The Unexpected Connection: Serendipity and Human Mediation in Networked Learning
Bibliographic record
Abstract
Major changes on the Web in recent years have contributed to an abundance of information for people to harness in their learning. Emerging technologies have instigated the need for critical literacies to support learners on open online networks in the mastering of critical information gathering during their learning journeys. This paper will argue that people will have to adapt to using information in a new way and will advocate the movement by learners into and inside information streams on open online networks. Their own control and aggregation of information, preferably through human mediation, should provide information not only relevant to their learning, but also slightly unexpected. We will highlight why this serendipity is important in a learning context and also take three emerging technologies under the loupe; recommenders, RSS and microbloggers,and their effectiveness in supporting serendipitous learning on open online networks. © International Forum of Educational Technology & Society (IFETS).
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".