Charting the growth of brain-based learning in higher education instructional design

This study aims to delineate the scientific literature and articulate the intellectual framework linking brain-based learning (BBL) with instructional design in higher education over the past decade (2013–2024). A scoping review was performed in accordance with the PRISMA-ScR and Joanna Briggs Institute (JBI) criteria. Bibliometric analysis was conducted on articles indexed in Scopus and Web of Science through performance analysis and scientific mapping, particularly utilizing author co-citation analysis (ACA) with VOSviewer. The performance analysis indicates a significant increase in interest in the field since 2020. The co-citation analysis, utilizing a stringent threshold of 14 citations, identified an intellectual core of five key authors, categorized into two primary groups. The primary cluster embodies the essential discourse on the critical and pedagogical utilization of neuroscience in education, whereas the secondary cluster emphasizes the application of these principles in curriculum development. Research on BBL and instructional design is an evolving domain centered on a fundamental discourse over the legitimacy and application of neuroeducation. Substantial deficiencies are recognized in the merging of classical cognitive psychology ideas with the establishment of a technological research frontier.

1 Introduction

Brain-Based Learning (BBL) is an educational strategy that focuses on understanding the human brain’s natural learning and information processing processes. This strategy aims to improve students’ comprehension, retention, and application of knowledge by designing learning environments and instructional methods that align with these brain processes (Abdolmaleki and Saeedi, 2024). This paradigm emerged from the intersection of neurobiology, psychology, and pedagogy, an interdisciplinary field known as neuroeducation (Thomas et al., 2019). The educational community’s enthusiasm for utilizing these discoveries to improve pedagogical practices, particularly in higher education, is remarkable and expanding.

The shift from neuroscience laboratory discoveries to classroom design has encountered problems and controversies. Since its start, the profession has grappled with a fundamental tension, eloquently expressed by Bruer (1997) in his article “Education and the Brain: A Bridge Too Far.” Bruer contended that the disparity between fundamental neuroscience and educational practice was sometimes too vast for direct and significant application, a critique that has influenced and impacted the discipline for decades. This caution highlights the peril of oversimplification and the commodification of misconstrued neuroscientific ideas, resulting in the spread of what are termed “neuromyths” (Bowers, 2016; OECD, 2007). Neuromyths, including the notion of learning styles (visual, auditory, kinesthetic) and the 10% brain fallacy, are erroneous beliefs regarding brain function that, although being scientifically discredited, continue to endure in educational practices (Waterhouse, 2023). The existence of these myths not only diminishes the credibility of the discipline but may also result in the adoption of ineffective or harmful instructional techniques.

The necessity for a thorough and methodical examination is particularly evident in this intricate topography of rewards and hazards. In order for neuroeducation to progress as an evidence-based field, it is essential to transcend slogans and comprehend its genuine intellectual framework. It is essential to identify the authors who are the foundation of the debate, the currents of thought that dominate the academic discourse, and the ways in which ideas are interconnected. The pedagogical foundations of BBL were established by figures such as Caine and Caine (1991), who established twelve principles of brain-based learning, which encompass concepts such as parallel processing, the innate search for meaning, and the integral role of emotions. Jensen (2008) then translated these principles into practical strategies for the classroom. These principles assert that learning is optimized when the brain is permitted to detect patterns in an orchestrated, threat-free immersion environment, emotions are managed, and the entire physiology is engaged.

Simultaneously, behaviorism and cognitivism (Córdova, 2002) have given way to more constructivist and sociocultural approaches in the field of instructional design, particularly in the context of online higher education (Londoño Palacio, 2011; Muñoz et al., 2019). Nonetheless, the incorporation of neuroscientific concepts into instructional design models is still a very new and frequently unorganized field of study (Alkhassawneh and Sharif, 2025). Even though both areas have expanded, their relationship has grown more complicated and disjointed. Although BBL techniques (Rasmitadila et al., 2019, 2020) and integrated learning tools (Eagleton, 2017; Rodgers, 2015) have been the subject of numerous studies, there is not a comprehensive overview that organizes the body of information.

In light of this predicament, a scoping review is essential to delineate the scope and characteristics of research in this domain. This study aims to create a mapping by addressing the following primary research question: What is the extent and intellectual framework of the scientific literature on BBL and instructional design in higher education produced from January 2013 to December 2024?

This review aims to answer the following secondary questions:

1) What is the volume and growth trajectory of the literature throughout this period?

2) Which nations, publications, and writers have the most productivity in the field?

3) What is the underlying intellectual structure, identifying currents of thought and their interrelationships using co-citation analysis?

4) What gaps or areas of future research do you see from this structural analysis?

This article attempts to give a clear path for researchers, educators, and instructional designers as they navigate the complicated terrain of neuroeducation, supporting better informed, evidence-based practice.

1.1 Conceptual framework

One of the most promising, yet complex, frontiers in contemporary pedagogy is the convergence of Brain-Based Learning (BBL) and Instructional Design (ID). It is imperative to first define each domain critically and academically in order to comprehend the intellectual structure of the field that unites them. ID offers a systematic and methodological “how” for structuring learning experiences, while BBL offers a potential neuroscientific “why” for effective learning. We will deconstruct these concepts, their theoretical underpinnings, and the models that govern their application in the following section, thereby establishing the conceptual framework for the current bibliometric analysis.

1.2 About brain-based learning (BBL)

The BBL is founded on the comprehension of brain processes that has been derived from research conducted by a collaboration of disciplines, including cognitive neuroscience, social neuroscience, biology, and artificial intelligence modeling. Concepts such as the brain’s parallel processing, the significance of emotions in learning, and the involvement of the entire physiology in learning are among the fundamental principles of brain-based learning (Jensen, 2000).

Caine and Caine (1991) identified ten key principles that explain the influence of cerebral functions on learning, grounded in brain research. Authors like Rahman et al. (2019) must familiarize themselves with brain-based principles to design curricula that foster a more effective learning environment. The fundamental principles of brain-based learning are as follows: (1) The brain operates as a parallel processor; (2) The lesson encompasses the entire field of physics. (3) The pursuit of meaning is an instinctive endeavor. (4) The search for meaning is achieved through the creation of patterns; (5) Patterns are fundamentally influenced by emotions; (6) The brain processes all components and the whole simultaneously. (7) Learning requires both concentrated attention and peripheral awareness. (8) Learning is consistently defined by the presence of both conscious and unconscious processes. (9) There are at least two distinct types of memory: a spatial memory system and various memory learning systems. (10) The integration of facts and abilities into natural spatial memory enhances comprehension and retention. (11) Challenges enhance the learning process, while threats have a detrimental effect. (12) Each brain exhibits uniqueness.

Three instructional techniques can be employed to incorporate brain-based learning into educational strategies. Rahman et al. (2019): (1) relaxed alertness, it is essential to establish a relaxed environment that eliminates fear and anxiety in students in order to optimize learning. A calm atmosphere and interest in the material being taught enhance information retention and motivation to learn (Hardiman, 2012; Stavrou, 2018) as cited in Rahman et al. (2019); (2) orchestrated immersion, teaching is more effective when students are fully focused and use their memory to explore the presented content in a comprehensive and interrelated manner and (3) active processing, teachers should allow students to actively consolidate and internalize information, connecting new knowledge with their prior cognitive structure to achieve meaningful learning (Caine and Caine, 1991).

