On multi-device use: Using technological modality profiles to explain differences in students' learning

Abstract

With increasing abundance and ubiquity of mobile phones, desktop PCs, and tablets in the last decade, we are seeing students intermixing these modalities to learn and regulate their learning. However, the role of these modalities in educational settings is still largely under-researched. Similarly, little attention has been paid to the research on the extension of learning analytics to analyze the learning processes of students adopting various modalities during a learning activity. Traditionally, research on how modalities affect the way in which activities are completed has mainly relied upon self-reported data or mere counts of access from each modality. We explore the use of technological modalities in regulating learning via learning management systems (LMS) in the context of blended courses. We used data mining techniques to analyze patterns in sequences of actions performed by learners (n = 120) across different modalities in order to identify technological modality profiles of sequences. These profiles were used to detect the technological modality strategies adopted by students. We found a moderate effect size (∈2 = 0.12) of students' adopted strategies on the final course grade. Furthermore, when looking specifically at online discussion engagement and performance, students' adopted technological modality strategies explained a large amount of variance (η2 = 0.68) in their engagement and quality of contributions. The result implications and further research are discussed.