A grounded theory of information sharing behavior in a personal learning space
Bibliographic record
Abstract
This paper presents a grounded theory of information sharing behavior of the users of a personal learning space. A personal learning space is an environment consisted of weblog, ePortfolio, and social networking functionality. It is primarily used within education as a tool to enhance learning, but is also used as a knowledge management tool and to develop communities of practice. Our results identify privacy as a main concern for users of a personal learning space and illustrate challenges users face in ensuring privacy of their information and strategies they employ to achieve the desired level of privacy. We then identify factors that affect users' decisions regarding disclosure of their personal artifacts to various people and groups in a personal learning space. The three main themes as emerged in our study include current stage in the information life cycle, the nature of trust between the owner and the receiver of information, and the dynamics of the group or community within which the information is being shared. Together, these themes portrayed a clearer picture of users' perspective on the privacy of their information in a personal learning space. The findings offer some ideas about how to create privacy management mechanisms for personal learning spaces that are based on users' mental model of information privacy. Practical implications of the results are also 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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".