing; an Introduction to Psychology. New York: Academic Press.

  • Google Scholar

• 144

  LindsayP. H.NormanD. A. (1977). Human Information Processing, 2nd Edn. New York: Academic Press.

  • Google Scholar

• 145

  LivingstonJ. A. (1997). Metacognition: An Overview. Available at: http://gse.buffalo.edu/fas/shuell/CEP564/Metacog.htm

  • Google Scholar

• 146

  MagnoC. (2010). Assessing academic self-regulated learning among Filipino college students: the factor structure and item fit. Int. J. Educ. Psychol. Assess.5, 61–78.

  • Google Scholar

• 147

  MagnoC. (2011). Validating the academic self-regulated learning scale with the motivated strategies for learning questionnaire (MSLQ) and learning and study strategies inventory (LASSI). Int. J. Educ. Psychol. Assess.7, 56–73.

  • Google Scholar

• 148

  MaloufD. B.TaymansJ. M. (2016). Anatomy of an evidence base. Educ. Res.45, 454–459.10.3102/0013189X16678417

  • CrossRef
  • Google Scholar

• 149

  MayerR. (2016). How can brain research inform academic learning and instruction?Educ. Psychol. Rev.11, 1–12.10.1007/s10648-016-9391-1

  • CrossRef
  • Google Scholar

• 150

  MayerR. E. (1992). Cognition and instruction: their historic meeting within educational psychology. J. Educ. Psychol.84, 405–412.10.1037/0022-0663.84.4.405

  • CrossRef
  • Google Scholar

• 151

  MayerR. E. (1998). Does the brain have a place in educational psychology?Educ. Psychol. Rev.10, 405–412.10.1023/A:1022837300988

  • CrossRef
  • Google Scholar

• 152

  MayerR. E. (2001). Multimedia Learning. New York: Cambridge University Press.

  • Google Scholar

• 153

  MayerR. E. (2003). “E. L. Thorndike’s enduring contributions to educational psychology,” in Educational Psychology: A Century of Contributions, eds ZimmermanB. J.SchunkD. H. (Washington, DC: American Psychological Association), 113–154.

  • Google Scholar

• 154

  MayerR. E. (2005). The Cambridge Handbook of Multimedia Learning. Cambridge, UK: Cambridge University Press.

  • Google Scholar

• 155

  MayerR. E. (2011). Applying the Science of Learning. Boston: Pearson.

  • Google Scholar

• 156

  McCombsB. L. (1982a). Learner satisfaction and motivation: capitalizing on strategies for positive self-control. Perform. Instr.21, 3–6.10.1002/pfi.4170210405

  • CrossRef
  • Google Scholar

• 157

  McCombsB. L. (1982b). Transitional learning strategies research into practice: focus on the student in technical training. J. Instr. Dev.5, 10–17.10.1007/BF02905450

  • CrossRef
  • Google Scholar

• 158

  McCombsB. L. (1984a). Process and skills underlying continuing intrinsic motivation to learn: toward a definition of motivational skills training interventions. Educ. Psychol.19, 199–218.

  • Google Scholar

• 159

  McCombsB. L. (1984b). CAI enhancements to motivational skills training for military technical training students. Train. Technol. J.1, 10–16.

  • Google Scholar

• 160

  McCombsB. L. (1985). Instructor and group process roles in computer-based training. Educ. Commun. Technol. J.33, 159–167.

  • Google Scholar

• 161

  McCombsB. L. (1986a). The role of the self-system in self-regulated learning. Contemp. Educ. Psychol.11, 314–332.

  • Google Scholar

• 162

  McCombsB. L. (1986b). Primary motivational variables in military career decision making. Paper Presented at the Annual Meeting of the Military Testing Association, Mystic, CT.

  • Google Scholar

• 163

  McCombsB. L. (1986c). The instructional systems development (ISD) model: factors critical to its successful implementation. Educ. Commun. Technol. J.34, 67–81.

  • Google Scholar

• 164

  McCombsB. L. (1987). “Issues in the measurement of standardized tests of primary motivational variables related to self-regulated learning,” in Measuring Student Self-Regulated Learning: Methods, Issues, and Outcomes. Symposium Conducted at the Annual Meeting of the American Educational Research Association, Washington, DC.

  • Google Scholar

• 165

  McCombsB. L. (1988). “Motivational skills training: combining metacognitive, cognitive, and affective learning strategies,” in Learning and Study Strategies: Issues in Assessment, Instruction, and Evaluation, eds WeinsteinC. E.et al (New York: Academic Press).

  • Google Scholar

• 166

  McCombsB. L. (1989). “Self-regulated learning: a phenomenological view,” in Self-Regulated Learning and Academic Achievement: Theory, Research, and Practice, eds ZimmermanB. J.SchunkD. H. (New York: Springer-Verlag), 51–82.

  • Google Scholar

• 167

  McCombsB. L. (1991a). Overview: where have we been and where are we going in understanding human motivation?J. Exp. Educ.60, 5–14.10.1080/00220973.1991.10806576

  • CrossRef
  • Google Scholar

• 168

  McCombsB. L. (1991b). “The definition and measurement of primary motivational processes,” in Testing and Cognition, eds WittrockM. C.BakerE. (New York: Prentice Hall), 63–81.

  • Google Scholar

• 169

  McCombsB. L. (1991c). “Computer-based technology (CBT): its current and future state,” in Problems and Promises of Computer-Based Training, ed. ShlecterT. M. (Norword, NJ: Ablex Publishers), 89–97.

  • Google Scholar

• 170

  McCombsB. L. (1993). “Learner-centered psychological principles for enhancing education: applications in school settings,” in The Challenges in Mathematics and Science Education: Psychology’s Response, eds PennerL. A.BatscheG. M.KnoffH. M.NelsonD. L. (Washington, DC: American Psychological Association), 287–313.

  • Google Scholar

• 171

  McCombsB. L. (1998). “Integrating metacognition, affect, and motivation in improving teacher education,” in How Students Learn: Reforming Schools through Learner-Centered Education, eds McCombsB. L.LambertN. (Washington, DC: APA Books), 379–408.

  • Google Scholar

• 172

  McCombsB. L. (1999). “What role does perceptual psychology play in educational reform today?” in Perceiving, Behaving, Becoming: Lessons Learned, ed. FreibergH. J. (Alexandria, VA: Association for Supervision and Curriculum Development), 148–157.

