The Effectiveness of Teaching Materials with TEE Patterns in Improving Students' Critical Thinking Skills and Scientific Attitudes
Bibliographic record
Abstract
The complexity of problems and information technology growth in the 21st-century demand better competence than in previous centuries.The 2016 Ontario discussion document states that three categories or competency domains must be developed: cognitive, interpersonal, and intrapersonal.The three domains consist of 50 competencies, where 13 competencies are included in the cognitive domain, 15 in the interpersonal domain, and 22 in the intrapersonal domain.However, of the many competencies above, the most important competencies in the international framework that benefit every life aspect are critical thinking, communication, collaboration, and creativity & innovation (Ontario, 2016:11).In the 2010 Pacific Polacy Research Center document (2010:1), there are 4 categorizations of 21st-century skills: digital literacy, thinking of discovering, effective communication, and high productivity.From these two documents, it is clear that the similarities in the expected competencies of the 21st century determine a person success.Based on those documents, 5 main competencies of the 21st century can be formulated into critical thinking, communication, creativity and innovation, collaboration, and digital literacy.In addition to the five basic 21st-century competencies above, other aspects that need to be improved are mastery of learning materials/outcomes and scientific attitudes.Although these two aspects are not the main competencies of the 21st century, these are essential as well.According to Piaget (Slavin, 2000:52), knowledge determines the breadth and depth of a person's actions and attitude.Therefore, education that encourages mastery of knowledge must be the learning outcome (Sumardi, et al., 2020; Irwanto, et al., 2023, Wahyudiati, et al., 2019).Moreover, from the 2018 PISA results, Indonesia is still ranked 70th out of 78 countries being studied (Harususilo, 2019).Of the 3 areas assessed, such as reading, mathematics, and science, the results of Indonesian students are still below the minimum competency (Ministry of Education and Culture of the Republic of Indonesia, 2019).According to the TIMSS rating, Indonesian students' aptitude is ranked 44th out of 44 nations (Sriyatun, 2020).
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".