Implementing a Group- and Project/Problem-Based Learning in a College Algebra Course
Bibliographic record
Abstract
The idea of this paper originated from reading the interesting article written by Mohammad A. Alseweed in Studies in Literature and Language (2013). In the article, the author defined and analyzed traditional learning, blended/hybrid learning and virtual learning. The result favored blended/hybrid learning in test scores and students’ attitudes suggests that students are more receptive when instructors use different teaching approaches. In this paper we describe an innovative approach to project-based learning in a group setting environment. Traditional science instruction has tended to exclude students who need to learn from contexts that are real-world, graspable, and self-evidence meaningful (Kolodner et al., 2003). As emphasized by Blumenfeld, one way of encouraging student engagement and addressing the contextualization of students’ inquiry is through project-based instruction (Bumenfeld et al., 1991; Petrosino, 2004). The learning sciences community agrees that deep and effective learning is best promoted by situating learning in purposeful and engaging activity (Bransford et al., 1999; Collins et al., 1989; Kolodner et al., 2003). Our goal for developing this collaborative project/problem-based learning technique is to engage the students in deep learning by encouraging them to write and explain all the steps of their reasoning when yielding to the answers.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".