Redefining learning strategies in SRL for student’s achievements in flipped classrooms

Abstract

Flipped classrooms require learners to actively regulate their learning processes, yet the relationships among self-regulated learning (SRL), engagement, social–emotional intelligence (SEI), and academic performance remain insufficiently integrated within learning analytics research. This study examined 96 third-year Computer Science and Engineering students enrolled in a semester-long Database Management Systems course implementing a flipped SRL framework. A mixed-methods approach was used to analyse academic performance across Continuous Assessment Tests 1 and 2 (CAT 1, CAT 2) and Final Assessment Test (FAT), engagement analytics from Microsoft Teams, SEI survey responses, affective indicators through Reflect app and machine learning models to explore associative and predictive relationships among these constructs. Results indicated strong positive associations between SRL behaviours, engagement, and final assessment outcomes, with engagement partially explaining the relationship between SRL and performance. Correlation and clustering analyses revealed alignment among self-regulation, cognitive engagement, and emotional competencies, while predictive modelling (XGBoost, R² = 0.83) demonstrated that SRL-related indicators effectively model academic performance patterns. Overall, the findings provide theoretically informed evidence of meaningful associations among cognitive, behavioural, and emotional regulation processes in flipped learning environments, highlighting the value of integrating SRL theory with learning analytics for data-informed instructional design in higher education.

1 Introduction

The educational shift in the realm of higher education towards blended learning environments has revolutionised the present-day teaching-learning process (Filiz et al., 2024). The flipped classroom is one such innovative model which has transformed passive and instructor-driven classroom sessions into interactive and student-centred experiences (Sun et al., 2023).

This strategy integrates two approaches to meet evolving educational needs and enhance academic performance, social skills, and engagement in the learning environment (Venkteshwar and Warrier, 2022). This model enables students to access learning materials and resources before class, facilitating collaboration on hands-on learning activities during class (Akti Aslan, 2022). The combination of the flipped classroom model and self-regulated learning (SRL) is a complete plan that encourages students to be independent, helps them set goals (Cheng et al., 2025), and improves their ability to learn by thinking about what they have learned (Zimmerman, 2013; Dignath et al., 2023). However, the incorporation of self-regulated learning (SRL) within flipped classrooms is rarely reported, as many studies have found that learners face motivational issues, underutilise effective learning strategies, and struggle to adapt to the cognitive demands of independent learning (Cheng et al., 2024). Emotional wellness, as one of the significant factors influencing learner engagement and academic achievement (Mustofa et al., 2022), is not systematically integrated into SRL-flipped learning models, thereby reducing their effectiveness. Despite the lack of evidence in integrating social-emotional intelligence (SEI) in the SRL context, the importance of SEI in recent research (Herut et al., 2024) is emphasised as it shapes successful learner attitudes, independence levels, and behaviours.

Social and Emotional Intelligence (SEI), by fostering empathy, emotional awareness (Heikkinen et al., 2023), and self-regulation, enhances Self-Regulated Learning (SRL) by integrating cognitive and affective dimensions of learning (Silva et al., 2023). The implementation of self-regulated learning strategies (O’Connell et al., 2023) and the monitoring of student progress (Courchesne, 2023), electronic portfolios (Nurjanah and Waluyo, 2024), and time management is facilitated through digital learning platforms like Microsoft Teams. Despite these advancements, there is limited research on the incorporation of advanced and varied technologies in self-regulated learning infused flipped classrooms, especially in the field of computer science. Moreover, there is an inadequacy of studies applying machine learning (ML) and affective analytics combinedly to forecast and assess learner engagement (Muasyaroh and Royanto, 2024), academic achievement (Helal et al., 2025), and emotional wellbeing (Martins Van Jaarsveld et al., 2025; Wong et al., 2025) in the domain of engineering education. Therefore, this study attempts to overcome existing shortcomings by proposing and analysing a flipped self-regulated learning framework specifically designed for Computer Science and Engineering students, enrolled in the DBMS (Database Management Systems) course. It employs a blend of quantitative and qualitative methodologies, supported by machine learning algorithms, to assess the relationship between SRL tactics, engagement metrics (Masood et al., 2022), and social–emotional intelligence (Lima et al., 2023). According to Khalil et al. (2024), the findings indicate that SRL-driven teaching can enhance learners’ academic performance and foster emotional development.

2 Literature survey

This section explores previous research on self-regulated learning, engagement, social–emotional intelligence, and technology-enhanced learning with special emphasis on theoretical frameworks and empirical evidence affirming the integration of self-regulated learning and learning analytics in flipped classroom settings. This review delineates the conceptual underpinnings and identifies deficiencies that the current work aims to address.

