Comparison of labatorials and traditional labs: The impacts of instructional scaffolding on the student experience and conceptual understanding
Bibliographic record
Abstract
Traditional, template physics labs are often associated with student dissatisfaction and superficial applications, and are known to leave students with fragmented knowledge. An alternative known as labatorials, a conceptually driven approach to labs, has been proposed. In a number of studies, labatorials have been shown to work well. However, what has been missing is a study comparing labatorials to traditional labs. In this study, labatorials are compared with traditional labs in terms of students' learning experience and the quality of their conceptual learning. Additionally, we identify the scaffolding mechanisms that impact these elements. In the context of Concordia University's introductory experimental mechanics course, we collect data spanning semistructured student and teaching assistant (TA) interviews, class observations, TA surveys, post-test and final exam scores and responses, and student writing products. Upon analysis and triangulation, we find that due to the scaffolding present in labatorials, students typically exhibit a high degree of collaboration and engagement with the material in a low-pressure environment, which allows students to focus on the learning. This is attributed to three primary forms of scaffolding inherent to the design of labatorials: peer scaffolding, instructor scaffolding, and scaffolding by the activity worksheet. In contrast, students in traditional labs have a tendency to rely on step-by-step instructions and focus on avoiding errors, which may inhibit their conceptual learning. These conclusions are supported by the students' differing performance and understanding exhibited in different types of questions; traditional lab students tend to perform better on questions involving standardized processes or simple, memorizationbased calculations, while labatorial students tend to perform better on conceptual questions.
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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 | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".