Direct Problem-Based Learning (DPBL): A Framework for Integrating Direct Instruction and Problem-Based Learning Approach
Bibliographic record
Abstract
Direct instruction approach has been widely used in higher education. Many studies revealed that direct instruction improved students’ knowledge. The characteristics of direct instruction include the subject delivered through face-to-face interaction with the lecturers and materials that sequenced deliberately and taught explicitly. However, direct instruction resulted in low creative thinking and teamwork skills among students. Therefore, problem-based learning activities were adapted to reform and create an innovation of a direct instruction approach in developing the new situation.Objective: This study aimed at exploring lecturers’ and students’ perspectives towards Direct Problem-Based Learning (DPBL) activities as a new approach for activities in the classroom.Design: A quasi-experimental design was used.Participants: Third-year students (N = 276) who signed up for Computer Networks subject from Dian Nuswantoro University, Indonesia and five lecturers were involved.Findings and Results: Learning outcomes were significantly positively (Sig. p=.00). Creative thinking skills score increased 8.4%, Teamwork skills score increased 11.5%, and knowledge score increased 25.9% of DPBL approach. The majority of students have difficulties in the direct instruction approach 4.71(.472). Whereas, 1.99(.655) students have low difficulty in DPBL approach. Expert participants agreed that DPBL approach can enhance creative thinking and teamwork skills 4.70(.50).
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".