A SYSTEMATIC LITERATURE REVIEW: A PRAGMATIC MODEL OF ONLINE ENGAGEMENT AND AFFORDANCES TO SUPPORT ADOLESCENT LEARNERS
Bibliographic record
Abstract
Adolescent learners, who often have fewer self-regulatory and metacognitive skills than adult learners, require more support and higher quality interactions for online learning (Borup, Graham & Davies, 2013; Cavanaugh, Barbour & Clark 2009).Through a systematic literature review, this paper identified a pattern of student feedback that collectively addressed the wide range of support they required and received from multiple stakeholders when learning online.This work is inspired by the theoretical framework of adolescent community of engagement (ACE) involving members who play different critical roles in supporting adolescent students who are enrolled in online courses (Borup, West, Graham & Davies, 2014).To thoroughly capture who, what, and how elements within online learning environments supported or failed to support adolescent students and influenced either their positive or negative perception of their online learning experiences, five major types of support were identified, and the evidence was analyzed using thematic analysis across studies included in this review.The five types consist of 1) support by the formalized teacher-student relationships and interactions, 2) support via peer relationships and interactions, 3) support from a Proximal Community of Engagement (PCE, Oviatt, Graham, Borup & Davies, 2016, p. 223), including support from a broad yet immediate cycle, the school, familial and community members, 4) support through technological affordances that are unique to online learning environments, facilitating communication synchronously and asynchronously, and 5) support regarding structured and enriched curriculum development in online course shells/learning management systems (LMSs) for self-direct learning.The shift to online learning due to the COVID 19 pandemic has greatly shaped adolescent students' perceptions of and experiences in distance learning, as well as their readiness for the future adoption of online learning.Therefore, it is important to enhance our understanding of "smart" designs and implementation of online courses, and the related teaching and learning strategies that can lend adolescents strong support to improve their learning experience and outcomes.
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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 | 0.128 | 0.272 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.028 | 0.018 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".