A Preliminary Investigation of Self-Directed Learning Activities in a Non-Formal Blended Learning Environment.
Bibliographic record
Abstract
This research considers how professional participants in a non-formal self-directed learning environment (NFSDL) made use of self-directed learning activities in a blended face-to-face and online learning professional development course. The learning environment for the study was a professional development seminar on teaching in higher education that was offered to ten novice professors over the course of one academic year in a western Canadian research-intensive university. Autonomous activities were compared to online and face-to-face social networking activities, and the effect of structure on the amount and type of self-directed engagement will be examined. We consider whether there is a need to adapt basic theory on formal virtual learning communities to understand self-directed learning and pedagogical practices in nonformal online learning environments. ---------------------------The purpose of this investigation was to examine the self-directed learning activities of learners in a nonformal professional development course that included online and face-to-face learning opportunities. We compare group characteristics and catalysts for learning we found in this non-formal learning environment with key elements of online learning communities we have found in formal environments in earlier studies (Schwier, 2007). This preliminary study was conducted in the 2008-09 academic year, and will be used to inform a research program that will span the next three years. We report preliminary findings in this paper, and discuss methodological issues that will drive future research. Specifically this pilot study examined two central questions: 1. Were characteristics identified in formal virtual learning communities found in a non-formal online learning environment, and did unique characteristics emerge? 2. How did the context and structure of the course influence self-directed learning by participants?
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".