Hooked By Avatars? Literature Studies in Upper Secondary School—A Simulation Study
Bibliographic record
Abstract
Several studies have shown that Swedish students’ reading comprehension and ability to understand fiction are decreasing year by year. Numerous alarming reports point to the unsustainable situation concerning young people’s reading engagement, but the ideas of how it can be remedied are few. This study aims to investigate and evaluate a didactic design that includes avatars as game elements in order to promote students’ reading of fiction. What opportunities and challenges might such a design present? In the study, an action research method and game theory were used, where teachers and researchers collaboratively explored and evaluated the outcome. The results showed that this design offered many opportunities and generated reading engagement. The students co-design their learning by creating an avatar and then entering the fictional world of a short story. Creating an avatar that interacts with the fictional text requires both participation, reading comprehension and meta analytic skills. However, the design also presented challenges, that some students did not link their avatars clearly to the text and instead did their own stories. The design could thus be further developed to provide more room for avatars to interact more with the chosen literary environment.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".