Designing for self-regulation: development and preliminary user evaluation of a student-facing learning analytics app in blended higher education

Abstract

Background Blended learning environments place substantial demands on students’ capacity to plan, monitor, and evaluate their own academic work, yet most technology-mediated interventions in higher education prioritize instructor-led monitoring over student-centered autonomy. Objectives This study aimed to characterize the challenges and self-regulated learning strategies of university students in blended learning contexts, design and validate a student-oriented learning analytics application natively integrated into a learning management system and explore the perceived experience and technological acceptability. Design An exploratory sequential mixed-methods design was employed. Participants: The qualitative phase included semi-structured interviews with 14 faculty members and 19 focus groups with 140 students (mean age = 21.53, SD = 2.87) from three Chilean universities, complemented by expert review conducted by seven specialists in educational technology; the quantitative phase involved two successive, non-equivalent pilot implementations with 5 faculty and 267 students enrolled in first-year high-risk courses. 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. Results 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. Conclusion 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.