  • Google Scholar

• 173

  McCombsB. L. (2000). Reducing the achievement GAP. Society37, 0.1034–1042.10.1007/s12115-000

  • CrossRef
  • Google Scholar

• 174

  McCombsB. L. (2001). “Self-regulated learning and academic achievement: a phenomenological view,” in Self-Regulated Learning and Academic Achievement: Theory, Research, and Practice, 2nd Edn, eds ZimmermanB. J.SchunkD. H. (Mahwah, NJ: Lawrence Erlbaum Associates), 67–123.

  • Google Scholar

• 175

  McCombsB. L. (2002). Knowing what teachers know: a response to Schraw and Olafson’s teachers’ epistemological world views and educational practices. Issues Educ.8, 181–187.

  • Google Scholar

• 176

  McCombsB. L. (2008). From one-size-fits-all to personalized learner-centered learning: the evidence. F. M. Duffy Rep.13, 1–12.

  • Google Scholar

• 177

  McCombsB. L. (2009). “Commentary: what can we learn from a synthesis of research on teaching, learning, and motivation?” in Handbook of Motivation at School, eds WentzelK. R.WigfieldA. (New York: Routledge), 655–670.

  • Google Scholar

• 178

  McCombsB. L. (2010). Learner-centered practices as a comprehensive theoretical model for enhancing college student success: a longitudinal study from 2006 to 2009 at San Antonio College. Paper Presented at the Annual Meeting of the American Educational Research Association, Denver.

  • Google Scholar

• 179

  McCombsB. L. (2011a). “Learner-centered practices: providing the context for positive learner development, motivation, and achievement,” in Handbook of Research on Schools, Schooling, and Human Development, eds MeeceJ.EcclesJ. (Mahwah, NJ: Erlbaum), 7.

  • Google Scholar

• 180

  McCombsB. L. (2011b). Developing Responsible and Autonomous Learners: A Key to Motivating Learners. An Online Web-Based Module for the APA Task Force on the Application of Psychological Science to Teaching and Learning (APS-TL). Washington, DC: American Psychological Association.

  • Google Scholar

• 181

  McCombsB. L. (2012). “Educational psychology and educational transformation,” in Comprehensive Handbook of Psychology, Volume 7: Educational Psychology, 2nd Edn, eds ReynoldsW. M.MillerG. E. (New York: John Wiley & Sons), 493–533.

  • Google Scholar

• 182

  McCombsB. L. (2013a). “The learner-centered model: implications for research approaches,” in Interdisciplinary Handbook of the Person Centered Approach: Research and Theory, eds Cornelius-WhiteJ. H. D.Motschnig-PitrikR.LuxM. (New York: Springer), 335–355.

  • Google Scholar

• 183

  McCombsB. L. (2013b). “The learner-centered model: from the vision to the future,” in Interdisciplinary Handbook of the Person Centered Approach: Connections beyond Psychotherapy, eds Cornelius-WhiteJ. H. D.Motschnig-PitrikR.LuxM. (New York: Springer), 261–275.

  • Google Scholar

• 184

  McCombsB. L. (2014). “Using a 360 degree assessment model to support learning to learn,” in Learning to Learn for All: Theory, Practice and International Research: A Multidisciplinary and Lifelong Perspective, eds Deakin-CrickR.SmallT.StringherC. (London: Routledge), 241–270. Series/discipline: freestanding book.

  • Google Scholar

• 185

  McCombsB. L.BackS. M.WestA. S. (1984). Self-Paced Instruction: Factors Critical to the Implementation of Self-Paced Instruction in Air Force Technical Training – A Preliminary Inquiry (AFHRL-TP-84-23). Lowry AFB, CO: Air Force Human Resources Laboratory, ID Division.

  • Google Scholar

• 186

  McCombsB. L.BruceK. L.LockhartK. A. (1986a). Enhancements to Motivational Skill Training for Military Technical Training Students: Phase I Evaluation Study Report. Alexandria, VA: Army Research Institute for the Behavioral and Social Sciences.

  • Google Scholar

• 187

  McCombsB. L.LockhartK. A.BruceK. L.SmithG. A. (1986b). Enhancements to Motivational Skill Training for Military Technical Training Students: Phase II Evaluation Study Report. Alexandria, VA: Army Research Institute for the Behavioral and Social Sciences.

  • Google Scholar

• 188

  McCombsB. L.DobrovolnyJ. L. (1980a). Comparison of Theoretical CMI Instructor Role Model and Actual Navy and Air Force CMI Instructor Roles and Behaviors. San Diego, CA: Navy Personnel Research and Development Center.

  • Google Scholar

• 189

  McCombsB. L.DobrovolnyJ. L. (1980b). Theoretical Definition of Instructor Role in Computer-Managed Instruction (NPRDC-TN-80-10). San Diego, CA: Navy Personnel Research and Development Center.

  • Google Scholar

• 190

  McCombsB. L.DobrovolnyJ. L. (1982). Student Motivational Skill Training Package: Evaluation for Air Force Technical Training (AFHRL-TP-82-31). Lowry AFB, CO: Air Force Human Resources Laboratory, Technical Training Division.

  • Google Scholar

• 191

  McCombsB. L.DobrovolnyJ. L.JuddW. A. (1979). Computer-Managed Instruction: Development and Evaluation of Student Skill Modules to Reduce Training Time (AFHRL-TR-79-20). Lowry AFB, CO: Air Force Human Resources Laboratory, Technical Training Division.

  • Google Scholar

• 192

  McCombsB. L.DobrovolnyJ. L.LockhartK. A. (1983). Evaluation of the CMI (Computer-Managed Instruction) Instructor Role Training Program in the Navy and Air Force (NPRDC-SR-83-43). San Diego, CA: Navy Personnel Research and Development Center.

  • Google Scholar

• 193

  McCombsB. L.DollR. E.BaltzleyD. R.KennedyR. S. (1987). Predictive Validates of Primary Motivation Scales for Reenlistment Decision-Making. (ARI Final Report). Alexandria, VA: Army Research Institute for the Behavioral and Social Sciences.

  • Google Scholar

• 194

  McCombsB. L.JuddW. A.DobrovolnyJ. L.O’NeilH. F.EschennbrennerJ. E. (1977). AIS Instructional Strategies Subsystem. Lowry AFB, CO: Air Force Human Resources Laboratory, Technical Training Division.

  • Google Scholar

• 195

  McCombsB. L.LockhartK. A. (1984). Personnel Roles and Requirements for Non-Conventional Instruction (NCI) in Air Force Technical Training (AFHRL Tech. Rep). Lowry AFB, CO: Air Force Human Resources Laboratory, ID Division.

  • Google Scholar

• 196

  McCombsB. L.MarcoR. A.SproulsM. W.EschenbrennerA. J.ReidG. R. (1973a). Media Adjunct Programming: An Individualized Media-Managed Approach to Technical Training (AFHRL-TR-73-7). Williams AFB, AZ: Air Force Human Resources Laboratory, Flight Training Division.

