Unravelling the Temporal Process of Learning Design and Student Engagement in Distance Education using Learning Analytics

Nguyen, Quan (2020). Unravelling the Temporal Process of Learning Design and Student Engagement in Distance Education using Learning Analytics. PhD thesis The Open University.

DOI: https://doi.org/10.21954/ou.ro.00010fea


Designing a curriculum in online and distance education can be challenging because the processes of what, when, and how students study are not always visible to teachers due to the limited opportunities for face-to-face interactions. The aim of this thesis is to explore how teachers design for learning, together with how the learning design impacts upon the students’ actual engagement with the learning materials, with the subsequent effect on their academic performance. One way forward, is to build on the intersection between the most recent work in learning analytics and learning design research. I have therefore argued for and investigated the potential of incorporating the design of learning activities into the analysis of student learning behaviour. On the one hand, the visualisation of learning activities designed by teachers provides the pedagogical context to improve the interpreta-tion of the observed learning behaviour and its effect on academic performance. On the oth-er hand, the analysis of online digital traces of learning activities offers a dynamic account of how students learn in practice in a distance learning environment. As a result, this thesis sheds new light on the implicit process of how learning design influences student engagement in distance education

By employing a mixed-method research design, I first examined how teachers design for learning using visualisations and network analysis of 37 modules over 30 weeks at The Open University. In the next step, I conducted an in-depth qualitative investigation with 12 teachers into the underlying factors that influenced their design decisions, as well as the perceived barriers and affordances of adopting approaches from the Open University Learning Design Initiative. The findings revealed common patterns as well as variations in learning design across modules and their disciplines of study. Analysis of the interviews revealed underlying tensions between teachers’ autonomy and the influence of management and institutional policies in the design process and the adoption of learning design tools.

After laying out the foundation for understanding the learning design processes, I carried out a large-scale analysis of 37 modules and 45,190 students to examine how learning design influences student engagement, satisfaction, and performance. The findings indicated that learning design explained up to 69% of the variance in student engagement, which was strongly driven by assimilative, assessment, and communication activities. Finally, I conducted a fine-grained analysis exploring the (in)consistencies between learning design and student behaviour and how different engagement patterns impact academic performance. The analysis found misalignments between how teachers designed for learning and how students actually studied. In most weeks, students spent less time studying the assigned materials compared to the number of hours recommended by instructors. High-performing students not only studied ‘harder’ by spending more time, but also ‘smarter’ by engaging in a timely manner.

Altogether, this thesis has contributed new scientific insights into the dynamic temporal aspects of how teachers design for learning and the relations between learning design, engagement, and academic performance in distance education. As an implication, the findings reported here demonstrated how learning design could improve the accuracy and interpretability of learning analytics models, and how learning analytics could help teachers identify potential inconsistencies between learning design and student behaviour.

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