Role of activity-based learning and ChatGPT on students' performance in education
Bibliographic record
Abstract
This study investigates the impact of activity-based learning and the utilization of ChatGPT on students' academic performance within the educational framework. The study aims to assess the effectiveness of activity-based learning in comparison to traditional methods, while also evaluating the potential benefits and drawbacks of integrating ChatGPT as an educational tool. The study employs a comparative approach, analyzing the outcomes of students exposed to activity-based learning versus those using conventional methods. Additionally, the study examines the usage of ChatGPT in education through surveys and trials to determine its contribution to personalized feedback, interactive learning, and innovative teaching methods. The findings reveal that activity-based learning enhances students' engagement, motivation, and critical thinking skills. Students participating in activity-based learning demonstrate improved academic achievement, which is attributed to their active involvement and practical application of knowledge. Similarly, the integration of ChatGPT offers novel avenues for interactive learning and individualized assistance, fostering students' understanding and exploration of complex concepts. In conclusion, activity-based learning proves to be a student-centered approach that enhances learning outcomes by fostering active participation and practical engagement. The utilization of ChatGPT in education showcases its potential to enhance educational experiences through interactive conversations and innovative teaching methodologies, despite considerations regarding potential limitations and ethical implications.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".