2.1 Self-regulated learning in flipped classroom settings

Self-regulated learning is an empirically derived concept that aids in managing and controlling the cognitive, affective, behavioural, and emotional state of the learners. The three cyclic phases of SRL (Zimmerman, 2002), involving the forethought phase for goal orientation, the self-monitoring phase for monitoring one’s performance (Osakwe et al., 2024; Hashem, 2021) and the final self-evaluation phase for self-reflection (Alqahtani, 2024), make it an iterative and dynamic learning process that keeps improving with time. However, incorporating SRL within a flipped classroom requires equal distribution of responsibility among instructors and learners.

Learners must engage independently (Goel, 2024) with the learning content, plan their schedules effectively, and be metacognitively aware in the pre-class phase (Ouyang et al., 2023). Proper strategic planning, problem-solving skills, and collaboration with peers are the prerequisites for the in-class phase to be beneficial. Further, the post-class hours focus on self-reflection (Alqahtani, 2024), which helps learners to reinforce their learning patterns (Panadero, 2017; Gligorea et al., 2023) and revise their learning pathways. Most experimental research supports the effectiveness of SRL in achieving learning outcomes and in supporting more persistent performance in blended learning environments (Elbourhamy et al., 2023). Evidence shows a deeper understanding of the concepts, stronger metacognitive abilities (Samadi et al., 2024), and high retention capabilities (Dignath et al., 2023; Wang et al., 2021) among learners who were successful enough to implement the cyclic phases in their learning process. Nevertheless, the lack of self-regulation skills in learners, lack of motivational factors, and absence of self-autonomy reported in a few studies demonstrate that the successful implementation of a flipped classroom is not only determined by the instructional design strategies but also by learners’ self-determination and regular monitoring of the learning process (Cheng et al., 2024).

Therefore, the SRL integration into a flipped classroom setup remains operational only through the incorporation of activities, tools, and videos without much theoretical grounding, as evident in previous works. Furthermore, despite the significance in enhancing learner’s interest, engagement, and resilience, the role of emotional and social dimensions in an SRL framework remains unexplored. This barrier creates future opportunities to explore and integrate the social and emotional aspects of learning into an SRL-driven flipped classroom.

2.2 Social and emotional dimensions of learning

In a blended or hybrid learning environment, it is always crucial to understand learners’ emotions, motivation and engagement levels, metacognitive abilities, and attitudes towards the learning process (Zheng et al., 2023). Moreover, learning in a digital environment does not constitute only the cognitive behaviour of the learner but also the social and emotional wellbeing (Su and Fung, 2024) of the learners. To address this conceptual gap in the prior works, this study employs the social–emotional intelligence (SEI), signifying both social intelligence (SI) and emotional intelligence (EI) as an integral construct of the SRL-oriented flipped classroom methodology. Interpersonal competencies like effective communication, resolving conflicts of interest, collaboration, and empathy (Akti Aslan, 2022) constitute social intelligence. In contrast, intrapersonal skills such as emotional awareness, resilience, stress management, and emotional regulation (Patil et al., 2023) fall under the category of emotional intelligence. Apart from these competencies, common emotions such as happiness, enthusiasm, enjoyment, anxiety, depression, and frustration play a significant role in shaping learners’ mental abilities (Du et al., 2023).

Furthermore, emotional stability and resilience (Goleman, 1998; Parker et al., 2004) are fostered through the integration of social–emotional intelligence (SEI) with self-regulated learning (SRL), which are essential in self-directed learning contexts (Herut et al., 2024). To enable learners to manage stress and anxiety, remain consistent during challenging tasks, and sustain self-motivation during the learning process, self-awareness of emotions and effective regulation become essential. Similarly, facilitation of social intelligence via collaborative and group tasks, peer interactions, and sharing productive feedback is the key to the in-class phase of the flipped classroom environment. Recent studies indicate that learners with higher emotional intelligence significantly show improved performance with better self-regulatory behaviour and perseverance (Silva et al., 2023).

Nevertheless, another significant aspect in proximity to SEI is the engagement metric, which is contemplated in this study as a multifaceted construct comprising cognitive, emotional, and behavioural components. Cognitive engagement denoted the learning strategies adopted by learners, emotional engagement recorded common feelings and affective state of learners, and behavioural engagement reflected the participation levels, interactions, and task completion rates (Fredricks et al., 2019; Bond et al., 2020). In this context, terms such as ‘student engagement’ and ‘learner engagement’ are used across formal, self-directed, and self-regulated learning contexts (Henrie et al., 2023). Following the engagement metric, ‘affect’ is another such construct representing the momentary emotions and feelings experienced by the learners during the learning process. Whereas affective analytics refers to the systematic analysis of affective states, functioning as a supportive measure to observe, evaluate and interpret the emotional intelligence of the learners (D’Mello and Graesser, 2021; Joksimović et al., 2023). Despite the growing significance of affective analytics in digital learning, only a few studies on the flipped classroom methodology have attempted to incorporate affective data with the SRL phases, thus leaving an empirical gap and opportunities for future researchers to address this challenge (Khalil and Ebner, 2023).

