Selective Assessment in Introductory Physics Labatorials
Bibliographic record
Abstract
In this paper, we present a selection methodology in the physics laboratory that lowers student anxiety and is beneficial to the instructors as well. At Mount Royal University, the traditional laboratory experimental exercises were replaced by a new style of laboratory called labatorials. In our previous research work, we found that labatorials integrate communications and discussions in a friendly environment. They decrease students’ anxiety and improve self-confidence. However, there have been some challenges associated with physics labs that are not specific to labatorials such as the final lab grades of students who miss a lab. Phys1201 and Phys1202 courses at MRU are 13-week courses with around 200 first-year students each semester. There are two training sessions for the lab instructors during the first two weeks of the semester to familiarize them with labatorial goals and strategies. Due to the training sessions and to make sure that the topics of the experiments have been covered in the classroom, introductory physics laboratories start the third week of the semester. There is no lab during the reading break at MRU and we are left with 10 weeks to cover 10 labs. A lab instructor cannot control student absences, and students should not be punished for missing a lab due to illness or a family situation. Each introductory physics course is divided into three to four lecture sections and around 15 lab sections each semester. One solution was to provide opportunities for students to go to another lab section when they miss a lab. However, this solution created new challenges for both students and instructors. It was not easy to find a lab section that matches the schedule of the students missing a lab. On the other hand, at MRU there is only one lab instructor for each 16-student lab section. Some feedback we have received from students is that they would prefer groups of two or three members as most of the time not every member of every group participates. Having one more student making up a lab resulted in having a group of five and made the group activity more difficult. We had received much negative feedback from students working with a new member in groups of five. Some lab instructors excused the missing lab grade and some provided a make-up lab opportunity during a time that worked for them and the students. There are many sessional lab instructors working in our department that cannot provide opportunities for students to make up labs during a time that they do not teach. On the other hand, they are not paid for the extra two-hour make-up labs as well. Not having a consistent solution for the students missing a lab in different sections increased the number of complaints. To address this challenge and use a consistent solution applicable in all lab sections, we decided to use a common practice in the Department of Mathematics and Computing at MRU that allows students to choose the best k quiz/activity grades from the n quizzes/activities written, a policy they call selective assessment.
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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 | 0.019 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".