In-class use of Laptop Computers to Enhance Engagement within an Undergraduate Biology Curriculum: Findings and Lessons Learned
Bibliographic record
Abstract
Laptop computers were provided for use in three biology classes with differing formats (a second year lecture course of 100 students, a third/fourth year lecture course of 50 students, and a second year course with > 250 students, in groups of 25 during the laboratory portion of the class) to assess their impact on student learning and engagement. In lecture courses, laptop computers were used for web-based exercises to collect and analyse information while in the laboratory, these computers were used to compile student data into a single dataset for analysis. Over a three year study period, student responses were generated using surveys with a Likert response scale. In the third/fourth year lecture course, student satisfaction was the greatest; students strongly agreed that the laptop computers were enjoyable and helpful and felt that the classroom environment was very interactive. However, comparison of examination scores between years with and without laptop computer-based instruction did not show a measurable difference in student learning. In the second year lecture course, students had similar experiences but the average student responses were lower. In the laboratory course, student responses were neutral, possibly due to difficulties associated with limited time to familiarise themselves with the technology and/or variation in laboratory instructors. Student feedback indicated that technology can have a positive impact on student experience but difficulties with technical issues can counteract benefits, necessitating careful preparation and implementation of technology use.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".