2.3 Technology-enabled self-regulated learning: learning analytics, feedback and coaching contexts

Digital learning platforms have increasingly enabled self-regulated learning by externalising learners’ cognitive, behavioural, and emotional processes, while technology can function as a metacognitive scaffold, providing learners with timely feedback on goal progress, engagement patterns, emotional states, and learning outcomes (Heikkinen et al., 2023; Silva et al., 2023). Such productive feedback helps transform passive learning processes into insightful actions to support the monitoring and reflection phases of SRL. Moreover, in a flipped classroom context, educational technologies facilitate SRL in multiple ways. While pre-class preparation is achieved via incorporating the usage of Learning management systems and collaboration platforms, support planning and resource management, digital tools enable peer interaction, formative assessment, and immediate feedback during in-class sessions, reinforcing self-monitoring and strategy adjustment (Ouyang et al., 2023; Osakwe et al., 2024). Further, the post-class reflective journals and portfolio systems promote self-evaluation and longitudinal reflection, helping learners track their progress over time (Cheng et al., 2025).

Research in learning analytics demonstrates that data-informed feedback and coaching can enhance learners’ self-awareness, strategic regulation, and academic persistence (Heikkinen et al., 2023; Khalil et al., 2024). Nevertheless, the current literature predominantly addresses technological support for SRL in a disjointed fashion. Numerous studies focus on cognitive analytics or performance prediction, while others examine engagement or emotional variables independently. There remains limited empirical work that integrates SRL processes, social–emotional intelligence, engagement analytics, and machine-learning-driven (Cardona et al., 2023) modelling within authentic flipped classroom implementations, particularly in engineering and computer science education (Wong et al., 2025). Therefore, there is a distinct need to develop a holistic framework that integrates pedagogical strategies, SRL theory, affective and engagement analytics, and advanced data-driven methods to comprehend how technology can support the complete SRL cycle, including its emotional and social dimensions. Addressing this gap creates opportunities for a more thorough investigation of the managerial skills of students to manage their learning in a technologically advanced flipped context on a cognitive, social, and emotional level. Educational platforms like Microsoft Teams and journaling applications have been implemented to enhance learner participation and engagement, along with positive feedback (Li and Wang, 2023). The essential aspects of self-regulated learning (van Alten et al., 2020), namely, team collaboration, workspace sharing, and self-monitoring of progress, are facilitated through these platforms. Nonetheless, there is an insufficient body of research connecting self-regulated learning techniques to social and emotional intelligence within a digital environment, hence affecting academic progression.

2.4 Overview of research gaps and key research inquiries

The current literature highlights specific inadequacies listing the first one as a deficiency of empirical research about the integration of self-regulated learning (SRL) and social–emotional intelligence (SEI) in flipped classrooms that employ digital resources, secondly the insufficient use of affective and engagement analytics in assessing the efficacy of self-regulated learning (SRL) (Psathas et al., 2023), and followed by insufficient application of machine learning for predicting student engagement (Arizmendi et al., 2023; Azizah et al., 2024) and academic success in hybrid learning contexts. The subsequent research questions (RQs) are constructed to address the highlighted deficiencies in the study.

• Research Question 1: What is the effect of integrating self-regulated learning (SRL) strategies into a flipped classroom on students’ academic performance, engagement, and mental wellbeing?

• Research Question 2: How effective are machine learning models in forecasting learner performance and engagement within a flipped self-regulated learning (SRL) environment?

• Research Question 3: What is the influence of emotional intelligence and engagement analytics on the understanding of the effectiveness of self-regulated learning interventions in hybrid learning environments?

This study addresses these shortcomings by aligning pedagogical methods with technological advancements, fostering psychological resilience, and equipping individuals to address contemporary practical issues.

3 Methodology

This section details the methodological procedures adopted to examine the interrelationships among self-regulated learning (SRL), engagement, social–emotional intelligence, and academic performance within a flipped classroom implementation. The study employs a mixed-methods design integrating assessment data, behavioural learning analytics, validated survey instruments, and predictive modelling techniques. Emphasis is placed on construct operationalisation, measurement reliability, and analytical validity to ensure methodological transparency. The methodology mainly aims to capture both the instructional structure and the underlying learning processes by integrating pedagogical design with data-driven analysis. The following subsections elaborates the study context, participants, instruments, data collection timeline, and statistical procedures in a structured and reproducible manner.

3.1 Research design and theoretical foundation of the flipped-SRL model

This study employs a mixed-methods quasi-experimental approach to evaluate the outcomes of a flipped SRL model on learners’ performance and engagement levels, as well as their social and emotional intelligence. The proposed model is primarily theory-driven by Zimmerman’s cyclic SRL model, complemented with social and emotional dimensions of learning.

Zimmerman visualised SRL as a 3-phase cyclic process comprising forethought, self-monitoring, and self-reflection, occurring iteratively. This iterative cycle closely aligns with the flipped classroom methodology, which spreads the learning process across 3 different phases of pre-class, in-class, and post-class sessions. Therefore, the proposed framework depicted in Figure 1 implements the three SRL phases incorporated within the flipped classroom environment as follows.

