We define engagement as “the intensity of productive involvement with an activity” (Ben-Eliyahu et al., 2018, p. 87), which includes facets such as involvement, focus, participation, and persistence (Fredricks et al., 2004). We consider motivation as the semi-stable student characteristics that interact with learning contexts to produce engagement (Ben-Eliyahu et al., 2018). By contrast, engagement varies by context and is shaped by pedagogy, demographics, course structure, and task attributes (Kahu, 2013; Sinatra et al., 2015; Wang et al., 2016).

Due to the frequent employment of large “grain sizes” of measurement (coarse, high-level assessments of general engagement as opposed to precise, contextually appropriate assessments, see Sinatra et al., 2015), research on learning engagement in higher education often lacks the precision evident in the primary and secondary learning engagement literature. Research on engagement in K-12 contexts tends to emphasize the multidimensional nature of engagement within a psychological framework (Kahu, 2013; Zhoc et al., 2019) and focuses on teaching practices associated with positive learning outcomes (Kahu, 2013; Krause and Coates, 2008), providing pedagogically useful frameworks and tools to educators and policymakers. By contrast, much research on engagement in higher education emphasizes student behaviors at a more general level (i.e., not within a specific learning context) and highlights the student-institution relationship, where students bear primary responsibility for learning and institutions provide support and resources (Coates, 2005; Krause and Coates, 2008; Zhoc et al., 2019). Furthermore, greater tertiary student autonomy permits more varied engagement strategies than in pre-college contexts, especially in STEM (Kahn, 2014). For example, an undergraduate could learn and develop quite well via independent study and taking exams while eschewing lectures. Resultantly, professors and teaching centers desiring to increase undergraduate engagement are often left with inadequate guidance. This challenge has become especially acute in the context of growing undergraduate disengagement from learning (Al-Furaih and Al-Awidi, 2021; Allison et al., 2024; Brint and Cantwell, 2012; Cech, 2014; Chipchase et al., 2017; Greener, 2018; Hockings et al., 2008; Jang et al., 2016; McCoy, 2016; Saito and Smith, 2017) and the acceleration of this disengagement by the COVID-19 pandemic (Branchu and Flaureau, 2022; Newton and Essex, 2024; Walker and Koralesky, 2021). To provide educators with more actionable tools for assessing and bolstering learning engagement, more research is needed on its specific nature within university courses.

Within-course engagement is typically conceptualized as a multidimensional construct (Appleton et al., 2006; Ben-Eliyahu et al., 2018; Kahu, 2013; Wang et al., 2016; Zhoc et al., 2019). The affective-behavioral-cognitive (A-B-C) model is the most empirically supported engagement structure (Fredricks et al., 2004). It comprises an affective/emotional factor, a behavioral factor composed of goal-directed actions, and a cognitive factor responsible for processing, thoughts, beliefs, and interpretations of the current task. While these factors may interact, their structure remains distinct.

Affective engagement reflects students’ emotional responses to peers, instructors, the course, the discipline, and the institution. Manifestations include interest versus boredom, happiness versus sadness, anxiety versus confidence, and pleasure versus discomfort. While affective engagement plays a critical role in student well-being (Bowden et al., 2021) and the development of attitudes toward learning (Bathgate and Schunn, 2017), it is conceptually and empirically distinct from behavioral and cognitive engagement (Flowerday and Schraw, 2003). Emerging evidence (McChesney et al., 2025) demonstrates affective engagement consistently separates from behavioral and cognitive dimensions and follows a different structural logic—organized around emotional valence (positive or negative) rather than the activity space-based patterns examined in this study. In line with prior work that focuses specifically on behavioral and cognitive dimensions to enable more targeted modeling (Poellhuber et al., 2016), this study deliberately limits its scope to those two forms. This narrowed focus allows for a deeper investigation into how behavioral and cognitive engagement are structured by educational activity spaces.

