The importance of interaction for academic success in online courses with hearing, deaf, and hard-of-hearing students
Bibliographic record
Abstract
This paper reports the findings of three studies within a program of research designed to better understand the factors contributing to the academic achievement of students in online courses and the contributions of interaction to online learning. The first study compared the academic achievement of students in the online and face-to-face (F2F) sections of multiple courses. In the second study, an online survey was used to obtain student perceptions of course satisfaction, learning, and communication. These factors were then related, using binary logistic regression analysis, to the amount of interaction that occurred in the students’ respective online courses; information from the myCourses course management system was used to quantify the amount of interaction that occurred in online courses. In the final study, both datasets were used to examine the academic achievement of students in online courses based upon the amount of interaction that had actually occurred. Whenever possible, a subgroup of deaf and hard-of-hearing students was included in the study to increase our understanding of the role that communication plays in the teaching-learning process. Our findings indicate that students enrolled in online courses, especially those designed with high levels of online interaction, receive higher grades and report greater learning than students in comparable F2F courses. In addition, online courses appear to provide deaf and hard-of-hearing students with special benefits in terms of academic achievement through online discussion. Overall, the studies illuminate how the quantity of interaction in online discussions relates to important success factors. Students in online courses with more interaction outperformed students in online courses with less interaction.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".