Learning analytics for online game-Based learning: a systematic literature review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Game-based learning researchers have been investigating various means to maximise learning in educational games. One promising venue in recent years has been the use of learning analytics in online game-based learning environments. However, little is known about how different elements of learning analytics (e.g. data types, techniques methods, and stakeholders) contribute to game-based learning practices within online learning environments. There is a need for a comprehensive review to bridge this gap. In this systematic review, we examined the related literature in five major international databases including Web of Science, Scopus, ERIC, IEEE, and compiled Proceedings of the International Conference on Learning Analytics and Knowledge. Twenty relevant publications were identified and analysed. The analysis was conducted using four core elements of learning analytics, namely the types of data that the system collects (what), the methods used for performing analytics (how), the reasons the system captures, analyzes, and reports data (why), and the recipients of the analytics (who). This study synthesises the existing literature, provides a conceptual framework as to how learning analytics can enhance online game-based learning practices in higher education, and sets the agenda for future research.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it