Bibliographic record
Abstract
Purpose This paper aims to examine how effective higher education institutions have been in harnessing the data capture mechanisms from their student information systems, learning management systems and communication tools for improving the student learning experience and informing practitioners of the achievement of specific learning outcomes. The paper seeks to argue that the future of analytics in higher education lies in the development of more comprehensive and integrated systems to value add to the student learning experience. Design/methodology/approach Literature regarding the trend for greater accountability in higher education is reviewed in terms of its implications for greater “user driven” direction. In addition, IT usage within higher education and contemporary usage of data captured from various higher education systems is examined and compared to common commercial applications to suggest how higher education management and teachers can gain greater understanding of the student cohort and personalise and enhance the learning experience much as commercial entities have done for their client base. A way forward for higher education is proposed. Findings If the multiple means that students engage with university systems are considered, it is possible to track individual activity throughout the entire student life cycle – from initial admission, through course progression and finally graduation and employment transitions. The combined data captured by various systems builds a detailed picture of the activities students, instructors, service areas and the institution as a whole undertake and can be used to improve relevance, efficiency and effectiveness in a higher education institution. Originality/value The paper outlines how academic analytics can be used to better inform institutions about their students learning support needs. The paper provides examples of IT automation that may allow for student user‐information to be translated into a personalised and semi‐automated support system for students.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.010 |
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".