• The Forethought phase involves goal-setting and planning strategies to carry out the learning process as a pre-class activity.

• The performance phase incorporates in-class activities such as collaboration, teamwork and receiving formative feedback.

• The self-reflection phase was implemented using journaling and e-portfolio creation for the regulation of emotions as the post-class activities.

Figure 1

The proposed framework integrates social and emotional intelligence as a moderator, along with the cognitive and behavioural dimensions. Therefore, this flipped SRL model combines the theoretical grounding of SRL, the structure of the flipped classroom and technology-enabled learning analytics as illustrated in Figure 1. The success of this integrated model is supported by prior evidence demonstrating that learners with better emotional awareness, management, and regulation tend to have higher engagement levels and sustained academic performance.

3.2 Participants and study sample

A total of 96 undergraduate students enrolled in a third-year Database Management Systems (DBMS) course in a Computer Science and Engineering programme participated in this study. Of the enrolled cohort, complete datasets were available for all 96 participants across academic performance measures, Continuous Assessment Test 1 (CAT 1), Continuous Assessment Test 2 (CAT 2) and Final Assessment Test (FAT), self-regulated learning artefacts (journals and portfolios), engagement logs, and social–emotional intelligence surveys. No participants were excluded from the analysis, as all students met the inclusion criteria of course enrolment and completion of required learning activities. The sample consisted of students within a relatively homogeneous academic context in terms of programme level and disciplinary background. Participation was incorporated within the course as part of regular instructional activities; however, the use of data for research purposes followed institutional ethical guidelines. Descriptive statistics for key variables, including academic performance, SRL indicators, engagement metrics, and social–emotional intelligence measures, are reported in the Results section to provide transparency and enable assessment of potential bias and variability across the sample.

3.3 Course context and structure

The study was conducted for third-year undergraduate students enrolled in the Database Management System course in the Computer Science and Engineering stream. The course followed a flipped classroom structure implemented with 3 SRL phases as given in Figure 2 and spanned across one semester.

Figure 2

The course began with an introduction to the SRL techniques, their principles, and their relevance to the course and the flipped learning methodology. Pre-class sessions included micro-learning content, relevant flipped lecture videos, digital resources, and formative assessments such as quizzes, which were shared on Microsoft Teams and other associated tools like Clockify and Evernote to encourage goal setting, planning, and the stimulation of prior knowledge. In-class sessions included group learning activities and discussions that focused on solving problems and getting immediate feedback from the instructors. These activities helped learners in self-monitoring their learning process. Digital journaling and e-portfolio development were involved as reflective practices (Robbins et al., 2020) in the post-class sessions, enabling learners to evaluate their emotional wellbeing and progress over time.

Numerous digital tools were incorporated in the course to facilitate the SRL model by mapping these tools explicitly with each phase of SRL. The central platform used to deliver content, conduct lectures, and interact with peers and facilitators was Microsoft Teams. To activate prior knowledge and engagement during the in-class activities, tools like Quizizz and Mentimeter were employed for formative assessments. Clockify was used to aid the learners in planning their tasks and maintaining time and schedules during rush hours, and to monitor the duration and time spent on each task. Additionally, the emotional states and feelings of the learners were recorded using the Reflect app during the semester. Furthermore, digital journaling was implemented using the Evernote application to record learner experiences, emotions, achievements and strategic plans, and E-Portfolio creation fostered self-evaluation and representation of the overall achievements and artefacts of learning, facilitating long-term reflection.

3.4 Measurement instruments

This subsequent subsection outlines the measurement instruments and their functions within the comprehensive proposed framework. The instruments were selected and modified to operationalise the study’s key constructs in the flipped classroom setting, aligning with the established theoretical frameworks. The multidimensional metrics of SRL were engineered to obtain both behavioural and perceptual dimensions of learning. Additionally, the reliability, construct validity, and the correspondence between theoretical definitions and empirical evidence are also emphasised in this sub-section, ensuring conceptual clarity and analytical rigour.

3.4.1 Academic performance indicators

The academic performance of the learners was evaluated using three assessments: Continuous Assessment Test 1 (CAT 1), Continuous Assessment Test 2 (CAT 2) and Final Assessment Test (FAT). CAT 1 assessment was conducted before the application of the flipped SRL methodology, whereas CAT 2 and FAT were conducted post-intervention of the proposed model. The CAT 1, 2 and FAT are a course assessment that is also used for grading and not specifically designed for the purposes of this study.

3.4.2 SRL indicators

The SRL component was functionalized using 2 major indicators: Digital journaling and E-Portfolios. Both indicators were measured via rubric-based evaluation (Radović and Seidel, 2025), given in Appendix A, based on Zimmerman’s SRL framework (2013) and Panadero’s rubric evaluation used for the SRL model (2017). The rubric assessed goal-setting skills, planning strategies, evidence of using digital tools, self-reflection of the learning process and consistency.

