Assessment as A Pedagogy and Measuring Tool in Promoting Deep Learning In Institutions of Higher Learning
Bibliographic record
Abstract
Traditionally, the key principle of assessment was based on the depth and intensity of the knowledge taught in class. In our modern state, the notion of assessment is more about learning and less about whether it is deep or surface learning. This could be attributed to challenges facing higher education, such as marketisation, massification, access and success. This article aims to demonstrate the significance of assessment as a pedagogical and measuring tool to promote deep learning in institutions of higher learning. It analyses how different types of assessment could contribute to deep learning while enhancing critical thinking and analytical skills. The article adopted the qualitative research approach to appraise critically and examine the literature on assessment in higher education. The sequence in which assessment tasks are presented, the pedagogical approaches adopted and measurement tools used should aim to present general non-threatening questions. The article recognises Bloom’s taxonomy as it classifies educational learning objectives in the manner that accommodates deep learning. This article suggests that assessment should be made explicit, aligned with learning outcomes that consider deep learning in terms of acquisition of knowledge, comprehension, application, analysis, synthesis and understanding of basic concepts in what is learnt. It concludes that students need to be engaged in their assessment to enable them to develop skills and dispositions that prepare them for the future as socially responsible citizens. Research needs to be conducted on the higher education challenges that compromise the quality of assessment as this could have negative effects on the development of deep learning.
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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 | 0.058 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".