Frontiers in psychology · 2026
Designing for self-regulation: development and preliminary user evaluation of a student-facing learning analytics app in blended higher education.
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Lobos Karla, Cobo-Rendón Rubia, Mora César, Arias Nelson, Fernández Carolyn, Abrigo Camilo
Аннотация
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.
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.