Despite its intuitive appeal, the field of BBL and neuroeducation in general has not been without rigorous criticism. The most frequently cited warning comes from Bruer (1997), who in his influential article “Education and the Brain: A Bridge Too Far” argued that there was a huge gap between the findings of basic neuroscience (at the synaptic and cellular level) and the macroscopic realities of classroom practice. Bruer argued that cognitive psychology, which studies behavior and mental processes, serves as a much more useful and applicable bridge for instructional design.

This criticism sparked a fundamental debate on neuromyths, which are misconceptions or oversimplifications of brain research that have gained popularity in the educational sector (OECD, 2007). Waterhouse (2023) cites several notable examples, such as the notion that the left and right hemispheres dominate personality and learning, the belief that only 10% of the brain is utilized, and the belief in learning styles (visual, auditory, kinesthetic) as a fixed determinant of effective instruction. As Tokuhama-Espinosa (2018) observes, these neuromyths frequently result from a misunderstanding of legitimate research and can result in pedagogical practices that are either ineffective or counterproductive. The necessity of a more precise and nuanced dialogue between neuroscientists and education professionals and a greater level of neuroscience literacy among educators is emphasized by the prevalence of these misconceptions. Consequently, an academic approach to BBL must be a critical approach that differentiates between substantial evidence and popular simplifications by definition.

1.3 Instructional design

If BBL provides the guiding principles, Instructional Design (ID) provides a systematic approach to creating effective, efficient, and engaging learning experiences. ID is a field and practice that uses learning and instructional theories to design, develop, implement, and assess educational products and environments (Reiser, 2012). Its goal is to ensure that learning is not an unintentional incident, but rather the predicted outcome of well-planned instruction.

The function of the instructional designer has been the primary focus of the discipline of instructional design in higher education, which has undergone significant evolution (Chiappe, 2008). Constructivist and sociocultural pedagogical approaches have significantly impacted this development (Barriga, 2005; Neves et al., 2012). The integration of learning theories into instructional design is regarded as essential (Londoño Palacio, 2011), and the utilization of technology in this process is also underscored (Góngora Parra and Martínez Leyet, 2012). Nevertheless, a more structured approach to instructional design is required, with an emphasis on the acquisition of real-world skills and practical implementation (Muñoz et al., 2019). Nevertheless, the implementation of instructional design in certain educational institutions is lacking in theoretical foundation and empirical evidence, despite these advancements (Núñez and Escobar, 2012). Instructional design is also affected by behaviorism and cognitivism (Córdova, 2002).

Instructional Design is not a stringent discipline; it has developed in response to the prevailing educational ideologies of the twentieth and twenty-first centuries. Comprehending these viewpoints is essential for grasping the rationale behind the creation of therapies.

• Behaviorism: According to behaviorists like B. F. Skinner, learning is a discernible and quantifiable shift in behavior brought about by the correlation between a stimulus and a response. Observable and quantifiable learning objectives, such as:

◦ The student will be able to list the five principles of BBL, are one way behaviorism appears in ID.

◦ Task analysis is the process of decomposing complicated jobs into manageable, sequential parts.

◦ Consolidating desired behaviors through positive reinforcement and instant feedback.

◦ Assessing whether a student has met the behavior specified in the objective is known as criterion-based evaluation.

• Cognitivism: Cognitivism, which emerged as a reaction to behaviorism, focuses on internal mental activities that cannot be clearly observed, such as thinking, remembering, and problem solving. It sees the learner as an active processor of information, not a passive receiver. Its impact on ID is significant:

◦ Emphasis on knowledge structure: The goal is to organize information such that it can be processed and encoded in long-term memory (e.g., using concept maps and outlines).

◦ Activating prior knowledge: Strategies are devised to assist pupils in linking new material with their existing knowledge.

◦ Cognitive load management: As previously stated, Cognitive Load Theory (Sweller et al., 1998) serves as a fundamental principle of cognitive-based instructional design, offering directives to prevent the overburdening of working memory.

• Constructivism: This viewpoint, based on the theories of Piaget, Vygotsky, and Bruner, asserts that information is not conveyed but actively created by the learner within a social and cultural framework. Learning constitutes the process of comprehending the world. In instructional design, constructivism informs methodologies such as:

◦ Problem-based learning (PBL): Students acquire knowledge by addressing genuine, intricate challenges.

◦ Collaborative learning: Knowledge is co-constructed through social interaction and dialogue.

◦ Authentic learning environments: Tasks and contexts are intended to be as similar to real-world situations as feasible, where knowledge will be applied.

◦ The instructor’s function as a facilitator or guide by the side instead of sage on the stage.

1.3.1 Instructional design theories

Learning theories influence the development of ID models, which are prescriptive frameworks that assist designers through the process’s stages. Although many of models exist, the most are variants or extensions of a few fundamental concepts.

• The ADDIE Model: This is the most well-known model and is typically regarded as the general framework for ID. It is an abbreviation for its five successive stages:

◦ Analysis: The learning needs, student characteristics, and overall goals are recognized.

◦ Design: Learning objectives are defined, instructional strategies are developed, activities are created, and assessments are established.

◦ Development: Educational resources are developed and generated, such as texts, films, and interactive exercises.

◦ Implementation: The intended audience receives instruction.

◦ Evaluation: Both formatively (during the process to make modifications) and summatively (at the end to determine overall achievement) the effectiveness of the training is measured.

• The Dick and Carey Model, recognized as a systems model, offers a detailed and systematic approach, significantly influenced by cognitivism and behaviorism. The process commences with the identification of an instructional goal, followed by nine interrelated steps: instructional analysis, learner analysis, objective writing, development of assessment instruments, formulation of instructional strategies, and both formative and summative evaluation.

• Merrill’s Principles of Instruction (Merrill, 2002) presents a set of first principles that are universally applicable across effective instructional design models and theories, rather than a process model. He posits that learning is enhanced when it is centered on real-world problems or tasks.

◦ Prior knowledge is activated.

◦ New knowledge is presented to the learner through demonstration.

◦ The learner is afforded the opportunity to apply the newly acquired knowledge.

◦ The new knowledge is incorporated into the learner’s existing framework (integration).

• Agile Models (e.g., SAM): Recently, iterative models like the Successive Approximation Model (SAM) have emerged, presenting a challenge to the linear structure of ADDIE. SAM facilitates a swift, iterative design process characterized by the phases of evaluate, design, develop. This approach enables rapid prototyping and timely feedback, rendering it especially beneficial for e-learning development and intricate projects with evolving requirements.

Neuroscience and Instructional Design come together in a field called Neuroinstructional Design. This field uses current understanding about how the brain functions to inform each phase and principle of ID models, progressing from a merely psychological basis to a neurocognitive basis.

2 Methods

To answer the research questions, a scoping review with a bibliometric analysis component was developed and implemented.

The study follows the Joanna Briggs Institute’s (JBI) methodological principles for conducting scoping reviews (Peters et al., 2020), as well as the PRISMA extension for Scoping Reviews (PRISMA-ScR) reporting standards (Tricco et al., 2018). This methodological approach was purposefully chosen to reflect the exploratory nature of the study topics. Unlike a systematic review, which attempts to answer a specific clinical or efficacy question, a scoping review is the appropriate methodology for mapping current data in a subject, identifying significant concepts, and highlighting gaps in the literature, all of which are well aligned with the objectives of this study.

2.1 Data sources and eligibility criteria

Because they are the most comprehensive and widely used bibliographic data sources in academic research, the Scopus and Web of Science (WoS) databases were selected. Additionally, they provide the detailed citation metadata that are essential for co-citation analysis (Pranckutė, 2021). The investigation was conducted on October 8, 2025.

