SRL is a multidimensional construct involving cognitive, motivational, and behavioral processes through which learners actively manage and direct their learning (Cenka et al., 2024). Major SRL models have conceptualized this process in different but complementary ways. Pintrich (2000), for example, described SRL as a dynamic and constructive process involving phases such as goal setting, strategic planning, monitoring, and reflection, whereas Zimmerman (2013) emphasized learners’ proactive regulation of cognition, motivation, and behavior. In addition to these perspectives, Boekaerts’ (1999; Boekaerts & Niemivirta, 2000) model highlights the role of motivational and emotional processes in self-regulation, particularly how learners balance learning goals with concerns about well-being and self-protection. From this perspective, motivational beliefs are central to SRL, as they shape learners’ engagement and their selection and use of learning strategies (Bandura, 2001; Poluektova et al., 2023). Across these perspectives, SRL is consistently associated with academic success and effective adaptation to learning demands. In the present study, these models are treated as complementary theoretical foundations for examining intrinsic motivation, trace-based SRL behaviors, and discussion-post behaviors, rather than as a single unified framework.

At the same time, the enactment of SRL strategies is shaped by learning context. Learners’ needs vary across disciplines and task types, and different activities may require different forms of regulation (Zhihong et al., 2023). Accordingly, disciplinary and task-related differences may shape learners’ motivation and regulatory behaviors across learning settings. Although strategic learning is generally beneficial, students often face constraints related to time, cognitive load, and motivational readiness, which may limit their ability to implement demanding strategies effectively. For example, some learners resort to surface-level tactics such as online searches or copy-paste methods to save time, which may be less conducive to deep learning (López-Pernas et al., 2021). By contrast, cognitively demanding strategies such as highlighting, summarizing, and note-taking have been shown to support comprehension and retention, but they require greater autonomy and cognitive engagement (Ponce et al., 2022; Rahiem, 2020). These observations suggest that the effectiveness and enactment of SRL strategies may vary across disciplines and task types. These contextual differences also make it important to consider how SRL is assessed, particularly when the aim is to capture strategy use as it unfolds in authentic learning settings.

Given this context-dependent nature of SRL, a related challenge concerns how self-regulation is assessed. Predominant approaches such as Likert-scale questionnaires and think-aloud protocols remain valuable tools for examining learners’ perceived cognitive, motivational, and emotional experiences (Fernández-Michels & Fornons, 2021; Sheats et al., 2024). However, when used alone, these methods may be less well suited to capturing the real-time and dynamic nature of learners’ strategy use, because they rely on learners’ ability to accurately recall and articulate their experiences, which may be constrained by limitations in self-awareness, memory, and verbal expression (Winne, Nesbit, et al., 2017a; Yang et al., 2023). Accordingly, real-time behavioral data may provide a useful complement to self-report measures when examining self-regulation processes in authentic learning contexts.

Conventional approaches to studying SRL, such as self-report questionnaires, interviews, and think-aloud protocols, provide useful information about learners’ perceived goals, strategy use, and reflections, but they are less well suited to capturing SRL as it unfolds in real time (Perry & Winne, 2006). In this regard, nStudy offers an important methodological advantage by recording learners’ time-stamped actions as they interact with information, tasks, and digital tools in authentic learning contexts (Winne, 2020; Winne et al., 2019). Rather than relying solely on retrospective accounts, nStudy makes it possible to examine what learners do, when they do it, and how they operate on information across learning episodes. This process-oriented evidence provides a stronger basis for investigating the dynamic and event-based nature of SRL than self-perception measures alone (Winne, 2019, 2020).

Earlier work in the gStudy environment, which served as a precursor to nStudy, laid important groundwork for this approach. The gStudy was designed as a multimedia learning environment that provided learners with cognitive tools such as note-taking, highlighting, concept mapping, searching, and glossary construction while simultaneously recording their actions in fine detail (Winne et al., 2006). Research using gStudy showed that trace data can reveal not only the frequency of study tactics but also how those tactics are sequenced and coordinated across learning activities (Malmberg et al., 2010; Perry & Winne, 2006). This line of work helped shift SRL research from viewing strategy use primarily as a self-reported construct toward examining SRL as an unfolding process embedded in learning activity.

