Project-Based Learning: A Case Study of Early Data Analytics Learning in Undergraduate Mathematics
Bibliographic record
Abstract
The overall goal of this study was to examine the design and impact of project-based learning (PBL) for early data analytics learning in undergraduate mathematics at a four-year research university in the United States. I documented the plans, activities and participant feedback gathered as part of the original funded program at the university, and delved further into the archival data to explore the feasibility and impact of introducing data analytics learning early in the undergraduate mathematics curriculum. The relationships between student development of knowledge, ability, and confidence during this PBL-infused program were also examined, and the benefits and challenges from the perspectives of the learner and the teacher were detailed.The study provided a successful example of how a PBL-infused approach could be used to effectively teach data analytics early in the mathematics undergraduate curriculum. The findings suggested that the early introduction to data analytics program resulted in additional learning, research and internship opportunities, and informed students’ undergraduate studies, choice of major, and other life choices such as plans for graduate studies and/or careers. Interactions and collaborations in visualizing problem solving in the context of projects using real-world data afforded the students additional opportunities to network with their peers, faculty, and university, industry, and/or community partners. This created a community of learners with shared goals and achievements. These collaborations and interactions supported students’ development of knowledge and ability, both of which were positively correlated with confidence in this early data analytics experience. This study illuminates the design features of the early data analytics learning and research experience, and offers a refined design framework for PBL-infused data analytics curricula in mathematics. The refined design framework includes: real-world content for greater accessibility, visualization of the problem solving, incorporating interactions and collaborations among all in the community of learners, and the promotion of knowledge, confidence and beliefs in support of lifelong learning. The potential of the case study and the refined framework to transform learning across STEM disciplines bolsters the potential broader impact.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".