Intelligent Computer Assisted Language Learning Research Articles

Heutagogical-based ICALL environment

The use of Intelligent Computer Assisted Language Learning (ICALL) in foreign language learning holds great promises, but the identification of rigorous design principles to develop students’ capability through practical learning design are still insufficient. As capability concerns how people use their competencies in the unknown rather than in familiar situations, ensuring students' capability development becomes crucial in today’s complex, dynamic, and rapidly changing system of modern society. One critical question to firstly answer regarding the numerous variety and available options of ICALL applications is what and how to choose appropriate and relevant ICALL applications which would effectively enhance students’ capability. This paper proposes a framework for the intervention design of the ICALL learning environment to develop EFL students’ capability based on the pedagogy-andragogy-heutagogy continuum and the authentic learning framework.

Using diagnostic feedback to enhance the development of phonetic knowledge of an L2: a CALL design based on the unified competition model and the implementation with the Pinyin Tutor

Second language acquisition (SLA) is complex and multidimensional. Using the framework of the unified competition model (UCM), the current study explores how robust learning and testing of Chinese Pinyin are fostered by optimal integration of different kinds of feedback in an intelligent computer-assisted language learning (CALL) environment offered by the Pinyin Tutor at TalkBank. The findings demonstrated that the repeated feedback-embedded training with the Pinyin Tutor significantly boosted the learners’ proficiency in all aspects of Pinyin knowledge for second language (L2) learners of Chinese whose first language (L1) backgrounds were varied and whose initial proficiencies in Chinese were elementary. Furthermore, there was a strong increase in Pinyin knowledge, as evidenced in the delayed posttest administered 3 months after finishing the training sessions. The results further showed that diagnostic feedback led to greater improvement than basic feedback. The significance of the results is attributed to the design of the Pinyin Tutor, which implements principles from psycholinguistic theory as well as corpus data on the speech production by L2 learners. The study sheds fresh light on improving the Pinyin Tutor, and CALL in general, by incorporating up-to-date findings in educational psychology.

A Systematic Review of Voice-based Intelligent Virtual Agents in EFL Education

Since its debut in the field of education nearly three decades ago, Artificial Intelligence (AI) has been considered as a powerful tool to facilitate new paradigms for instructional design and innovative educational practice in the form of intelligent tutoring systems, chatbots, teaching robots and adaptive learning systems among others. Recent technological advances in the adjacent areas of natural language processing, machine learning and computer graphics focusing primarily on design features that can improve their human-like qualities of naturalness and believability as human interlocutors have also amplified new application opportunities for Intelligent Virtual Agents (IVAs) or Animated Pedagogical Agents (APAs) within the area of Intelligent Computer-Assisted Language Learning (ICALL). Although AI-powered IVAs hold the potential to enhance the learning process in nearly any knowledge domain and personalize automation in teaching by embodying different roles in the learning environment, strikingly few studies have empirically attempted to assess IVAs impact on L2 learners’ academic achievement when learning English as a Foreign (EFL) so far. This study addresses this issue via a systematic review of relevant interventionist IVA studies that were conducted in EFL settings and published within the 2015-2020 timeframe examining IVAs key affordances, major barriers in their adoption for language learning purposes, and the CALL research trends currently prevalent on the topic. Pedagogical implications for the effective implementation of IVA technology in L2 contexts are discussed and future research avenues in the area are highlighted.

Instructional design and learning outcomes of intelligent computer assisted language learning: Systematic review in the field

Instructional design and learning outcomes of intelligent computer assisted language learning: Systematic review in the field

Review of the 2021 ChinaCALL conference

Abstract This paper aims to provide an overview of the 2021 ChinaCALL conference. The topics range from the traditional ones of teaching, learning and assessment to the emerging fields of intelligent computer-assisted language learning (iCALL), conversational agents and language development, the reconceptualisation of teacher digital literacy, reflections upon perfunctory gamification in language learning and particularly pedagogy in the post-pandemic era. In addition, the ability of technology to facilitate language education is explored, along with its potential to foster teacher and learner well-being.

