The implementation of pre‐lecture resources to reduce in‐class cognitive load: A case study for higher education chemistry
Bibliographic record
Abstract
Abstract This case study describes an effective method to ameliorate the cognitive load caused by new terminology and concepts in lectures. Ten online pre‐lecture resources whose design was underpinned by the principles of cognitive load theory were provided to a class of 49 first year university level chemistry students. Each resource introduced a number of key concepts to the forthcoming lecture and included a quiz for students to test understandings and identify misconceptions. The evaluation of the implementation of resources was measured by considering the difference in exam marks for in‐semester test and end of module exam. These showed that the marks for students who had no prior knowledge of chemistry before coming to college significantly improved to the point that there was no difference between students with and without prior knowledge. A key outcome of this work is that providing students with resources to prepare for lectures can help in reducing their cognitive load. Practitioner Notes What is already known about this topic Prior knowledge (eg, from school level) is a strong predictor factor for future performance (eg, at college level). Cognitive load theory describes how the working memory has a limited capacity to process new information. E‐resources can be designed so as to minimise the difficulty of extracting new information from the resources. What this paper adds Designing e‐resources to introduce some core concepts for a lecture can help students identify these in a lecture with a lot of new terminology. These e‐resources can be easily embedded into the virtual learning environment so that students can access resources, complete quiz and receive feedback and a grade with little extra work for the lecturer. These e‐resources can provide a basis for in‐lecture discussion between students and between lecturer and students to further discuss content using core terminology. Implications for practice/policy Embedding of the resources into the module design is important to attribute them value. The lecture should build on the material introduced in the e‐resource. Feedback should be as rich as possible, correcting wrong ideas for novices to the discipline and misconceptions for those with prior knowledge. Identifying core concepts in a structured way before each lecture and providing feedback on students' understanding of these gives students an opportunity to take control of their own learning both before and after a lecture.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".