A framework for interaction and cognitive engagement in connectivist learning contexts
Bibliographic record
Abstract
<p class="BODYTEXT">Interaction has always been highly valued in education, especially in distance education (Moore, 1989; Anderson, 2003; Chen, 2004a; Woo &amp; Reeves, 2007; Wang, 2013; Conrad, in press). It has been associated with motivation (Mahle, 2011; Wen-chi, et al., 2011), persistence (Tello, 2007; Joo, Lim, &amp; Kim, 2011), deep learning (Offir, et al., 2008) and other components of effective learning. With the development of interactive technologies, and related connectivism learning theories (Siemens, 2005a; Downes, 2005), interaction theory has expanded to include interactions not only with human actors, but also with machines and digital artifacts. This paper explores the characteristics and principles of connectivist learning in an increasingly open and connected age. A theory building methodology is used to create a new theoretical model which we hope can be used by researchers and practitioners to examine and support multiple types of effective educational interactions. Inspired by the hierarchical model for instructional interaction (HMII) (Chen, 2004b) in distance learning, a framework for interaction and cognitive engagement in connectivist learning contexts has been constructed. Based on cognitive engagement theories, the interaction of connectivist learning is divided into four levels: <em>operation interaction</em>, <em>wayfinding</em> <em>interaction</em>, <em>sensemaking interaction</em>, and <em>innovation interaction</em>. Connectivist learning is thus a networking and recursive process of these four levels of interaction.</p>
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".