Building on this trajectory, the nStudy was developed as a web-based system for researching information problem solving, learning processes, and SRL in naturalistic digital environments. Winne, Nesbit, et al. (2017b) described the nStudy as a system that gathers fine-grained, time-stamped trace data about how learners search for, select, analyze, organize, and revise information while working online. Similarly, Winne et al. (2019) characterized nStudy as software for learning analytics that captures learners’ interactions with information and tools and provides trace data that can be used to infer cognitive, metacognitive, and motivational processes. In practical terms, nStudy records actions such as visiting pages, selecting content, creating and editing notes, highlights, bookmarks, and terms, linking artifacts, and revisiting saved materials across work sessions. Such records enable researchers to move beyond static indicators of SRL and toward a process-oriented account of how learning unfolds over time.

An important contribution of the nStudy-based research is that it has advanced not only data collection but also the theoretical interpretation of learning analytics. Winne (2020) argued that trace data should not be treated as self-evident indicators of learning; rather, their value depends on reliability, validity, and theory-guided interpretation. In this sense, the nStudy is valuable not simply because it logs behavior, but because it supports the representation of theoretically relevant learning events in real time and in authentic contexts. For the present study, the nStudy provides a methodological bridge between learners’ reported experiences and their trace-based behaviors. The system was used to record time-stamped learner actions in a web-based environment, and these traces were used to derive indicators of SRL that complement questionnaire-based measures of motivation, critical thinking, and discussion-post behaviors. Thus, the nStudy is positioned as a process-sensitive approach that strengthens the assessment of the SRL in higher education contexts (Winne, Nesbit, et al., 2017b).

In the present study, discussion-post behaviors are conceptualized as learners’ self-reported engagement in different types of written contributions in online course-related discussions, including questions, responses, claims, elaborations, and other forms of interaction with peers or instructors. This conceptualization is consistent with prior work examining the quality and functions of online discussion contributions (e.g., Bender, 2023; Rakovic et al., 2020). In online learning environments, such behaviors may support student engagement, peer interaction, and opportunities for reflection, but their educational value depends on how discussion activities are designed and facilitated rather than being uniformly positive (Bender, 2023; Neuwirth et al., 2020). In this context, high-quality discussion refers to contributions that go beyond simple participation or agreement and instead involve cognitively engaging moves such as asking questions, giving reasons, elaborating on others’ ideas, comparing perspectives, and making claims (Chi & Wylie, 2014; Rakovic et al., 2020).

Such discussion behaviors may provide opportunities for learners to articulate understanding, monitor their thinking, and respond to others’ perspectives, thereby supporting SRL processes (Marnola et al., 2024; Rakovic et al., 2020). Students with stronger SRL abilities typically demonstrate higher levels of engagement and more effective problem-solving in online discussions, drawing on metacognitive strategies such as goal setting, self-monitoring, and reflection to enhance learning outcomes (Marnola et al., 2024; Paul & Criado, 2020). Related research has also examined regulation in collaborative contexts through co-regulation and socially shared regulation of learning, which emphasize how regulation may emerge through interaction among learners (Hadwin et al., 2017; Isohätälä et al., 2017). However, the present study does not model shared regulation at the group level; instead, it focuses on discussion-post behaviors as socially situated indicators related to individual regulation.

The effectiveness of discussion posts is influenced by several instructional and contextual factors, including the quality of instructor guidance, the structure of participation, and the provision of feedback (Neuwirth et al., 2020). Distinguishing between mandatory and voluntary participation may also affect student engagement and the development of SRL skills (Jin et al., 2023). Structured discussion activities, particularly those guided by instructors, can help students cultivate goal-setting and self-assessment skills while encouraging reflection on both learning processes and outcomes (Gikandi & Morrow, 2016; Schultz & Sandidge, 2022). These findings suggest that discussion posts are most beneficial when they are embedded in supportive instructional designs that promote meaningful engagement.

To better understand the learning processes embedded in online discussions, researchers have increasingly drawn on analytical frameworks such as the ICAP model (interactive, constructive, active, passive), which categorizes students’ learning behaviors into four types of cognitive engagement (Chi & Wylie, 2014). This framework provides a basis for analyzing message content and examining how the nature of students’ contributions relates to learning outcomes. Using ICAP, Rakovic et al. (2020) coded students’ online posts and responses to peers and found that more elaborated and well-developed contributions were associated with stronger performance on course assessments. Their findings also indicated that posts characterized by comparison, justification, and perspective-taking were associated with stronger learning outcomes than posts that primarily expressed disagreement. In the present study, ICAP is used only as a conceptual reference for understanding discussion quality rather than as a direct coding framework for students’ actual posts.

