WORK IN PROGRESS Understanding Student Learning Profiles in Second Year Problem-Solving Engineering Classes
Bibliographic record
Abstract
Student motivation, mindset, and learning styles play a role in student success and satisfaction, and research in engineering education is beginning to link these factors to student retention and learning outcomes.In this work in progress, we add to that prior work by surveying students in a second-year bioengineering course to identify their motivations, mindsets, and learning styles and check which correlates with student success.This set might be specific to this course because it necessitates conceptual problem-solving which requires a unique set of skills that are often new to students.They require thinking through the problem and gaining an abstract conceptual understanding before proceeding.During the first week of Fall 2021, 84 second-year engineering students at the University of Illinois Urbana-Champaign answered a questionnaire with 60 questions taken from validated instruments related to the factors mentioned previously.We conducted a statistical analysis on our data which consists of student performance data (i.e.midterm and final grade) and quantitative data from the questionnaire.We found that the students in our study as a whole have a mindset, intrinsic motivation and sense of belonging that should be conducive to positive learning outcomes.Final grades were correlated with students' responses to questions related to "thinking" as a preferred strategy.We also observed a correlation between grade improvement and questions taken from the Intrinsic Motivation Inventory and sense of belonging.In future work, we plan to use this for designing interventions that are specifically tailored to students in this class.We plan to extend our work to other conceptual problem solving Engineering courses.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".