Using Kahoot! As A Formative Assessment Tool in Science Teacher Education
Bibliographic record
Abstract
The development of 21st century competencies and skills in science teaching and learning is a key strategic imperative. Game-based learning platforms can be used to promote pedagogic innovation in various educational settings. Game-based applications such as Kahoot! have been increasingly used in education to facilitate meaningful enactment of formative assessment practices. Within the realm of science education, formative assessment is largely perceived as an assessment practice with pedagogic potential to enhance students’ academic performance, motivation and engagement during the teaching and learning process. Kahoot! is an interactive game-based learning platform which can essentially be utilised to enhance students’ academic performance, motivation and engagement in the classroom. This paper explores the role of Kahoot! as a formative assessment tool to enhance students’ academic performance, motivation and engagement with a view to help students to achieve stipulated learning outcomes during remote teaching and learning in undergraduate Physical Sciences teacher education. The research study adopted a generic qualitative design and involved 21 purposively selected preservice Physical Sciences teachers at a South African university. Data was collected through semi-structured interviews and the administration of qualitative user-generated online quizzes with the participants. The findings demonstrated that Kahoot! plays a significant role in enhancing students’ academic performance, motivation and active engagement during remote teaching and learning. Theoretical implications for technology-enhanced teaching and learning are discussed.
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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 | 0.023 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| 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".