Use of interactive storytelling trailers to engage students in an online learning environment
Bibliographic record
Abstract
Lack of student engagement in online learning is reported as the major challenge contributing to poor academic performance and completion rates. When transforming an in-person undergraduate remote sensing course to online, this study implemented interactive storytelling lecture trailers (ISLTs) as a tool to effect changes in the realms of behavioral, cognitive, emotional, and student-instructor engagement. We collected survey data to examine students’ own perception of how ISLTs impacted their online learning, and analyzed students’ course participation and performance on tests. Results indicated that ISLTs enhanced some aspects of students’ behavioral engagement such as page views, effectively engaged students’ emotions when viewing ISLTs, and improved student-instructor engagement. Regarding cognitive engagement, ISLTs were able to improve short-term learning skills like remembering and applying levels of thinking. A majority of students recognized that ISLTs enhanced their learning experience and made learning more accessible, while a few considered them burdensome and overwhelming. However, there was no clear evidence indicating that ISLTs enhanced participation or promoted students’ emotional engagement in the follow-up lectures. Further, the improvement of student-instructor engagement we observed through quantitative data analysis lacked representative qualitative support. In summary, this study demonstrates the utility of ISLTs as an online learning engagement tool for stimulating students’ interest and improving their performance in lower levels of cognitive thinking. Further work is required to explore ways to further enhance students’ participation and emotional engagement throughout the semester and confirm the usefulness of ISLTs for student-instructor engagement.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".