Computer-assisted EFL writing and evaluations based on artificial intelligence: a case from a college reading and writing course

PurposeThe aim of this study is to explore students' expectations and perceived effectiveness of computer-assisted review tools, and the differences in reliability and validity between human evaluation and automatic evaluation, to find a way to improve students' English writing ability.Design/methodology/approachBased on the expectancy disconfirmation theory (EDT) and Intelligent Computer-Assisted Language Learning (ICALL) theory, an experiment is conducted through the observation method, semistructured interview method and questionnaire survey method. In the experiment, respondents were asked to write and submit four essays on three online automated essay evaluation (AEE) systems in total, one essay every two weeks. Also, two teacher raters were invited to score the first and last papers of each student. The respondents' feedbacks were investigated to confirm the effectiveness of the AEE system; the evaluation results of the AEE systems and teachers were compared; descriptive statistics was used to analyze the experimental data.FindingsThe experiment revealed that the respondents held high expectations for the computer-assisted evaluation tools, and the effectiveness of computer scoring feedback on students was higher than that of teacher scoring feedback. Moreover, at the end of the writing project, the students' independent learning ability and English writing ability were significantly improved. Besides, there was a positive correlation between students' initial expectations of computer-assisted learning tools and the final evaluation of learning results.Originality/valueThe innovation lies in the use of observation methods, questionnaire survey methods, data analysis, and other methods for the experiment, and the combination of deep learning theory, EDT and descriptive statistics, which has particular reference value for future works.

Towards Post-postmethodism: Embracing a New Generation of Methodism

Postmethod is the offspring of language teaching methods failure in meeting their objectives, a failure which led to disillusionment with the concept of method. To solve this pedagogical challenge, postmethod stigmatized those taking the initiative to develop new methods as making a futile attempt. Therefore, it exerted a deterrent effect on the developmental trend of language teaching methods. All these measures were taken following postmethodism’s major precept: it is the concept of method which should be to blame. True, the concept of method, as grasped so far, is both “limited and limiting” (Kumaravadivelu, 2003, p. 1). But what if there is another possibility? What if future methods can surpass the boundaries of the past methods? By making an analogy between the developmental trend of assessment and that of language teaching methods and by referring to the potentials of Artificial Intelligence, especially those of intelligent computer assisted language learning (ICALL), this study attempts to provide compelling evidence about the inevitability of a methodism resurgence in the language teaching profession and the emergence of dynamic methods as a new generation of methodism.

Preparing teachers for the application of AI-powered technologies in foreign language education

AbstractAs any other area of human lives, current state of foreign language education has been greatly influenced by the latest developments in the modern information communication technologies. The paper focuses specifically on the incorporation of artificial intelligence (AI), which includes a wide range of technologies and methods, such as machine learning, adaptive learning, natural language processing, data mining, crowdsourcing, neural networks or an algorithm, into foreign language learning and teaching.First, the paper is concerned with changes brought to foreign language education specifically through the application of AI-powered tools and discusses ICALL (intelligent computer assisted language learning) as a subset of CALL. Second, it summarizes eight types of AI-powered tools for foreign language education and related results of the existing research, however scarce it is. Third, it discusses the frame for effective preparation of foreign language teachers in order to integrate AI-powered tools into their teaching to make it easier, less time-consuming and more effective. The author argues for reconsideration of the existing frames of requirements for CALL teachers.

The effects of working memory and declarative memory on instructed second language vocabulary learning: Insights from intelligent CALL

The extent to which learners benefit from instruction may be largely dependent on their individual abilities. However, there is relatively little work on the interaction between instructional effectiveness in second language learning and learner individual factors. In this study, we investigated the relationship between instruction, individual differences in cognitive abilities (working memory and declarative memory), and second language vocabulary acquisition in the context of web-based intelligent computer assisted language learning (ICALL). To this end, 127 adult learners of English, predominantly advanced-level, German-speaking learners, read news texts on the web for about two weeks using an ICALL system under two instructional conditions: form-focused and meaning-focused instruction. Learners in the form-focused condition read and completed automatically-generated multiple-choice gaps where phrasal verbs appeared in the text, while learners in the meaning-focused condition simply read and did not complete any gaps. Mixed-effects regression analyses showed that working memory was associated with vocabulary acquisition and that this association depended on the instructional context, with working memory being predictive of learning only in the form-focused condition, suggesting an aptitude-treatment interaction. Furthermore, declarative memory abilities were related to learning only as measured by the Continuous Visual Memory Task, and the relationship was not moderated by instructional condition. Overall, the study contributes to accounting for variability in second language learning in general, as well as in instructional contexts supported by intelligent CALL.

