Internet-based infrastructures and online architectural education on design studios: perceived instructional strategies and students engagement

Abstract

The current study investigated the impact of perceived instructional strategies in online design studios on student engagement as a multidimensional construct. Given the rapid shift to online learning during the COVID-19 pandemic, understanding effective teaching strategies and their impacts on student engagement is crucial for enhancing overall student outcomes. The study also examined the role of learning motivation as a mediator in these relationships. A mixed-methods approach was employed, measuring a second-order construct of student engagement based on three reflective dimensions: cognitive, affective, and behavioral. Qualitative interviews with instructors revealed three main instructional strategies utilized in online design studios: learning by doing, learning with interaction, and learning from feedback. The present study analyzed self-report survey responses from 230 undergraduate architecture students across four major universities in Iran, utilizing partial least squares structural equation modeling (PLS-SEM). Based on the results, the perceived learning by doing (PLD) and learning with interaction (PLI) significantly improve student engagement. In contrast, the perceived learning from feedback (PLF) fails to influence student engagement directly. Additionally, intrinsic and extrinsic motivation mediated the impact of PLI and PLF on student engagement. These findings highlight the importance of effective instructional strategies in online design studios. Additionally, the results provide valuable insights for future pedagogical practices in online education, offering guidance for instructors aiming to enhance student outcomes in this dynamic learning environment.

1 Introduction

On 11 March 2020, WHO proclaimed the novel coronavirus (COVID-19) epidemic a pandemic (Cucinotta and Vanelli, 2020). In common with other human social activities, COVID-19 has significantly impacted all levels of education (Nicola et al., 2020) and has changed many education delivery methods worldwide (Yasmin, 2022). After the outbreak, some universities around the world ceased educational operations. Still, because there was no end to the pandemic, they had to choose a way to continue their education (Akin, 2002; Muthuprasad et al., 2021). Therefore, Online learning has become the primary alternative to face-to-face learning in many educational institutions (Almahasees et al., 2021). While distance and online education existed before the outbreak, their use was not as widespread as face-to-face classes (Andrade-Vargas et al., 2021), so teachers and students faced challenges along the way (Salahshori et al., 2022; Selvaraj et al., 2021; Al-Mawee et al., 2021), especially in studio-based design courses (Nubani and Lee, 2022). The first experience using online studios in design education dates back to the early 1990s (Akar et al., 2012). In parallel, research in this field began (McCullough et al., 1990; Sheldon et al., 1995; Kvan, 2001). However, online design education research has not kept pace with growing research in other disciplines in scope and number (Güler, 2022; Fleischmann, 2021). Hence, there is a need for more research in this field that can be helpful both in similar critical situations and in the future of design education because the impact of experiences gained during the pandemic on the future of design education cannot be ignored. In the meantime, engagement is critical to student academic achievement (Fredricks et al., 2004) and academic success (Carvalho et al., 2014; Fisher et al., 2018). Due to the distance between students and instructors, engaging them online is exceptionally challenging (Martin et al., 2020; Fisher et al., 2021). A deep understanding of this concept in online learning environments helps to improve their effectiveness. It is imperative to focus on student engagement to ensure an effective online learning environment. Therefore This study focuses on the relationship between perceived instructional strategies and student engagement in online design studios.

2 Literature review and theoretical framework

2.1 Student engagement

A concept known as student engagement has been the subject of numerous studies in education (Vayre and Vonthron, 2016; Karimian Shamsabadi et al., 2022). Also, according to Martin et al. (2020), Kolhe (2017), Kuo et al. (2014), student engagement has been a prominent theme throughout the past two decades of research into online learning. Many studies have explored its relationship to critical educational outcomes, including academic achievement and success (Wang and Degol, 2014; Pietarinen et al., 2014), academic performance (Wang and Holcombe, 2010), Learning satisfaction (She et al., 2021; Gao et al., 2020; Fisher et al., 2018). Studies have also addressed the possible predictors of student engagement, particularly the personality traits of the students (Durón-Ramos et al., 2020; Qureshi et al., 2016; Yeganeh and Kamalizadeh, 2018; Mclaughlan et al., 2021) and contextual and institutional factors (Christenson et al., 2012; Manwaring et al., 2017). Information in Table 1 shows the influence of various factors on student engagement in blended and online learning. However, there is no agreement on student engagement’s definition, dimensions, and measurement (Bond et al., 2020). Despite these challenges, studies consider it a complex and multidimensional construct that links the affective, cognitive, and behavioral factors in an integrated way. Observable behaviors like involvement and participation in learning activities describe behavioral engagement (Fredricks et al., 2004). Affective engagement refers to students’ emotional response to the learning environment, institution, and instructor (Pietarinen et al., 2014; Mohamed, 2021). Students’ cognitive engagement can be defined as their psychological investment in academic activities, their preference for mentally challenging activities, and their use of metacognitive strategies to regulate their cognition while performing tasks (Fredricks et al., 2004; Nazidizaji et al., 2014).

