Rethinking Assessment and Evaluation: Towards a Holistic Approach to Measuring Student Success
Bibliographic record
Abstract
This research explores the integration of critical thinking skills into educational curricula to meet 21st-century social and economic demands. Critical thinking empowers students to analyze information, make informed decisions, and solve problems creatively, making it vital in modern education. The study reviews practices in Finland, Singapore, and Canada, focusing on student-centered methods like inquiry-based learning and collaborative activities that enhance cognitive skills and academic performance. However, challenges include inconsistent teaching, varied assessments, and inadequate teacher training. A qualitative approach was used, employing case studies and comparative analysis of Finland, Singapore, and Canada’s educational systems. Data were collected through interviews with educators, surveys of students and parents, and curriculum analysis. Findings emphasize the need for standardized critical thinking education, clear assessment frameworks, and more educator support to optimize implementation, enhancing student engagement, academic success, and lifelong learning. Future research should explore long-term impacts and best practices to ensure equitable access to critical thinking education.
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 | 0.134 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".