3.4.3 Derived measures via feature engineering

To translate theoretical constructs of self-regulated learning (SRL), engagement, and social–emotional intelligence into analyzable variables, a structured feature engineering process was conducted. Operationalisation was guided by Zimmerman’s cyclical SRL model (forethought, performance, self-reflection), Panadero’s rubric-based SRL assessment framework, and multidimensional engagement theory (Fredricks et al., 2004). Each derived feature was explicitly mapped to observable behavioural or artefact-based indicators generated within the flipped classroom environment.

Feature construction followed a three-stage procedure. First, theoretical mapping aligned latent constructs (e.g., reflective regulation, behavioural consistency, cognitive engagement) with measurable indicators such as rubric scores, participation logs, and assessment progression. Second, operational translation transformed raw indicators into composite metrics using normalisation and aggregation procedures to ensure scale comparability. Mean-based aggregation was applied to preserve comparability among indicators and prevent scale distortion, while weighting was introduced only where theoretical models suggest differentiated emphasis (e.g., reflective depth relative to frequency-based indicators). Third, the construct alignment review ensured coherence between computational formulation and theoretical intent. All variables were normalised before aggregation to prevent scale distortion. Proportional growth measures were used for longitudinal indicators (e.g., performance change) to reflect adaptive progression consistent with SRL theory. These features were subsequently employed in regression, mediation, clustering, and predictive modelling analyses (Heikkinen et al., 2023). Some of the derived metrics are listed below as follows.

• a. Self-regulated learning (SRL) score

Log transformation reduces inflation from high consistency values.

• b. Cognitive engagement score (CES)

Represents behavioural investment and task completion depth.

• c. Portfolio-based performance impact (PPI)

Captures alignment between portfolio engagement and final assessment outcomes.

• d. Performance growth rate (PGR)

Captures average proportional performance improvement across assessments.

These derived metrics should therefore be interpreted as theory-informed behavioural proxies rather than psychometric scales. Their validity rests on explicit theoretical alignment and transparent computational documentation (Refer to Appendix B for the remaining measures), and their interpretation should remain confined to the conceptual scope of SRL-related behavioural representation. Table 1 below provides a structured mapping between theoretical constructs and computational derivations to enhance interpretive transparency.

3.4.4 Social and emotional (SEI) intelligence surveys

The SEI questionnaire used in this study was adapted from two established emotional intelligence frameworks emphasising social–emotional learning needs (Goleman, 1998; Parker et al., 2004; Fredricks et al., 2004): (1) a Daniel Goleman–based Emotional Intelligence Framework questionnaire and (2) the Quick Emotional Intelligence Self-Assessment derived from Paul Mohapel’s model. The adaptation retained various sub-components under the social and emotional intelligence domains (Refer to Appendix C). Multi-item subscales were preserved to maintain construct integrity; single-item constructs were not employed. Items were contextually modified to suit the flipped classroom and SRL activities (e.g., journaling, collaborative tasks, portfolio development) while preserving conceptual equivalence with the original frameworks. All items and the course’s instructional language were administered in English, using the original 5-point Likert scale format.

3.4.5 Engagement and affective learning analytics

Power BI, integrated within Microsoft Teams, was used to collect the engagement data and interaction logs to perform engagement analytics, and the Reflect app was implemented to record the emotional states of learners at different times during the semester. The responses generated by the Reflect app helped with affective learning analytics rather than producing direct psychological outcomes.

3.5 Instrument reliability and validity

Internal consistency reliability (Cronbach’s α) was computed for each domain to evaluate internal coherence among items. Consistent with psychometric standards, reliability indices do not constitute evidence of construct validity; therefore, the adapted SEI instrument should be interpreted as a theoretically grounded measure rather than a fully validated scale. The complete item set and domain structure are provided in Appendix C for transparency.

4 Results

This section presents the empirical findings of the study with reference to the research questions framed. It is further organised into three phases: First, to present the descriptive statistics of the academic achievement of the learners with respect to the assessments conducted. Second, correlational and descriptive findings between learner engagement and social–emotional constructs are reported, followed by the results obtained through machine learning analytics to assess predictive patterns and classify learners based on SRL-based features.

RQ1 indicates the associations among SRL, engagement, academic performance, and social–emotional intelligence. RQ2 analyses predictive modelling using machine learning, and RQ3 explores the role of social–emotional factors in SRL-related constructs.

