Development of Open Textbooks Learning Analytics System
Bibliographic record
Abstract
Textbook costs have skyrocketed in recent years, putting them beyond the reach of many students, but there are options which can mitigate this problem. Open textbooks, an open educational resource, have proven capable of making textbooks affordable to students. There have been few educational development as promising as the development of open textbooks to lower costs for students. While the last five years have witnessed unparalleled interest and significant advances in the development and dissemination of open textbooks, one important aspect has, until now, remained unexplored: the praxis of learning analytics for extracting information regarding how learners interact and learn with open textbooks, which is crucial for their evaluation and iterative improvement process. Learning analytics offers a faster and more objective means of data collection and processing than traditional counterparts, such as surveys and questionnaires, and—most importantly—with their capability to provide direct evidence of learning, they present the opportunity to enhance both learner performance and environment. With such benefits on offer, it is hardly surprising that the optimism surrounding learning analytics is mounting. However, in practice, it has been pointed out that the technology to deliver its potential is still very much in its infancy, which is true in the case of open textbooks. Against this background, the main aim of our study was to develop a prototype open textbook learning analytics system to track individual learners’ online and offline interactions with their open textbooks in electronic publication (EPUB) format, and to present its developmental work as building blocks for future development in this area. We conclude with a discussion of the practical implications of our work and present directions for future work.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.013 |
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".