Bibliographic record
Abstract
In undergraduate science courses, students typically attend lectures without preparing in advance and are often overwhelmed with the quantity of new material they are being taught. In addition, instructors often teach new material without adequately assessing students’ prior knowledge and any misconceptions that students may have in relation to key concepts. These challenges can be addressed by incorporating pre-lecture activities, which help to prepare students for lecture by introducing them to key concepts in a structured way.\nPre-lecture activities facilitate student learning in three ways. First, students are introduced to core concepts before lecture to help identify misconceptions, activate prior knowledge and familiarize students with lecture material. Secondly, instructors can incorporate the pre-lecture questions into the lecture itself and provide student responses as discussion openers. Lastly, students are more likely to participate if they are familiar with the material and feel confident in their understanding. Therefore, pre-lecture activities help students prepare for lectures and provide a basis for interactive learning, without compromising the amount of content that can be taught during the lecture.\nThe purpose of this workshop is to prepare instructors to effectively incorporate pre-lecture activities into their courses. In addition to providing specific examples of pre-lecture activities, the workshop will also highlight how this teaching approach is supported by several learning theories, including constructivism, cognitive learning theory and Just-in-Time Teaching (JiTT) pedagogy. Lastly, the workshop will emphasize the adaptability of pre-lecture activities and how they can be incorporated into any science course. Workshop participants will have the opportunity to discuss different types of pre-lecture activities and different ways of implementing them into their courses.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.040 | 0.022 |
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".