4.1 Descriptive statistical analysis

Table 2 displays the descriptive statistics for the principal variables examined in this study, incorporating assessment scores, reflective learning indicators, engagement metrics, and social–emotional intelligence (SEI) scores for the 96 samples considered. The results from the baseline (CAT 1), mid-semester (CAT 2), and final assessment (FAT) exhibit moderate variability among students, with FAT reflecting a higher mean than both CAT 1 and CAT 2, indicating an enhancement in academic performance over the semester. Journaling and portfolio activity scores demonstrated significant variability, indicating disparities in students’ involvement with reflective learning tasks and course-based assignments. SEI scores exhibit less variability, signifying a more uniform distribution of social–emotional abilities among the cohort. Indicators related to engagement further corroborate these patterns. Additionally, inconsistencies in completing the given learning tasks within the course were indicated through moderate variability in completion rates, whereas feedback scores showed lesser variance, signifying a consistent quality of feedback and student engagement throughout the group.

The supplementary data facilitating the interpretation of the descriptive statistics are provided in Appendix D, including supportive engagement analytics and platform interaction visualisations obtained through Microsoft Teams and Power BI applications. The revised SEI questionnaire domains and their corresponding statistical item mappings are comprehensively detailed in Appendix E, while the feedback of students after the semester has concluded is presented in Appendix F.

4.2 Results for RQ1: associations among SRL, engagement, social–emotional intelligence, and academic performance

Regression and mediation analyses were performed to examine associative and explanatory relationships among self-regulated learning, learner engagement, social–emotional intelligence, and students’ academic performance across different exams (CAT 1, CAT 2, and FAT). These analyses do not claim causal effect experimentally, but rather test theoretically informed pathways and directional assumptions.

A simple linear regression model revealed that the SRL Score was a strong predictor of learner engagement (β = 1.77, p < 0.001), accounting for 40.4% of the variance in engagement (R² = 0.404). The second regression model indicated that engagement strongly predicted FAT scores (β = 3.73e-07, p < 0.001, R² = 0.288), with a modest effect, suggesting that engagement is a contributing factor rather than the exclusive driver of academic achievement. Another regression model confirmed a significant direct effect (β = 1.359e-07, p < 0.001, R² = 0.345) of SRL on academic performance, indicating that SRL influences FAT both directly and indirectly via engagement. Consequently, the current analyses demonstrate the statistical relationships via associations, temporal order between SRL, CAT 2, and FAT results, and nonspuriousness as far as previous theoretical and empirical work supports it. Given the sample size (N = 96), effect estimates should be interpreted cautiously, particularly for mediation pathways. These restrictions justify the exploration of the mediation pathways as theoretically viable explanatory mechanisms rather than conclusive causal claims.

Additionally, the illustration in Figure 3, depicting a scatter plot with a regression line, represents the association between reflective SRL activities, like journaling and portfolio management, with FAT scores. A clear positive trend is identified, indicating that students with higher self-regulated learning (SRL) engagement patterns tend to produce superior performance in a flipped classroom environment. Nevertheless, despite lower self-regulated learning skills, a few students excelled, highlighting the influence of external factors. Overall, the Variability observed in the scatter plot reflects heterogeneity in SRL application across learners.

Figure 3

4.3 Results for RQ2: predictive modelling of academic performance and engagement

The extent to which machine learning models can predict academic performance outcomes (Hussain and Khan, 2023) and classify learners based on their engagement levels, using derived SRL-metrics, social–emotional factors, and engagement indicators, is addressed by RQ2.

Regression-based ensemble models, namely, Random Forest (RF) (Badal and Sungkur, 2023), Gradient Boosting Machine (GBM), and XGBoost, were evaluated (Alhazmi and Sheneamer, 2023) using Mean Squared Error (MSE), R², Adjusted R², and Mean Absolute Percentage Error (MAPE) metrics. Among these models, XGBoost outperformed the other models with the lowest mean squared error (12.82) and the highest R² value (0.83) as given in Table 3, closely followed by Gradient Boosting with an R² of 0.82, while Random Forest had the lowest predictive accuracy (R² = 0.74). A Five-fold cross-validation was applied to mitigate overfitting risk, and the feature importance analysis identified SRL composite score and portfolio-related indicators as the most influential predictors. Given the modest sample size (N = 96), predictive performance should be interpreted as exploratory rather than generalisable. However, model complexity was balanced against sample size to reduce variance inflation.

Students were classified into performance-based groups based on academic achievement in FAT using Support Vector Machine (SVM) and Decision Tree (DT) models (Arashpour et al., 2023). The SVM model outperformed the DT model in key performance metrics across accuracy (75% vs. 55%), precision, recall, F1-score, and MCC, as given in Table 4, indicating greater stability and discriminative capacity.

These findings suggest that the SRL-related features and the engagement metrics can be leveraged to model and forecast learner’s performance outcomes with reasonable accuracy, hence validating the effectiveness of learning analytics in an SRL-integrated flipped classroom environment.

