Using the Community of Inquiry Framework to Scaffold Online Tutoring
Bibliographic record
Abstract
<p class="3">Tutoring involves providing learners with a suitable level of structure and guidance to support their learning. This study reports on an exploration of how to design such structure and guidance (i.e., learning scaffolds) in the Chinese online educational context, and in so doing, answer the following two questions: (a) What scaffolding strategies are needed to design online tutoring, and (b) How should different levels of scaffolding intensity be emphasized in different stages of online tutoring in such educational contexts? A model for online tutoring using the Community of Inquiry framework was developed and implemented in this study. It focused attention on both the critical role of the tutor in online learning and the importance of scaffolding in online tutoring. Both qualitative and quantitative methods were used to collect data, including questionnaires, interviews, and content analysis. In considering the variation of scaffolding throughout the online course, results showed that: (a) As long as a high degree of social presence is established in the initial phase, scaffolds for social presence can be withdrawn gradually throughout the course; (b) High-intensity teaching presence is much more important in the mid-phase of the course than in other phases; (c) “Discourse facilitation” should be emphasized for teaching presence in the mid-phase, while “direct instruction” scaffolding is needed in the last phase; and (d) The greatest need for scaffolding of cognitive presence occurs in the final phase of the course.</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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| 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".