The behavioral domain denotes goal-directed actions that support learning and includes on-task behavior, effort, persistence, and concentration (Fredricks et al., 2004). Manifestations include studying, class attendance, completing activities, and writing papers. These behaviors, which vary by course deliverables and structure, correlate with academic performance (Wang et al., 2016) and changes in self-efficacy and self-esteem (Bathgate and Schunn, 2017; Bowden et al., 2021).

The cognitive domain denotes mental effort in pursuit of learning outcomes, and includes thoughtfulness, depth of processing, development of goals, self-regulation, and metacognitive strategies (Fredricks et al., 2004). Manifestations include active reflection upon one’s thinking processes and learning strategies, synthesizing information from multiple sources, concept mapping, and applying conceptual structures to complex information. Cognitive engagement has been consistently observed to support academic performance (Greene, 2015; Rotgans et al., 2018).

Conceptually, there should be a strong separation between cognitive and behavioral engagement, but empirical evidence on their separation has been mixed. Generally, these factors correlate more strongly with each other than with affective engagement (Ben-Eliyahu et al., 2018; Wang et al., 2016), particularly in secondary education. This trend may reflect reciprocal causation (thinking influences action and vice-versa). But it may also reflect measurement limitations. For example, younger students may struggle to self-assess their cognition and, therefore, base responses on behavioral elements (Fredricks et al., 2004). In addition, in efforts to create instruments with validity evidence that have universal educational applicability, survey instruments are often focused on instantiations of behavioral and cognitive engagement that might poorly match the varied ways students can engage in particular contexts. In other words, better separation of cognitive and behavioral engagement might occur with instruments better tuned to each context. Such limitations highlight the need to reassess engagement not as static traits, but as dynamic, contextually structured phenomena.

Learning does not occur in a psychological vacuum. Rather, contemporary research acknowledges the importance of task characteristics, activity contexts, and environmental affordances in shaping learning engagement (Bedenlier et al., 2020; Branchu and Flaureau, 2022; Choi et al., 2025; Li and Xue, 2023; Limniou et al., 2022; Schmidt et al., 2018; Walker and Koralesky, 2021). We synthesize these findings through the concept of activity spaces. An activity space is a physical, social, cultural, and historical environment that forms the domain of specific behaviors (for example, studying for a quiz, listening to a lecture, or completing a team project; Bakhurst, 2009). The concept is derived from activity theory—a sociological perspective developed by Engeström and others into cultural-historical activity theory (see Engeström, 2000). Often used in psychological and educational research, activity spaces are composed of subjects (a student), their objective (studying communally for an exam), available tools (textbooks, whiteboards, other mediating artifacts), the community (several classmates and their social characteristics), norms (active information sharing), and division of labor (one student searches the textbook, another reviews notes) (Gyasi et al., 2021). Systematic reviews report STEM learning encompasses diverse activity spaces—labs, study sessions, coding tasks—each fostering distinct engagement behaviors and cognitive strategies (Gyasi et al., 2021). Research suggests engagement is shaped by activity processes (Ben-Eliyahu et al., 2018). Therefore, we argue that activity spaces do not merely modulate behavioral and cognitive engagement levels -they may shape how engagement is structured psychologically.

This study addresses key limitations in prior research on behavioral-cognitive learning engagement in higher education STEM contexts. First, there is a lack of studies that use engagement measures capable of directly studying the robustness of engagement’s structure across contexts. Some studies use a small pool of items assessing generic, decontextualized forms of engagement that could exist in any course context and thus, they struggle to capture the ways in which engagement might vary across activity spaces within a course. Other studies examine engagement within only one specific context rather than across contexts. Resultantly, different contexts are assessed with distinct item pools, which makes it difficult to determine whether differences in engagement are due to genuine contextual variation or simply differences in instrumentation. This heterogeneity prevents cross-context comparisons, limiting the generalizability of findings (Buntins et al., 2021). Large-grained instruments also frequently obscure engagement’s within-course variation, ignoring how students may engage differently across activity settings even within a single class. Addressing these shortcomings, this study employs a coherent item pool tested across multiple STEM contexts selected for their disciplinary variability (Granovskiy, 2018; see also Appendices B, D).