  • Google Scholar

• 197

  McCombsB. L.O’NeilH. F.Jr.HeinrichD. L.HansenD. N. (1973b). Effect of anxiety, response mode, subject matter familiarity, and program length on achievement in computer-assisted learning. J. Educ. Psychol.64, 310–324.10.1037/h0034601

  • CrossRef
  • Google Scholar

• 198

  McCombsB. L.MarzanoR. J. (1989). Integrating skill and will in self-regulation: putting the self as agent in strategies training. Teach. Think. Probl. Solv.11, 1–4.

  • Google Scholar

• 199

  McCombsB. L.MarzanoR. J. (1990). Putting the self in self-regulated learning: the self as agent in integrating skill and will. Educ. Psychol.25, 51–69.

  • Google Scholar

• 200

  McCombsB. L.McDanielM. A. (1981). On the design of adaptive treatments for individualized instructional systems. Educ. Psychol.16, 11–22.10.1080/00461528109529228

  • CrossRef
  • Google Scholar

• 201

  McCombsB. L.McDanielM. A. (1983). Individualizing through treatment matching: a necessary but not sufficient approach. Educ. Commun. Technol. J.31, 213–225.

  • Google Scholar

• 202

  McCombsB. L.McNabbM. (2001). Cultures of Web-based Learning and Assessment. Consultation Session at Cultures of Learning Conference. Bristol: University of Bristol.

  • Google Scholar

• 203

  McCombsB. L.McNeelyS. (eds) (1994). Psychology in the Classroom: A Mini-Series on Applied Educational Psychology. Washington, DC: APA Books.

  • Google Scholar

• 204

  McCombsB. L.PopeJ. E. (1994). “Motivating hard to reach students,” in Psychology in the Classroom: A Mini-Series on Applied Educational Psychology, eds McCombsB. L.McNeelyS. (Washington, DC: APA Books).

  • Google Scholar

• 205

  McCombsB. L.PriceC. C. (2008). San Antonio College – Effective Learner-Centered Practices: A Compendium. San Antonio, TX: Murguia Learning Institute.

  • Google Scholar

• 206

  McCombsB. L.QuiatM. A. (2002). What makes a comprehensive school reform model learner-centered?Urban Educ.37, 476–496.10.1177/0042085902374002

  • CrossRef
  • Google Scholar

• 207

  McCombsB. L.VakiliD. (2005). A learner-centered framework for e-learning. Teach. Coll. Rec.107, 1582–1600.10.1111/j.1467-9620.2005.00534.x

  • CrossRef
  • Google Scholar

• 208

  McCombsB. L.WhislerJ. S. (1989). The role of affective variables in autonomous learning. Educ. Psychol.24, 277–306.10.1207/s15326985ep2403_4

  • CrossRef
  • Google Scholar

• 209

  McKeachieW. J. (1986). Teaching Tips: A Guidebook for the Beginning College Teacher. Lexington, MA: Heath.

  • Google Scholar

• 210

  McKeachieW. J. (1988). “The need for study strategy training,” in Learning and Study Strategies: Issues in Assessment, Instruction, and Evaluation, eds WeinsteinC. E.GoetzE. T.AlexanderP. A. (San Diego, CA: Academic), 3–9.

  • Google Scholar

• 211

  McKeachieW. J. (1990). Research on college teaching: the historical background. J. Educ. Psychol.82, 189–200.10.1037/0022-0663.82.2.189

  • CrossRef
  • Google Scholar

• 212

  McKeachieW. J.PintrichP. R.LinY. G. (1985). Teaching learning strategies. Educ. Psychol.20, 153–160.10.1207/s15326985ep2003_5

  • CrossRef
  • Google Scholar

• 213

  McLuhanM. (1967). The Medium Is the Massage: An Inventory of Effects. Corte Madera, CA: Gingko Press.

  • Google Scholar

• 214

  MeeceJ. L. (2002). Child and Adolescent Development for Educators, 2nd Edn. Boston: McGraw-Hill.

  • Google Scholar

• 215

  MillsR. C. (1991). A new understanding of self: the role of affect, state of mind, self-understanding, and intrinsic motivation. J. Exp. Educ.60, 67–81.10.1080/00220973.1991.10806580

  • CrossRef
  • Google Scholar

• 216

  NaimanN.FrolichM.SternH.TodescoA. (1978). The Good Language Learner. Research in Education Series No 7. Toronto: Ontario Institute for Studies in Education.

  • Google Scholar

• 217

  NambiarR. (2009). Learning strategy research—where are we now?Read. Matrix9. Available at: http://www.readingmatrix.com/articles/sept_2009/nambiar.pdf

  • Google Scholar

• 218

  NauertR. (2016). Creating an Ideal Learning Environment. Available at: http://psychcentral.com/news/2016/11/14/creating-an-ideal-learning-environment/112505.html

  • Google Scholar

• 219

  NewellA.SimonH. A. (1972). Human Problem Solving. Englewood Cliffs, NJ: Prentice Hall.

  • Google Scholar

• 220

  NolanT. E. (1991). Self-questioning and prediction: combining metacognitive strategies. J. Read.35, 132–138.

  • Google Scholar

• 221

  NormanD. A. (1969). Memory and Attention: An Introduction to Human Information Processing. New York: Wiley.

  • Google Scholar

• 222

  NormanD. A. (1976). Memory and Attention, 2nd Edn. New York: Wiley.

  • Google Scholar

• 223

  NormanD. A. (1977). Teaching Learning Strategies. Advanced Research Projects Agency (DOD). Washington, DC: Office of Naval Research, Arlington, Va. Available at: http://files.eric.ed.gov/fulltext/ED151677.pdf

  • Google Scholar

• 224

  NormanD. A. (2014). Don Norman: Living with Complexity. Available at: https://www.youtube.com/watch?v=Tj96KyC9zdI

  • Google Scholar

• 225

  NormanD. A.RumelhartD. E. (1975). Explorations in Cognition. London: Freeman.

  • Google Scholar

• 226

  NormanD. A.StappersP. J. (2016). DesignX: design and complex sociotechnical systems. She Ji J. Des. Econ. Innov.1, 1–24.10.1016/j.sheji.2016.01.002

  • CrossRef
  • Google Scholar

• 227

  OaklanderM. (2016). The mindful classroom. Time188(13), 44–47.

