An Exploratory Study of Levels of Interaction Occurring with Graduate Students in an Online Literacy Course.
Bibliographic record
Abstract
This study surveyed graduate students prior to, and immediately following, a literacy course offered online to determine their interactions with the content, interactions with the instructor, and interactions with peers throughout the semester. The study also examined graduate students’ opinions about the convenience and perceived benefits of taking this course in an online format. Findings indicated that, prior to the course, less than one quarter of the graduate students (22%) expected the online experience to enhance their understanding of course content at a high level. Prior to the course more than half of the students (52%) also expected a high level of frequent and meaningful interactions with the instructor. When asked about their expectations of meaningful interactions with peers, more than half (52%) of the students indicated high-level expectations on this item. Following the course, students were again asked to rate their interactions with course content, with the instructor, and with peers. In all instances interactions were described as high level and increased following the course. This exploratory study provides interesting insight into the importance of aligning course content and instruction with student expectations when taking online courses. More research is needed to evaluate the impact interactions with content, instructors, and peers has on the learning experience for graduate students enrolled in online courses.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".