ANALISIS EFEKTIVITAS KEGIATAN PRAKTIKUM SEBAGAI UPAYA PENINGKATAN HASIL BELAJAR MAHASISWA
Bibliographic record
Abstract
The current research intends to clarify the lab activities program of Basic Chemistry course (including its planning and implementation) at Science Education Department of IAIN Mataram and the factors affecting the quality of the program in order to maximize students’ learning achievement. It is a qualitative research where that data are obtained from students, lecturers, and laboratory assistances. The research found the following remarks: 1) the planning process shows that students have weaknesses on their understanding of the topic being practiced, their capability on using the tools and stuff used in the practices, and on the quantity of consultation to laboratory assistances; 2) During the implementation process, there some blind spots such as inadequate equipment to do lab activities, students’ difficulties in compiling report, unsynchronized schedule of lab activities, and inadequate explanation and consultation to supporting lecturers and laboratory assistances. To anticipate the problems above, an intensive evaluation is held together with students, laboratory assistances, and supporting lecturers to optimize their roles. The factors contributing to the quality of the lab activity are student motivation, the lecturers and laboratory assistances contributions, and the lab equipment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".