Conventional Critical Reading Practices and 21st Century Learner Preference
Bibliographic record
Abstract
Today’s generations of learners possess learning preferences that differ from previous generations: active, independent and team player. The acquisition of critical reading skills in the 21st century demands learners to take active roles. However, conventional practices of teaching and learning critical reading are still taking precedence in the changing world and may not work well with learning preferences of today’s learners. Thus, this systematic literature review aims to explore the extent of the disparity of traditional practices and learning styles of the current generation by identifying the occurrence of conventional practices, distinguishing today’s learner preferences, and determining whether conventional methods of teaching critical reading align with the learning styles of students today. Past literature on teaching critical reading was selected, underwent a screening process based on the inclusion and exclusion criteria, then synthesised and integrated. The results revealed that conventional methods of teaching critical reading remained prominent, producing a clear pattern of misalignment between old and new practices in teaching and learning critical reading skills. The disconnection between conventional practices and today's learning preferences suggests the need to adopt more innovative models for teaching critical reading and to enhance teacher training in innovative critical literacy to align with 21st century learning.
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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".