  • Google Scholar

• 228

  OECD. (2016). Longitudinal Study of Children’s Social and Emotional Skills in Cities (LSEC). Available at: http://www.oecd.org/edu/ceri/social-emotional-skills.htm

  • Google Scholar

• 229

  O’MalleyJ. M.ChamotA. U.Stewner-ManzanaresG. (1985). Learning strategies used by beginning and intermediate ESL students. Lang. Learn.25, 21–36.10.1111/j.1467-1770.1985.tb01013.x

  • CrossRef
  • Google Scholar

• 230

  O’NeilH. F.Jr.HedlJ. J.Jr.McCombsB. L.GrantR. D.Jr.FitzpatrickJ. L.JuddW. A. (1972). The Effects of Anxiety Reduction Techniques on Anxiety and Computer-Assisted Learning and Evaluation of College Students (Grant No. OEG-4-71-0027, U.S. Office of Education). Tallahassee: Florida State University, Computer-Assisted Instruction Center. (ERIC Document Reproduction Service No. ED 076 060).

  • Google Scholar

• 231

  OwensD. (2016). What Student Choice and Agency Actually Looks Like. Available at: http://www.eschoolnews.com/2016/11/14/what-student-choice-and-agency-actually-looks-like/?ps=bmccombs%40du.edu-0013000000j04S9-0033000000q5P0Z and http://practices.learningaccelerator.org/about-this-project

  • Google Scholar

• 232

  OxfordR. L. (1990). Language Learning Strategies: What Every Teacher Should Know. New York: Newbury House Harper Collins.

  • Google Scholar

• 233

  OxfordR. L. (2001). “Language learning styles and strategies,” in Teaching English as a Second or Foreign Language, ed. Celce-MurcieM. (Boston: Heinle & Heinle), 359–366.

  • Google Scholar

• 234

  OxfordR. L. (ed.) (1996). Language Learning Strategies around the World: Cross Cultural Perspectives (Technical Report No 13). Honolulu: University of Hawaii Press.

  • Google Scholar

• 235

  PaivioA. (1986). Mental Representations: A Dual Coding Approach. New York: Oxford University Press.

  • Google Scholar

• 236

  PalincsarA. (1986). Metacognitive strategy instruction. Except. Child.53, 118–124.10.1177/001440298605300203

  • CrossRef
  • Google Scholar

• 237

  PalinscarA. S.BrownA. L. (1986). Interactive teaching to promote independent learning from text. Read. Teacher39, 771–777.

  • Google Scholar

• 238

  PamM. S. (2016). What Is Learning Strategy? Definition of Learning Strategy (Psychology Dictionary)? Available at: http://psychologydictionary.org/learning-strategy/

  • Google Scholar

• 239

  ParisS. G. (1998). Fusing skill and will: the integration of cognitive and motivational psychology. Paper Presented at the Annual Meeting of the American Educational Research Association, New Orleans, LA.

  • Google Scholar

• 240

  ParisS. G.CrossD. R. (1983). “Ordinary learning: pragmatic connections among children’s beliefs, motives, and actions,” in Learning in Children, eds BisanzJ.BisanzG.KailR. (New York: Springer-Verlag), 137–169.

  • Google Scholar

• 241

  ParisS. G.CrossD. R.LipsonM. Y. (1984). Informed strategies for learning: a program to improve children’s reading awareness and comprehension. J. Educ. Psychol.76, 1239–1252.10.1037/0022-0663.76.6.1239

  • CrossRef
  • Google Scholar

• 242

  ParisS. G.LindauerB. K. (1976). The role of inference in children’s comprehension and memory for sentences. Cogn. Psychol.8, 217–227.10.1016/0010-0285(76)90024-4

  • CrossRef
  • Google Scholar

• 243

  ParisS. G.LindauerB. K.CoxG. L. (1977). The development of inferential comprehension. Child Dev.48, 1728–1733.10.2307/1128546

  • CrossRef
  • Google Scholar

• 244

  ParisS. G.LipsonM. Y.WixsonK. K. (1983). Becoming a strategic reader. Contemp. Educ. Psychol.8, 293–316.10.1016/0361-476X(83)90018-8

  • CrossRef
  • Google Scholar

• 245

  ParisS. G.ParisA. H. (2001). Classroom applications of research on self-regulated learning. Educ. Psychol.36, 89–101.10.1207/S15326985EP3602_4

  • CrossRef
  • Google Scholar

• 246

  ParisS. G.TurnerJ. C. (1991). “The development of strategic readers,” in Handbook of Reading Research, eds BarrR.KamilM. L.MosenthalP.PearsonP. D. Vol. 2 (New York: Longman), 609–640.

  • Google Scholar

• 247

  ParisS. G.WinogradP. (1990). “How metacognition can promote academic learning and instruction,” in Dimensions of Thinking: A Framework for Curriculum and Instruction, eds JonesB. F.IdolL. (Hillsdale, NJ: Lawrence Erlbaum Associates, Inc), 15–51.

  • Google Scholar

• 248

  ParisS. G.WixsonK. K.PalincsarA. M. (1986). “Instructional approaches to reading comprehension,” in Review of Research in Education, ed. RothkopfE. (Washington, DC: American Educational Research Association), 91–128.

  • Google Scholar

• 249

  ParkhurstP. E.McCombsB. L. (1979). Applying the ATI concept in an operational environment. J. Instr. Dev.31, 33–39.10.1007/BF02908999

  • CrossRef
  • Google Scholar

• 250

  PerezL. (2016). We All Vary: Why Universal Design Is for All of Us. More Than Cool: FETC’s Discussion on EdTech with Leading Educators. Available at: https://www.facebook.com/edchatinteractive

  • Google Scholar

• 251

  PerkinsD. N.SalomonG. (1987). “Transfer and teaching thinking,” in Thinking: The Second International Conference, eds PerkinsD. N.LochheadJ.BishopJ. (Hillsdale, NJ: Erlbaum), 285–303.

  • Google Scholar

• 252

  PerkinsD. N.SalomonG. (1992). Transfer of Learning. International Encyclopedia of Education, 2nd Edn. Oxford, UK: Pergamon Press.

  • Google Scholar

• 253

  PiagetJ. (1926). The Language and Thought of the Child. London: Routledge and Kegan Paul.

  • Google Scholar

• 254

  PintrichP. R. (1989). “The dynamic interplay of student motivation and cognition in the college classroom,” in Advances in Motivation and Achievement: Vol. 6. Motivation Enhancing Environments, eds AmesC.MaehrM. (Greenwich, CT: JAI Press), 117–160.

