Developing an Affordance-Rich Curriculum to Enhance English Reading Proficiency Among Chinese College Students Through Blended Learning
Bibliographic record
Abstract
Affordances, defined as "potential opportunities" enabling learners to engage in goal-directed actions, serve as a pivotal concept in curriculum design. This study explores the implementation of an affordance-rich curriculum, developed based on affordance theory and delivered through a blended learning approach, to improve the English reading proficiency of Chinese college students. A quasi-experimental design was employed, involving 106 non-English major undergraduates from a Chinese vocational university, with 53 students assigned to an experimental class (EC) and 53 to a control class (CC). Over 12 weeks, the EC received instruction through an affordance-rich curriculum, which systematically integrated linguistic, social, and technological affordances, while the CC underwent conventional lecture-based teaching. Pre- and post-test assessments of reading proficiency were administered to both groups. Statistical analyses revealed significant improvements in the EC’s reading performance, demonstrating the efficacy of the affordance-rich curriculum in fostering reading proficiency. These findings underscore the potential of integrating affordance theory within blended learning frameworks to address challenges in English language acquisition in non-native contexts.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".