Learning Analytics Intervention: A Review of Case Studies
Bibliographic record
Abstract
Intervention has been claimed to be the greatest challenge in learning analytics. As the provision of just-in-time and personalised support for learners, intervention has yet to be widely implemented in learning analytics practices. This paper reviews intervention practices in higher education in 23 case studies. The cases were categorised into four types – direct message, actionable feedback, categorisation of students, and course redesign – according to the nature of the methods of intervention; and the intervention methods were summarised. Most of the intervention cases belonged to the first two types. Direct message involves contacting at-risk students via channels such as emails or phone calls to encourage their participation, provide additional learning resources, or remind them of deadlines. Actionable feedback involves the provision of suggestions or information for students to help them understand their performance and possible ways of improving it. Only a few cases were identified for the other two types of intervention, which involve categorisation of students into different groups based on their risk levels for taking specific remedial actions for each group, and redesigning the course structure or contents based on the analysis of data. The results of this review serve to facilitate the formulation of intervention strategies for higher education institutions which practise learning analytics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".