Distance students' readiness for and interest in collaboration and social media
Bibliographic record
Abstract
Purpose The purpose of this paper is to describe how researchers from four large Canadian distance education or dual mode institutions conducted a survey aiming to describe the use of and interest in social software and Web 2.0 applications by distance education students and to measure their interest in collaborating with peers. Design/methodology/approach In order to do this, an online questionnaire was distributed to students from four large Canadian distance education institutions. A systematic sampling procedure led to 3,462 completed questionnaires. The results show that students have diverse views and experiences, but they also show strong and significant age and gender differences in a variety of measures, as well as an important institution effect for interest in collaboration. Findings Males and younger students score higher on almost all indicators, including cooperative preferences. In this paper the authors review quantitative results from the survey from earlier work (Poellhuber et al.) and present an analysis of the qualitative data gathered from open‐ended questions in the survey. Answers to open‐ended questions regarding the expectation and interest in using social software in their courses, show that students have positive expectations about interactions and course quality, but also concerns about technical, time, and efficiency issues. Research limitations/implications The probabilist sampling, as well as the high number of respondents, are forces. The limits of the research are linked to its survey methodology, possible self‐selection bias, history effect and social desirability effect. Practical implications The study opens avenues to those who consider the integration of social software or Web 2.0 tools in distance courses. Social implications It also offers guidance to those who consider using social software for learning purposes in general. Originality/value While social media and social networking tools offer new educational affordances and avenues for students to interact, that may alleviate the drop‐out rate problem faced by distance education institutions. Little is known about distance students' expertise with social media or their interest in using them to learn individually or to collaborate with peers.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".