Developing Teacher Knowledge about Gamification as an Instructional Strategy
Bibliographic record
Abstract
There is a need for teachers and higher education faculty to develop knowledge about instructional strategies that engage digital learners and accommodate digital learning preferences in order to deliver instruction that digital learners perceive as relevant. This chapter discusses how gamification can be used in higher education as an instructional strategy to meet the needs of the digital learner. Findings from a design-based research study of how gamification was used in a Teacher Education technology methods course, to engage pre-service teachers in activities that develop Technological Pedagogical Content Knowledge (TPACK) (knowledge about teaching with technology) (Mishra & Koehler, 2006), are discussed. The findings provide guidance for teachers and technology educators on how to design courses incorporating gamification as an instructional strategy appropriate for meeting the needs of digital learners. Issues concerning design and implementation as it influenced student engagement and learning are highlighted, and recommendations are made for course development.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 teacher head, 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".