Investigating Characteristics of Learning Environments During the COVID-19 Pandemic: A Systematic Review
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
Dramatic change in learning environments during the COVID-19 pandemic highlighted the significance of virtual learning and led to more interactive learning environments. Quick adoption of online and social interactive learning in many universities around the world raised challenges and emphasized the importance of investigating different learning environments. This paper investigates the accelerated transition in education from traditional learning environments through online learning environments to social innovative learning environments, and the latest trends of this change. The stages of transition were divided into three parts: before, during, and after the COVID-19 pandemic, which was the reason for this accelerated change. Features and characteristics of each stage of transition were analyzed and discussed, based on the following factors: edu-space and classrooms, the learning and teaching process, curricular choices, information and communication technology applications, students’ and educators’ perceptions, edu-approaches, and knowledge transformation. A systematic review approach was used to investigate learning environments based on the literature reviews of previous publications. Analysis of these features revealed the main characteristics and differences in each stage. New trends in online learning environments and social innovative learning environments were identified including cloud platforms, massive open online courses, digital learning management systems, open educational resources, open educational practices, m-learning, and social network applications. Finally, this study makes two recommendations: 1) the adoption of online learning environments and social innovative learning environment applications to continue the e-learning process during the pandemic, and 2) the enhanced usage of online learning environments and social innovative learning environment applications in the future by educational institutions and governments.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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