MEASURING THE IMPACT OF STUDENT LED TUTORIALS ON FIRST YEAR STUDENTS' LEARNING OUTCOMES
Why this work is in the frame
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Bibliographic record
Abstract
ENGR 141: Engineering Mechanics is afoundational course in the UVic Engineering Faculty thatserves all of the engineering degree programs:biomedical, civil, mechanical, electrical, computer andsoftware. Between the 2013 and 2014 offerings of thecourse, the ENGR 141 population grew dramatically, bywell over 50%, necessitating changes in the coursestructure and methods of student assessment. In additionto addressing logistical challenges, the changes weredesigned to develop the students’ confidence in theirability to wield fundamental mechanical principlesindependently and in peer-to-peer working environments.This was done by repurposing the tutorial sections of thecourse to create student driven exploration, analysis andsolution of complex three dimensional mechanicsproblems.A series of 22 problems lying outside the domain of theassignment problem sets were addressed-two in eachweek of the tutorials. The assignments and midtermsproblems were constructed so that the impact of tutorialwork on students mastery of the course Intended LearningOutcomes could be extracted.Under the new tutorial format, instructors monitoredgroup dynamics, helped troubleshoot and providedencouragement. Presentation of solution strategies weremade by select student groups each week. The currentwork describes the motivation for the changes made,observations made at implementation and somepreliminary results from analyses of the impact of the newcourse structure on student mastery of the course learningoutcomes. Important conclusions are that the student-ledtutorials should be accompanied with additionalinstructor contact hours that provide opportunity forstudents to receive tutelage on a one-to-one basis andthat individual testing should stress the procedures andtools emphasized in the tutorials.In addition, students found that assessments madethrough multiple choice testing contradicted values andprinciples being stressed in the tutorial and seminarsessions.
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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 | 0.002 |
| 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 it