Board 271: Evaluating the Effect of Multi-Attempt Digital Assessments on Student Performance in Foundation Engineering Courses
Bibliographic record
Abstract
This paper discusses the design and implementation of multi-attempt digital assessments in the foundation engineering courses of Statics and Dynamics as part of an NSF-funded project entitled "Enhancing Student Success in Engineering Curriculum through Active e-Learning and High Impact Teaching Practices (ESSEnCe)."Statics and Dynamics are fundamental courses that are critical in the graduation pathway of almost all engineering majors.At the authors' institution, the average ten-year student success rate in these courses is typically low, and the success rates of Hispanic transfer students are even lower.To address this, the authors introduced multi-attempt digital assessments to improve student success rates.Prior research has shown that frequent testing is beneficial for student learning as it allows the realization of knowledge gaps via self-regulated learning and metacognitive monitoring strategies.In this semester-long study, the authors redesigned the major assessments for multi-attempt testing in both Statics and Dynamics by creating extensive test question banks in the learning management system of Canvas.The assessments were administered digitally to the students using a Lockdown browser in Canvas at a proctored testing facility.End-of-semester surveys were administered in both courses to gauge student satisfaction and experience with this testing method.Preliminary results indicate very promising positive effects of the multi-attempt digital assessments in Statics and Dynamics courses on student performance, satisfaction, and selfreported motivation and self-regulation for all students, including Hispanic transfer students.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".