Innovating pathology learning via Kahoot! game-based tool: a quantitative study of students` perceptions and academic performance
Bibliographic record
Abstract
Pathology learning for medical undergraduate students is a challenging task. Kahoot! is a mobile game-based online digital formative assessment tool that can engage students in its learning. This study is the first to assess the effect of Kahoot! use on Pathology learning outside classroom using a comparative group with assessment done at the end of the course. The study was carried out on the first-year Pathology students at Helwan University, Faculty of Medicine, after ending a basic Pathology course. The study is a retrospective quasi-experimental quantitative study. Academic performance of students in Pathology was compared between Kahoot! and non-Kahoot! users (55 students each). In addition, an online survey was introduced to the 55 Kahoot! user students to investigate their perceptions on it. Survey and test score data were analyzed by appropriate tests using IBM-SPSS (Statistical Package for Social Sciences). The level of significance was P < 0.05. Kahoot! enhanced Pathology understanding (83.6%), retaining knowledge (87.3%), made learning fun and motivating (89.1%). Other mentioned advantages of Kahoot! were practicing for exam (40%), simple and easy to use (36.4%), competitive (18.2%), self confidence booster (10.9%), forming a comprehensive image of the lecture (9%), quick (9%), and imagining skills booster (5.5%). Mentioned disadvantages included no explanation for the answers to questions (20%). A quarter of the students stated that the time limit for the questions was short (27.3%). Kahoot! use was significantly associated with better Pathology academic performance (P = 0.001), and it was not related to the general academic performance of the students (P = 0.06). Most users (85.4%) recommended its continuous future use. The study offers an endorsement to the use of Kahoot! for gamifying formative assessment of Pathology and can provide a basis for the design of an online Kahoot! -based continuous formative assessment plans implemented outside-classroom in the Pathology curricula.
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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 | 0.003 | 0.008 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".