Although prior research has shown that online discussion can support engagement and higher-order learning, relatively few studies have examined SRL strategies and discussion-post behaviors together within the same analytic framework, particularly when discussion-post behaviors are treated as explicit behavioral variables. This gap warrants further attention because discussion-post behaviors may reflect how learners regulate their understanding, respond to peers, and engage in cognitively demanding exchanges. The present study addresses this gap by examining discussion-post behaviors together with intrinsic motivation, trace-based SRL indicators, and learning outcomes in authentic higher education contexts.

Intrinsic learning motivation, as conceptualized by the Self-Determination Theory (Deci & Ryan, 2000), refers to the internal drive that encourages learners to engage in educational activities out of genuine interest, curiosity, and a desire for self-fulfillment. Unlike extrinsic motivation, which relies on external rewards or pressures, intrinsic motivation has often been associated with sustained engagement and deeper learning. Research has consistently shown that intrinsic motivation enhances persistence, creativity, and enjoyment in the learning process (Yin et al., 2020). This effect is particularly pronounced in tasks requiring high cognitive engagement, such as participating in discussions and articulating personal viewpoints, where intrinsic motivation encourages active thinking and meaningful interaction. Moreover, when learning environments satisfy the three basic psychological needs identified by the Self-Determination Theory, learners tend to show stronger intrinsic motivation and higher levels of learning engagement (Kilinc & Buyuk, 2022).

Intrinsic motivation is closely related to learning outcomes, as intrinsically motivated students are more likely to invest effort in challenging tasks, persist through difficulty, and engage deeply with learning materials, which in turn support academic performance and higher-order learning (Deci & Ryan, 2000; Kilinc & Buyuk, 2022; Yin et al., 2020). In this sense, intrinsic motivation is an important factor associated with learners’ engagement and achievement across learning contexts (Deci & Ryan, 2000; Yin et al., 2020). In the present study, intrinsic motivation is examined in relation to discussion-post behaviors, critical thinking, and course grades.

To examine whether students from different disciplinary backgrounds differ in intrinsic motivation, SRL, discussion-post behaviors, and learning outcomes.

H1: Students from different disciplinary backgrounds differ in intrinsic motivation, SRL, discussion-post behaviors, and learning outcomes.

To examine whether students completing different assignment types (implementation vs. written) differ in intrinsic motivation, SRL, discussion-post behaviors, and learning outcomes.

H2: Students completing implementation versus written assignments differ in intrinsic motivation, SRL, discussion-post behaviors, and learning outcomes.

To examine whether disciplinary background and assignment type interact in shaping students’ intrinsic motivation, SRL, discussion-post behaviors and learning outcomes and whether intrinsic motivation, SRL, and discussion-post behaviors are associated with critical thinking and academic performance.

H3: Disciplinary background and assignment type jointly influence students’ intrinsic motivation, SRL, discussion-post behaviors, and learning outcomes.

H4: Intrinsic motivation, SRL, and discussion-post behaviors are positively associated with critical thinking and academic performance.

Pre-course average grade referred to students’ cumulative academic average prior to taking the target course. This variable was obtained from the university’s academic records system rather than from a self-report scale or questionnaire. In the regression analyses, pre-course average grade was included as a control variable to account for students’ prior academic performance.

Intrinsic learning motivation was assessed using two validated subscales (effort/importance and value/usefulness) from the Intrinsic Motivation Inventory (IMI). The inventory is based on the research by Ryan (1982) and Ryan et al. (1983). It is a multidimensional measurement tool that includes seven dimensions: interest/enjoyment, perceived competence, effort/importance, pressure/tension, perceived choice, value/usefulness, and relatedness. The purpose of the IMI is to assess participants’ subjective experiences related to target activities in laboratory experiments. This study selected two subscales effort and value/usefulness which are emphasized by the school, as measurement tools for intrinsic learning motivation. The effort/importance scale evaluates the degree of effort a person invests in an ongoing activity. It consists of 5 items, such as “I put a lot of effort into this.” The value/usefulness scale measures the extent to which people engage in more self-regulatory activities when they perceive an activity as personally valuable and useful. It includes 5 items, such as “I believe this activity could be of some value to me.” Both scales use a seven-point Likert scale (1 = not at all true, 7 = very true). The Cronbach’s α coefficient for the effort scale was .90, and for the value/usefulness scale, it was .89.

