Socialization and Cognitive Apprenticeship in Online Doctoral Programs
Bibliographic record
Abstract
Online doctoral programs are gaining in popularity, both among students and institutions. However, research to date on the effectiveness and popularity of such programs has looked largely at either quantitative measures of student satisfaction or of administrative effectiveness and design. Further, previous research has also tended to focus on the early part of doctoral study; in specific, the coursework. This qualitative study reports findings from four online doctoral programs in one UK university, contributing to the literature in two important ways. First, we aim to look specifically at current and recently graduated students’ experiences of doing their thesis using a demographic and experiential survey. This will be followed up by in-depth interviews to better understand the kinds of academic experiences and knowledge they both bring to, and receive from their program. Second, we aim to analyse the data through the lens of cognitive apprenticeship to help us better understand the individual trajectories of students in the thesis portion of their programs. By so doing, this research will contribute both theoretically and practically to our understanding of student experience of the thesis process in online doctoral programs. In particular, we conclude that there is a lack of knowledge and frameworks for how to design online/distance post-graduate programmes that best support the cognitive apprenticeship model. We suggest a shift in the research agenda on this issue: Perhaps, the first step towards a more effective direction is to focus less on quantitative measures for success, like enrolment statistics or graduation rate but rather to employ qualitative judgements for the evolution of the post-graduate experience. What might be the guidelines for such qualitative judgments? The answer may lie within the principles of Networked Learning: knowledge is not confined to an individual; rather, it is distributed across individuals within the environment. That is, learning is not an in-the-head phenomenon but a matter of engagement with, participation in, and membership to a community. We argue that it is through this notion of learning that we may develop a more effective framework to reconceptualise the theory and practice of online/distance post-graduate education within the cognitive apprenticeship model of 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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".