Promoting Active Learning when Teaching Introductory Statistics and Probability Using a Portfolio Curriculum Approach
Bibliographic record
Abstract
The use of a portfolio curriculum approach, when teaching a university introductory statistics and probability course to engineering students, is developed and evaluated. The portfolio curriculum approach, so called, as the students need to keep extensive records both as hard copies and digitally of reading materials, interactions with faculty, interactions with other students and work they have completed on their own, is designed to encourage active learning, mainly in the areas of cooperation and collaboration. In order to investigate the effectiveness of the portfolio curriculum, a controlled experiment applying a pre-test-post-test control group design is conducted. Two tests are conducted, one before the commencement of the course (pre-test) and one after the completion of the course (post-test). The effectiveness is evaluated by comparing within-subject post-test and pre-test scores and by comparing the scores between subjects in the experimental group, i.e., those who learned using the portfolio curriculum approach and subjects in the control group, i.e., those who learned using a traditional method of teaching. In addition to analysis of the controlled experiment, a Survey of Attitudes Toward Statistics (SATS) was completed on the first and last day of the semester by the participants so as to give a measure of student confidence, understanding, liking, and difficulty of the portfolio curriculum approach as opposed to using a traditional method of teaching and learning. The findings of these investigations are reported and discussed, as are the merits and problems encountered regarding the methodology and student attitudes regarding the portfolio curriculum approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".