Second, many studies use a “large grain size” approach, measuring engagement broadly (e.g., institutional-level engagement) rather than at a fine-grained level (e.g., a student’s engagement in specific learning tasks) (Schmidt et al., 2018; Sinatra et al., 2015). However, fine-grained measures are crucial for assessing the psychological underpinnings of the learning process—a matter of particular significance for researchers, practitioners, and theorists (Azevedo, 2015).

Third, while extensive research acknowledges and examines the impact of contextual factors on engagement (Astin, 1993; Lewin, 1936; Li and Xue, 2023; Mayhew et al., 2016; Schmidt et al., 2018) this study moves beyond the common assumption that the dimensionalityof learning engagement is fixed across contexts. Previous work has productively explored context-engagement interactions along axes such as in-person versus electronic modalities (Altomonte et al., 2016), and how instructional choices shape overall engagement intensity (Zhao et al., 2021). However, few studies have tested how contextual effects differentially shape specific domains of learning engagement. Those that have (Lam et al., 2012; Şeker, 2023) typically treat contextual factors as antecedents that moderate engagement in particular domains (e.g., behavioral) without considering the internal structure within domains or the heterogeneity of context-engagement interactions that may occur there. While such approaches explain why learning engagement varies by context, this investigation goes further by examining how engagement itself is constructed -treating context as inseparable from engagement activities.

Finally, much research confounds engagement with its antecedents (e.g., motivation; see Ben-Eliyahu et al., 2018) or its consequents (e.g., academic achievement; see Kahu, 2013). This conceptual blurring obscures engagement’s structure and hinders tests of engagement’s internal structure and assessments of its predictive validity (DeVellis, 2016). This study disentangles these phenomena, clarifying engagement’s structure to help researchers generate specific insights and enable practitioners to develop more effective engagement strategies.

A novel learning engagement instrument was developed to apply the BC engagement model in university STEM course contexts (see Appendix B for full instrument development details). It was piloted in Spring 2022 on 149 engineering students. Confirmatory factor analysis revealed the anticipated behavioral-cognitive factor structure did not fit the data, while an alternative model including in-exam cognitive engagement alongside the two expected factors did. Pursuing this unexpected finding, the scale was then iteratively revised and tested to better capture variation by activity spaces across economics (n = 324), organic chemistry (n = 198), general chemistry 2 (n = 346), and engineering (n = 810) contexts (Appendix B). To improve instrument quality, identify response errors, and enhance clarity, cognitive interviews were conducted with participants from diverse STEM fields and sociocultural backgrounds and the findings used to strengthen the scale further and contribute to its validity argument (Appendix C). We here test the finalized scale in new populations and analyze its structure via exploratory factor analyses.

The Kaiser-Meyer-Olkin (K-M-O) test indicated adequate sampling for factor analysis (0.73 engineering, 0.82 genetics) (Flora and Flake, 2017). Eigenvalue screeplots are in Appendix Figures A1, A2. The optimal factor solution was determined using (1) eigenvalues larger than 1, (2) scree plot interpretation, (3) rotated factor coherence (4) sufficient items per factor, (5) minimal cross-loadings (6) minimizing weakly loading (<0.4) items, and (7) theoretical interpretability (Flora and Flake, 2017).

In contrast to the expectations of prevailing theory, the data did not break down into a two-factor solution composed of (1) behavioral and (2) cognitive engagement. Instead, a five-factor solution emerged for engineering, consisting of (1) in-exam cognitive focus, (2) in-class cognitive strategies, (3) cognitive engagement with group assignments, (4) pre-exam studying behaviors, and (5) time spent on group assignments. An identical four-factor solution emerged for genetics except for the absence of the two group assignment factors (group assignments were not part of the course) and the addition of a behavioral factor for attending and completing activities in recitations (the engineering course did not have recitations). The maximum inter-factor correlation was 0.44 for engineering and 0.30 for genetics, indicating acceptable factor separation. Rotated factor loadings are in Table 1 with replication studies confirming the factor structure (Appendix B).

