Rediscovering the Uptake of Dashboard Feedback: A Conceptual Replication of Foung (2019)
Bibliographic record
Abstract
Learning analytics has been widely used in the context of language education. Among the studies that have used this approach, many have developed a dashboard that aims to provide students with recommendations based on data so that they can act on these suggestions and improve their performance. To further our understanding of dashboard research, this study aims to replicate an earlier study using a new data mining strategy, association rule mining, to explore if the new strategy can (1) generate comparable results; and (2) provide new insights into feedback uptake in dashboard systems. The original study was conducted with 423 students at a Hong Kong university and implemented a dashboard for a suite of first-year composition courses. It used a classification tree to identify factors that could predict the uptake of tool-based and general recommendations made by the dashboard. After performing association rule mining with the original data set, this study found that this approach allowed for the identification of additional useful factors associated with the uptake of general and tool-based recommendations with a higher accuracy rate. The results of this study provide new insights for dashboard research and showcase the potential use of association rule mining in the context of language education.
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 | 0.029 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".