The paper states that predictive analytics in learning analytics can identify at-risk learners and predict dropout risk, but does not specify the specific dropout prediction algorithms, the compositio
Research gap analysis derived from 3 education papers in our local library.
The gap
The paper states that predictive analytics in learning analytics can identify at-risk learners and predict dropout risk, but does not specify the specific dropout prediction algorithms, the composition of training datasets used across diffe
Consensus across the literature
Clustered from 3 gap mentions across 3 papers via embedding cosine ≥ 0.62.
Research trend
Established — well-defined area with open sub-problems.
Supporting evidence — 3 representative gaps
- The impact of artificial intelligence on students’ academic development, critical thinking, cognitive skills, and learning outcomes (2026) · doi
The paper states that predictive analytics in learning analytics can identify at-risk learners and predict dropout risk, but does not specify the specific dropout prediction algorithms, the composition of training datasets used across different institutional contexts, or how algorithm bias affects prediction accuracy across diverse student populations.
Keywords: predictive analytics learning analytics dropout prediction algorithm bias student retention - Artificial intelligence vs traditional teaching methods on student performance: Effectiveness and challenges (2026) · doi
The excerpt describes predictive analytics applications for identifying at-risk students using attendance, activity rates, and quiz results, but does not specify the datasets required, the threshold values for defining 'at-risk' status, or comparative effectiveness across different institutional contexts (K-12 vs. tertiary education, online vs. blended learning environments).
Keywords: predictive analytics early warning systems student retention blended learning educational data mining at-risk prediction - A systematic review of generative artificial intelligence in education: Pedagogical impacts, ethical risks, and future directions (2026) · doi
The paper describes predictive analytics applications for identifying at-risk students and preventing dropouts in large universities and online learning environments, but does not specify which student data features (attendance patterns, assignment submission timing, assessment score trajectories) are most predictive or how prediction accuracy varies across different institutional contexts and student demographics.
Keywords: learning analytics predictive analytics dropout prediction student engagement intervention retention
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