Discussion posts were assessed using a study-developed 11-item self-report questionnaire adapted from Rakovic et al. (2020) and informed by the ICAP framework proposed by Chi and Wylie (2014). In this study, ICAP served as a conceptual lens for distinguishing discussion behaviors by levels of cognitive engagement; it was not used as a direct coding protocol for analyzing students’ actual online messages. Rather than coding discussion transcripts, we transformed discussion characteristics identified in prior research into questionnaire items that asked students to retrospectively report how frequently they engaged in each type of discussion behavior during the course.

The questionnaire included 11 categories (Table 1): non-contribution, agree, disagree, give reason, request justification, ask a question, build on, share, compare, make a claim, and answer. Each category was represented by one item describing a specific discussion behavior. For example, the category agree was represented by an item asking whether the student affirmed a peer’s idea (e.g., “I agreed with others’ viewpoints”). Responses were rated on a 7-point Likert scale ranging from 0 (never) to 6 (always). Higher scores indicated more frequent engagement in the corresponding discussion behavior. Table 1 presents the categories, operational definitions, and sample item wording. The Cronbach’s alpha coefficient for the 11-item scale was .76, indicating acceptable internal consistency.

Students were asked to reflect on their experiences during the course and assess whether these discussion characteristics appeared when they interacted with classmates, group members, teaching assistants, or instructors. Although retrospective self-report data may be subject to recall bias, this approach was adopted as a complementary measure alongside the nStudy trace data in order to capture students’ perceived patterns of discussion participation.

SRL was assessed using behavioral trace data collected through the nStudy real-time tracking system (Winne, Teng, et al., 2017). Figure 1 presents the nStudy system architecture, and Figure 2 illustrates examples of learner-generated artefacts recorded in the system. nStudy records timestamped learner actions in a web-based environment, including logins and logouts, page visits, the creation and editing of bookmarks, highlights, notes, and terms, tagging and accessing of these artefacts, searches, and browser tab switching. To protect participants’ privacy, all event data were linked to randomly generated identifiers, and analyses were conducted on anonymized records. Students who did not correctly log in to or log out of the nStudy system during the study period were excluded from the analysis to ensure the completeness of session records.

Five trace-based behaviors recorded by nStudy were selected as SRL indicators: bookmarking, highlighting, note-taking, accessing saved artefacts/links, and interacting with the interface. These indicators were chosen because they represent observable manifestations of regulatory activity in digital learning environments and have been used in prior trace-based SRL research (Winne, 2021; Winne, 2023; Winne, Nesbit, et al., 2017a). Specifically, bookmarking and highlighting were treated as indicators of information selection and organization, note-taking as an indicator of externalization and elaboration, accessing saved artefacts or links as an indicator of monitoring and resource revisiting, and interface interactions such as tagging, searching, and editing fields as indicators of task management and strategic interaction with the learning environment. Following prior nStudy research, each behavior was transformed into a quantitative indicator for analysis. Time on task was calculated as the duration of a learner’s recorded nStudy session, from login to logout.

Learning outcomes were assessed using two indicators: critical thinking and course grade. Critical thinking was measured using the validated critical thinking subscale of the Motivated Strategies for Learning Questionnaire (MSLQ) developed by Pintrich et al. (1991). This subscale assesses the extent to which students apply prior knowledge to new situations in order to solve problems, make decisions, or evaluate ideas according to standards of excellence. It consists of five items, such as “I treat the course material as a starting point and try to develop my own ideas about it.” Responses were rated on a 7-point Likert scale ranging from 1 (not at all true of me) to 7 (very true of me). In the present study, the Cronbach’s α coefficient for the critical thinking subscale was .80. Course grade served as the second indicator of learning outcomes. Because the course adopted a multi-evaluation approach, course grade was considered to reflect students’ overall performance across multiple abilities (Luo et al., 2024). Therefore, course grade was recorded on a 100-point scale (0-100) and included as an additional indicator of learning outcomes.