Features fostering academic and social integration in blended synchronous courses in graduate programs
Bibliographic record
Abstract
Abstract The purpose of this study was to examine the features that foster the academic and social integration of students enrolled in blended synchronous courses (BSC). Many studies and models have considered academic and social integration to be important determinants of student persistence and success in higher education programs and courses. In keeping with current research on blended courses that builds on models and theories developed for both online courses and face-to-face courses, we draw on Tinto’s model (Tinto, Review of Educational Research 45:89–125, 1975; Tinto, Leaving college: Rethinking the causes and cures of student attrition, 1993) and those of Rovai (The Internet & Higher Education 6:1–16, 2003) and Park (Proceedings of the 2007 Academy of Human Resource Development Annual Conference, 2007) to better define the academic and social integration of students in blended synchronous courses. To meet the study objective, a qualitative methodology was adopted. A convenience sampling technique was used in the study. The study participants were students (n = 8) enrolled in a graduate program in education offering only blended synchronous courses, as well as their instructors (n = 5). Semi-structured interviews (60–120 min in length) were selected as the data collection method. All qualitative data were analyzed using a general inductive approach (Thomas, American Journal of Evaluation 27:237–246, 2006). The results show that many features appear to promote academic and social integration, including the pedagogical strategies used. Moreover, this integration depends on the attitudes of both instructors and face-to-face students towards online students. This study highlights some challenges associated with blended synchronous courses. Further, it appears to suggest that instructors will need to work more on the inclusion of online students, and that training should be provided to assist them in this regard.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".