INVESTIGATING DIFFERENCES BETWEEN INSTRUCTOR EXPECTATION AND STUDENT WORKLOAD IN UNDERGRADUATE ENGINEERING
Bibliographic record
Abstract
For the past two years at the University of X, first-year engineering undergraduate students have been asked to fill out workload questionnaires. These questionnaires were sent to random samples of the first-year class weekly, where they were prompted to answer questions regarding how much time they devoted outside of the classroom to each particular class. Workload data for 2018 and 2019 showed upward of 30 hours of work outside of the classroom, after the first few weeks of classes once major assignments and examinations began. Evidence in the literature [1,2] suggests that university students face a shortage of time, specifically with first-year students lacking the essential time management skills to be efficient. In the present study, we aim to find a correlation between how long the first-year engineering students spend on a class each week versus how long instructors anticipate the average student would spend on their respective class. In order to do so, we examined the data gathered for 2018 and 2019 fall terms from each student for a specific class and week. Furthermore, additional relevant information will be gathered from the instructors and course coordinators to obtain an estimate on how many anticipated hours a student would have to spend on a class each week versus how long instructors anticipate the average student would spend on their respective class. In order to do so, we examined the data gathered for 2018 and 2019 fall terms from each student for a specific class and week. Furthermore, additional relevant information will be gathered from the instructors and course coordinators to obtain an estimate on how many anticipated hours a student would have to spend on their course that week, given what assessments are in that week. Through analyzing multiple courses, we expect to find a relationship that would suggest whether the hours students spend on assignments is less than, equal to, or greater than what instructors expect for first-year engineering students at University of X to spend. The outcome of this analysis would be beneficial to understand the workloads as perceived by professors and experienced by first-year engineering students. Furthermore, it can highlight potential misjudging of difficulty of each course and assignment, helping instructors to update their expectations and propose fair deadlines and grades for assessments. It can also assist program coordinators to distribute major assessments better towards a steadier and more manageable workload for the students. The students can also benefit from the findings to understand their time commitments.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".