Implementation of Project-Based Learning (PjBL) Assisted by E-Learning through Lesson Study Activities to Improve the Quality of Learning in Physics Learning Planning Courses
Bibliographic record
Abstract
This study aims to improve the quality of learning in physics learning planning courses through the implementation of Project Based Learning (PjBL) assisted by E-Learning through Lesson Study activities. This type of research was qualitative research through the stages of Lesson Study activities. Subjects in this study were the 5th-semester students who program 11 physics learning planning subjects in the 2018-2019 academic year in the Department of Physics Education, University of Papua. The research data was obtained through the student learning outcomes test instrument that was given after the submission of each topic of study, observation sheet of student activities, interview guidelines, documentation in the form of video recordings during open class implementation, and student response questionnaire. Data were analyzed through Rasch modeling with the help of the Winstep application to analyze student responses after learning. Lesson Study activities consist of three phases of activities, namely Plan, Do, and See. In the Plan stage discussions with the team of lecturers were held to develop Chapter Design and Lesson Plan. In the Do stage, the model lecturer based on the tools that have been prepared does learning. In the See stage, the reflection was done to find out weaknesses and strengths during learning which is then followed up on further learning. The results showed that student-learning outcomes increased student responses to good learning and learning atmosphere seemed very fun. Therefore, it can be concluded that through the implementation of PjBL assisted E-Learning through Lesson Study activities can improve the quality of learning in physics learning planning subjects.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".