Social presence in technology-rich learning environments: how real we are feeling connected and how does it matter for learning?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose Social presence (SP), which refers to individuals’ perception of others being engaged as “real people” in the same situation, is a crucial component in technology-rich learning environments (TREs). This study aims to identify major learning design, antecedents and outcomes of SP within TREs, and identify common findings from the past two decades. Design/methodology/approach Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses review principles and a qualitative analysis of selected articles, a final review of 72 studies that met inclusion criteria was obtained. Key information, including education level, discipline, sample size, study type and measurements, was extracted and studies were further analyzed and synthesized based on design features and learning modes. Findings The study identifies five crucial factors for instructional design to foster SP in TREs: technology affordances, multimedia features, social factors, instructional principles, learner characteristics and learning management systems. The authors compare two learning modes across three dimensions and identify popular technologies used in studies related to SP over the past two decades. Practical recommendations are provided for educators and educational technology developers to enhance SP within technology-rich learning environments. Originality/value This research contributes to the discourse on online learning and computer-supported communication, particularly in the post-COVID-19 era. By examining factors influencing SP and providing implications for instruction and educational technology development, this study offers evidence-based support to educators for engaging learners and fostering authentic learning experiences through adaptive selection of educational technologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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 it