Improving effectiveness of learning through class activity assessment : a case study
Bibliographic record
Abstract
Most education reformers agree that effective learning in the classroom is based on both teachers? and students? co-operation and creativity in generating a good learning atmosphere. Many educational institutions are exploring new dimensions in class activity and assessment which not only complement the curriculum but form an integral part it. Reports have shown that in today?s environment, scoring straight As will not necessary guarantee a student a place in university or in the working sector. Rather, students require greater exposure especially through having class activities and assessments inside and outside the classroom such as talks, workshops, seminars, forums and field trips. Such activities can improve the effectiveness of the students? learning process, and will not only contribute towards a higher final subject grade, but also better prepare the pre-university students for their next stage of education in the university. This paper therefore focuses on the steps that could be taken to improve the effectiveness of learning through class activities and assessments. Specifically, it aims to gain feedback from students and lecturers at the preuniversity level on how such activities can be conducted and assessed to improve the learning and teaching processes. The authors also investigate some possible activities that can fulfill this purpose such as the methods of assessment, challenges encountered by lecturers and students, and possible solutions. The information is mainly distilled from a survey conducted on students and staff involved in the Mathematics-Science and Social Sciences subjects taught in the Canadian International Matriculation Programme (CIMP).
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".