Using LectureTools to enhance student–instructor relations and student engagement in the large class
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Positive student–instructor relationships are important for student engagement, motivation, retention and achievement. Yet, as class sizes grow, these relationships can be increasingly difficult to develop. This study explores LectureTools – a web-based student response and learning platform that facilitates communication between instructors and students – as a possible solution to this issue by analysing survey data collected from students in a second-year communication class at a large Canadian university. This study builds on previous evidence that using LectureTools results in an increase in student engagement, attentiveness and level of learning, while expanding on this work to include the concept of student instructor relationships. Ultimately, the functionality of LectureTools was found to facilitate the development of student–instructor relationships in the large class while also enhancing student engagement.Keywords: pedagogical design; undergraduate; mixed method; e-learning; technology(Published: 11 November 2015)Citation: Research in Learning Technology 2015, 23: 27197 - http://dx.doi.org/10.3402/rlt.v23.27197
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it