The time span was defined for January 2013 to December 2024. The inclusion and exclusion criteria were predefined and are described in Table 1.

Table 1

Table 1. Inclusion and exclusion criteria.

2.2 Search strategy

A two-phase sensitivity search strategy was devised and implemented to resolve concerns regarding an overly restrictive initial search approach and to ensure thorough coverage. The search was limited to the Title, Abstract, and Keywords fields (TITLE-ABS-KEY in Scopus and Topic [TS] in WoS). To guarantee the inclusion of exclusively peer-reviewed journal articles, manual filtering procedures were applied. Since English and Spanish are the predominant languages of publication within the selected databases and are the languages in which the research team is competent, a restriction based on language was considered appropriate.

Phase 1: Centralized Search. The purpose of this investigation was to determine the direct intersection among the primary constructs. To improve sensitivity, two conceptual modules (Module A and Module B) were established utilizing a broad spectrum of synonyms: Block A (Neuroscience and Education): (“brain-based learning” OR “neuroeducation” OR “brain-compatible learning” OR “neuroscience” OR “neuromyths” OR “educational neuroscience” OR “neuropedagogy” OR “neurolearning” OR “neuropsicoeducation” OR “neurodidactics” OR “mind-brain-education”)

• Block B (Instructional Design and Pedagogy): (“instructional design” OR “learning design” OR “educational design” OR “learning experience design” OR “instructional systems design” OR “instructional technology” OR “didactic design” OR “instructional schema”)

Phase 2: Sensitivity Search. A subsequent search was performed by integrating Block A with a new conceptual block to identify articles that address brain-based pedagogy without employing formal terminology related to instructional design. Block C (Ample Pedagogical Context): (“active learning” OR “flipped classroom” OR “blended learning” OR “e-learning” OR “online learning” OR “student-centered learning” OR “teaching method*”).

An initial engagement with field experts was undertaken to validate these phrase clusters, and essential terminology from relevant review articles was analyzed. Comprehensive and precise search strings customized for each database are provided in Supplementary material 1 to ensure full reproducibility.

The complete Boolean chains (Block A AND Block B) and (Block A AND Block C) were executed in Scopus and WoS. It is important to note that the term “Higher Education” was employed as a manual screening criterion rather than a search term to prevent the exclusion of pertinent articles that did not explicitly reference the context in their title or abstract.

2.3 Statement on the use of AI in search strategy

To overcome the constraints of the first search and to enhance the robustness of the results, generative artificial intelligence technology was employed as a methodological aid in the iterative development of search chains. Specifically, the Gemini Model 2.5 Pro, developed by Google, was utilized. The principal author established the preliminary conceptual frameworks, and through interaction and refinement, the AI model contributed to the enhancement of synonym lists for the conceptual categories and the organization of Boolean logic to create a more effective two-phase search approach. This collaborative effort was crucial for achieving broader coverage of the field. The entire history of prompts and generated responses is contained in the Supplementary files for this publication.

2.4 Study selection and data extraction

A second expert approved the selection procedure once one reviewer completed it.

1. Identification phase: Both databases were searched. Duplicate records were eliminated in Zotero after exporting the results.

2. Titles and abstracts of all unique records were reviewed first. Thematically unsuitable entries were excluded. After that, the complete text of the remaining articles was collected for a final eligibility evaluation, applying all inclusion and exclusion criteria, especially context in Higher Education.

Inclusion/exclusion decisions were recorded in an Excel matrix (Screening Analysis Sheet - Screening.xlsx) for data extraction. Finally, the metadata of the 80 articles was exported in.csv format for performance analysis and directly from Scopus and WoS with cited references for scientific mapping.

2.5 Data synthesis and analysis

Data from the final 80 articles were exported from Scopus and WoS in.csv format for performance analysis and co-citation analysis. The analysis comprised two components:

1) Performance Analysis: A descriptive analysis of the metadata was conducted using Microsoft Excel. The scientific production was measured annually, including the geographical distribution of authors’ affiliations, the most productive journals (core journals), and the authors with the highest publication counts within the corpus.

2) Scientific Mapping (ACA): An Author Co-citation Analysis (ACA) was conducted to illustrate the intellectual framework of the discipline.

2.6 Specifications of the scientific mapping and sensitivity analysis

The mapping was conducted utilizing VOSviewer software (version 1.6.20). The Scopus/WoS.csv data file has been successfully imported. The analysis was structured as follows:

1) Analysis Type: Citation

2) Analytical Unit: Cited Authors

3) Counting Method: Comprehensive counting (each co-citation is assigned equal weight).

4) Normalization Technique: Association Strength. This approach standardizes co-citation data to emphasize the most robust and important associations, irrespective of an author’s overall citation count (van Eck and Waltman, 2010).

5) The “Smart local moving” clustering technique from VOSviewer was employed with a default resolution parameter of 1.0 to create the clusters.

6) Pre-processing: Thesaurus files and author disambiguation techniques were not utilized, depending solely on VOSviewer’s citation data aggregation.

An appropriate threshold was identified to reconcile rigor in filtering out fewer relevant writers and interpretability in producing a clear map. A minimum criterion of 14 citations per author was instituted. This selection produced a core cohort of five authors, facilitating a thorough and concentrated examination of the most impactful intellectual figures in the discipline.

A sensitivity analysis was conducted utilizing other thresholds to validate the robustness of this structure and ascertain that the findings were not contingent upon a singular parameter.

• The network expanded to 22 authors and introduced figures from adjacent disciplines (general psychology, pedagogy) that diluted the central structure of the neuroeducational debate when the threshold was lower (e.g., 10 citations). However, the network became less coherent.

• The network was reduced to only two authors (Bowers, J.S. and Dubinsky, J.M.) when the threshold was raised to 20 citations, which confirmed their centrality but obscured their structural relationship with the other three main authors. The central structure of five authors who comprise the foundational debate of the discipline was, therefore, most effectively revealed by the 14-citation threshold. It was unnecessary to exclude unconnected authors, as all five of the identified authors were interconnected in a single primary component.

3 Results

The scoping review’s findings are presented in this section in two segments. Initially, it delineates the process of selecting a study and offers a performance analysis that delineates the general characteristics of the corpus. Secondly, it provides a scientific cartography by analyzing the co-citations of authors to uncover the fundamental intellectual framework of the field.

3.1 Study selection

Figure 1 depicts the PRISMA-ScR flow diagram, which summarizes the study search and selection procedure. After deleting duplicates, the two-phase search approach used on the Scopus and Web of Science databases yielded 340 unique records in total. The screening of titles and abstracts (Phase 1) was carried out using a fast exclusion criterion to eliminate plainly irrelevant studies. Following this method, 205 articles were identified as potentially eligible and proceeded to full-text review. In Phase 2 of the screening, the eligibility of these papers was evaluated extensively using all inclusion criteria. At this point, 125 articles were removed, primarily because they were not written in the context of higher education or did not directly address the thematic convergence of neuroscience and instructional design/pedagogy. Also, a corpus of 80 articles met all of the criteria and was used for the qualitative synthesis and bibliometric analysis.

Figure 1

Figure 1. PRISMA-ScR flow diagram detailing the steps in the identification and selection of sources.

3.2 Performance analysis

The descriptive study of 80 publications illustrates the characteristics and trends in scientific production during the last decade.

