Investigating Epistemic Stances in Game Play Through Learning Analytics
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
The authors apply techniques of statistical computing to data logs to investigate the patterns in students' play of The Fuzzy Chronicles and how these patterns relate to learning outcomes related to Newtonian kinematics. This chapter has two goals. The first goal is to investigate the basic claims of the proposed two-system framework for game-based learning (or 2SM) that may serve as part of a general-use explanatory framework for educational gaming. The second goal is to explore and demonstrate the use of automated log files of student play as evidence of learning through educational data mining techniques. These goals were pursued via two research questions. The first research question examines whether students playing the game showed evidence of dichotomous fast/slow modes of solution. A second research question investigates the connection between conceptual understanding and student performance in conceptually-laden challenges. Implications in terms of game design, learning analytics, and refinement of the 2SM are discussed.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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