Student understandings of learning at the end of an undergraduate program using networked learning: A case study
Bibliographic record
Abstract
Course-based online learning has grown significantly in the last decade, yet the understanding of students’ experience of this form of learning is only just starting to emerge. Practitioners and researchers are already starting to explore post course-based networked learning scenarios, including networked lifelong learning. Now would seem to be an opportune time to investigate students' learning experiences in course-based networked environments, in order to inform the development of these post course-based learning environments. The aim of this case study was to examine students’ understandings of learning gained through course-based networked learning, with the aim of shedding some light on how students might engage with post course-based networked learning environments. Specifically, the study sought to understand what aspects of identity as learners and understandings of ways to learn were shown by students who had been through a program using course-based networked learning. Through interviews with six students who were close to completion of an undergraduate program making significant use of networked learning at a west coast Canadian University, this research explored the understandings about learning that these students had developed through their program. Results showed that students were faced with an onslaught of technologies and found it challenging to develop new ways to learn. This suggests that newer ways to learn will have to be explicitly taught if students are to be successful with networked lifelong learning. The study concluded with implications for the development of post course-based networked learning environments, for educational programs using networked learning and for future research on students’ experiences of networked learning.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".