HOLISTIC APPROACHES TO ASSESSMENT: BRIDGING GAPS IN TEACHING AND LEARNING
Bibliographic record
Abstract
Assessment techniques are the keys that can turn a good teacher into a great teacher. By doing the right thing, they can produce high quality output in teaching and learning. On the contrary, the traditional ways of assessing students tend to miss the all-around growth of students, because they mostly concentrate on the cognitive abilities and knowledge retention. This paper is in favour of the implementation of the holistic methods of evaluation which would include a wider variety of skills, attitudes, and competencies. The paper gives examples of different educational theories and practices, such as constructivism, socio-cultural perspectives, and competency-based education, and thus, it offers the strategies for integrating holistic assessment into teaching and learning processes. These strategies comprise of the process of performance evaluation, portfolios, self-assessment, peer evaluation, and authentic assignments. In addition, the publication examines the possible advantages of the holistic approach, for example, the development of deeper learning, the promotion of metacognitive skills, the improvement of student engagement, and the provision of more detailed feedback. Through the connection of conventional assessment methods to the multidimensionality of learning, holistic approaches to assessment are opening up the way for students to become well-rounded learners with the skills and the dispositions necessary for success in the world that is rapidly changing.
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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 | 0.034 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.003 | 0.005 |
| 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".