3.2.1 Growth trajectory

The annual scientific output (see Table 2) depicts the evolution of academic interest in the topic. During the initial period (2013–2019), productivity was consistent but low, averaging 5 articles per year. However, a noticeable turning point may be seen beginning in 2020, with a steady increase culminating in 2023. This acceleration indicates a rising consolidation of the area and increased interest, probably fueled by the global shift to online education during the COVID-19 epidemic, which may have stimulated the quest for novel and evidence-based pedagogies (Riva et al., 2021).

Table 2

Table 2. Annual scientific output (2013–2024).

3.2.2 Geographical distribution

An examination of the authors’ affiliations (see Table 3) indicates a geographical distribution predominantly led by the United States, which accounts for over half of the corpus (46 articles, 57.5%). This leadership suggests that a significant portion of the academic discourse and foundational research emanates from US universities. The inclusion of 16 additional countries from Europe, Asia, Oceania, and Latin America illustrates the field’s worldwide scope and increasing international interest, albeit a more fragmented production landscape.

Table 3

Table 3. Most productive nations.

3.2.3 Publication focus

The field’s interdisciplinary nature is reflected in the distribution of publications across a variety of journals. Nevertheless, the Journal of Undergraduate Neuroscience Education (JUNE) is the primary publication forum (refer to Table 4), with 16 articles, solidifying its status as a dissemination center for neuroscience pedagogy. The relevance of the subject in professional training is emphasized by the existence of journals on medical education (Medical Science Educator) and education sciences in general (Education Sciences).

Table 4

Table 4. Core journals.

3.2.4 Prolific writers

The authorship analysis (refer to Table 5) indicates that the field is not monopolized by a limited cohort of academics. While some authors possess two publications inside the corpus, the predominant number of articles originates from distinct authors. This indicates a wide-ranging and varied study domain, characterized by a growing number of contributors rather than being concentrated in a limited number of laboratories or research teams.

Table 5

Table 5. Most prolific authors.

3.3 Intellectual structure of the field: examination of author co-citation

An examination of author co-citation was conducted to illustrate the intellectual structure of the field. Using a very strict threshold of at least 14 citations, we found a core group of five authors whose work is always cited in the literature. Figure 2 shows the resulting map, which shows a cohesive network divided into two primary clusters that represent the most important schools of thought.

Figure 2

Figure 2. Co-citation map of authors in the literature on BBL related to instructional design in higher education.

It is essential to understand the visual elements of Figure 2 to aid in its interpretation. The map constitutes a network in which:

• Nodes (Circles): These represent the authors. The magnitude of the node is directly proportional to the total number of citations the author has received within our corpus of 80 articles.

• Links (Lines): The strength of co-citation is represented by the proximity and thickness of the lines. Authors who are more closely related and linked by thicker lines are cited together more often, signifying a strong conceptual association.

• Clusters (Colors): VOSviewer automatically groups authors with a high co-citation frequency into color-coded clusters, each representing a distinct “school of thought” or research domain.

3.3.1 The core of praxis and criticism in neuroeducation: the red cluster

Four authors—Bowers, J.S., Dubinsky, J.M., Hinesley, V., and Chang, Z.—make up this cluster, which is the largest and most central. This group is not a monolithic perspective; rather, it is a dynamic academic dialogue. On the one hand, Bowers, J.S., is a critical academic voice in the field, recognized for his advocacy for empirical rigor and his skepticism regarding the direct application of brain findings to the classroom. However, Dubinsky, J.M., in conjunction with collaborators such as Hinesley and Chang, is at the forefront of the practical application of neuroscience in teacher training and the enrichment of pedagogical choices. Consequently, this cluster serves as the focal point of the academic discourse on neuroeducation, which is characterized by the fundamental conflict between the potential of neuroscience and the obstacles to its legitimate and evidence-based application.

The red cluster, the most extensive in the network, has four authors: Bowers, J.S., Dubinsky, J.M., Hinesley, V., and Chang, Z. This group embodies the vigorous scholarly discourse around the utilization of neuroscience in educational contexts. The prominence of Bowers, J.S., represented by the largest node, signifies his substantial citation influence as a pivotal authority in the subject. Bowers (2016) research has been essential in fostering healthy skepticism, challenging the direct relevance of neurological results to educational settings, and advocating for enhanced empirical rigor to mitigate the proliferation of neuromyths.

Conversely, within the same cluster, Dubinsky, J.M. and his colleagues exemplify a proactive initiative to establish robust and pragmatic connections between neuroscience and teaching. Their research centers on teacher training and the enhancement of pedagogical judgments through precise neuroscientific information in the classroom (Dubinsky et al., 2019; Schwartz et al., 2019). Consequently, this cluster does not represent a singular school of thought; instead, it serves as the primary platform for discourse in neuroeducation, where the advocacy for practical application coexists with the necessity for critical examination.

3.3.2 Green cluster: the vanguard of neuroscience-informed curriculum development

Schwartz, M.S., is the sole author of this cluster. His placement in a distinct cluster, despite its close association with the red cluster, implies that the scientific community acknowledges his contribution as a specialization within the broader debate. Schwartz’s research is dedicated to the practical application of neuro-pedagogical principles in the development of curriculum design and lesson preparation (Schwartz et al., 2019).

The link between Dubinsky, J.M. (Red Cluster) and Schwartz, M.S. (Green Cluster) is the most important and strongest one on the map. This relationship serves as an intellectual bridge, representing the crucial transition from theoretical-critical discourse and educator training (Dubinsky’s domain) to practical application within the curriculum (Schwartz’s expertise). This link shows that the field is trying to move from the why? to the how? in using neuroscience in education.

4 Discussion

This scoping review aimed to delineate the intellectual framework of the research domain that converges Brain-Based Learning (BBL) with Instructional Design in higher education. The analysis of author co-citation (ACA) has offered a comprehensive examination of the theoretical foundations and essential issues that have shaped scientific production in the past decade. The findings indicate a domain that, albeit exhibiting continuous increase in interest and output, is currently undergoing theoretical consolidation, centered on a principal dispute, with significant areas of integration yet to be explored.

The intellectual structure of the field that connects BBL and DI in higher education has been mapped in this scoping review. A significant concentration of production in the United States is confirmed by the performance analysis, which indicates a growing and accelerating academic interest since 2020. The most noteworthy discovery, which is based on the co-citation analysis of authors, is the identification of a highly concentrated intellectual core of five authors. This core is organized around a central debate on the validity and application of neuroscience in education, rather than a broad range of learning theories.

This analysis will thoroughly interpret the findings of the bibliometric study, starting with the importance of the identified intellectual clusters. Subsequently, the identified gaps or disconnections highlighted by the map in relation to established learning theories will be critically examined. Practical implications for educators and instructional designers will be delineated, and recommendations for future research will be suggested. The limitations of the current study will be synthesized, followed by a conclusion regarding the present state of the field.

4.1 Debate defines the field of intellectual structure interpretation

The most significant discovery of this study is that the intellectual core of the field, as defined by a rigorous co-citation threshold, is not comprised of a multitude of applied theories, but rather a highly concentrated set of five authors who embody the fundamental debate on the validity and practice of neuroeducation. The co-citation map does not indicate a placid application of principles; rather, it reveals a dynamic and self-critical field that is currently in the process of establishing its own epistemological identity.

