A Prelaboratory Framework Toward Integrating Theory and Utility Value with Laboratories: Student Perceptions on Learning and Motivation
Bibliographic record
Abstract
Laboratory-based learning can be weakened by a lack of connection with underlying theory and limited contextualization to enhance motivation. To address these shortcomings, a framework for the development of web-based multimedia prelaboratory modules is proposed. The framework incorporates supportive information (content), utility value (context), multimedia design principles (design), and questions/explanatory feedback (formative assessment). On the basis of this framework, prelaboratory modules were developed for three second-year organic chemistry experiments in a chemical engineering course. Each module consists of a few short animation videos and a few questions. The videos include explanation of theories and justification for experimental procedures (supportive information), as well as explanation of utility value to increase student motivation. The effectiveness of the modules was assessed through multiple strategies including a survey with learning and utility value/motivation constructs, student grades for the modules, time spent on the modules, and the number of times videos were watched. Students in general expressed positive views regarding the prelaboratory modules in terms of understanding and relating theory to procedures, and understanding the utility value of the material. Half of the students reported increased motivation as a result of understanding the utility value of the knowledge they acquired. Thus, prelaboratory exercises based on this framework may alleviate some of the educational challenges in undergraduate laboratories.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| 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".