A Comparison of the Online Learning Activities and Learning Style Preferences of Young Adult Video Game Players and Nonplayers
Bibliographic record
Abstract
A study is presented that compared the online learning activities and learning style preferences of video game players and nonplayers. A total of 1,258 students across seven postsecondary institutions near Seoul, South Korea, rated their experiences with video game play alongside their online learning activities and preferences toward learning styles that share characteristics with many of today’s games. Utilizing a causal-comparative approach, descriptive and inferential statistical analyses were used to quantitatively examine the groups. At first glance, the findings revealed that the players were more involved in online learning activities than the nonplayers. Namely, the players (a) took more online courses and/or training per year; (b) shared ideas, documents, information, and/or knowledge online; (c) read and/or contributed to blogs; (d) used the Internet to complete school assignments; and (e) used email, instant message, chat (or other means) to communicate with instructors and peers. However, further examination revealed that the nonplayers held a stronger preference than the players for most of the learning styles examined. That is, the nonplayers preferred online courses and/or training that (a) presented graphics before text; (b) provided opportunities to multitask; (c) offered the ability to selectively access different parts of courseware, rather than linearly stepping through; and (d) were play- rather than work-centric. Although exceptions were found, on the whole, these findings suggest that arguments about today’s youth and their different learning preferences, as a result of exposure to and experience with technology, to include video games, may be premature and much more in-depth empirically supported research is needed before definitive conclusions can be safely drawn.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".