Maximizing feedback for language learning: English language learners' attention, affect, cognition and usage of computer-delivered feedback from an English language reading proficiency assessment
Bibliographic record
Abstract
Attention to personalised feedback for language learning is increasing as computer-based assessment increases practicality, but little attention has been paid to how language learners interact with and use feedback from computer-based assessments. The purposes of the present research were two-fold: to investigate how adult immigrant English language learners engaged with and processed computer-based feedback on their English reading skills, and to explore how these learners used feedback depending on their processing outcomes, psychological characteristics, and English proficiency. To examine these issues, six data sources were analysed using mixed methods for complementary and developmental purposes through interviews, surveys, language assessments, and eye tracking with 102 adult immigrant English language learners in Canada. Data were analysed using qualitative coding and analysis and quantitative methods such as regression analyses and latent class profiling. Results were organized and synthesized by research questions. Study findings were that the personalised sections received most attention, particularly visual results, but detailed descriptive text was useful at intermediate stages of feedback processing and usage. Learnersâ cognitive and affective strategies for negotiating feedback included emotional reactions, deflecting responsibility for negative feedback, critically evaluating report content, negotiating comprehension difficulties, and relating the report to their own lives. Learners were generally positive about personalised feedback, adapted it for their own purposes, and used known affective and cognitive strategies, confirming earlier research in these areas. In addition, confirming other previous research, major factors impacting understanding and usage were external circumstances such as English language environment and language proficiency. A mastery goal orientation, trust in report content, reflection on English skills, and desire to use the report, were positively associated with report usage. Implications included an observed need to fully factor feedback design into test design where impact/effects/outcomes are a guiding principle in test validation processes. From an instructional perspective, a key implication was the need to embed feedback in a high-quality, regular, and social learning environment. Further research is required to understand how feedback design can be personalized to promote more constructive feedback usage in learners with different background characteristics.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".