An examination of the effect of capsi as a learning system in developing knowledge and critical thinking in two blended learning courses
Bibliographic record
Abstract
The growth of the Internet has begun to make an impact on how course information is delivered, leading to a new pedagogical approach called blended learning (Concannon, Flynn & Campbell, 2005; Dzubian et al. 2004). One pedagogical design that facilitates this changing approach combines Keller's Personalized System of Instruction (Brothen & Wambach, 1999; Keller, 1968) with Web-based technology, resulting in a learning-management method called Computer-Aided Personalized System of Instruction (CAPS!) (Pear & Kinsner, 1988). Three experiments were conducted in order to assess CAPSI in regard to student course knowledge and critical thinking development. These experiments incorporated three different research designs. Experiments 1 and 2 were conducted in a first year Introduction to University course at the University of Manitoba. In Experiment 1 two lecture sections received a CAPSI assignment while two received an extra paper assignment. The results indicate that the CAPSI sections performed significantly better on a final exam and a critical thinking question in the final exam. There was also a positive, but non-significant difference between the CAPSI and the non-CAPSI sections on the content questions and on a measure of critical thinking. In Experiment 2, one lecture section of Introduction to University received a CAPSI assignment, while another lecture section was assigned a research paper. Students in both lecture sections were assessed at the same level for critical thinking on the ACTM prior to the CAPSI or paper assignment. There was a significant difference in scores between sections in favour of the CAPSI section after the completion of the CAPSI or paper assignment. Experiment 3 involved students in two sections of first year Introduction to Psychology course at the University of Winnipeg. In this experiment, the CAPSI group performed better on four multiple-choice exams administered during the course. These difference, however, were not statistically significant, possibly due to a significantly higher dropout rate in the non-CAPSI section than in the CAPSI section. Over all three experiments, the CAPSI sections consistently outperformed the sections with which they were compared with, indicating that CAPSI is an effective empirically based educational methodology.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".