Readiness and Use of Big Data Analytics in Selected Canadian Higher Education Institutions
Bibliographic record
Abstract
The rapid evolution of information technologies has driven the exponential growth of big data, creating opportunities to leverage data analytics across sectors. In higher education, Big Data Analytics (BDA) holds promise for improving decision-making, enhancing student outcomes, and driving institutional efficiency. However, its implementation remains limited due to technological, organizational, and environmental challenges. This study examines the readiness and use of BDA within selected Canadian higher education institutions, focusing on Southwest Ontario. Utilizing the Technology-Organization-Environment (TOE) framework, the research adopts a qualitative approach, drawing on semi-structured interviews with 10 academic and administrative staff from selected universities in Southwestern Ontario. The result identifies several barriers to BDA readiness and use, including a fragmented data landscape, integration challenges, and resource constraints. The study emphasizes the need for strategic investments in technological infrastructure, leadership engagement, and updated policies to improve BDA adoption. The study concludes with recommendations addressing barriers within the technological, organizational, and environmental contexts to enhance institutional performance and student outcomes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.020 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".