E-Learning Process of Maharah Qira'ah in Higher Education during the Covid-19 Pandemic
Bibliographic record
Abstract
The learning process has changed totally since the implementation of the distance learning policy (Pembelajaran Jarak Jauh-PJJ). Lecturers and students are required to be able to optimize the internet. This article discusses the process of Maharah Qira’ah using e-learning in UIN Imam Bonjol Padang. This research is a qualitative study by describing data found in the field in depth. The data were collected through Google Forms, observation, distribution of questionnaires through Google Forms, and online interviews through social media and documentation. The results showed that the e-learning media used in Maharah Qira’ah classes are WhatsApp, Zoom, Youtube, Instagram, and Facebook applications, where Whatsapp is more significant than other media. In an effort to realize reading skills, the lecturers designed the lesson by demanding students to understand Qira'ah texts sent through WhatsApp Group, by writing new vocabulary found in the text, recording their readings, and sending them to WhatsApp Group, followed by solving 10 problems, and then discuss them with the lecturers and the other students. This study found that there is a shift in learning maharah qira’ah using e-learning from student center to media center. It means that the process depends on technology is more significant than dependence on teachers. This study also found that learning mahara qira'ah with conventional methods is more preferred by students than using e-learning. Although the teacher explains the material in depth and provides assignments that support student learning.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".