  • Google Scholar

• 255

  PintrichP. R.SmithD. A. F.GarciaT.McKeachieW. J. (1993). Reliability and predictive validity of the motivated strategies for learning questionnaire (MSLQ). Educ. Psychol. Meas.53, 801–813.10.1177/0013164493053003024

  • CrossRef
  • Google Scholar

• 256

  PintrichP. R.ZushoA. (2002). “Student motivation and self-regulated learning in the college classroom,” in Higher Education: Handbook of Theory and Research, Vol. 17, eds SmartJ. C.TierneyW. G. (The Netherlands: Kluwer Academic Press), 55–128.

  • Google Scholar

• 257

  PressleyM. (1976). Mental imagery helps eight-year olds remember what they read. J. Educ. Psychol.68, 355–359.10.1037/0022-0663.68.3.355

  • CrossRef
  • Google Scholar

• 258

  PressleyM. (1977). Children’s use of the keyword method to learn simple Spanish vocabulary words. J. Educ. Psychol.69, 465–472.10.1037/0022-0663.69.5.465

  • CrossRef
  • Google Scholar

• 259

  PressleyM.HarrisK. (1990). What we really know about strategy instruction. Educ. Leadersh.48, 31–34.

  • Google Scholar

• 260

  ReigeluthC. M. (1994). “The imperative for systemic change,” in Systemic Change in Education, eds ReigeluthC. M.GafinkleR. J. (Englewood Cliffs, NJ: Educational Technology Publications). Available at: http://www.indiana.edu/~syschang/decatur/2007_fall/documents/1-3_2-1_reigeluth_imperative.pdf

  • Google Scholar

• 261

  ReigeluthC. M.BeattyB. J.MyersR. D. (2017). Instructional-Design Theories and Models, Volume IV: The Learner-Centered Paradigm of Education. New York, NY: Routledge. Available at: https://sites.google.com/a/nau.edu/educationallearningtheories/home

  • Google Scholar

• 262

  ReigeluthC. M.GarfinkleR. J. (eds) (1994). Systemic Change in Education. Englewood Cliffs, NJ: Educational Technology Publications. Available at: http://www.indiana.edu/~syschang/decatur/2007_fall/documents/1-3_2-1_reigeluth_imperative.pdf

  • Google Scholar

• 263

  ResnickL. B.WangM. C. (1969). Approaches to the validation of learning hierarchies. Paper Presented at the 18th Annual Western Regional Conference on Testing Problems, San Francisco, CA.

  • Google Scholar

• 264

  RichardsonM.AbrahamC.BondR. (2012). Psychological correlates of university students’ academic performance: a systematic review and meta-analysis. Psychol. Bull.138, 353–387.10.1037/a0026838

  • CrossRef
  • Google Scholar

• 265

  RidleyD. S. (1991). Reflective self-awareness: a basic motivational process. J. Exp. Educ.60, 31–48.

  • Google Scholar

• 266

  RidleyD. S.McCombsB. L.TaylorK. (1994). Walking the talk: fostering self-regulated learning in the classroom. Middle Sch. J.26, 50–55.10.1080/00940771.1994.11494411

  • CrossRef
  • Google Scholar

• 267

  RigneyW. (1978). “Learning strategies: a theoretical perspective,” in Learning Strategies, ed. O’NeillH. F. (New York: Academic Press), 70–75.

  • Google Scholar

• 268

  RobsonC. (2002). Real World Research, 2nd Edn. Malden, MA: Blackwell Publishing.

  • Google Scholar

• 269

  RogersC. R. (1961). On Becoming a Person. Boston: Houghton Mifflin.

  • Google Scholar

• 270

  RothkopfE. Z. (1970). The concept of mathemagenic activities. Rev. Educ. Res.1, 325–336.10.3102/00346543040003325

  • CrossRef
  • Google Scholar

• 271

  RubinC. M. (2016). Interviews: How to Integrate the Development of Essential Skills into the Curriculum. Available at: http://edtechreview.in/voices/interviews/2566-student-skills-curriculum-for-schools-teachers?utm_source=EdTechReview%E2%84%A2+Weekly+Newsletter&utm_campaign=6c990bb5c6-EMAIL_CAMPAIGN_2016_11_17&utm_medium=email&utm_term=0_94aed71205-6c990bb5c6-103017145

  • Google Scholar

• 272

  RubinJ. (1975). What the “good language learner? can teach us. TESOL Q.9, 41–51.10.2307/3586011

  • CrossRef
  • Google Scholar

• 273

  RubinJ. (1981). Study of cognitive processes in second language learning. Appl. Linguist.11, 117–131.10.1093/applin/II.2.117

  • CrossRef
  • Google Scholar

• 274

  RubinJ.ThompsonI. (1982). How to Be a More Successful Language Learner. Boston: Heinle and Heinle.

  • Google Scholar

• 275

  RudduckJ. (1998). “Student voices and conditions of learning,” in Didaktikk: Tradisjon og Fornyelse, Festskrift til Bjorg Brandtzaeg Gundem, eds KarsethB.GudmundsdottirS.HopmannS. (Oslo: Universitet i Oslo), 131–146.

  • Google Scholar

• 276

  RudduckJ. (2006). The past, the papers and the project. Educ. Rev.58, 131–143.

  • Google Scholar

• 277

  RyanR. M.DeciE. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am. Psychol.55, 68–78.10.1037/0003-066X.55.1.68

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 278

  RyleG. (1949). The Concept of Mind. London: Hutchinson.

  • Google Scholar

• 279

  SalomonG. (ed.) (1993). Distributed Cognitions. New York: Cambridge University Press.

  • Google Scholar

• 280

  ScharmerC. O. (2011). Leading from the Emerging Future Minds for Change: Future of Global Development. Ceremony to Mark the 50th Anniversary of the BMZ Federal Ministry for Economic Cooperation and Development. Berlin: MIT Sloan School of Management.

  • Google Scholar

• 281

  SchrobsdorffS. (2016). The kids are not all right. Time188(19), 44–51.

  • Google Scholar

• 282

  SchunkD. H. (1989). Self-efficacy and achievement behaviors. Educ. Psychol. Rev.1, 173–207.10.1007/BF01320134

  • CrossRef
  • Google Scholar

• 283

  SchunkD. H. (1994). “Self-regulation of self-efficacy and attributions in academic settings,” in Self-Regulation of Learning and Performance: Issues and Educational Applications, eds SchunkD. H.ZimmermanB. J. (Hillsdale, NJ: Erlbaum), 75–99.

  • Google Scholar

• 284

  SchunkD. H.ZimmermanB. J. (eds) (2007). Motivation and Self-Regulated Learning: Theory, Research, and Applications. Hillsdale, NJ: Lawrence Erlbaum.

