The Development of Direct-Contextual Learning: A New Model on Higher Education
Bibliographic record
Abstract
The development of Islamic education demands a change in the teaching system that leads to the availability of a constructivist-oriented learning model in constructing Fiqh knowledge more logically and rationally through analyzing the context of people's life. This study aims to develop a Direct-Contextual Learning (DCL) model by integrating the characteristics of direct instruction and contextual learning and improving the learning outcomes of comparative Fiqh in higher education. This type of research included the type of research development (R&D) with a systematic approach using the Dick and Carey model. The participants involved in this study were 100 first-level undergraduate degrees at Gontor Islamic University who were selected using the purposive sampling technique. The data collection techniques used include questionnaires, literature reviews, and test learning outcomes. This study's results were the DCL model, and the DCL teaching plan that was developed led to better Fiqh learning outcomes. The DCL phases developed to consist of an introduction, presentation, context exploration, confirmation, and closing. This study provides a new learning model at Modern Islamic University that can be used by lecturers to impart student fiqh knowledge without leaving the lecturer's role in facilitating students through critical analysis of the relationship between Fiqh and the context of social life.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".