A Case Study of Undergraduate and Graduate Student Research in STEM Education
Bibliographic record
Abstract
Student research in STEM education is an important learning component for both undergraduate and graduate students. It is not sufficient for students to learn passively in lecture-based classrooms without engaging and immersing themselves in the educational process through real-world research learning. Experiential learning for STEM students can involve conducting research, alongside and through the guidance of their professors, early in a student’s undergraduate or graduate program. The authors consider such experiences to be the hallmark of a high-quality STEM education and something every student, undergraduate and graduate, should have during the course of their programs. The purpose of this case study is to document the faculty authors’ experiences in student-faculty research and provide guidance and recommendations for best practices based upon the authors’ experience, data, and literature findings. Moreover, the study presents the experience of the faculty authors’ international student researchers in STEM with focus on two student researchers, one undergraduate and one graduate, who are international STEM. The students served as co-authors on this project. Findings from this case study indicate that students were highly engaged in the research process and found these skills valuable preparation for further study and career. Moreover, the students expressed enthusiasm and engagement for the research process.
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 | 0.020 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".