The Use of Digital Video Games in an Elementary Teacher Education Program: Exploring Teacher Candidates’ Attitudes and Interest towards Physics
Bibliographic record
Abstract
For decades, digital video games (DVGs) have been used in various educational contexts. However, there has been limited exploration into how they could be used in teacher education programs to investigate how DVGs, in their play, could affect teacher candidates’ (TCs) attitudes towards and interests in physics and physics education. Using a mixed method approach, and a multiple case study design and methodology, data were collected from a single Ontario university for two cases - primary/junior (PJ) and junior/intermediate (JI) TCs enrolled in science education – to explore how TCs engaged with, and responded to physics-focused DVGs. Portraits for each case were constructed from data collected on influencers, (in)formal physics education, physics experiences, physics teacher self-efficacy, as well as personal and professional gaming habits and exposure, to understand their experiences with two DVGs: Huje Tower and Vita: Saving Sutom. Data included: surveys, audio recordings of gameplay think-alouds (GPTA), post-gameplay small group discussions (SGD), field notes and photographs. Case portraits revealed that prior to gameplay PJ TCs had taken fewer formal physics courses, engaged with fewer hands-on practical experiences, and had less gaming experience than JI TCs. Using a gender lens, female participants were also found to have significantly lower physics-teacher self-efficacy than male participants. A multiple case analysis showed DVGs affected participants’ physics learning and physics identity, and a modest increase in physics teacher self-efficacy was found, which some participants directly attributed to gameplay. This study’s findings have implications for researchers, teacher educators and video game designers. For researchers, DVGs continue to occupy a space for yet unexplored growth, in terms of how DVGs could be used to shape a player’s feelings towards subject matter, affect TCs’ efficacy, and support independent learning. For teacher educators, meaningfully selected DVGs have untapped potential to provide virtual experiences for players, and highlight areas of need for further support. For designers, DVGs are still met with skepticism beyond being used as either a motivator or time filler within the classroom. As such, there is a need for better designed serious games (e.g. storyline, purpose, learning outcome) to make them part of a larger learning strategy.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".