Speaking practice is important for learners of a second language. Computer assisted language learning (CALL) systems can provide attractive opportunities for speaking practice when combined with automatic speech recognition (ASR) technology. In this paper, we present a CALL system that offers spoken practice of word order, an important aspect of Dutch grammar. The system uses ASR technology to process the learner's responses and to detect errors so that immediate corrective feedback (CF) can be provided on learner errors. We evaluate the system as a learning environment by analyzing proficiency gains in pre-and post-tests, the logs of the practice sessions, and the learners' appreciation of the system. In this paper, we present two learning conditions: (1) the learners received oral practice and immediate CF on spoken performance and (2) learners received oral practice and NO CF on spoken performance. We found that our system was successful in providing L2 speaking practice. Results show that both groups improve their proficiency on the target feature as a result of treatment. Between the groups there is no significant difference in learning, but the groups proceeded differently through the sessions, and the learners in the group that received automatic CF evaluated the system more positively than the NO CF group. We discuss the performance of the system as an environment for language learning and the obtained proficiency test results, and relate them to current views on second language acquisition.

A major aim of computer-assisted language learning (CALL) is to create computer environments that facilitate students' second language (L2) acquisition. To achieve this aim, CALL employs technological innovations to create novel types of language practice. Evaluations of the new practice types serve the important role of distinguishing effective practice environments from less effective environments, while simultaneously informing educational practices and second language acquisition (SLA) theory. Accordingly, evaluations of CALL systems necessarily deal with multiple criteria. Most researchers would probably agree that motivation is an important criterion in CALL evaluations: a system can provide sufficient L2 input and opportunities for L2 output, yet fail to be pedagogically effective if learners are unwilling to participate. Furthermore, knowledge of the motivational impact of practice can provide valuable context linking individual language learners, practice effort and learning outcomes. From the perspective of recent theoretical developments in L2 motivation theory, this paper surveys a representative sample of CALL system evaluations that include motivational impact. Our analysis suggests not only that CALL needs to do more to align its treatment of motivation with recent L2 motivation theories, but also that it is well positioned to do so. We find that (1) few CALL studies treat motivation as it relates to practice as a dynamic variable, (2) behavioural practice logs are underexploited and (3) very few evaluations take into account learners' individual interests and goals. Drawing on these and other findings, we suggest four new directions for developing the motivation dimension in CALL evaluations.

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This paper presents a detailed study on the role of corrective feedback (CF) in the development of second language (L2) oral proficiency. Learners practiced speaking with a computer-assisted language learning (CALL) system that employs automatic speech recognition (ASR) technology to provide CF. The system tracks learner behaviour by logging the system-user interactions. Two language learning conditions are compared. In the CF condition learners received immediate, automatic CF on the grammaticality of their spoken output. In theOCF condition, learners practiced speaking with the option to self-correct. The target structure under investigation is Dutch verb second (V2) in the main clause. The results show that learner proficiency improved in both conditions. The CF condition shows an additional benefit for learning that is related to the learner’s initial knowledge of the target structure (which we call V2 proficiency). Learners at a lower V2 proficiency level benefitted more from practice with CF than learners in the NOCF condition. Learner evaluations are in line with these results: both the CF and the NOCF groups positively evaluated practice with the system, but the CF condition was preferred by learners starting at a lower V2 proficiency level. For more information on these outcome measures, we investigated the learners’ behaviour during practice. The two groups were found to receive equal amounts of input, but learners in the CF condition produced more (grammatically correct) output during treatment. We found that the CF group repaired their errors in fewer attempts as they progressed through practice. Learners in the NOCF condition generally did not (attempt to) repair their errors. However, the learners answered correctly more often as they progressed in the training. The log data, therefore, shows learning of the target structure in both conditions. We discuss these results and how learning outcome is related to learner behaviour.