Symposium 5: Undergraduate experiences of coping with networked learning
Bibliographic record
Abstract
UK Higher Education’s recent focus on enhancing learning through technology has taken root in educational policy. The Funding Council for Wales (HEFCW, 2008) has stressed to universities in Wales that “we will ask you to report on your use of technology-enhanced learning in future Learning and Teaching Strategies.” Trinity University College, Wales matriculates largely undergraduate students and is faced with the challenge offered up by the funding council. Considerable research has already been conducted on the use of ‘networked learning technologies’, but is often based in the context of post-graduate professionals undertaking more flexible off-campus delivery modes of learning (Asensio et al., 2000; McConnell, 2006; Fung, 2004).The aim of this study was to examine campus based learners’ reflections of their experience when they were moved from the familiar face-to-face learning to a networked learning environment. To achieve this, the following questions emerged: How do campus-based learners initially react to using discussion forums? What did they offer that traditional face-to-face approaches did not? How did they cope? What benefits did they gain? What did they lose? What can be learned from the experience? The methodological approach adopted for this was qualitative and based on the grounded theory method provided by Charmaz (2006), as the research seeks to explore and examine a complex and detailed phenomenon from the perspective of the learner’s experience. From the results of this grounded study four themes were identified from the reflections of the ‘lived experiences’ offered by full time undergraduate learners participating in a research methods programme. The themes identified were categorised as: Familiarisation with the networked environment; grappling with collaboration; learning anew the ‘text as talk’ medium and coping strategies – reverting to the familiar. Networked learning often places great emphasis on text as the medium of mediation between learners, their tutors and their resources. The findings identify benefits from networked learning that face to face interactions rarely offer. However, the study questions the efficacy of relying solely on a text based medium for communication with undergraduate learners and offers possibilities for the future.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".