Table 1_Designing for self-regulation: development and preliminary user evaluation of a student-facing learning analytics app in blended higher education.docx

A 20-item Likert-type scale incorporating elements on self-regulation of learning and the Technology Acceptance Model was administered after each implementation. Qualitative data were analyzed through qualitative content analysis with inductive orientation; quantitative data were examined descriptively, with no inferential comparisons between pilots given differences in sample size and context.</p>Results<p>Five overarching challenge domains were identified alongside four student strategy clusters.

The final application, comprising seven interfaces aligned with Zimmerman’s three-phase model of self-regulated learning, received descriptively higher perceived impact ratings in the final pilot (forethought: M = 4.0–4.8; performance: M = 3.7–4.4; self-reflection: M = 3.7–4.4) than the preliminary version (all dimensions M = 2.2–3.0), with higher perceived usefulness (M = 4.1) and perceived ease of use (M = 3.8) than in the preliminary version.</p>Conclusion<p>These exploratory findings suggest that student-facing learning analytics, when embedded within institutional virtual classrooms, may be associated with higher perceived support for self-regulatory processes across all three phases of the model, positioning students as active agents in interpreting their own learning data rather than passive subjects of instructor-facing monitoring systems.

These findings should be considered preliminary and hypothesis-generating rather than confirmatory.</p>