2.2 Learning motivation

In previous studies, the motivation of students has been investigated in both traditional and online learning contexts (Deci and Ryan, 1985; Vallerand et al., 1997; Yeganeh and Kamalizadeh, 2018; Boekaerts, 1999; Pintrich, 2002; Chen and Jang, 2010). Also, the effect of learning motivation on academic performance and achievement has been extensively investigated from various perspectives (Castillo-Merino and Serradell-López, 2014). Several theories and concepts have been proposed in the motivation field (Maslow, 1943; Herzberg et al., 1959; Deci and Ryan, 1985). Although not explicitly mentioned, intrinsic and extrinsic motivation are inherent in these theories (Singh et al., 2022). The definition of motivation relies on the distinction between extrinsic and intrinsic motivation (Ryan and Deci, 2000). An inherent tendency toward expiration and learning can describe the learner’s intrinsic motivation. In contrast, extrinsic motivation refers to learning to obtain a different outcome or when external reinforcement, such as rewards or pressure, is present (Ryan and Deci, 2000). At the same time, researchers examined the reciprocal relationship between motivation and engagement (Reeve and Lee, 2014). Appleton et al. (2008) suggested that learning motivation leads to student engagement. Intrinsic and extrinsic motivation predict student engagement. Studies showed the impact of intrinsic motivation on student engagement (Froiland and Worrell, 2016; Saeed and Zyngier, 2012). Oga-Baldwin et al. (2017) demonstrated how promoting autonomy and intrinsic motivation enhanced engagement. In addition, many studies highlight the links between students’ motivation and the learning environment factors (Matthews, 1991; Meece et al., 2003; Zare. et al., 2022).

2.3 Instructional strategies in the online design studio

Studio learning is broadly accepted in design education as the dominant delivery method (Crowther, 2013; Park, 2011). The significant factors in design education comprise: the studio environment, The manner of instructor-student communication, and teaching methods and studio management. Some studies investigated the characteristics and role of an effective instructor in the design studio (Goldschmidt et al., 2010), teaching practices (Salama, 2006), training strategies (Quayle, 1985), and teaching and learning strategies (Noels et al., 2019). The literature also has emphasized the importance of instructors’ instructional practices in enhancing students’ learning (Day et al., 2021; Sakhaei et al., 2020). Understanding instructors’ instructional strategies can help assess teaching and instructors (Kendall and Schussler, 2013; Sökmen, 2019; Motie et al., 2023) and reveal how they influence learning outcomes (Young et al., 2003). Instructional strategies are studied under different labels, such as teaching practices (Olivier, 2020), instructional Support (Marchand and Gutierrez, 2012; Maren et al., 2021), instructional practices (Webb et al., 2014), and classroom practices (Smith et al., 2005; Tucker and Rollo, 2006). Instructional strategies as contextual factors influence student engagement (Heilporn et al., 2022; Kahu, 2013; Christenson et al., 2012). Lekwa et al. (2018) divided teacher practices into two categories to understand their impact on student engagement: instructional and behavior management strategies. According to their study, both categories of strategies significantly enhanced students’ engagement with course content. Applying strategies from one domain can affect the strategy’s effectiveness in the other (Wang et al., 2016; Lekwa et al., 2018). Different approaches can be used to measure instructional strategies, including observation, self-report, and perception of students of teaching (Beck and Blumer, 2021; Goharian et al., 2023). Each of these approaches has weaknesses. However, perceptions of students can be used to measure teaching strategies in the classroom and improve teaching strategies (Wubbels et al., 2006; Ghanbari et al., 2022), and assess educational quality (Wang, 2010; Scherer et al., 2016). According to previous studies, Table 2 shows the most important instructional strategies used in the design studios.