Internal reliability (Cronbach’s α) ranged from 0.69–0.86 (engineering) and 0.56–0.79 (genetics) (Appendix Table A4). While α > 0.60 is generally acceptable for new scales (DeVellis, 2016), Cronbach’s α is downwardly biased by brief scale lengths, ordinal items, and the assumption of essential tau-equivalence (Raykov, 1997; Zumbo et al., 2007). Thus, scholars have identified lower thresholds (α > 0.50) as indicating adequate reliability for shorter scales (2–3 items) (Briggs and Cheek, 1986). Dropped versus loaded items are in Appendix Tables A5, A6.

Contradicting prevalent theory (Fredricks et al., 2004), the exploratory study provided evidence that behavioral-cognitive learning engagement is shaped not only by behavioral-cognitive distinctions but also by activity spaces. While findings were consistent across two STEM disciplines, small but meaningful differences emerged that were aligned with engagement affordances (e.g., group assignments) and learning contexts. Replication studies (Appendix B) and factorial invariance analysis (Appendix D) further support the potential generalizability of the hypothesized structure across STEM disciplines and courses. Replications consistently showed a clear separation of activity spaces dominated by either cognitive or behavioral engagement, with replication studies yielding the same factor structures as the main study. These results underscore the robustness of the engagement structure across STEM contexts.

While EFA revealed a consistent factor structure, such analysis is interpretative, and only confirmatory techniques can formally test hypothesized models. We administered the scale to a new sample and compared the fit of two alternative models: (1) the traditional cognitive-behavioral structure and (2) the activity space-based structure based on the exploratory study.

The traditional behavioral-cognitive model failed to provide an acceptable fit to the data (CFI = 0.68; TLI = 0.60; RMSEA = 0.118, [90% CI = 0.109, 0.127, p-RMSEA < 0.05 = 0.000]; SRMR = 0.105; CD = 0.94). Adding covariance paths did not improve model fit, and factor loadings were weak, with one behavioral item not significantly loading onto its latent factor (Appendix Figure A3; Appendix Table A10; Brown, 2015; Flora and Flake, 2017; Kline and Little, 2023). This notable lack of fit to the data implies the traditional behavioral-cognitive model does not accurately describe the fine-grained structure of engagement.

Contrarily, the activity space model showed adequate-to-good fit (CFI = 0.94; TLI = 0.92; RMSEA = 0.054, [90% CI = 0.043, 0.064, p-RMSEA < 0.05 = 0.275]; SRMR = 0.048; CD = 1.00, Figure 1). The best-fitting model included significant covariance between all latent factors except exam studying and exam focus. This model also had the strongest TLI and RMSEA values (metrics that penalize overfitting). Unlike the behavioral-cognitive model, all standardized factor loadings were statistically significant (Table 2). The much superior fit and strong parsimony-focused metrics indicate this model better explains the observed structure of behavioral-cognitive engagement, and that its increased complexity is justified (Flora and Flake, 2017).

These results indicate a purely behavioral-cognitive model fails to capture the complexity of learning engagement, whereas activity spaces provide a more empirically robust explanation. Findings support a fundamental shift in engagement conceptualization and suggest potential cross-disciplinary generalizability.

These two studies challenge existing paradigms by demonstrating that, contrary to prevailing theory (Fredricks et al., 2004), behavioral and cognitive engagement may not operate as distinct psychological traits, but rather as emergent properties structured by students’ activity contexts. Addressing our research question, in the examined courses behavioral and cognitive learning engagement’s structure consisted of a consistent core of factors (in-lecture cognition, in-exam cognitive focus, and exam studying behaviors) accompanied by context-dependent factors that emerge when their concomitant activity spaces are available (discussion/recitation behaviors, group assignment behaviors, and group assignment cognition). The spontaneous emergence of this factor structure in EFAs and its fit to the data in the CFA model, as well as the non-emergence of unified behavioral and cognitive factors in the EFA and the severe lack of fit of the same in the CFA suggest the traditional behavioral-cognitive model of learning engagement is insufficient to describe contextualized engagement and should be reconsidered.

