Learning Beyond the Laboratory: A Web Application Framework for Development of Interactive Postlaboratory Exercises
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Information overload and limited laboratory time usually result in students focusing mostly on procedural tasks to get the results, rather than being engaged with what they are doing. Moreover, traditional laboratory curricula rarely provide students opportunities to repeat experiments. This study presents the design of a web-based application framework for the development of interactive postlaboratory exercises that complement and extend the hands-on laboratories. The framework has four main modules: (1) a video module to present information and concepts through embedded videos, (2) a virtual lab module to perform experiments, (3) a reaction mechanism module to practice drawing reaction mechanisms, and (4) a formative assessment module including questions and explanatory feedback to guide students and to encourage reflection. On the basis of this framework, postlaboratory exercises have been developed for two experiments in an undergraduate chemistry laboratory course. The exercises allowed students to reinforce and extend the laboratory-gained knowledge in a new virtual context by comparing two different chemical reactions related to the experiments they had performed. A survey conducted at the end of the exercises revealed that a majority of students found the postlaboratory exercises helpful and thought they provided an opportunity for application and helped with reinforcing theory and integrating concepts.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".