4.4 Results for RQ3: correlation between social–emotional intelligence and SRL outcomes

RQ3 investigated the associations and relationships among Social–Emotional Intelligence, Emotional States, and SRL outcomes using the Reflect app and SEI questionnaires to explore students’ emotional conditions and interactions. The analysis of emotional clusters revealed two dominant categories: Positive Emotion Cluster, characterised by calmness, focus and attentiveness, and the Negative Emotion Cluster associated with stress, anxiety, and loneliness. Designated as Whirlwind Emotions. About 36% of students belonged to the positive emotions cluster named Lake emotions, while merely 4% of the student population was categorised within the negative emotions cluster termed as Whirlwind emotions (Refer to Appendix D).

The majority group exhibited consistent engagement with higher participation levels in the self-regulated learning (SRL) activities. Additionally, the correlation analysis indicated moderate positive relationships among empathy, emotional regulation, adaptability, and academic performance (r = 0.64, p < 0.01), indicating that SEI coexist with sustained engagement and self-regulation in flipped classroom contexts. The results validate the third inquiry, highlighting the intricate interplay between emotional states, engagement, and performance outcomes in the context of SRL and higher education. Furthermore, the correlation heatmap presented in Figure 4 examines relationships among SRL indicators, SEI dimensions, engagement metrics, and performance outcomes. “Portfolio Performance Impact” and “Self-Regulation Effectiveness” showed strong positive associations (r = 0.94), in addition to “Cognitive Engagement” and “Self-Regulation Effectiveness” (r = 0.83).

Figure 4

Moreover, positive connections were identified between “CAT 2” scores with “Portfolio” management and “Self-Regulation Effectiveness,” and students with better initial scores showed lesser improvement over time, indicating negative associations between “Performance Growth Rate” and initial assessment scores. Thus, the heatmap illustrates significant interrelationships among self-regulation, cognitive engagement, and academic outcomes within the flipped SRL environment. Considering the sample size (N = 96), the correlation and clustering results should be interpreted cautiously. While the analyses reveal meaningful patterns linking social–emotional intelligence, engagement, and SRL outcomes, cluster stability and correlation estimates may vary in larger datasets. Therefore, these findings should be regarded as exploratory indicators requiring validation in broader samples.

Further, K-Means clustering was applied to group students as given in Figure 5 based on SRL scores and participation levels, identifying three engagement profiles. The Elbow method was applied to choose the number of clusters, and further, the three-cluster solution yielded a Silhouette Score of 0.42, indicating moderate cluster cohesion and acceptable separation. The clusters formed demonstrated that the high-engagement group showed consistently stronger SRL indicators, whereas the low-engagement group showed limited participation. Although cluster boundaries are not sharply defined, reflecting the continuous nature of learner engagement, the solution captures meaningful variability in SRL-related participation patterns.

Figure 5

4.5 Sample size and analytical considerations

The sample size (N = 96) provides adequate power for moderate regression effects but limits the detection of small effects. Mediation pathways mentioned in RQ1 should be interpreted cautiously due to sample size constraints. In RQ2, despite the cross-validation of the machine learning models, predictive stability may vary in larger populations. Furthermore, the clustering solutions given in RQ3 should be considered exploratory due to potential sensitivity to sample size.

5 Findings and discussion

This findings and discussion section encapsulates the educational implications and pedagogical implementation of the flipped classroom model. It examines how the observed relationships among self-regulated learning (SRL), engagement, social–emotional intelligence, and academic performance can inform instructional design and learner support strategies. Particular attention is given to practices such as goal setting, reflective activities, and feedback integration. This section further translates analytical results into actionable insights for higher education contexts by connecting empirical patterns to classroom application.

5.1 Interpretation of findings in relation to prior research

The findings of this study align with and extend prior research highlighting the relationship between self-regulated learning (SRL), learner engagement, and academic performance in flipped and technology-enhanced learning environments. Consistent with earlier studies, learners who demonstrated stronger planning, monitoring, and reflection behaviours also exhibited higher engagement levels and improved academic outcomes (Dignath et al., 2023; Samadi et al., 2024). These patterns support the theoretical perspective that SRL operates as a cyclical process in which goal setting, strategic action, and reflection collectively shape learning outcomes (Zimmerman, 2013; Panadero, 2017).

The observed association between engagement indicators and academic performance further corresponds with engagement frameworks that conceptualise cognitive, behavioural, and emotional participation as key drivers of learning persistence and achievement (Fredricks et al., 2019; Henrie et al., 2023). In flipped classroom environments specifically, previous work has shown that structured pre-class preparation and in-class collaboration promoted deeper engagement and active knowledge construction (Sun et al., 2023; Samadi et al., 2024). The present findings therefore reinforce the interpretation that engagement functions as an important mechanism through which SRL-related behaviours are reflected in observable academic outcomes.

In addition, the association between social–emotional intelligence (SEI) indicators and reflective learning behaviours highlights the importance of emotional regulation and interpersonal awareness within self-regulated learning contexts. Prior research shows that learners with stronger emotional awareness and stress-management skills demonstrate greater persistence and adaptability while facing academic challenges (Silva et al., 2023; Su and Fung, 2024). Similarly, emotional intelligence has been correlated with enhanced collaborative learning and sustained motivation in technology-supported environments (Herut et al., 2024). The current findings, are a valuable addition to the expanding body of literature suggesting that SRL processes are not exclusively cognitive but are also influenced by engagement and affective dimensions of learning.