The findings call for an ecological perspective on learning engagement, extending beyond classroom dynamics to capture the full academic experience. Our findings indicate engagement is not merely multidimensional as suggested by prior studies (Appleton et al., 2006; Ben-Eliyahu et al., 2018; Zhoc et al., 2019). Rather, it is a partially-context-based dynamic structure with certain features that are stable across different populations, settings, and pedagogical approaches. This finding substantially extends previous work on the situatedness of learning engagement (Altomonte et al., 2016; Lam et al., 2012; Şeker, 2023; Zhao et al., 2021), suggesting potential inseparability between learning engagement and the contexts in which it occurs. Partially addressing calls for the same (see Azevedo, 2015), a more sophisticated and contextually-appropriate understanding of this psychological structure could support more effective interventions and pedagogy, helping address opportunity gaps for minoritized and underprepared students and advancing STEM equity efforts (Beasley and Fischer, 2012; Estrada et al., 2016; Museus et al., 2011; Witherspoon et al., 2019).

Our findings regarding cognitive versus behavioral dominance within engagement spaces may be influenced by survey item selection. The interdisciplinary research team designed items for theoretical and contextual relevance and validated them through extensive cognitive interviews with diverse participants (see Appendix C). However, further research is needed to determine whether alternative item pools would yield different factor structures.

The low inter-factor correlations among engagement spaces (0.06–0.54) raise important questions: Do these factors represent distinct skills, preferences, or both?; How can engagement theory evolve to better explain these observed structures?; and Do differences in factor loadings across courses reflect differences in course structure or student populations?

The engagement scale developed here provides a tool for testing whether specific forms of cognitive and behavioral engagement matter more than overall engagement levels for outcomes like performance, retention, and cognitive development (Schmidt et al., 2018). Analyzing how students engage in different contexts (including those not assessed here, e.g., labs) could help explain learning outcome variability among otherwise similar students and inform targeted interventions to enhance student success (Estrada et al., 2016; Riegle-Crumb et al., 2019).

Future research should explore how engagement patterns evolve across developmental stages, aiding efforts to support learners through key transition points associated with attrition (Akiha et al., 2018). Furthermore, testing how stable factors like in-class cognitive engagement are across different pedagogies, disciplinary groupings (e.g., humanities), and institutions will produce a clearer view of how generalizable the observed factor structure is. As our sample was drawn from selective, research-intensive institutions, it is an open question whether this structure will generalize to courses with high disengagement (e.g., STEM courses for non-majors), less selective institutions, and teaching-focused institutions. Another open question is whether psychological traits (e.g., metacognitive skill, motivation, or anxiety) moderate engagement within different spaces. Finally, linking these engagement structures to downstream outcomes—such as academic persistence, conceptual learning, or identity development—would further clarify their psychological function.

Psychometrically, our findings suggest that engagement should not be measured with generic scales divorced from activity context. Instruments must be designed or interpreted with awareness of the environments in which engagement occurs. Practically, this has important implications: given the minimal covariation between engagement spaces, instructors should build multiple pathways to engagement to accommodate diverse learning approaches. Students engaged in one domain may not engage in others, so teaching strategies should broaden engagement opportunities to foster inclusion and persistence in STEM and remove barriers to high-impact spaces of engagement. For example, recent studies report behavioral engagement in discussion/recitation spaces are critical to academic success (McChesney et al., 2025), but these sessions remain poorly attended. Diversifying engagement strategies may particularly benefit marginalized students, who face multiple challenges that could lead to disengagement from STEM (Barbatis, 2010; McGee et al., 2021; Museus et al., 2011). Finally, practitioners might use the factor structure and instrument presented here to precisely evaluate and address spaces of low engagement in their courses, enacting targeted interventions to counteract growing student disengagement (Allison et al., 2024; Newton and Essex, 2024).