Integrating Problem Solving and Critical Reflection Opportunities in First- and Second-Year Science Courses.
Bibliographic record
Abstract
The development of problem solving and critical reflection skills is neglected in early-level science courses; however, such skills are necessary in upper-year science courses and scientific careers (Gupta 2005). Early-year science teaching seems to be about memorization and recall (McDonald and Dominguez 2009) because teachers feel that they have insufficient time to integrate problem solving and critical reflection components into their courses while covering the subject matter (Kronberg and Griffin 2000). Yet, integrating problem solving and critical reflection opportunities into science courses does not have to take too much time and can cover the same curriculum subject matter (Kronberg and Griffin 2000; McDonald and Dominguez 2009); students usually learn more and have a greater understanding of concepts resulting in better grades (e.g., Chaplin 2009); and teachers have more frequent assessments of what their students are learning and can make instructional changes as required (McDonald and Dominguez 2009). This seminar will demonstrate methods (that are not greatly time consuming or drastically change the current curriculum) to integrate problem solving and critical reflection opportunities into lectures, laboratories, and tutorials of early-level science courses. Participants also have the opportunity to actively demonstrate the methods. The benefits of developing problem solving and critical reflection skills earlier in university science education are better grades, better integration of complex topics, and a better understanding of what students are actually learning.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".