Individual and collaborative Performance and Level of Certainty in MetaVals
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
In the context of Higher Education (HE) in general, and management education in particular, the use of Serious Games (SG) is spreading, and solutions are increasingly developing. Nevertheless, the implementation of this learning methodology deserves further study, in particular concerning pedagogical and psychological aspects such game performance and players’ metacognitive processes. This paper aims to study the relation among these two variables, based on the review of the results of MetaVals SG during the last 3 years. MetaVals is a collaborative, computer-based SG designed to facilitate collaboration and metacognitive awareness among HE students. It has been played by 250 students in 16 different experiences since its first version, in 2011. Overall results show a higher performance for collaborative than individual phases of the game, furthermore, students’ elicitation of their Level of Certainty (LC), although not significantly, could be related to a better performance. These results can be a basis for further studies focused on the implementation of collaborative GBL in formal and informal adult learning contexts. However, some challenges are also identified and discussed on the present version of MetaVals game, and solutions are proposed in order to continue with the design of SGs for wider application and learners’ needs in the current contexts.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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