Item Response Theory Utilization for Developing the Student Collaboration Ability Assessment Scale in STEM Classes
Bibliographic record
Abstract
Collaboration is an ability that develops in STEM learning and is very influential in 21st-century life. Thus, students' collaboration abilities must be detected properly. This study aims to produce a quality and easy-to-use instrument for assessing student collaboration skills in STEM classes. The research is development research that contains three steps, namely preliminary research, making prototypes, and conducting product evaluations. Methods of data collection using FGD and questionnaires. The FGD was carried out with experts to produce descriptive data and assessment instruments as well as questionnaires which were also development products with data in the form of graded scales 1, 2, 3, and 4. The study involved 187 junior high school students who took lessons in STEM classes. The instrument is a questionnaire with 4 graded answer choices. To ensure the quality of the instrument, the researcher conducted FGD and expert validation and proved the construct with CFA. The instrument profile was traced using the unidimensional graded response model (GRM) method of response analysis. The results showed that the final instrument containing 17 items was declared valid in terms of content and constructs, as well as reliable. The results of the item analysis show that all items have good sequential step parameters (b1 < b2 < b3), all items have a good discriminant index (0.995 ≤ ai ≤ 1.764), and the instrument is reliable for measuring students with an ability range of -6.15 < θ < 4.05. Thus, this instrument can define students' abilities well in a wide range of abilities.
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 | 0.023 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".