Learner affect in computerised L2 oral grammar practice with corrective feedback

ABSTRACT

Although corrective feedback (CF) has received much interest in the second language acquisition literature, relatively little research has investigated the relationship between CF and learner affect in concrete practice situations. The present study investigates learners’ affective states and practice behaviour in a novel context: oral grammar practice with a computer-assisted language learning (CALL) system employing automatic speech recognition (ASR) technology to analyse learners’ speech and provide feedback. Thirty-one adult learners of Dutch practiced with this system in one of two conditions: the no-feedback condition (NOCF) and the feedback condition (CF) which provided immediate CF through ASR. Despite concerns that CF can elicit negative affective reactions and although practice with feedback forced learners to reformulate more often, CF did not appear to have a negative impact. Our analysis finds no significant differences between the NOCF and CF groups. A significant correlation between practice performance and self-efficacy was found in the CF only. These findings suggest that ASR-enabled CALL systems may be suitable environments for oral grammar practice where CF on oral productions can be provided without negative affective responses, and that without feedback, learners may develop self-efficacy beliefs which do not necessarily reflect their actual performance.

KEYWORDS:

• Corrective feedback
• affect
• grammar
• computer-assisted language learning
• speech recognition

Acknowledgements

We would like to thank the two anonymous reviewers whose comments have helped to improve this paper. This work is part of the research program Feedback and the Acquisition of Syntax in Oral Proficiency (FASOP) NWO-Dossier Number 360-75-010, which is funded by the Netherlands Organisation for Scientific Research (NWO).

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. Another strand of research has investigated how more stable affective characteristics (e.g. trait anxiety or motivational orientations) influence the effectiveness of CF in classroom contexts (DeKeyser, 1993; Sheen, 2008; see also Dasse-Askildson, 2008), but this is not further discussed.

2. A skip button was also added to allow learners to move to the next question, to handle the rare event that their utterances were repeatedly misclassified as incorrect. The button was disabled until the learner completed three attempts.

3. The first time this item was presented, the item was phrased Ik denk dat het oefenen met GREET ... zal zijn (Helemaal niet leuk - Heel leuk). I think that the exercise with GREET will be ... because the learner had not yet practiced with the system.

4. In the CF case, this measure also indicates performance: the CF condition required learners to produce the correct utterance before advancing to the next question. In the NOCF condition, attempts per question do not necessarily reflect performance (learners were free to attempt a question multiple times and may make multiple correct attempts).

5. Values reported for session and micro session levels are the mean of the individual data sets for that level.

6. Additionally, inspection of the data for the NOCF group revealed three outliers. When these participants are excluded, the standard deviation drops markedly (SD = 0.051), suggesting very little variation in the NOCF condition.

Additional information

Funding

This work was supported by Stichting voor de Technische Wetenschappen [grant number 360-75-010].

Notes on contributors

Stephen Bodnar

Stephen Bodnar is a postdoctoral fellow at McGill University's Advanced Technologies for Learning in Authentic Settings (ATLAS) lab. His areas of interest include affective factors influencing language learning and applications of speech and language technology to CALL.

Catia Cucchiarini

Catia Cucchiarini is a senior researcher at CLST. Her research activities include speech processing, CALL and the application of automatic speech recognition (ASR) to language learning and testing.

Bart Penning de Vries

Bart Penning de Vries was employed as a researcher at the university of Nijmegen on speech technology for CALL and L2 learning, and on corpus linguistic analyses of educational texts. He currently works in the field of educational testing.

Helmer Strik

Helmer Strik is an associate professor in Speech Science and Technology at the Radboud University Nijmegen. His fields of expertise include phonetics, speech production, ASR, spoken dialogue systems, e-learning and e-health.

Roeland van Hout

Roeland van Hout is a professor in applied linguistics and variationist linguistics at the Radboud University Nijmegen. He publishes in the fields of sociolinguitics, dialectology and second language acquisition and has a special interest in research methodology and statistics.