Differentiated instruction in digital video games: STEM teacher candidates using technology to meet learners’ needs
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
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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 it