Investigating the Impact of Model Eliciting Activities on Development of Critical Thinking
Bibliographic record
Abstract
This paper presents a portion of a study on how model eliciting activities (MEAs) impact critical thinking development in first year engineering. Model eliciting activities (MEAs) are realistic problems used in the classroom that require learners to document not only their solution to the problems, but also their processes for solving them. Studies have shown MEAs to be valuable in helping students to develop conceptual understanding, knowledge transfer , and generalizable problem--‐solving skills. This study is investigating the impact of the MEA- integrated course on students’ development of critical thinking skills. Ultimately, the team aims to determine whether the MEA-integrated course facilitates students’ critical thinking. During the fall semester of the 2012/2013 academic year three instruments will be used to evaluate the critical thinking skills (CTS) of first year engineering students. These instruments will be used as both a pre--‐ and post--‐test in order to benchmark CTS of the incoming first year students, and determine the effectiveness of MEA instruction on developing student critical thinking ability. These instruments are the Cornell Critical Thinking Test Level Z (Cornell Z), the International Critical Thinking Essay Test (ICTET) and the Collegiate Learning Assessment (CLA). This paper will present the preliminary findings from analysis of the MEA results and pre and post tests from the study.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".