2.4 Present study

The emergence of COVID-19 has transformed the educational system in numerous ways, making online, blended, and distance learning no longer a choice but a necessity. Some of these changes in the education system will persist after the epidemic. Previous studies have investigated the different aspects of online design education during the pandemic and emphasized the critical role of instructional strategies in the effectiveness of online learning (Xiang et al., 2018; Alsuwaida, 2022; Azizibabani et al., 2021). However, they still need to study the instructional strategies used in the online design studio context and their impact on student engagement. This investigation is the first to be conducted in architecture design education. The understanding student engagement concept can provide a deeper insight into the learning process. In different studies, student engagement has been conceptualized as a multidimensional construct with two to eight dimensions (Lanasa et al., 2009; Ashtari et al., 2021; Yeganeh, 2022).

Furthermore, there is evidence that engagement can be considered a higher-order construct (Martin, 2007; Maroco et al., 2016; Hosseini Alamdari et al., 2022). This study presents a model that examines the effect of perceived instructional strategies by students on their engagement. This model assumes that student engagement is a second-order concept characterized by three first-order dimensions: affective, behavioral, and cognitive. Considering the links among instructional strategies, learning motivation, and student engagement that were established separately, this model has a logical mediating relationship; instructional strategies indirectly influence student engagement through motivation (Figure 1)

FIGURE 1

In this paper, the following research hypotheses are specifically formulated:

3 Materials and methods

3.1 Sampling

Two groups, university instructors and students, participated in this study. The samples were selected using the cluster sampling method. In the first phase, four universities in Iran were randomly selected. An email outlining the purpose of study was sent to instructors teaching architecture design at these four universities, inviting them to participate in the qualitative phase of the study. In the next phase of the study, an email containing the study’s purpose and a link to the survey was sent to students, inviting them to participate by completing the online questionnaire created using Google Forms.

3.2 Qualitative phase

In the first phase of research, qualitative methods in the form of open-ended interviews were utilized to comprehensively explore teachers’ views and experiences regarding their instructional strategies in online architecture studios. This approach allowed teachers to articulate their experiences in their own language, capturing the nuances of their teaching practices. The qualitative method is particularly effective in exploratory research, especially in contexts like online learning, where there is limited existing information (Cohen et al., 2018), and researchers aim to delve into the experiences and insights of teachers and students (Bdair, 2021; Liu, 2023). The sampling strategy employed was cluster sampling; for the qualitative phase of the research, ten teachers from four universities were selected, comprising six women and four men. In December 2021, data were collected from the qualitative phase. At the time of the interviews, participants had taught three semesters in the online design studio, with teaching experience ranging from two to 14 years. The primary researcher sent emails to teachers inviting them to participate in the study. The email included a detailed description of the study, including its aims and processes, as well as the researchers’ contact information. Recipients interested in participating were invited to reach out to the researchers to schedule an interview. Due to the COVID-19 pandemic, interviews were conducted via Skype to ensure the safety and convenience of all participants. Each interview lasted between 30 and 50 min and was recorded for analysis purposes. Participants were asked five questions during the interview, which can be found in Supplementary Appendix B. The recordings were transcribed verbatim. Two researchers analyzed the data collaboratively following Braun and Clarke’s six-step approach (Braun and Clarke, 2006). The following themes emerged from teachers’ interviews regarding their instructional strategies in the online design studio: (1) learning by doing, (2) learning with interaction, and (3) learning from feedback.

The findings obtained from these qualitative interviews subsequently contributed to the development of the scale for perceived instructional strategies in the following phase of the research, thereby enhancing the overall validity of the research findings.

3.3 Quantitative phase

3.3.1 Participants

The sample comprised students from ten online studios. Studios came from four different universities. Cluster sampling was the method used for selecting the sample. Online questionnaires created using Google Forms were distributed to students via email. The demographic characteristics of the study sample are summarized in Table 3. The study participants were 230 architecture undergraduates (68.7% female, 31.3% male). Their ages ranged from 19 to 36 (M = 22.08, SD = 2.95). Participants were spread across five semesters of study (32.6% in the fourth semester, 13% in the fifth semester, 23% in the sixth semester, 18.3% in the seventh semester, and 13.1% in the eighth semester). Students were examined at the end of the course to maximize the ecological validity of the study. They were asked to rate their learning motivation, perception of educational strategies, and engagement in the course.