  • Google Scholar

• 285

  SchunkD. H.ZimmermanB. J. (1998). Self-Regulated Learning – From Teaching to Self-Reflective Practice. New York: Guilford Press. Available at: https://books.google.com/books?hl=fr&lr=&id=FQnLHRQJUccC&oi=fnd&pg=RA1-PA1&dq=Understanding+self-regulated+learning&ots=DBI3VOswZ5&sig=yHtX63yolvL0VxQsnjrloegbwcw#v=onepage&q=Understanding%20self-regulated%20learning&f=false

  • Google Scholar

• 286

  ScottT. (2016). The Role of Big Data, EdTech and Militarized Austerity within America’s Authoritarian Democracy. Personalized Learning, Surveillance and Counterinsurgency within the State-Finance Matrix. Available at: https://narrativedisruptions.wordpress.com/surveillance-counterinsurgency-and-schooling-within-the-state-finance-matrix/

  • Google Scholar

• 287

  SeidelR. J. (1969). Computers in Education: The Copernican Revolution in Education Systems. Computers and Automation. Alexandria, VA: HumRRO Professional Paper, 16–69.

  • Google Scholar

• 288

  SeidelR. J. (1971). Theories and Strategies Related to Measurement in Individualized Instruction. Alexandria, VA: HumRRO Professional Paper, 2–71.

  • Google Scholar

• 289

  SeidelR. J. (1973). Research on Instructional Decision Models. Alexandria, VA: HumRRO Final Report FR-D1-73-6.

  • Google Scholar

• 290

  SeidelR. J.PerencevichK. C.KettA. L. (2005). From Principles of Learning to Strategies for Instruction: Empirically Based Ingredients to Guide Instructional Development. New York: Springer.

  • Google Scholar

• 291

  SeifertT. (1993). Learning Strategies in the Classroom. Available at: http://www.mun.ca/educ/faculty/mwatch/vol2/seifert.html

  • Google Scholar

• 292

  SengeP. (2011). Collaborative Culture: Insights from Peter Senge on the Foundations of Organizational Learning. Available at: https://sourcepov.com/2011/01/11/

  • Google Scholar

• 293

  SengeP. (2012). The necessary revolution: how we got into this predicament. Ref. SoL J.9, 20–33. Available at: http://c.ymcdn.com/sites/www.solonline.org/resource/resmgr/Docs/Senge_9.2.pdf

  • Google Scholar

• 294

  SengeP.Cambron-McCabeN.LucasT.SmithB.DuttonJ.KleinerA. (2000). Schools That Learn: A Fifth Discipline Field Book for Educators, Parents, and Everyone Who Cares About Education. New York: Doubleday. Available at: http://www.indiana.edu/~syschang/decatur/2007_fall/documents/1-2_senge_stl_35-57.pdf

  • Google Scholar

• 295

  ShavelsonR.WebbN.BurnsteinL. (1986). “Measurement of teaching,” in Handbook of Research on Teaching, ed. WittrockM. C. (London: MacMillan), 50–91.

  • Google Scholar

• 296

  SkinnerB. F. (1953). Science and Human Behavior. New York: Free Press.

  • Google Scholar

• 297

  SlavinR. E. (1980). Cooperative learning. Rev. Educ. Res.50, 315–342.10.3102/00346543050002315

  • CrossRef
  • Google Scholar

• 298

  SlavinR. E. (2011). “Classroom applications of cooperative learning,” in Application to Learning and Teaching: Vol. 3. APA Educational Psychology Handbook, eds HarrisK. R.GrahamS.UrdanT. (Washington, DC: American Psychological Association), 359–378.

  • Google Scholar

• 299

  SmithH. K. (1967). The responses of good and poor readers when asked to read for different purposes. Read. Res. Q.1967–1968, 53–83.10.2307/747204

  • CrossRef
  • Google Scholar

• 300

  SnowR. (1989). “Aptitude-treatment interaction as a framework for research on individual differences in learning,” in Learning and Individual Differences, eds AckermanP.SternbergR. J.GlaserR. (New York: W.H. Freeman), 13–59.

  • Google Scholar

• 301

  SnowR.FedericoP.MontagueW. (1980). Aptitude, Learning, and Instruction, Vol. 1 & 2. Hillsdale, NJ: Erlbaum.

  • Google Scholar

• 302

  SnowR. E. (1974). Representative and quasi-representative designs for research on teaching. Rev. Educ. Res.44, 265–292.10.3102/00346543044003265

  • CrossRef
  • Google Scholar

• 303

  SnowR. E. (1976). Theory and Method for Research on Aptitude Processes. Technical Report #2, Aptitude Research Project. Stanford, CA: School of Education, Stanford University.

  • Google Scholar

• 304

  SnowR. E. (1977). “Research on aptitudes: a progress report,” in Review of Research in Education, ed. ShulmanL. S. (Itasca, IL: Peacock), 4.

  • Google Scholar

• 305

  SpielbergerC. D. (1977). “Computer-based research on anxiety and learning: an overview and critique,” in Anxiety, Learning and Instruction, eds SieberJ.TobiasS.O’NeilH. F. (New York: LEA/Wiley), 119–132.

  • Google Scholar

• 306

  SpielbergerC. D. (ed.) (1972). Anxiety: Current Trends in Theory and Research, Vol. 1. New York: Academic Press.

  • Google Scholar

• 307

  SternbergR. (1997). Thinking Styles. New York: Cambridge University Press.

  • Google Scholar

• 308

  SuppesP. (1972). “Computer-assisted instruction,” in Display Use for Man-Machine Dialog, eds HandlerW.WeizenbaumJ. (Munich: Hanser), 155–185.

  • Google Scholar

• 309

  SuppesP. (1973). “Facts and fantasies of education. Phi Delta Kappa Monograph,” in Changing Education: Alternatives from Educational Research, ed. WittrockM. C. (Englewood Cliffs, NJ: Prentice Hall), 6–45.

  • Google Scholar

• 310

  SuppesP. (1974). The place of theory in educational research. Educ. Res.3, 3–10.10.3102/0013189X003006003

  • CrossRef
  • Google Scholar

• 311

  SuppesP. (2002). Representation and Invariance of Scientific Structures. Wayne, PA: CSLI (distributed by the University of Chicago Press).

  • Google Scholar

• 312

  SwartzR. J.PerkinsD. N. (1989). Teaching Thinking: Issues and Approaches. Pacific Grove, CA: Midwest Publications.

  • Google Scholar

• 313

  TaylorR. D.WangM. C. (1997). Social and Emotional Adjustment and Family Relations in Ethnic Minority Families. Mahwah, NJ: Lawrence Erlbaum Associates.

  • Google Scholar

• 314

  ThorndikeE. L. (1912). Education: A First Book. New York: MacMillan.