4.1.1 Red cluster: central to criticism-praxis dialogue

The red cluster, the most prominent and central in the network, signifies the core of the academic discourse. The composition, featuring key figures like Bowers, J.S., and advocates of practical application such as Dubinsky, J.M., together with collaborators Hinesley and Chang, is highly illuminating. Bowers (2016), recognized for his reasoned skepticism regarding the direct applicability of neuroscience in educational settings, underscores the scientific community’s appreciation for and dependence on self-critique in this domain. Researchers are not uncritically endorsing the assertions of neuroscience; instead, they are contextualizing their work within a discourse that recognizes cautions regarding neuromyths and the necessity for a robust methodological bridge, a concept that aligns with Bruer’s seminal thesis (Bruer, 1997).

In this same group, Dubinsky and his team (Dubinsky et al., 2019) are a prominent presence, which is a good response to this criticism. Their study centers on teacher training and the ways in which well-integrated neuroscientific knowledge can enhance pedagogical decisions and empower educators. So, this group is not a single “school of thought,” but rather a lively place where theory and practice come together. It stands for the useful tension between being careful with science and the urge to improve instruction. Dubinsky’s significant position in this network indicates his function as a pivotal individual who articulates a vision of neuroeducation that is both aspirational and pragmatic, fostering application without reductionism.

4.1.2 Green cluster: curriculum design’s practical facet

The emergence of Schwartz, M.S., in a distinct although closely related cluster represents another significant structural result. While the red cluster deliberates the “what” and “why” of neuroeducation, the green cluster, represented by Schwartz, concentrates on the “how.” His work, frequently in conjunction with Dubinsky’s group (Schwartz et al., 2019), is regarded by the community as a unique specialization: the conversion of neuro-pedagogical principles into concrete curriculum design.

The most significant intellectual bridge on the map is the firm connection between Dubinsky and Schwartz. This link signifies the critical transition from the development of a neuroscientific perspective in educators (Dubinsky’s domain) to the development of specific lesson materials and structures (Schwartz’s domain). This discovery implies that the field fundamentally acknowledges that effective neuroeducation necessitates a two-step process: first, a robust conceptual and critical foundation, and second, meticulous instructional design that converts that foundation into tangible learning experiences.

The co-citation map indicates that the area is characterized less by the implementation of recognized neuroscientific concepts and more by the discourse regarding the nature of that implementation. The red cluster marks the center of this discussion, where both Bowers’ skeptical criticism and Dubinsky’s informed praxis can be found. This discovery is significant as it illustrates that the advancement of the discipline is contingent upon its ability for critical self-examination. This discipline does not accept dogmas or postulates; instead, it builds its identity through a conversation between possibility and caution. The Dubinsky-Schwartz bridge connects the discussion cluster (red) to the curriculum application cluster (green). This is an example of how this process works, showing how the educator’s thinking leads to the design of instruction.

4.2 Disconnections and gaps in the structure

What the map does not show is just as important as what it does show. By emphasizing the most frequently shared theoretical foundations, co-citation analysis also reveals places where conceptual integration is deficient or absent. Three major structural gaps have been found.

4.2.1 Gap 1: instructional design’s break from classical cognitive psychology

The most significant omission from the intellectual foundation of the map is the lack of the pioneering theorists of cognitive psychology, particularly John Sweller and Richard E. Mayer, who have influenced instructional design for the past 30 years. Sweller’s Cognitive Load Theory (CLT) (Sweller et al., 1998) provides a comprehensive and empirically substantiated theory for the constraints of working memory, a fundamentally neuroscientific notion. Principles drawn from Cognitive Load Theory, such as minimizing extraneous load and enhancing germane load, are directly applicable to the design of any learning environment, including those based on Blended Learning. Nonetheless, despite this conceptual alignment, Sweller is absent from the co-citation map, suggesting that while individual articles in our corpus may reference him, his work is not integrated into the common and central theoretical discourse that characterizes the neuroeducational discussion.

Richard Mayer’s Cognitive Theory of Multimedia Learning (CTML) (Mayer, 2014) is predicated on the assumptions of the brain’s dual processing capabilities (visual and auditory channels), the constraints of these channels, and the necessity for active processing in knowledge production. Coherence, signaling, redundancy, and spatial and temporal contiguity are all ways of presenting information that are based on how the human brain works. These principles provide pragmatic, evidence-based recommendations that should form the foundation of any brain-based instructional design. Mayer’s exclusion from the co-citation core indicates a worrying deficiency: the discourse of neuroeducation seems to be evolving concurrently, rather than through profound integration, with the discourse of multimedia learning grounded in cognitive psychology.

This gap suggests that neuroeducation may be missing decades of study in cognitive psychology that have already looked at many of its goals from a black box view of the brain as it tries to make a name for itself in neuroscience. So, the chance is in clear integration in the future. To build a real link between behavior, cognition, and the neural substrate, future study should not only use the ideas behind CLT and CTML, but also use neuroscientific tools like EEG or fNIRS to test and improve these ideas (Gerjets et al., 2014; Mayer, 2019).

The omission of John Sweller and Cognitive Load Theory (CLT) from the principal co-citation is both noteworthy and troubling, given that this theory offers a thorough structure connecting human cognitive architecture with instructional design (Sweller et al., 1998). The tenets of CLT, which address the management of intrinsic, extraneous, and germane loads, possess broad applicability, grounded in the evolutionary biology of human cognition, specifically the limitations of working memory and its association with long-term memory (Sweller et al., 2019). Consequently, disregarding this framework, the BBL discourse risks undertaking unnecessary endeavors or, more critically, advocating for ‘immersion’ strategies that inadvertently exceed the cognitive capacity of novice learners.

The importance of CLT to BBL predominantly lies in its mechanical explanation of learning as a transformation within long-term memory. Although BBL often emphasizes ‘active processing’ and ‘meaning-seeking’ in a general sense, CLT delineates these concepts by defining learning as the formation and automatization of cognitive schemas (Paas et al., 2003). This perspective is crucial for instructional design in higher education, as complex information requires careful management of attentional resources.

Furthermore, recent research emphasizes the potential synergy between CLT and neurobiology. Kirschner et al. (2006) contend that instructional design must align with human cognitive architecture to ensure effectiveness, a premise that clearly aligns with BBL’s objectives but is underpinned by a more substantial empirical basis. Ayres (2020) investigated the impact of cognitive load management on improving learning outcomes within complex digital environments, a domain of growing importance for BBL. Neglecting these advancements limits BBL’s ability to offer practical, evidence-based solutions to current educational challenges.

4.2.2 Gap 2: the lack of a theoretical foundation in neuro-educational technology

The performance analysis indicated a notable rise in publications in recent years, corresponding with the growth of online education. BBL, characterized by its focus on immersion, feedback, and threat reduction, appears particularly appropriate for implementation via well-structured educational technologies. The co-citation map fails to identify any significant clusters or authors at the intersection of technology, neuroscience, and instructional design.

This does not imply that the articles do not address technology; in fact, a significant number of them do. This implies that the community does not consistently and transversally cite any set of authors or theoretical framework on neuro-educational technology. Rather than being based on a shared theoretical foundation, the discussion of technology appears to be more applied and study-specific (e.g., the use of a specific platform, a type of simulation, etc.). For instance, despite the fact that Caine and Caine's (1991) concept of “orchestrated immersion” could be directly implemented through virtual reality (VR) or augmented reality (AR), we do not envisage VR theorists in education arising as central figures in this map.

This gap indicates that the field is primed for the creation of theoretical frameworks to inform the design and assessment of educational technology through a neuroscientific lens. This may encompass, for instance, frameworks illustrating how user interfaces can enhance cognitive load, how virtual environments can influence emotional states to promote learning, or how adaptive feedback systems can tailor instruction based on neurophysiological indicators of student condition (Gerjets et al., 2014). The lack of this cluster necessitates a transition in the discipline from employing technology to reasoning about it based on its inherent principles.

