Student Perceptions of Twitters’ Effectiveness for Assessment in a Large Enrollment Online Course
Bibliographic record
Abstract
During the Winter and Spring 2014 semesters students registered in the online offering of Human Kinetics and Recreation 1000 (N=589) were asked to participate in two Twitter events encompassing two of the course’s assessment activities. In each Twitter event, students were required to post, at minimum, one original tweet and respond to another student’s tweet. The use of a tweet feeder widget in the course’s learning management system provided a current summary of the dialogue. An aggregate tool was used to assist with tracking of student tweets for assessment purposes. At the end of the semester students were asked to complete an online survey that sought to ascertain their experience of using Twitter within the course, including its effectiveness as a component of the assessment, and as a means to enhance social presence within the class. The survey also inquired about students’ previous and current Twitter use, and requested recommendations on how to use it in future courses. Results of this survey data indicate students perceived Twitter as an effective means of assessment, and an effective means to integrate social presence in the high enrollment course allowing them to feel more connected to their classmates and the course content. Students suggested several ways micro-blogging could be used in future classes. Implications for the use of Twitter for assessment purposes or as a means to enhance social presence are discussed.
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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 | 0.004 | 0.017 |
| 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 | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".