  • Google Scholar

• 315

  TobiasS. (2010). Generative learning theory, paradigm shifts, and constructivism in educational psychology: a tribute to Merl Wittrock. Educ. Psychol.45, 51–54.10.1080/00461520903433612

  • CrossRef
  • Google Scholar

• 316

  TobiasS. (2016). “No panacea garden,” in Acquired Wisdom. Lessons Learned by Distinguished Researchers. Series of Essays in Education Review, eds TobiasS.FletcherD.BerlinerD.. Available at: https://www.researchgate.net/publication/301280026_No_Panacea_Garden

  • Google Scholar

• 317

  TobiasS.DuffyT. E. (eds) (2009). Constructivist Instruction: Success or Failure?New York: Routledge.

  • Google Scholar

• 318

  TobiasS.FletcherJ. D.BerlinerD. (eds) (2016). About Acquired Wisdom (A Publication Series That Preserves and Transmits Knowledge and Skills Obtained Through Experience by Distinguished Educational Researchers). Available at: https://www.researchgate.net/project/Acquirede-Wisdom-Lessons-Learned-by-Distinguished-Researchers, http://edrev.asu.edu/index.php/ER and http://edrev.asu.edu/index.php/ER/pages/view/preface

  • Google Scholar

• 319

  TsaiM.-J. (2009). The model of strategic e-learning: understanding and evaluating student e-learning from metacognitive perspectives. Educ. Technol. Soc.12, 34–48.

  • Google Scholar

• 320

  Vander ArkT. (2016a). Intelligence Unleashed: How Artificial Intelligence Will Improve Education. Available at: http://gettingsmart.com/2016/03/intelligence-unleashed-how-artificial-intelligence-will-improve-education/

  • Google Scholar

• 321

  Vander ArkT. (2016b). What’s Next? Personalized, Project-Based Learning. Available at: http://www.gettingsmart.com/2016/11/next-generation-school-design/

  • Google Scholar

• 322

  VygotskyL. (1962). Thought and Language. Cambridge, MA: MIT Press.

  • Google Scholar

• 323

  VygotskyL. (1986). Thought and Language. Cambridge, MA: MIT Press.

  • Google Scholar

• 324

  VygotskyL. S. (1978). Mind in Society. Cambridge, MA: Harvard University Press.

  • Google Scholar

• 325

  WangM. C. (1992). Adaptive Education Strategies: Building on Diversity. Baltimore, MD: Paul H. Brookes Pub. Co.

  • Google Scholar

• 326

  WangM. C. (1997). Next steps in inner-city education: focusing on resilience development and learning success. Educ. Urban Soc.29, 255–276.10.1177/0013124597029003002

  • CrossRef
  • Google Scholar

• 327

  WangM. C. (ed.) (1968). Criterion-Referenced Achievement Tests for the Early Learning Curriculum of the Primary Education Project. Pittsburg, PA: University of Pittsburgh.

  • Google Scholar

• 328

  WangM. C.ReynoldsM. C.WalbergH. J. (1994). Serving students at the margins. Synthesis of research/the effects of inclusion on learning. Educ. Leadersh.52, 12–17.

  • Google Scholar

• 329

  WangM. C.WalbergH. J. (1985). Adapting Instruction to Individual Differences. Berkeley, CA: McCutchan Publishing Corp.

  • Google Scholar

• 330

  WeinerB. (2016). The Legacy of Attribution Theory. Available at: https://www.researchgate.net/profile/Bernard_Weiner

  • Google Scholar

• 331

  WeinsteinC. E. (1978). “Elaboration skills as a learning strategy,” in Learning Strategies, ed. O’NeilH. F. (New York: Academic Press), 31–56.

  • Google Scholar

• 332

  WeinsteinC. E.HusmanJ.DierkingD. R. (2000). “Self-regulation interventions with a focus on learning strategies,” in Handbook of Self-Regulation, eds BoekaertsM.PintrichP. R.ZeidnerM. (San Diego, CA: Academic Press), 727–747.

  • Google Scholar

• 333

  WeinsteinC. E.MayerR. E. (1986). “The teaching of learning strategies,” in Handbook of Research on Teaching, ed. WittrockM. C. (New York: Macmillan), 315–327.

  • Google Scholar

• 334

  WeinsteinC. E.McCombsB. L. (1994). “Putting learning strategies research into practice,” in Psychology in the Classroom: A Mini-series on Applied Educational Psychology, eds McCombsB. L.McNeelyS. (Washington, DC: APA Books).

  • Google Scholar

• 335

  WeinsteinC. E.McCombsB. L. (1998). Strategic Learning: The Merging of Skill, Will and Self-Regulation. Hillsdale, NJ: Lawrence Erlbaum Associates.

  • Google Scholar

• 336

  WeinsteinC. E.PalmerD. (1990). LASSI-HS User’s Manual. Clearwater, FL: H&H Publishing.

  • Google Scholar

• 337

  WeinsteinC. E.PalmerD. R.AceeT. W. (2016). User’s Manual Learning and Study Strategies Inventory – Third Edition. Clearwater, FL: H&H Publishing.

  • Google Scholar

• 338

  WeinsteinC. E.PalmerD. R.SchulteA. C. (1987). LASSI-Learning and Study Strategies Inventory. Clearwater, FL: H&H Publishing.

  • Google Scholar

• 339

  WeissbergR. P.GreenbergM. T. (1998). “School and community competence-enhancement and prevention programs,” in Handbook of Child Psychology: Vol. 4. Child Psychology in Practice, 5th Edn, eds DamonW.SiegelI. E.RenningerL. A. (New York: Wiley), 877–954.

  • Google Scholar

• 340

  WescheM. B. (1975). The Good Adult Language Learner: A Study of Learning Strategies and Personality Factors in an Intensive Course. Unpublished Doctoral Thesis, University of Toronto, Canada.

  • Google Scholar

• 341

  WigfieldA.EcclesJ. S. (1992). The development of achievement task values: a theoretical analysis. Dev. Rev.12, 265–310.10.1016/0273-2297(92)90011-P

  • CrossRef
  • Google Scholar

• 342

  WigfieldA.EcclesJ. S. (2002). “The development of competence beliefs, expectancies for success, and achievement values from childhood through adolescence,” in Development of Achievement Motivation. A Volume in the Educational Psychology Series, eds WigfieldA.EcclesJ. S. (San Diego, CA: Academic Press), 91–120.

  • Google Scholar

• 343

  WitherspoonM.SykesG.BellC. (2016). Leading a Classroom Discussion: Definition, Supportive Evidence, and Measurement of the ETS^® National Observational Teaching Examination (NOTE) Assessment Series (Research Memorandum No. RM-16-09). Princeton, NJ: Educational Testing Service.

