Using LectureTools to enhance student–instructor relations and student engagement in the large class
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.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".