The Efficiency of Online Learning Environment for Implementing Project-Based Learning: Students' Perceptions
Bibliographic record
Abstract
Project-based learning is gaining increasing popularity supported by research studies regarding its effectiveness for teaching and learning. In addition, the widespread of digital technologies and sudden disruptions to traditional in-person teaching have accelerated the adoption of online learning. The current study examined students' perceptions of the impact of online learning environment on project-based teaching method. Most universities worldwide have considered online learning encouraging their faculty to use online learning tools, and Hashemite University in Jordan is no exeption. 154 students studying Computers in Education course were selected during the first semester of the academic year 2019/2020 and were devided into two groups. The experimental group consisted of 75 students who studied the course online and 79 students in the control group who studied the course in a face-to-face mode. For the purpose of gathering data, a questionnaire was developed which consisted of 17 items and students' answers were on a four-point Likert scale: 4= strongly agree, 3= agree, 2= disagree, and 1= strongly disagree. Means, standard deviations, and One-Way ANOVA were used to analyze the data. The results of the study showed positive attitudes among students (both online and in-class) toward project-based learning. In addition, the results showed that in-class students had a stronger views of project based-learning than online learning students.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".