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Generalizability of the value of methods based on learning analytics remains one of the big challenges in the field of learning analytics. One approach to testing generalizability of a method is to apply it consistently in different learning contexts. This study extends a previously published work by examining the generalizability of a learning analytics method proposed for detecting learning tactics and strategies from trace data. The method was applied to the datasets collected in three different course designs and delivery modalities, including flipped classroom, blended learning, and massive open online course. The proposed method combines process mining and sequence analysis. The detected learning strategies are explored in terms of their association with academic performance. The results indicate the applicability of the proposed method across different learning contexts. Moreover, the findings contribute to the understanding of the learning tactics and strategies identified in the trace data: learning tactics proved to be responsive to the course design, whereas learning strategies were found to be more sensitive to the delivery modalities than to the course design. These findings, well aligned with self-regulated learning theory, highlight the association of learning contexts with the choice of learning tactics and strategies. Notes for Practice• Explorations of the detected learning tactics and strategies need to consider both sequential and temporal characteristics.• Learning tactics and strategies are context dependent; therefore, specific learning tactics and strategies have to be interpreted in the particular learning context from which the data originate.• Detected learning tactics should reflect the instructional course design.

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Problem-based learning (PBL) is a constructivist approach to learning which is believed to promote reflective thinking in students. This study investigated how students in one particular institution developed in their reflective thinking habits-Habitual Action, Understanding, Reflection, and Critical Reflection-as they went through the daily practice of PBL. A 16-item questionnaire measuring the four levels of reflective thinking habits was administered to four cohorts of students: an incoming cohort, first-years, second-years, and third-years. First-year students rated themselves higher on Reflection and Critical Reflection, while third-years reported the highest levels of Habitual Action. Discriminatory and scatterplot analysis on the third year cohort revealed that while a proportion of students (47%) reported higher levels of Habitual Action with lower levels of Reflection, there was a small subgroup who also reported higher levels of both Habitual Action and Reflection. Overall, the results showed that PBL does promote the development of reflective thinking, particularly for first-year students, but that this development is not sustained consistently after that. This pointed to other possible factors that could hinder students' development of reflective thinking in PBL.

Although technological advances have brought about new opportunities for scaling feedback to students, there remain challenges in how such feedback is presented and interpreted. There is a need to better understand how students make sense of such feedback to adapt self-regulated learning processes. This study examined students’ sense-making of learning analytics–based personalised feedback across four courses. Results from a combination of thematic analysis and epistemic network analysis show an association between student perceptions of their personalised feedback and how these map to subsequent self-described self-regulated learning processes. Most notably, the results indicate that personalised feedback, elaborated by personal messages from course instructors, helps students refine or strengthen important forethought processes of goal-setting, as well as to reduce procrastination. The results highlight the need for instructors to increase the dialogic element in personalised feedback in order to reduce defensive reactions from students who hold to their own learning strategies. This approach may prompt reflection on the suitability of students’ current learning strategies and achievement of associated learning goals. Implications for practice or policy: Personalised feedback based on learning analytics should be informed by an understanding of students’ self-regulated learning. Instructors implementing personalised feedback should align this closely with the course curriculum. Instructors implementing personalised feedback in their courses should consider the relational element of feedback by using a positive tone. Personalised feedback can be further enhanced by increasing the dialogic element and by including more information about learning strategies.