The Efficacy of Problem-Based Learning in an Instrumental Analyse Laboratory
Bibliographic record
Abstract
In the context of the study, an instrumental analysis laboratory course offering Problem-Based Learning (PBL) was designed as an alternative to traditional laboratory practices. The study was conducted with a total of 36 volunteer, prospective chemistry teachers consisting of fourth year undergraduates and graduates. While PBL activities were conducted with 19 of the prospective teachers, instrumental analysis laboratory activities were conducted with 17 of them using the traditional approach. The first aim of this study was to determine the levels of perception of problem-solving ability and self-regulatory learning strategies of prospective teachers after and before all the applications. The second aim was to compare the effects of PBL instrumental analyze laboratory course and traditional instrumental analyze laboratory course on the perceptions of problem-solving ability and self-regulatory strategies of prospective teachers. A pre-test-post-test control group design was used. In this study, data were obtained using the “Problem Solving Inventory (PSI)” and “Self-Regulatory Strategy Scale (SRSS)”. The pretest-posttest results of the SRRS test showed that the prospective teachers in the experimental group used self-regulatory learning strategies more often when compared to the ones in the control group. According to the results obtained within the scope of the study, it can be said that the effect of PBL on the perception levels of problem solving skills and self-regulatory learning skills of prospective teachers is more effective than the traditional laboratory teaching application.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".