3.3.2 Data collection and measures

3.3.3 Analysis

To analyze the data, SmartPLS 3.0 was used as a partial least squares structural equation modeling (PLS-SEM) tool, capable of assessing measurement models (associations between indicators and related constructs) and structural models (relationships between constructs) simultaneously. PLS-SEM was chosen as the appropriate model for testing small sample sizes (Hair al., 2021) and complex models such as higher-order models (Sarstedt et al., 2019). This paper conceptualizes student engagement as a two-order construct with cognitive, affective, and behavioral dimensions. According to prior studies, reflective-reflective and reflective-formative constructs in PLS-SEM be prominent in various fields (Becker et al., 2012; Sarstedt et al., 2019). According to the literature review, a reflective-reflective second-order construct is presented in the present study. Two commonly used methods for validating and assessing higher-order constructs include the two-stage and repeated indicator approaches. In addition, two versions of the two-stage approach have been proposed, (1) the embedded and (2) the disjoint approach. (Sarstedt et al., 2019). This paper establishes and estimates a path model based on the two-stage disjoint approach. Therefore, there were two significant steps to analyze the two-order path model conceptualized for this article. As a first step, we modeled the path only based on the first-order components of the second-order construct (without considering the second-order construct). Each of these is directly connected to all constructs to which the second-order construct is theoretically associated. We then examined the relationship between first-order components and measurement items via a measurement model assessment. We then recorded the latent variables (LVs) of three components of the second-order construct (BSE, ASE, and CSE). LVs were saved in the original data file and used In step two to analyze the two-order construct. This analysis was performed to estimate the model and validate the hypothesis (Figure 2).

FIGURE 2

3.3.4 Results

3.3.4.1 First-order measurement model

The assessment and interpretation of the PLS model consist of two parts: (1) assessing the reliability and validity of the measurement model and (2) evaluating the structural model. In this paper, the confirmatory factor analysis (CFA) was performed as part of the evaluation process of the measurement model to refine and confirm the items and construct. In the current step, we evaluated three criteria: internal consistency reliability, convergent validity, and discriminant validity (Sarstedt et al., 2019).

This study uses Cronbach’s alpha and composite reliability (CR) to measure reliability. Cronbach’s alpha values above 0.7 indicate admissible reliability (Nunnally, 1978). According to Table 4, Cronbach’s alpha values are between 0.709 and 0.919, which is acceptable. Additionally, the value of the combined reliability is more significant than 0.7 for each variable in Table 4, indicating an excellent internal consistency for the model (Nunnally, 1978). The average variance extracted (AVE) was used to evaluate convergence validity, confirmed for all structures that exceeded the threshold value 0.50.

The discriminant validity of a construct refers to how it distinguishes itself from others. To assess each construct’s discriminant validity, we compared the square root of AVEs with their correlations with other latent constructs (i.e., the Fornell-Larcker criterion). In each construct pair, the square root of the AVE was higher than the correlation with other constructs, which indicates good discriminant validity (Fornell and Larcker, 1981). The discriminant validity test for the constructs can be seen in Table 5. In summary, evaluations of the first-order measurement model conformed to internal consistency reliability, convergent validity, and discriminant validity requirements.

3.3.4.2 Second-order measurement model

In the next step, the model of second-order measurement was evaluated. According to the conceptual model suggestion, student engagement (SE) has three reflective components: ‘cognitive engagement,’ ‘affective engagement,’ and ‘behavioral engagement.’ Stage two focuses on evaluating the second-order measurement model. Table 6 indicates factor loading, CA, rho_A, CR, and AVE of higher-order construct. Thus, the reflective nature of SE was supported (Table 7).

We also check the model’s fitness before evaluating the structural model. The SRMR value was 0.063, below the recommended value of 0.08, and the NFI value was 0.95, which Hu and Bentler (1999) consider acceptable. Therefore, the model has a good level of fit.

3.3.4.3 Direct effects

The structural model was tested using the bootstrapping procedure with 5,000 samples in step two. Table 8 presents the results of this analysis. Hair et al. (2014) state that when the t-value exceeds 1.96, the path relationship is assumed to be significant. Two instructional strategies directly affected student engagement (H1b, H1a): PLI and PLD. No direct effect was found for PLF (H1c). It can be concluded that student engagement was positively influenced by factors such as PLI (ßPLI→SE = 0.192, t = 3.065, p = 0.002), PLD (ßPLD→SE = 0.225, t = 3.753, p = 0.000). In contrast, PLF did not directly affect student engagement (ßPLF→SE = 0.073, t = 1.331, p = 0.183 > 0.05), Unlike the proposed H1c. Furthermore, PLF and PLI were significantly associated with IM, with coefficient values of 0.567 (p = 0.000) and 0.178 (p = 0.019), respectively. Also, PLF and PLI were significantly associated with EM, with coefficient values of 0.316 (p = 0.000) and 0.302 (p = 0.000), respectively. Contrary to this, PLD did not affect the EM and IM (ßPLD→EM = 0.052, t = 699, p = 0.485 > 0.05) (ßPLD→IM = 0.002, t = 0.036, p = 0.971 > 0.05).

