JOURNAL OF DIGITAL LEARNING AND DISTANCE EDUCATION

Designing Self-Regulated Learning Scaffolds in Distance Education: Enhancing Student Engagement and Online Persistence

Ruth Rize Paas Megahati S — 2026-09-28

The transition to distance education requires learners to possess high levels of autonomy and self-regulated learning (SRL) capabilities. However, many distance learners struggle with time management, goal setting, and cognitive fatigue due to physical isolation and the absence of real-time instructional pacing. While most online learning environments focus on content delivery, few systematically incorporate instructional scaffolds to support SRL processes. This study investigates the impact of integrating structured SRL scaffolding—comprising goal-setting templates, weekly reflective prompts, and automated progress feedback—into a 12-week distance education course. Using a mixed-methods approach with 142 undergraduate students, we evaluated changes in student engagement, self-efficacy, and academic persistence. Quantitative results revealed a significant improvement in academic task completion and course persistence among students exposed to SRL scaffolding compared to the control group. Qualitative feedback highlighted that metacognitive reflection prompts reduced feelings of transactional distance and enhanced study discipline. These findings suggest that designing intentionally scaffolded learning environments is critical for fostering long-term engagement and reducing dropout rates in digital and distance higher education.

Student Leaders’ Social Media Engagement Shaping Online-to-Offline Activism in a Teacher’s College

Rebecca Bargo — 2026-09-28

Social media is an emerging arena where students are practicing leadership in both virtual and physical realities. While previous studies have established the association between social media engagement and activism, there is a need to document how such online activity is connected to extra-online manifestations, particularly in selected teacher education institutions in the Philippines. Hence, this study explored how social media helped these students perform their empowerment-based activism in the real world. Informed by Kelman’s Social Influence Theory, the descriptions provided by nine (9) organization chairpersons and vice chairpersons selected through purposive sampling became the basis of this study’s findings. After transcribing the responses to the semi-structured interview guide presented through Google Forms, the researchers applied Braun and Clarke’s (2006) thematic analysis. This study found that students’ activism was actualized through the online medium via increased mobilization, leadership skill development, ethically guided conduct, and response to online challenges. Contrary to activism’s performative nature, the respondents prioritized information gathering, decision-making, cooperation, and leadership in promoting civic responsibility. As such, this study’s results added to the existing scholarship on social media’s role in activism while highlighting the importance of empowerment-based leadership in teacher education.

BiLSTM-Based Deep Learning Model for Early Prediction of Student Academic Failure Risk in Digital and Distance Education

Liza Yuliana — 2026-09-28

Digital and distance learning platforms generate abundant interaction log data from students. However, leveraging temporal log data for early prediction of academic failure risks remains challenging due to time sensitivity and sequence data processing in remote learning contexts. This study aims to develop a Deep Learning model based on Bidirectional Long Short-Term Memory (BiLSTM) to predict student academic performance and failure risks based on weekly interaction patterns within a Learning Management System (LMS). A dataset comprising interaction logs from 1,250 distance education students over one semester was analyzed by extracting features such as material access frequency, forum participation, and assignment submission timing. Experimental results demonstrate that the BiLSTM model achieves an accuracy of 91.4%, Precision of 89.2%, and Recall of 92.6% as early as Week 6 of the course. This early prediction allows educators to implement timely pedagogical interventions to reduce dropout rates in digital and distance learning environments.