The Effects of Integrating Social Learning Environment with Online Learning
Bibliographic record
Abstract
<p class="3">The aim of this paper is to present the learning and teaching styles using the Social Learning Environment (SLE), which was developed based on the computer supported collaborative learning approach. To avoid burdening learners with multiple platforms and tools, SLE was designed and developed in order to integrate existing systems, institutional learning, management systems, and Facebook. With SLE, a learner is exposed to instructional learning material, problem-based, project-based, and social learning. This work focuses on presenting and analyzing effectiveness of implemented teaching and learning scenarios that are used by means of SLE. This study investigates the use of SLE as a learning environment and it examines student satisfaction and online activity while using it. The aim is to evaluate the overall effectiveness of integrating SLE with online learning and to determine how using SLE effects student learning. This analysis points out the advantages and disadvantages of using SLE in online learning and provides recommendations for future improvements.</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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".