Furthermore, the application of predictive modelling and learning analytics in this study illustrated how behavioural indicators derived from digital learning environments provided additional insights into SRL processes. Present research in learning analytics emphasises that data from digital platforms reveal patterns of engagement, persistence, and strategic learning behaviours that are not easily observable through conventional assessment methods (Heikkinen et al., 2023; Khalil et al., 2024). The predictive models explored in this study should therefore be interpreted as exploratory analytical tools that help identify potential relationships among SRL indicators, engagement metrics, and performance outcomes rather than as definitive predictors of student success.

5.2 Theoretical implications

From a theoretical perspective, this study supports a multidimensional interpretation of self-regulated learning that integrates cognitive, behavioural, social, and emotional components within technology-enhanced learning environments. While classical SRL frameworks acknowledge motivational and affective elements, empirical studies in flipped learning contexts have often emphasised cognitive strategy use without fully incorporating emotional and social dimensions (Panadero, 2017; Dignath et al., 2023). The present findings suggest that social–emotional intelligence and affective states should be conceptualised as supporting conditions that facilitate SRL processes rather than independent causal drivers of academic performance. Learners’ emotional awareness, interpersonal communication, and collaborative skills appear to coexist with higher engagement and reflective learning practices, reinforcing the idea that SRL is a context-dependent and socially situated process.

Additionally, the integration of learning analytics with SRL theory demonstrates how digital trace data and artefact-based indicators can operationalise theoretical constructs within authentic classroom environments. Recent research has highlighted the growing potential of learning analytics to support adaptive feedback, monitor engagement patterns, and upgrade instructional design in higher education (Heikkinen et al., 2023; Wong et al., 2025). However, these analytical approaches should complement rather than replace pedagogical theory, as meaningful interpretation of behavioural data requires grounding in established educational frameworks.

5.3 Limitations and future research directions

This study must be considered in the context of certain limitations. First, the restricted sample size of 96 participants confines the generalizability exclusively to the DBMS course and its related domain. Although this supports internal validity for analysing correlations among SRL, engagement, and performance within the DBMS course, it hinders the generalisability of findings beyond analogous engineering education contexts. Second, self-reported social–emotional intelligence may result in biased responses. Third, the cross-sectional study design presents challenges in establishing causal relationships across semesters. This model should be extended over time to include a range of universities in future research. Fourth, employing multimodal affective sensors, behaviour indicators and emotion detectors has the potential to improve data accuracy. A further limitation concerns the psychometric validation of the adapted SEI instrument. While content and face validity were supported through alignment with established emotional intelligence frameworks, and internal consistency reliability was confirmed, full construct validity (e.g., via factor analytic techniques) was not independently established within the present sample. Consequently, the SEI measures should be interpreted as context-specific and theoretically grounded indicators rather than fully validated psychometric instruments. Future research could further employ larger samples and conduct exploratory and confirmatory factor analyses to evaluate measurement validity and dimensional structure.

The overall findings corroborate that the implementation of structured Self-Regulated Learning (SRL) interventions in flipped classrooms enhances academic achievement while simultaneously strengthening emotional resilience and fostering greater engagement. By incorporating Machine Learning (ML)-driven analytics to investigate adaptive AI systems that tailor learning trajectories based on an individual’s interests and emotional involvement, a data-driven structure could be established for upcoming hybrid educational models, leading towards the development of emotionally intelligent, self-regulating students in higher education.

6 Conclusion

This study examined the associations among self-regulated learning (SRL) constructs, learner engagement, social–emotional intelligence, and academic performance within a flipped classroom environment. Drawing on assessment data, engagement analytics, self-reported measures, and machine learning techniques, the analysis provides theoretically grounded evidence of meaningful interrelationships among cognitive, behavioural, and emotional dimensions of learning. The results further demonstrate enhanced self-regulatory behaviours correlated with higher engagement and more favourable academic outcomes, where engagement functions as a mediating factor between SRL and academic performance. Social–emotional competencies and affective states co-occurred with sustained engagement and reflective practices, underscoring the relevance of emotional regulation and interpersonal skills in self-directed learning contexts.

Methodologically, the study demonstrates how learning analytics and predictive modelling can be integrated with SRL theory to examine complex learner behaviours while maintaining interpretive caution regarding causality. Adaptive analytics-driven interventions. The findings indicate that flipped classroom designs facilitate goal setting, monitoring, reflection, and emotional awareness, which in turn correlate with enhanced engagement and academic achievement. Future research should broaden this research to improve learners’ self-regulatory and emotional profiles by employing longitudinal and experimental methodologies to analyse causal mechanisms, expanding disciplinary contexts to enhance generalisability, and investigating adaptive data-driven interventions.

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