3.3.4.4 Mediation analysis

According to the results of the mediation test, there was a significant indirect effect between the PLF and SE (H3c) (ßPLF→EM→SE = 0.088; t = 3.540; p = .000), and the PLF and SE (H2c) (ßPLF→IM→SE = 0.148; t = 3.793; p = .000) showing that learning motivation mediates the relationship between PLF and SE. In addition, the mediation effects of learning motivation on the relationship between PLI and SE (H3b) (ßPLI→EM→SE = 0.083; t = 3.177; p = .001) and PLI and SE (H2b) (ßPLI→IM→SE = 0.047; t = 1.968; p = .049) revealed in this test. In contrast, IM and EM did not have a mediation effect on the relationship between PLD and SE (H3a, H2a) (ßPLD→EM→SE = 0.014, t = 667, p = 0.505 > 0.05) (ßPLD→IM→SE = 0.001, t = 0.036, p = 0.971 > 0.05).

3.3.4.4 Total effect

The effects of different factors on student engagement are summarized in Table 9. Based on Table 9, among instructional strategies, the total effect of PLI on SE (β = 0.321, p = 0.000) was the most significant, followed by PLF (β = 0.309, p = 0.000), PLD (β = 0.270, p < 0.000), respectively.

3.3.4.5 Predictive capability evaluation

In assessing the SEM, the R2 value and Q2 value are the essential criteria for predictive accuracy and relevance. The coefficient of determination (R2) value assesses predictive accuracy. According to Hair et al. (2014), R2 values vary from 0 to 1, with a higher value of R2 indicating a higher predictive accuracy. The SE construct had an R2 value of 0.685 (>0.67), which Henseler et al., 2009 regarded as a significant level of predictive accuracy. While a moderate level of R2 was found for IM and EM (>0.33). The predictive relevance was evaluated using the blindfolding method to calculate the Q2 value. As Henseler et al. (2009) described, Q2 values above zero indicate good reconstruction of the observations and predictive relevance for the model. The results of this study indicate that Q2 values for constructs are above zero, indicating a sufficiently predictive model.

4 Discussion

There is limited evidence to support theorized associations between student engagement, perceived instructional strategies, and learning motivation. The current study developed previous research by investigating the relationships among three instructional strategies (learning by doing, learning with peers, and learning from feedback), learning motivation (intrinsic and extrinsic motivation), and student engagement in online studio contexts. This research provides significant insight into the instructional strategies specific to the architecture design online studio that can lead to high levels of student engagement. The study’s results suggest that perceived instructional strategies by the student can play essential roles in student engagement. In the first place, among the instructional strategies examined in this study, perceived learning with interaction (PLI) was the most significant predictor of student engagement. Specifically, our results clarify the need to use learning with interaction as an instructional strategy and emphasize the values of group discussion, teamwork, description of the design process, and interaction through online tools. Therefore, interaction is critical in online studio settings and for engagement in course content. Learning with interaction strategies that increase student communication should be applied in the future. Our result would align with Wang and Eccles’s (2013) research, showing that students’ peer interaction significantly affects their engagement. Our finding also recalls Xu et al. (2020) results indicating that student behavioral and cognitive engagement increase when instructors facilitate online discussion. Intrinsic and extrinsic motivation mediated the relationship between perceived learning with interaction and student engagement. It meant that perceived learning with interaction could predict intrinsic and extrinsic motivation, predicting student engagement. Previously, the neural bases of the relationship between social interaction and intrinsic motivation in educational environments have been confirmed by Clark and Dumas (2015) and how instructors use social strategies to Develop motivation to learn.

