Are online learners frustrated with collaborative learning experiences?
Bibliographic record
Abstract
<p>Online education increasingly puts emphasis on collaborative learning methods. Despite the pedagogical advantages of collaborative learning, online learners can perceive collaborative learning activities as frustrating experiences. The purpose of this study was to characterize the feelings of frustration as a negative emotion among online learners engaged in online computer-supported collaborative learning (CSCL) experiences and, moreover, to identify the sources to which the learners attribute their frustration. With this aim, a questionnaire was designed to obtain data from a sample of online learners participating in the Master of ICT and Education program of the Universitat Oberta de Catalunya (UOC). Results revealed that frustration is a common feeling among students involved in online collaborative learning experiences. The perception of an asymmetric collaboration among the teammates was identified by the students as the most important source of frustration. Online learners also identified difficulties related to group organization, the lack of shared goals among the team members, the imbalance in the level of commitment and quality of the individual contributions, the excess time spent on the online CSCL tasks, the imbalance between the individual and collective grades, and difficulties in communication, among other factors leading to frustration. The analysis of the students’ sources of frustration in online CSCL is followed by a list of recommendations to the distance education stakeholders, aiming to reduce students’ frustration and improve the quality of their experiences in online CSCL contexts such as the UOC.</p>
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".