Intelligent Language Tutoring System

Intelligent computer-assisted language learning (ICALL) is a multidisciplinary area of research that combines natural language processing (NLP), intelligent tutoring system (ITS), second language acquisition (SLA), and foreign language teaching and learning (FLTL). Intelligent tutoring systems (ITS) are able to provide a personalized approach to learning by assuming the role of a real teacher/expert who adapts and steers the learning process according to the specific needs of each learner. This article reviews and discusses the issues surrounding the development and use of ITSs for language learning and teaching. First, the authors look at ICALL history: its evolution from CALL. Second, issues in ICALL research and integration will be discussed. Third, they will explain how artificial intelligence (AI) techniques are being implemented in language education as ITS and intelligent language tutoring systems (ITLS). Finally, the successful integration and development of ITLS will be explained in detail.

Intelligent Computer Assisted Language Learning (ICALL) fornêhiyawêwin: An In-Depth User-Experience Evaluation

Intelligent computer assisted language learning (ICALL) applications for Indigenous languages are a relatively new avenue for computer assisted language learning (CALL). CALL allows language learners to practise a wide range of grammatical exercises and receive feedback on their answers outside of class time. ICALL is essential for dynamically producing these exercises for polysynthetic Indigenous languages with complex morphology. To better understand user perceptions and behaviours within an ICALL setting, an in-depth user evaluation of nêhiyawêtân (a university-level ICALL application for Plains Cree) was initiated. Five second language learners of Plains Cree were recorded using nêhiyawêtân as they completed various grammatical exercises. They were encouraged to report their opinions, thoughts, and observations aloud. Subsequently, observed user reactions and strategies were recorded. This supplied us with potential user errors, strategies, and preferences that allowed us to improve answer feedback and the design and interface of the exercise templates. Moreover, the results of surveys and observations highlighted sociocultural issues that are not seen in mainstream CALL for majority languages. We hope that this evaluation will serve as a guideline for evaluating future ICALL programs for Indigenous and other minority languages.

Research into the Effects of an ICALL Program on Teaching Word Classes to Learners of English

This study investigated the effectiveness of an intelligent computer-assisted language learning (ICALL) program on learning word classes to Turkish learners of English. Learning word classes is a common problem for Turkish students, so the main aim of this research is to help students to overcome this problem. Within the scope of this research, an ICALL application with a word class analyzer was developed and used on Foundation English School Students to measure its effects on students’ achievement in the mastery of word classes and also their attitudes toward such an ICALL environment. The study employed a pre-test—post-test control group design. The sample consisted of 38 B1 level students who were divided into experimental and control groups. The t test was used to investigate the differences between the experimental and control groups. Learners’ achievements in the knowledge of word classes were measured. The results showed that the reading activities with this system had positive effects on students’ word classes learning and their attitudes toward the intelligent computer-assisted language learning tool. According to the statistical results obtained from the study, there is a significant difference between the groups in favor of the experimental group.

Language Learning Research at the Intersection of Experimental, Computational, and Corpus‐Based Approaches

Language Learning Research at the Intersection of Experimental, Computational, and Corpus‐Based Approaches

Providing graduated corrective feedback in an intelligent computer-assisted language learning environment

AbstractCorrective 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 agraduated(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.

How can Writing Tasks be Characterized in a way serving Pedagogical Goals and Automatic Analysis Needs?

The paper works out and addresses a central question in the field of Intelligent Computer-Assisted Language Learning (ICALL): How can language learning tasks be conceptualized and made explicit in a way that supports the pedagogical goals in modern Foreign Language Teaching and Learning (FLTL) and at the same time provides an explicit characterization of the Natural Language Processing (NLP) requirements to provide feedback to learners completing those tasks? We argue that the successful implementation of language learning tasks to be automatically assessed by means of NLP-based feedback generation strategies demands a design process considering both pedagogical and computational requirements as equally important.
 
 Extending well-established work in Task-Based Language Teaching (TBLT) and TBLT testing we propose a framework that helps us (i) elucidate the formal features of foreign language learning activities, (ii) characterize the gap between expected and actually elicited learner language, and (iii) assess how variability in learner responses impacts computational techniques for the automatic analysis of learner language.
 