4.2.3 Gap 3: the lack of insights from computational neuroscience

The size of the intellectual core is a finding. It is important that a strict requirement of 14 citations narrows down a field of thousands of possible references to only five authors. There are two ways to look at this. It can mean that the field is still new or not fully developed, having a shared theoretical framework that is still limited and being built. Research may exhibit significant diversity yet remain fragmented, as numerous scholars operate within conceptual silos, unable to establish a unified set of basic references.

Conversely, it may be perceived as an indication of concentration. Neuroscience is not immature; rather, it is intently focused on a fundamental question: the debate regarding its validity and application. The five authors of the map are not the only significant individuals; however, they are the most significant for this particular discussion.

This implies that the scientific community is inclined to address this fundamental epistemological inquiry prior to pursuing a broader range of applications. The notion that these five authors are firmly interconnected, resulting in a coherent and closed debate, is substantiated by the network’s density, despite its small size.

Furthermore, our analysis has revealed a significant disconnect with related disciplines that integrate neuroscience and learning from a computational perspective. The educational discourse (Red Cluster) underscores the importance of epistemological validity, although advancements in computational neuroscience have led to the development of models that simulate learning pathways, offering valuable insights that could improve instructional design.

Stephen Grossberg’s research and his Adaptive Resonance Theory (ART) provide a sophisticated model of how the brain processes information and maintains stable learning within a dynamic environment without forfeiting previously acquired knowledge (the stability-plasticity dilemma) (Grossberg, 2013, 2021). Grossberg asserts that conscious learning occurs through conditions of ‘resonance’ between incoming sensory information and internal expectations (top-down). For instructional design, this indicates that effective learning encompasses not only the transmission of information but also the development of experiences that cultivate this resonance, thereby affirming and enhancing learner expectations. Similarly, the study carried out by Carpenter and Grossberg (2016) investigates how these resonance mechanisms can clarify attention and category learning, offering a comprehensive theoretical framework for understanding how learners organize and improve their knowledge.

At the same time, advancements in artificial neural networks (ANNs), led by researchers including Geoffrey Hinton, offer a complementary viewpoint. Hinton (2007) and LeCun et al. (2015) have demonstrated that deep learning algorithms, modeled after neural architecture and utilizing the backpropagation technique, are capable of acquiring complex representations from extensive datasets. Although these are artificial models, Hinton asserts that they offer valuable insights into the ways the human brain may modify its synaptic connections to minimize errors and enhance predictive precision. Furthermore, Bengio et al. (2021) have examined how principles of deep learning can improve our understanding of human cognition, suggesting that the learning processes in both machines and humans are guided by fundamental principles of optimization and generalization.

Ultimately, the work of Chris Eliasmith and his team exemplifies an effort to develop cognitive models with substantial biological plausibility, as demonstrated by the Semantic Pointer Architecture Unified Network (SPAUN) (Eliasmith et al., 2012). Unlike solely functional artificial neural networks, these models aim to replicate both the behavior and the underlying architecture and dynamics of the brain. Incorporating the ‘biological realism’ perspective, in conjunction with the insights of Stewart and Eliasmith (2014) concerning the composition of cognitive representations, has the potential to propel the field of BBL beyond mere abstract metaphors of the brain, thereby promoting instructional design models that recognize the true limitations and capacities of biological neural systems.

4.3 Practical implications and future research directions

The findings of this structural mapping have immediate consequences for both practitioners in the area and future research priorities. This study provides a guide for educators, instructional designers, and higher education administrators or leaders to navigate the topic of neuroeducation from a more critical and educated perspective.

Initially, assume a position of informed skepticism: The significance of the crucial cluster (Bowers, Bruer) should remind practitioners to exercise caution regarding “brain-based” assertions. Prior to the adoption of a new instrument or methodology, it is essential to inquire: What evidence substantiates it beyond mere neuroscientific plausibility? Does it originate from comprehensive educational studies or from extrapolations of fundamental research? Educators should critically evaluate “brain-based” initiatives, emphasizing evidence from educational research rather than relying solely on neuroscientific plausibility.

Secondly, focus on teacher training as a bridge: The connection between Dubinsky and Schwartz emphasizes that the effective implementation of neuroeducation does not involve the distribution of “recipes” to teachers, but rather a comprehensive training process that enables them to comprehend the underlying principles and make their own informed pedagogical decisions (Dubinsky et al., 2019). Institutions should allocate resources to professional development programs that, similar to those suggested by Dubinsky, prioritize the acquisition of fundamental knowledge over the promotion of pedagogical trends.

Ultimately, incorporate the tenets of cognitive psychology: In light of the discovered gap, instructional designers possess the opportunity to function as integrators. They can enhance BBL methodologies by explicitly integrating elements from Cognitive Load Theory and Cognitive Multimedia Learning Theory. For instance, in creating a learning environment aimed at fostering orchestrated immersion (a BBL principle), Mayer’s cognitive load management principles, including the coherence principle (removing extraneous information) and the signaling principle (directing student attention), should be implemented concurrently.

4.4 Future research directions

This bibliometric approach not only describes the past, but may also point the way forward. Three primary directions for future research are proposed; On one hand, theoretical integration research: The most notable disparity observed is the disjunction with cognitive psychology. Future research must directly confront this deficiency. Research is required to develop educational interventions that intentionally integrate the principles of BBL with those of CLT and CTML. A research might compare four conditions: a traditional design, a BBL-only design, a CLT/CTML-only design, and an integrated design. This would facilitate both the comparison of effectiveness and the comprehension of potential synergistic effects.

Another approach is the creation of theoretical frameworks for neuroeducational technologies. To close the second gap, researchers must go beyond analyzing specific technology and provide theoretical frameworks. For example, research might look into how the sense of presence in virtual reality links to the brain’s attention and memory mechanisms, as well as how to optimize the design of these experiences for maximum neural engagement and learning. Models are required to correlate technological design characteristics with specific neuropsychological assumptions that can be evaluated.

Another potential avenue of investigation is longitudinal bibliometric studies: The sector is expanding swiftly. Replicating this co-citation analysis over a five-year period might be beneficial to examine the evolution of the intellectual structure. Have novel clusters arisen? Have characters such as Sweller and Mayer been incorporated into the core? Has a technical cluster developed? Longitudinal observation of the field’s structure can yield an objective assessment of its maturity and the clarification of its ongoing disputes.

It is important to acknowledge that, by prioritizing peer-reviewed journal articles, this review may have neglected significant theoretical contributions published in book form, such as the ‘learning ecologies’ perspective (e.g., King and Thibault, 2024). This perspective provides a crucial layer of context, interactivity, and developmental framework that enriches the cognitive structure of BBL. Furthermore, research such as Barnett and Jackson's (2020) on learning ecologies for life highlights the importance of perceiving learning as a continuous and contextually integrated process that extends beyond the confines of formal education. Future research should integrate these extensive, book-length theoretical frameworks to attain a comprehensive comprehension of the contextual features of learning in higher education.

5 Conclusions and limitations

To adequately understand the results of this study, it is crucial to recognize its inherent limitations. The search was limited to the Scopus and Web of Science databases. These are the most complete for bibliometric study, although they might not include important material from other sources, including novels, book chapters, or non-indexed regional journals, which could have changed the corpus composition. Second, co-citation analysis tends to focus on well-known and frequently cited authors, which could hide the work of newer or more niche researchers who have not yet reached the citation threshold. Lastly, the interpretation of clusters, while grounded in a thorough analysis of the identified authors’ work, inherently involves a degree of qualitative judgment by the researchers.

