Sami P. Heikkinen

This study investigates the dynamic interplay between student engagement and self-regulated learning (SRL) in an online project management course. Using learning management system trace data combined with longitudinal self-reported SRL measures, we analysed data from 165 first-year business students through multi-channel sequencing and clustering algorithms. Results revealed three distinct patterns of learning behaviour with significant implications for intervention design: Low-regulating improvers (4.85%) who showed potential for growth with targeted support, Disengaged low regulators (44.85%) requiring comprehensive intervention in both engagement and self-regulation skills, and High-regulating improvers (50.30%) who demonstrated successful adaptation to online learning. Our innovative approach of analysing student progress individually rather than using fixed time points enabled more precise identification of support needs in asynchronous learning settings. The findings demonstrate that both engagement and self-regulation skills can develop over time with appropriate support, suggesting opportunities for adaptive intervention throughout the course. This research provides actionable insights for developing targeted support strategies, including early warning systems for at-risk students and personalized scaffolding approaches based on students’ behavioural patterns. These findings advance learning analytics by providing a framework for real-time identification of student needs and evidence-based intervention design in online education. The results particularly emphasize the importance of continuous, pattern-based support for developing self-regulation skills in online environments.

This study introduces a novel methodological approach for analyzing customer journey data using eye-tracking technology and multimodal AI. Customer journey research faces significant limitations due to reliance on resource-intensive qualitative methods that are difficult to scale. We address this gap by employing vision language models (VLMs) to automate the interpretation of eye-tracking data, eliminating the manual coding bottleneck while enabling analysis of larger and more diverse datasets. Our research evaluates five locally-deployable VLMs to determine the optimal balance between semantic accuracy and computational efficiency. Using data collected with Tobii eye-tracking glasses, we developed an analytical pipeline that synchronizes gaze location data with verbal think-aloud protocols to create a comprehensive multimodal dataset. Results demonstrate that Gemma3 (4B parameters) achieved 100% semantic accuracy on our test set while maintaining reasonable processing efficiency (43.26 s per image). When validated against human coding across the complete dataset, the model achieved a 74.2% recall rate. The integration of eye-tracking and verbal data revealed distinctive attention patterns including “navigational uncertainty,” “confirmatory scanning,” and “socially-mediated attention” throughout the customer journey. Our approach provides objective behavioral evidence of visual attention that complements traditional self-reported measures, enabling more comprehensive touchpoint analysis while aligning with event-driven perspectives from process mining research. This methodology offers promising applications for service design by identifying discrepancies between reported and actual customer attention patterns and providing a foundation for developing automated behavioral indicators to detect moments of customer confusion, decision-making, or confirmation throughout service journeys.

This thesis investigates the complex interplay between self-regulated learning, student engagement, and learning analytics in online educational environments. Through three related studies, the research examines how learning analytics can support self-regulated learning, how students’ self-efficacy beliefs influence their learning behaviors, and how engagement patterns evolve alongside self-regulation throughout online courses. The research employs a mixed-methods approach that combines systematic literature review, sequence analysis, process mining, and multi-channel clustering techniques. Data were collected from multiple sources including learning management system trace data, self-reported surveys, and longitudinal self-regulated learning measures across various cohorts of business students. Key findings reveal that while learning analytics interventions show promise in supporting self-regulated learning, only 46% demonstrated positive impacts on learning outcomes. The research identified distinct patterns in how students with different self-efficacy beliefs approach online learning, with high self-efficacy students demonstrating more varied and adaptive learning tactics. Analysis of engagement patterns revealed three distinct clusters of students: low-regulating improvers (4.85%), disengaged low regulators (44.85%), and high-regulating improvers (50.30%), each showing different trajectories in their self-regulation development. The thesis makes several theoretical and practical contributions to the field of educational technology. It elucidates how self-regulation and engagement co-evolve in online learning environments, provides evidence-based recommendations for designing learning analytics interventions, and demonstrates the value of combining multiple analytical approaches to understand student learning processes. These findings have important implications for the design of online courses and learning support systems, particularly their adaptation to support students with varying levels of self- regulation skills and engagement patterns.

During the past years scholars have shown an increasing interest in supporting students’ self-regulated learning (SRL). Learning analytics (LA) can be applied in various ways to identify a learner’s current state of self-regulation and support SRL processes. It is important to examine how LA has been used to identify the need for support in different phases of SRL cycle, which channels are used to mediate the intervention and how efficient and impactful the intervention is. This will help the learners to achieve the anticipated learning outcomes. The systematic literature review followed PRISMA 2020 statement to examine studies that applied LA interventions to enhance SRL. The search terms used for this research identified 753 papers in May 2021. Of these, 56 studies included the elements of LA, SRL, and intervention. The reviewed studies contained various LA interventions aimed at supporting SRL, but only 46% of them revealed a positive impact of an intervention on learning. Furthermore, only four studies reported positive effects for SRL and covered all three SRL phases (planning, performance, and reflection). Based on the findings of this literature review, the key recommendation is for all phases of SRL to be considered when planning interventions to support learning. In addition, more comparative research on this topic is needed to identify the most effective interventions and to provide further evidence on the effectiveness of interventions supporting SRL.