Differentiated instruction in digital video games: STEM teacher candidates using technology to meet learners’ needs
Bibliographic record
Abstract
Differentiated instruction (DI) is a teaching approach that aims to achieve learning for diverse students. This study reports on promoting STEM teacher candidates’ (TCs’) implementation of technology-enhanced DI in teacher education courses. The research questions are: (1) How do TCs develop digital video games (DVGs) to be inclusive of DI?, and (2) If, and to what extent are DVGs effective tools to implement DI in secondary science classes? The analysis of eight DVGs, developed by the TCs, shows that most TCs were able to proficiently integrate DI practices in their DVGs. Furthermore, DVGs are effective tools to differentiate instruction by facilitating pacing variation for different students, differentiating difficulty levels, scaffolding, integrating multimodalities to present the content in different formats, utilizing engaging features, representing different learners of various backgrounds, promoting conceptual understanding, and enabling different assessment forms especially formative and diagnostic assessments. This research is significant as it highlights how digital resources such as DVGs can be used to address individual learners’ needs, interests, profiles, and academic achievement levels. Additionally, this research informs instructional designers, game developers, and curriculum specialists on ways to incorporate equity, diversity, and inclusion pedagogies such as DI in digital educational resources.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".