Development in Designing Competency-Based Learning Management According to the Guidelines for Driving the Economy (BCG Model) by Using the Concept of Proactive Learning Management for Students Practice Teaching Professional Experience
Bibliographic record
Abstract
This research piece has the following goals: (1) to create a curriculum for creating a competency-based learning management system based on the economically motivated approach (BCG Model) by utilizing the proactive learning management concept for students practice teaching professional experience, (2) to assess the success of the curriculum in creating competency-based learning management utilizing the idea of proactive learning management for student practice teaching professional experience in accordance with the economic driving model (BCG Model), namely; (2.1) to compare the understanding of developing compe-tency-based learning management in accordance with the economic driving model (BCG Model) utilizing the idea of proactive learning management before and after learning, and (2.2) to assess the capacity to create a competency-based learning man-agement plan utilizing the proactive learning management concept in accordance with the economy-driven approach (BCG Model) compared to the criteria of 80%. In the second semester of the academic year 2022, there are 50 first-year teaching professional internship students in attendance. In the second semester of the academic year 2022, a total of 30 first-year teacher training students made up the sample. It makes use of a straightforward random sampling technique and a research and development (R&D) paradigm, which is typical in behavioral and social science research. The following resources were used in the study: (1) a test of knowledge on competency-based learning management system design, and (2) an operational capacity assessment form for creating a competency-based learning management strategy. Using proactive learning management concepts and knowledge comparison in the design of learning management, manage pre-learn and post-learn competency-based learning, using the t-test for dependent, data analysis is used to evaluate the suitability and effectiveness of the curriculum in the design of competency-based learning management in accordance with the economic-driven approach (BCG Model). Then use the t-test for one sample to assess the capacity to create a learning management plan based on post-learning competency versus the threshold of 80%. The results of the study showed that; (1) the development of a curriculum in the design of competency-based learning management according to the economic-driven approach (BCG Model) using the concept of proactive learning management for students practice teaching professional experience, the innovation of competency-based learning management combined with work to develop competence for students, practice, teaching, and professional experience was successful at 81.74/82.19, the mean was 4.60, the standard deviation was 0.50, and the level at which it was most suited was 4.60, and (2) use the idea of proactive learning management for students’ practice teaching professional experience to evaluate the efficiency of the curriculum in de-veloping competency-based learning management in accordance with the economic driving model (BCG Model), it was found that (2.1) students practice teaching professional experience knowledge in designing a competency-based learning management after learning was significantly higher than before learning at the .01 level, and (2.2) students practice teaching professional experience had the ability to prepare a learning management plan based on post-learning competency higher than the criteria of 80 percent at the statistical significance level of .01.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".