 To validate our approach we apply our framework to two specific writing tasks for learners of English as a Foreign Language (EFL). Our analysis highlights the relevance of spelling out the pedagogical and linguistic goals of language learning tasks in order to successfully characterize the language variation in learner responses needed to design effective pedagogical and NLP strategies.Given the combination of design- and data-driven perspectives, the framework supports an iterative approach to the creation of language learning tasks and ICALL materials.

Tutorial computer-assisted language learning

‘Sometimes maligned for its allegedly behaviorist connotations but critical for success in many fields from music to sport to mathematics and language learning, practice is undergoing something of a revival in the applied linguistics literature’ (Long & Richards 2007, p. xi). This research timeline provides a systematic overview of the contributions of computer-assisted language learning (CALL) to the role, nature, and development of individual practice in language learning. We focus on written language practice in Tutorial CALL, corrective feedback and language awareness-raising in Intelligent CALL (ICALL), and individualization of the learning process through tailoring of learning sequences and contingent guidance.

Efficacy of an ICALL tutoring system and process-oriented corrective feedback

A Web-based form-focused intelligent computer-assisted language learning (ICALL) tutoring system equipped with a process-oriented corrective feedback function was developed to investigate the extent to which such a program may serve as a viable method of teaching grammar to Korean secondary and elementary students. The present study was also intended to explore the overall efficacy of two different types of corrective feedback on errors made by students while using the process-oriented tutoring program. In addition, the current research attempted to identify the distribution pattern of errors made by English as a foreign language (EFL) learners across error types combining grammar and cognitive strategies. Finally, the research surveyed subjects’ attitudes toward the ICALL tutoring program and assessed its ability to help them acquire grammatical concepts. Overall, the statistical analysis reveals that a form-focused ICALL tutoring system that provides process-oriented feedback may serve to facilitate EFL learners’ acquisition of grammatical concepts, and that the majority of subjects think favorably of the ICALL tutoring program.

An Environment for Learner Corpus Research and Error Analysis

This article presents an environment developed for Learner Corpus Research and Error Analysis which makes it possible to deal with language errors from different points of view and with several aims. In the field of Intelligent Computer Assisted Language Learning (ICALL), our objective is to gain a better understanding of the language learning process. In the field of Natural Language Processing (NLP), we work on the development of applications that will help both language learners and teachers in their learning/teaching processes. Using this environment, several studies and experiments on error analysis have been carried out, and thanks to an in-depth study on determiner-related errors in Basque, some contributions in the above mentioned fields of research have been made.

Design and implementation of an intelligent virtual environment for improving speaking and listening skills

In this paper, we present an intelligent architecture, called intelligent virtual environment for language learning, with embedded pedagogical agents for improving listening and speaking skills of non-native English language learners. The proposed architecture integrates virtual environments into the Intelligent Computer-Assisted Language Learning. This architecture supports visual, auditory, and haptic channels of interaction. It allows pedagogical ideas about language skills to be implemented and validated with a minimum design time. Moreover, we design a computational model to evaluate learner's proficiency level, and an automatic adaptation mechanism which adjusts to the learner's learning curve. We have implemented two scenarios based on the proposed architecture to teach learners how to communicate in public places such as airports and TV stores. Inputs to this system include learner's speech and hand motion, and outputs include graphical scenes, force feedback, and speech by a few embodied agents. Throughout interactions, agents discover the proficiency level of the learner and customize the level of communication complexity accordingly. The system is tested on 10 subjects. Experimental results show 14% increase in the number of proper replies, 3% decrease in grammatical errors, 16% decrease in pronunciation duration, and 11% increase in learners' proficiency level within three trials.

WordBricks: a virtual language lab inspired by Scratch environment and dependency grammars

AbstractThis paper explains design decisions forming a foundation of WordBricks — an intelligent computer-assisted language learning environment, recently initiated at our institution. WordBricks is intended to serve as a “virtual language lab” that supports open experiments with natural language constructions. Being based on dependency grammars, this instrument illustrates the use of modern natural language processing technologies in language learning. The latest prototypes of WordBricks also show how dependency-styled constructions can be represented in a more natural sequential form that facilitates easier user interaction.