How can state-of-the-art computational linguistic technology reduce the workload and increase the efficiency of language teachers? To address this question, we combine insights from research in second language acquisition and computational linguistics to automatically generate text-based questions to a given text. The questions are designed to draw the learner’s attention to target linguistic forms – phrasal verbs, in this particular case – by requiring them to use the forms or their paraphrases in the answer. Such questions help learners create form-meaning connections and are well suited for both practice and testing. We discuss the generation of a novel type of question combining a wh- question with a gapped sentence, and report the results of two crowdsourcing evaluation studies investigating how well automatically generated questions compare to those written by a language teacher. The first study compares our system output to gold standard human-written questions via crowdsourcing rating. An equivalence test shows that automatically generated questions are comparable to human-written ones. The second crowdsourcing study investigates two types of questions (wh- questions with and without a gapped sentence), their perceived quality, and the responses they elicit. Finally, we discuss the challenges and limitations of creating and evaluating question-generation systems for language learners.

Corrective feedback (CF), a response to linguistic errors made by second language (L2) learners, has received extensive scholarly attention in second language acquisition. While much of the previous research in the field has focused on whether CF facilitates or impedes L2 development, few studies have examined the efficacy of gradually modifying the explicitness or specificity of CF as a function of a learner’s response to the feedback. Yet, the type and extent of CF needed by a learner, as suggested by Vygotsky (1978), sheds light on whether a learner is developing his or her abilities in a particular area and the ways in which they do it. This paper reports on a study that explores the design, effectiveness and learners’ perception toward a graduated (Aljaafreh & Lantolf, 1994) approach to CF, i.e., feedback that progresses from very general and implicit to very specific and explicit, in an intelligent computer-assisted language learning (ICALL) environment. The results show that the graduated approach to CF is effective in helping learners to self-identify and self-correct a number of grammatical issues, although an onsite tutor provides necessary remedies when the ICALL system occasionally fails to do its part. Implications for CF research, particularly on the notion of individualized feedback, are also discussed.

This study introduces the educational assistant robots that we developed for foreign language learning and explores the effectiveness of robot-assisted language learning (RALL) which is in its early stages. To achieve this purpose, a course was designed in which students have meaningful interactions with intelligent robots in an immersive environment. A total of 24 elementary students, ranging in age from ten to twelve, were enrolled in English lessons. A pre-test/post-test design was used to investigate the cognitive effects of the RALL approach on the students’ oral skills. No significant difference in the listening skill was found, but the speaking skills improved with a large effect size at the significance level of 0.01. Descriptive statistics and the pre-test/post-test design were used to investigate the affective effects of RALL approach. The result showed that RALL promoted and improved students’ satisfaction, interest, confidence, and motivation at the significance level of 0.01.