In conclusion, this scoping review provides a comprehensive overview of the intellectual framework at the intersection of Brain-Based Learning and Instructional Design in higher education, despite its limitations. The field is characterized not by universally accepted principles, but by an active scholarly discourse concerning the validity, critique, and application of neuroeducation. The primary discourse focuses on establishing a robust connection between neuroscience and educational settings, a discussion represented by both critical and pragmatic individuals. The identified gaps, particularly the disconnection with classical cognitive psychology and the absence of a technological theoretical core, should be regarded not as weaknesses but as promising avenues for future research. This study provides practitioners with a framework for informed and critical practice, while offering researchers a guide to the key questions that will shape the next decade of this field.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors.

Author contributions

JR: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing – original draft, Writing – review & editing. AV: Conceptualization, Formal analysis, Funding acquisition, Project administration, Resources, Supervision, Validation, Writing – original draft, Writing – review & editing. OB: Data curation, Formal analysis, Supervision, Validation, Writing – review & editing. FA: Data curation, Formal analysis, Supervision, Validation, Visualization, Writing – review & editing.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This work was supported by the Universidad de La Sabana [Technologies for Academia - Proventus Research Group (EDU-8-2024)].

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that Generative AI was used in the creation of this manuscript. The authors declared that first search results were lower than AI chains. Google Gemini 1.5 Pro. After the primary author provided conceptual frameworks, the AI model improved conceptual category synonym lists and Boolean logic for two-phase search. Field coverage required cooperation. The publication's additional files contain all prompts and answers. The article used AI to check for and fix spelling and grammar mistakes.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feduc.2026.1677395/full#supplementary-material

References

Abdolmaleki, N., and Saeedi, Z. (2024). Brain-based CALL in flipped higher education GE courses held through LMS: boosting vocabulary learning and reading comprehension. Int. J. Educ. Technol. High. Educ. 21. doi: 10.1186/s41239-024-00442-9

Crossref Full Text | Google Scholar

Alkhassawneh, S., and Sharif, H. A. (2025). Perspectives of brain research (educational neuroscience) on the design and implementation of teaching strategies in educational technology. J. Neuro Oncol. 5. doi: 10.1344/joned.v5i2.47695

Crossref Full Text | Google Scholar

Ayres, P. (2020). Something old, something new from cognitive load theory. Comput. Human Behav. 113:106503. doi: 10.1016/j.chb.2020.106503

Crossref Full Text | Google Scholar

Barnett, R., and Jackson, N. (Eds.) (2020). Ecologies for learning and practice: Emerging ideas, sightings, and possibilities. London: Routledge.

Google Scholar

Barriga, F. D. (2005). Principios de diseño instruccional de entornos de aprendizaje apoyados con TIC: Un marco de referencia sociocultural y situado. Rev. Tecnol. Comunic. Educ. 41, 5–16.

Google Scholar

Bengio, Y., Lecun, Y., and Hinton, G. (2021). Deep learning for AI. Commun. ACM 64, 58–65. doi: 10.1145/3448250

Crossref Full Text | Google Scholar

Bowers, J. S. (2016). The practical and principled problems with educational neuroscience. Psychol. Rev. 123, 600–612. doi: 10.1037/rev0000025,

PubMed Abstract | Crossref Full Text | Google Scholar

Bruer, J. T. (1997). Education and the brain: a bridge too far. Educ. Res. 26, 4–16. doi: 10.3102/0013189x026008004

Crossref Full Text | Google Scholar

Caine, R. N., and Caine, G. (1991). Making connections: Teaching and the human brain : Association for Supervision and Curriculum Development. Alexandria, Virginia: ASCD Association for Supervision and Curriculum Development.

Google Scholar

Carpenter, G. A., and Grossberg, S. (2016). “Adaptive resonance theory” in Encyclopedia of machine learning and data mining (New York: Springer US), 22–35.

Google Scholar

Chiappe, A. (2008). Diseño instruccional: Oficio, fase y proceso. Educ. Educ. 11, 213–226. doi: 10.14201/eks.22483

Crossref Full Text | Google Scholar

Córdova, D. (2002). El diseño instruccional: Dos tendencias y una transición esperada. Docencia Univ. 3, 11–26.

Google Scholar

Dubinsky, J. M., Guzey, S. S., Schwartz, M. S., Roehrig, G., MacNabb, C., Schmied, A., et al. (2019). Contributions of neuroscience knowledge to teachers and their practice. Neuroscientist 25, 394–407. doi: 10.1177/1073858419835447,

PubMed Abstract | Crossref Full Text | Google Scholar

Eagleton, S. (2017). Designing blended learning interventions for the 21st century student. Adv. Physiol. Educ. 41, 203–211. doi: 10.1152/advan.00149.2016,

PubMed Abstract | Crossref Full Text | Google Scholar

Eliasmith, C., Stewart, T. C., Choo, X., Bekolay, T., DeWolf, T., Tang, Y., et al. (2012). A large-scale model of the functioning brain. Science, 338, 1202–1205. doi: 10.1126/science.1225266

Crossref Full Text | Google Scholar

Gerjets, P., Walter, C., Rosenstiel, W., Bogdan, M., and Zander, T. O. (2014). Cognitive state monitoring and the design of adaptive instruction in digital environments: lessons learned from cognitive workload assessment using a passive brain-computer interface approach. Front. Neurosci. 8:385. doi: 10.3389/fnins.2014.00385,

PubMed Abstract | Crossref Full Text | Google Scholar

Góngora Parra, Y., and Martínez Leyet, O. L. (2012). Del diseño instruccional al diseño de aprendizaje con aplicación de las tecnologías. Educ. Knowl. Soc. 13, 342–360. doi: 10.14201/eks.9144

Crossref Full Text | Google Scholar

Grossberg, S. (2013). Adaptive resonance theory: how a brain learns to consciously attend, learn, and recognize a changing world. Neural Netw. 37, 1–47. doi: 10.1016/j.neunet.2012.09.017,

PubMed Abstract | Crossref Full Text | Google Scholar

Grossberg, S. (2021). Conscious mind, resonant brain: how each brain makes a mind. Oxford: Oxford University Press.

Google Scholar

Hardiman, M. M. (2012). The brain-targeted teaching model for 21st-century schools. Oxford: SAGE Publications.

Google Scholar

Hinton, G. E. (2007). Learning multiple layers of representation. Trends Cogn. Sci. 11, 428–434. doi: 10.1016/j.tics.2007.09.004,

PubMed Abstract | Crossref Full Text | Google Scholar

Jensen, E. (2000). Brain-based learning: a reality check. Educ. Leadersh., 57, 76–80. Available online at: https://www.ascd.org/el/articles/brain-based-learning-a-reality-check (Accessed May 25, 2024).

Google Scholar

Jensen, E. (2008). Brain-based learning: The new paradigm of teaching. 2nd Edn. Thousand Oaks, California: Corwin Press.

Google Scholar

King, A., and Thibault, P. J. (2024). Learning as interactivity, movement, growth and becoming. London: Routledge.