In the second place, the direct effect of perceived learning from feedback on student engagement was not significant. Learning motivation fully mediates perceived learning from feedback and student engagement. Specifically, this strategy involved receiving timely and sufficient feedback from the instructor, peer feedback, using drawings and case studies as feedback, and self-feedback. It shows that this strategy does not directly affect student engagement, but it does so indirectly by causing changes in student motivation. One possible explanation for this finding is that feedback may only directly engage students if perceived as helpful and relevant to their learning goals. Therefore, the impact of feedback on student engagement may be mediated by the level of learning motivation, which is influenced by the perceived value and relevance of the feedback. Based on the results, it is clear that as students get feedback that is clear and timely, that helps them realize their mistakes, that helps them learn, that helps them improve design quality, that makes them think, there is a higher probability that they will be motivated in the online studio. Our findings follow the results proposed in the research that feedback can improve intrinsic motivation. Overall, the findings of this study highlight the importance of understanding the complex relationship between feedback, motivation, and engagement in the learning process. By considering these factors, instructors can develop more effective instructional strategies that promote student engagement and learning outcomes.

Finally, perceived learning by doing (PLD) significantly affected student engagement. Specifically, this strategy involved providing step-by-step instructions for designs, analyzing and presenting examples, making physical models, diagrams, and sketches, solving a specific design problem with the instructor, and providing similar assignments for students.

The mediating relationship between learning by doing and student engagement through intrinsic and extrinsic motivations was not confirmed. This finding indicates that the levels of intrinsic or extrinsic motivation did not influence the impact of learning by doing on student engagement. One possible explanation for this finding is that learning by doing is inherently active and engaging for many students because it involves participation, problem-solving, and practical application. This suggests that the act itself can directly enhance engagement, regardless of students’ motivational levels. Moreover, the assignments related to learning by doing in online studios may be challenging, which could limit the impact of this instructional strategy on motivation to learn. Students may feel overwhelmed or discouraged by the difficulty of the assignments, which could limit their motivation to engage in the learning process. Online learning environments often introduce additional cognitive demands, such as navigating technology and managing distractions at home. These factors, combined with the challenges inherent in learning by doing, can increase cognitive load, making it more difficult for students to stay focused and motivated. Finally, if the tasks associated with learning by doing are perceived as monotonous or excessively challenging without direct teacher involvement, students may struggle to maintain their motivation. As a result, the relationship between learning by doing, motivation, and engagement is likely complex and may involve other mediating or moderating variables not accounted for in the study, such as self-efficacy or peer influence or instructor’s supervision. Further research is needed to explore these additional factors and their potential impact on student engagement in online learning contexts.

Consequently, while the PLD strategy effectively promoted student engagement, the lack of a mediating effect through intrinsic and extrinsic motivation suggests that instructional modifications may be needed to optimize its impact on student motivation and engagement.

5 Limitations and future directions

Several limitations exist in the present study. First, it is difficult to generalize the study’s conclusions due to the inadequate sample size. More students and universities should be selected to make the conclusion more generalizable. Second, in this study, we used a cross-sectional research design in that all variables are self-reported simultaneously in this study. In future research, longitudinal designs can be used to test causal relationships among variables.

Moreover, although self-reported data may provide a view into students’ subjective perceptions and actions that cannot be observed directly (Fredricks and McColskey, 2012), they only provide a limited picture (Kirschner and Van Merrienboer, 2013). Class observations can be used to supplement it. The present study tests the relationships among perceived instructional strategies, motivation, and engagement dimensions. In future research, multilevel analysis can be used to test relationships between instructional strategies and student engagement. While the findings confirmed the impact of instructional strategies on student engagement in online studios, future studies could include additional factors, such as learning outcomes (e.g., performance and satisfaction).

6 Conclusion

The current study is the first to examine the relationship between perceived instructional strategies in online studios and student engagement. As a result of these findings, it is clear that instructional strategy plays a crucial role in engaging students, both directly and via learning motivation. Based on the results, among the instructional strategy, learning with interaction significantly impacted students’ engagement in architectural design online studios. This study has several theoretical implications. Our findings extend the theoretical framework by demonstrating differential relationships between instructional strategies (e.g., learning with interaction, learning from feedback, and learning by doing) and student engagement. This research also offers several practical implications for establishing how instructors provide instructional strategies to students and emphasizes the importance of motivating students to learn in online studios. Instructors must encourage their students to interact with one another and collaborate while learning.

7 Ethical considerations

The study received ethical approval from the Research Ethics Committees of Tarbiat Modares University. In both the qualitative interviews and quantitative surveys, participants were provided with an explanation in easy-to-understand language that comprehensively informed them about the study’s aims and procedures. Participants in the qualitative phase signed an Electronic Consent Statement prior to interview, while those in the quantitative phase provided informed consent by answering a question at the beginning of the online survey. Participation was voluntary, and individuals had the right to withdraw at any time. To ensure participant privacy, all data were kept confidential and used exclusively for research purposes.

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