Assessment Activities in Teaching First-year Engineering Mechanics
Bibliographic record
Abstract
Abstract Assessment activities play a significant role in teaching first-year engineering mechanics courses to ensure that accreditation standards are met. Traditionally, for in-person lecture-based course delivery, assessments typically involved a mix of problem-solving assignments, labs and examinations. In terms of exams, they were often delivered in a time restricted in-person invigilated setting for first-year students to uphold academic integrity and ensure that assessments demonstrate individual learning for students. With the accelerated move to online delivery of lecture material during the Covid pandemic many instructors were required to rapidly transform to a completely different mode of assessment. This paper speaks to that rapid transformation of first-year assessments and how prior research in engineering education was used to ease the difficulty of this transition. Prior to the pandemic, there had been a need to investigate the use of online resources to assist in delivery of course materials, from online learning management systems (LMSs) to online resources (quizzes, worksheets, problem databases, interactive activities and simulation to name a few) that were often used to demonstrate competency of learning outcomes. In the past several years, the authors of this paper conducted research on large problem-solving databases, the use of active learning in accelerating efficacy for student learning, automatically generated assessment activities, and the development of online quizzes and exams. The paper summarizes the techniques used for assessments in first-year mechanics and makes a comparison to traditional methods as well as the tools (such as Crowdmark) used that facilitated uploading and marking of more traditional textbook assignments and seminar problems. A discussion and reflection of the successes and challenges (such as frequency of assessment) is then presented. Finally, the paper reflects on the more serious consequences of the move to online assessments in terms of issues with online invigilation.
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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 | 0.000 | 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 | 0.001 |
| 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".