Feedback explicitness, working memory, and explicit knowledge in online classroom-based second language Mandarin tone learning
Bibliographic record
Abstract
The impact of corrective feedback explicitness on second language acquisition remains a critical area of inquiry, yet research on classroom-based learning of Mandarin tones – particularly challenging for first language (L1) English speakers – remains scarce. Furthermore, most feedback studies are conducted in laboratory settings, which may not reflect real-world second language (L2) classroom learning. The few classroom-based studies that exist often lack internal validity, such as failing to include a control group. Additionally, individual learner differences are rarely considered when investigating feedback effectiveness. To address these gaps, this study compares the effectiveness of two feedback types – recasts and metalinguistic feedback – in U.S. university students’ learning of Chinese tones within an online communicative classroom environment. It also examines whether feedback effects are modulated by learners’ explicit knowledge of vocabulary tone values and working memory capacities. Forty-eight novice learners of Chinese were assigned to three groups (recasts, metalinguistic feedback, control) and completed an online synchronous course comprising four 65–85-minute sessions over two weeks. Feedback effects were assessed through controlled (sentence reading) and spontaneous (picture description) oral production tasks administered before, immediately after, and two weeks post-treatment. Phonological short-term memory was evaluated via a nonword recall test, while executive working memory was measured with a listening span test. Results revealed that recasts produced larger and more sustainable gains than metalinguistic feedback, particularly in spontaneous tone use. While phonological short-term memory had minimal impact, executive working memory predicted pretreatment tone accuracy and enhanced the effects of recasts but was negatively associated with the utility of metalinguistic feedback. Vocabulary tone knowledge was linked to pretreatment tone accuracy; however, improvements in this knowledge resulting from instruction did not influence feedback effectiveness. This study highlights the efficacy of recasts in Mandarin tone learning, reinforcing the superiority of implicit over explicit metalinguistic instruction for similar L2 phonological targets.
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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 | 0.001 | 0.005 |
| 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".