  • Google Scholar

• 344

  WittrockM. C. (1974a). A generative model of mathematics learning. J. Res. Math. Educ.5, 181–197.

  • Google Scholar

• 345

  WittrockM. C. (1974b). Learning as a generative process. Educ. Psychol.II, 87–95.

  • Google Scholar

• 346

  WittrockM. C. (1978). “Education and the cognitive processes of the brain,” in Education and the Brain: The 77th Yearbook of the National Society for the Study of Education, Part II, eds ChallJ.MirskyA. (Chicago, IL: University of Chicago Press), 61–102.

  • Google Scholar

• 347

  WittrockM. C. (1980). “Learning and the brain,” in The Brain and Psychology, ed. WittrockM. C. (New York: Academic Press), 371–403.

  • Google Scholar

• 348

  WittrockM. C. (1981). “Reading comprehension,” in Neuropsychological and Cognitive Processes of Reading, eds PirozzoloF. J.WittrockM. C. (New York: Academic), 229–259.

  • Google Scholar

• 349

  WittrockM. C. (1986a). Handbook of Research on Teaching. New York: Macmillan.

  • Google Scholar

• 350

  WittrockM. C. (1986b). “Education and recent research on attention and knowledge acquisition,” in Brain, Cognition, and Education, eds FriedmanS. L.KlivingtonK. A.PetersonR. W. (New York: Academic), 151–169.

  • Google Scholar

• 351

  WittrockM. C. (1989a). Generative processes of comprehension. Educ. Psychol.24, 345–376.10.1207/s15326985ep2404_2

  • CrossRef
  • Google Scholar

• 352

  WittrockM. C. (1989b). “A classification of sentences used in natural language processing in the military services,” in Advanced Research Projects Agency (DOD), Report No CSE-TR-294 and University of California, Los Angeles. Center for the Study of Evaluation (CSE), Technical Report 294, Washington, DC.

  • Google Scholar

• 353

  WittrockM. C. (1990). Generative processes of comprehension. Educ. Psychol.24, 345–376.10.1207/s15326985ep2404_2

  • CrossRef
  • Google Scholar

• 354

  WittrockM. C. (1991). Generative teaching of comprehension. Elem. Sch. J.92, 167–182.10.1086/461686

  • CrossRef
  • Google Scholar

• 355

  WittrockM. C. (1992). Generative learning processes of the brain. Educ. Psychol.27, 531–541.10.1207/s15326985ep2704_8

  • CrossRef
  • Google Scholar

• 356

  WittrockM. C.AlesandriniK. (1990). Generation of summaries and analogies and analytic and holistic abilities. Am. Educ. Res. J.27, 489–502.10.3102/00028312027003489

  • CrossRef
  • Google Scholar

• 357

  ZhangZ. (1993). Literature review on reading strategy research. Paper Presented at the Annual Meeting of the Mid-South Educational Research Association, New Orleans, LA.

  • Google Scholar

• 358

  ZhuC.ValckeM.SchellensT. (2008). A cross-cultural study of Chinese and Flemish university students: do they differ in learning conceptions and approaches to learning?Learn. Individ. Differ.18, 120–127.10.1016/j.lindif.2007.07.004

  • CrossRef
  • Google Scholar

• 359

  ZhuC.ValckeM.SchellensT. (2009). A cross-cultural study of online collaborative learning. Multicult. Educ. Technol. J.3, 33–46.10.1108/17504970910951138

  • CrossRef
  • Google Scholar

• 360

  ZimmermanB.Martinez-PonsM. (1988). Construct validation of a strategy model of student self-regulated learning. J. Educ. Psychol.80, 284–290.10.1037/0022-0663.80.3.284

  • CrossRef
  • Google Scholar

• 361

  ZimmermanB. J. (1989). “Models of self-regulated learning and academic achievement,” in Self-Regulated Learning and Academic Achievement: Theory, Research and Practice, eds BarryJ. Z.DaleH. S. (New York: Springer), 1–25.

  • Google Scholar

• 362

  ZimmermanB. J. (1990). Self-regulated learning and academic achievement: an overview. Educ. Psychol.25, 3–17.10.1207/s15326985ep2501_2

  • CrossRef
  • Google Scholar

• 363

  ZimmermanB. J. (2000). “Attaining self-regulation: a social cognitive perspective,” in Handbook of Self-Regulation, eds BoekaertsM.PintrichP. R.ZeidnerM. (San Diego, CA: Academic Press), 13–39.

  • Google Scholar

• 364

  ZimmermanB. J. (2001). “Theories of self-regulated learning and academic achievement: an overview and analysis,” in Self-Regulated Learning and Academic Achievement: Theoretical Perspectives, 2nd Edn, eds ZimmermanB. J.SchunkD. H. (Mahwah, NJ: Erlbaum), 1–37.

  • Google Scholar

• 365

  ZimmermanB. J.Martinez-PonsM. (1986). Development of a structured interview for assessing student use of self-regulated learning strategies. Am. Educ. Res. J.23, 614–628.10.3102/00028312023004614

  • CrossRef
  • Google Scholar

• 366

  ZimmermanB. J.Martinez-PonsM. (1990). Student differences in self-regulated learning: relating grade, sex, and giftedness to self-efficacy and strategy use. J. Educ. Psychol.82, 51–59.10.1037/0022-0663.82.1.51

  • CrossRef
  • Google Scholar

• 367

  ZimmermanB. J.SchunkD. H. (eds) (1989). Self-Regulated Learning and Academic Achievement: Theory, Research and Practice. New York: Springer, 1–25.

  • Google Scholar

• 368

  ZimmermanB. J.SchunkD. H. (eds) (2001). Self-Regulated Learning and Academic Achievement: Theoretical Perspectives, 2nd Edn. Mahwah, NJ: Erlbaum.

  • Google Scholar

• 369

  ZimmermanB. J.SchunkD. H. (eds) (2003). Educational Psychology: A Century of Contributions. Mahwah, NJ: Erlbaum.

  • Google Scholar

• 370

  ZinsJ. E.EliasM. J.GreenbergM. T.WeissbergR. P. (2000). “Promoting social and emotional competence in children,” in Preventing School Problems – Promoting School Success: Strategies and Programs That Work, eds MinkeK. M.BearG. G. (Bethesda, MD: National Association of School Psychologists), 71–99.

  • Google Scholar

• 371

  ZinsJ. E.WeissbergR. P.WangM. C.WalbergH. J. (eds) (2004). Building Academic Success on Social and Emotional Learning: What Does the Research Say?New York: Teachers College Press.

  • Google Scholar