Google Scholar

Kirschner, P. A., Sweller, J., and Clark, R. E. (2006). Why minimal guidance during instruction does not work: an analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educ. Psychol. 41, 75–86. doi: 10.1207/s15326985ep4102_1

Crossref Full Text | Google Scholar

LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature 521, 436–444. doi: 10.1038/nature14539,

PubMed Abstract | Crossref Full Text | Google Scholar

Londoño Palacio, O. J. (2011) El diseño instruccional en la educación virtual: Más allá de la presentación de contenidos Educ. Desarr. Soc. 5 112–127. Available online at: https://dialnet.unirioja.es/servlet/articulo?codigo=5386237 (Accessed July 23, 2024).

Google Scholar

Mayer, R. E. (Ed.) (2014). The Cambridge handbook of multimedia learning. 2nd Edn. Cambridge: Cambridge University Press.

Google Scholar

Mayer, R. E. (2019). Thirty years of research on online learning. Appl. Cogn. Psychol. 33, 152–159. doi: 10.1002/acp.3482

Crossref Full Text | Google Scholar

Merrill, M. D. (2002). First principles of instruction. Educ. Technol. Res. Dev. 50, 43–59. doi: 10.1007/BF02505024

Crossref Full Text | Google Scholar

Muñoz, S. Y., Ortiz, D. M., Martínez, L. V., and Cruz, A. A. (2019). Dominar el proceso de diseño instruccional: Un enfoque sistemático. Ingen. Concienc. Bol. Cient. Esc. Super. Ciudad Sahagun 6, 73–73. doi: 10.29057/ess.v6i11.3749

Crossref Full Text | Google Scholar

Neves, M., Centeno, C., Fruet, F., Otte, J., and Orth, M. (2012). Design educacional construtivista: O papel do design como planejamento na educação a distância. Anais CIET: Horizonte, 1–12. Available online at: https://ciet.ufscar.br/submissao/index.php/ciet/article/view/1532/1548 (Accessed July 23, 2024).

Google Scholar

Núñez, L., and Escobar, E. (2012). Algunas precisiones sobre el diseño instruccional. Rev. Virtual Univ. Catol. Norte, (35), 1–4. Available online at: https://www.redalyc.org/pdf/1942/194224362001.pdf (Accessed July 23, 2024).

Google Scholar

OECD (2007). Understanding the brain: The birth of a learning science. Paris, France: OECD Publishing.

Google Scholar

Paas, F., Renkl, A., and Sweller, J. (2003). Cognitive load theory and instructional design: recent developments. Educ. Psychol. 38, 1–4. doi: 10.1207/S15326985EP3801_1

Crossref Full Text | Google Scholar

Peters, M. D. J., Marnie, C., Tricco, A. C., Pollock, D., Munn, Z., Alexander, L., et al. (2020). Updated methodological guidance for the conduct of scoping reviews. JBI Evid. Synth. 18:2119. doi: 10.11124/JBIES-20-00167,

PubMed Abstract | Crossref Full Text | Google Scholar

Pranckutė, R. (2021). Web of science (WoS) and Scopus: the titans of bibliographic information in today’s academic world. Publica 9:12. doi: 10.3390/publications9010012

Crossref Full Text | Google Scholar

Rahman, A., Wibawa, B., and Sumantri, S. (2019). Developing e-module of English for tourism based on brain-based learning approach at state polytechnic of Lampung. Int. J. Innov. Creat. Change 6, 29–47. https://www.ijicc.net/images/Vol6Iss2/6203_Rahman_2019_TD_R.pdf (Accessed May 25, 2025).

Google Scholar

Rasmitadila, R., Rachmadtullah, R., Samsudin, A., Suryanti Tambunan, A. R., Khairas, E. E., and Nurtanto, M. (2020). The benefits of implementation of an instructional strategy model based on the brain’s natural learning systems in inclusive classrooms in higher education. Int. J. Emerg. Technol. Learn. 15:53. doi: 10.3991/ijet.v15i18.14753

Crossref Full Text | Google Scholar

Rasmitadila, R., Widyasari, W., Prasetyo, T., Rachmadtullah, R., Samsudin, A., and Suryanti Tambunan, A. R. (2019). Design of instructional strategy model based on the brain’s natural learning system (MS-SIPAO) in inclusive classrooms in higher education. Univ. J. Educ. Res. 7, 2352–2360. doi: 10.13189/ujer.2019.071113

Crossref Full Text | Google Scholar

Reiser, R. A. (2012). “What field of study is instructional design and technology?” in Trends and issues in instructional design and technology (3rd ed., pp. 1–9). eds. R. A. Reiser and J. V. Dempsey (Boston, MA: Pearson).

Google Scholar

Riva, G., Wiederhold, B. K., and Mantovani, F. (2021). Surviving COVID-19: the neuroscience of smart working and distance learning. Cyberpsychol. Behav. Soc. Netw. 24, 79–85. doi: 10.1089/cyber.2021.0009,

PubMed Abstract | Crossref Full Text | Google Scholar

Rodgers, D. L. (2015). The biological basis of learning: neuroeducation through simulation. Simul. Gaming 46, 175–186. doi: 10.1177/1046878115590585

Crossref Full Text | Google Scholar

Schwartz, M. S., Hinesley, V., Chang, Z., and Dubinsky, J. M. (2019). Neuroscience knowledge enriches pedagogical choices. Teach. Teach. Educ. 83, 87–98. doi: 10.1016/j.tate.2019.04.002

Crossref Full Text | Google Scholar

Stavrou, P.-D. (2018). Psychoanalytic psychotherapy as a treatment for depression in adolescents: a case study. Glob. J. Soc. Sci. Stud. 4, 91–101. doi: 10.20448/807.4.2.91.101

Crossref Full Text | Google Scholar

Stewart, T. C., and Eliasmith, C. (2014). Large-scale synthesis of functional spiking neural networks. Proc. IEEE 102, 881–898. doi: 10.1109/JPROC.2014.2306061

Crossref Full Text | Google Scholar

Sweller, J., van Merrienboer, J. J. G., and Paas, F. G. W. C. (1998). Cognitive architecture and instructional design. Educ. Psychol. Rev. 10, 251–296. doi: 10.1023/A:1022193728205

Crossref Full Text | Google Scholar

Sweller, J., van Merrienboer, J. J. G., and Paas, F. (2019). Cognitive architecture and instructional design: 20 years later. Educational Psychology Review, 31, 261–292. doi: 10.1007/s10648-019-09465-5

Crossref Full Text | Google Scholar

Thomas, M. S. C., Ansari, D., and Knowland, V. C. P. (2019). Annual research review: educational neuroscience: progress and prospects. J. Child Psychol. Psychiatry 60, 477–492. doi: 10.1111/jcpp.12973,

PubMed Abstract | Crossref Full Text | Google Scholar

Tokuhama-Espinosa, T. (2018). Neuromyths: Debunking false ideas about the brain. New York: W. W. Norton & Company.

Google Scholar

Tricco, A. C., Lillie, E., Zarin, W., O’Brien, K. K., Colquhoun, H., Levac, D., et al. (2018). PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann. Intern. Med. 169, 467–473. doi: 10.7326/M18-0850,

PubMed Abstract | Crossref Full Text | Google Scholar

van Eck, N. J., and Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84, 523–538. doi: 10.1007/s11192-009-0146-3,

PubMed Abstract | Crossref Full Text | Google Scholar

Waterhouse, L. (2023). Why multiple intelligences theory is a neuromyth. Front. Psychol. 14:1217288. doi: 10.3389/fpsyg.2023.1217288,

PubMed Abstract | Crossref Full Text | Google Scholar