Bibliographic record
Abstract
Abstract – As programs drive to innovate in educational delivery they are increasingly seeking to apply and connect diverse data sets, including student performance data arising from learning outcomes assessment, student activity data in learning management systems, and survey data. This research study is aimed at developing a model to support visualization of educational data to support a range of data needs from individual reflection to program improvement and change management. A literature review around assessment data usage indicated that there is currently a gap in the assessment cycle between collecting the educational data and putting it to use towards educational data needs. Facilitating ways to close this gap served as the motivation for the study. The first phase of this study, as approved by the instructional research ethics board, was to find out how instructors, faculty administration, and educational developers use data and the role it has in improving the student experience. We conducted semi-structured interviews of twelve faculty and educational staff who collect, analyze, and reflect on educational data. Interviews were created according to McCracken and analyzed using Charmaz’s abductive approach to grounded theory. The emerging data presents a number of emergent themes around the affective aspects of how stakeholders feel about how data is and is not being used. This paper will describe the method to approach to the qualitative data gathering and analysis procedure, and the ideas that emerged from the interviews that we think are of interest to readers.
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 | 0.072 | 0.203 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".