Bibliographic record
Abstract
The barriers to accessing electronic learning materials have significantly decreased in the rapidly evolving field of education technology. Learning Analytics (LA) has emerged as a valuable tool for improving institutional decision-making and student outcomes. This paper addresses the lack of detailed research focusing on the foundational stages of LA in Canadian universities by developing an automatic LA system to identify engineering students needing support. The initial phase involves examining LA practices in other institutions and evaluating the current landscape of LA implementation at scale among Canadian universities. Additionally, the paper explores the limitations of the LA features in Brightspace Learning Management System (LMS), specifically the Student Success System (S3), for detecting at-risk students and providing interventions. While statistical results demonstrating the effectiveness of LA in Canadian universities are lacking, proactive strides have been made, and more than 50% of universities investigated have implemented or planned to implement LA tools. This underscores the growing recognition of the need for LA to enhance academic performance and student success. The paper concludes by proposing a robust LA architecture as a reference model that prioritizes early identification of at-risk students which tackle the limitations of current LA features in Brightspace LMS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.051 | 0.008 |
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".