Assessing Students' Progress in Chemistry: Using Multiple-Choice Questions and Performance-Based Assessments
Bibliographic record
Abstract
This article aims to investigate the progress of preparatory year students in a single-quarter chemistry course at King Faisal University, using multiple-choice questions (MCQs) and performance-based assessments. The article evaluates students' conceptual understanding and application of chemistry concepts, compares the effectiveness of these assessment methods, and identifies areas for instructional improvement. The results of MCQs showed significant improvement in conceptual understanding throughout the quarter, with average scores steadily increasing. Performance-based assessments, such as projects, demonstrated enhanced problem-solving and application skills, consistently producing higher scores than MCQs. Moreover, comparative analysis highlighted the complementary nature of these methods in evaluating students’ proficiency. In addition, interviewing students provided additional insights and emphasized the importance of a balanced assessment approach. The study concludes that integrating traditional and performance-based assessments effectively captures students’ progress in chemistry. Recommendations include diversifying assessment formats and using technology to increase evaluation practices. Future research should explore larger sample sizes and additional instructional contexts to refine assessment strategies.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".