Intelligent CALL

This chapter describes the provision of corrective feedback in Tutorial CALL, sketching the challenges in the research and development of computational parsers and grammars. The automatic evaluation and assessment of free-form learner texts paying attention to linguistic accuracy, rhetorical structures, textual complexity, and written fluency is at the centre of attention in the section on Automatic Writing Evaluation. Reading and Incidental Vocabulary Learning Aids looks at the advantages of lexical glosses, or look-up information in electronic dictionaries for reading material aimed at language learners. The conclusion looks at the role of ICALL in the context of general trends in CALL

한국인 고등학생의 영어 형용사 타동결과구문 학습에서의 인공지능 챗봇 기반 교수의 효과

학위논문(박사) -- 서울대학교대학원 : 사범대학 외국어교육과(영어전공), 2022.2. 김기택.English adjectival transitive resultative constructions (VtR) are notoriously challenging for Korean L2 English learners due to their syntactic and semantic differences from their L1 counterparts. To deal with such a complex structure, like English adjectival VtR, Korean L2 English learners need instructional interventions, including explicit instructions and corrective feedback on the target structure. Human instructors are virtually incapable of offering adequate corrective feedback, as providing corrective feedback from a human teacher to hundreds of students requires excessive time and effort. To deal with the practicality problems faced by human instructors in providing corrective feedback, numerous artificial intelligence (AI) chatbots have been developed to provide foreign language learners with corrective feedback on par with human teachers. Regrettably, many currently available AI chatbots remain underdeveloped. In addition, no prior research has been conducted to assess the effectiveness of corrective feedback offered by an AI chatbot, a human instructor, or additional explicit instruction via video material. The current study examined the instructional effects of corrective feedback from an AI chatbot on Korean high school students’ comprehension and production of adjectival VtR. Also, the current study investigated whether the corrective feedback generated by the AI chatbot enables Korean L2 English learners to expand their constructional repertoire beyond instructed adjectival VtR to uninstructed prepositional VtR. To investigate these issues, text-based Facebook Messenger AI chatbots were developed by the researcher. The effectiveness of the AI chatbots’ corrective feedback was compared with that of a human instructor and with additional video material. Students were divided into four groups: three instructional groups and one control group. The instructional groups included a chatbot group, a human group, and a video group. All learners in the three instructional groups watched a 5-minute explicit instruction video on the form and meaning pairings of the adjectival VtR in English. After that, learners were divided into three groups based on their preferences for instructional types. The learners volunteered to participate in the instructional procedures with corrective feedback from a text-based AI chatbot, a human instructor, or additional explicit instruction using a 15-minute video. Moreover, they took part in three testing sessions, which included a pretest, an immediate posttest, and a delayed posttest. The control group students were not instructed, and only participated in the three testing sessions. Two tasks were used for each test session: an acceptability judgment task (AJT) and an elicited writing task (EWT). The AJT tested participants’ comprehension of instructed adjectival VtR and uninstructed prepositional VtR. The EWT examined the correct production of instructed adjectival VtR and uninstructed prepositional VtR. The results of the AJT revealed that the instructional treatment (e.g., corrective feedback from the AI chatbot or a human instructor, or additional explicit instruction from the video material) was marginally more effective at improving the comprehension of adjectival VtR than was the case with the control group. On the other hand, the instructional treatment on the adjectival VtR failed in the generalization to prepositional VtR which was not overtly instructed. In the EWT, the participants in the corrective feedback groups (e.g., the chatbot and human groups) showed a more significant increase in the correct production of the instructed adjectival VtR more so than those in the video and control groups. Furthermore, the chatbot group learners showed significantly higher production of uninstructed prepositional VtR compared to any other group participants. These findings suggest that chatbot-based instruction can help Korean high school L2 English learners comprehend and produce complex linguistic structures—namely, adjectival and prepositional VtR. Moreover, the current study has major pedagogical implications for principled frameworks for implementing AI chatbot-based instruction in the context of foreign language learning.영어 형용사 타동결과구문(English Adjectival Transitive Resultative Construction)은 한국인 영어 학습자들에게 모국어의 대응 구문이 갖는 의미 통사론적 차이로 인해 학습하기 매우 어려운 것으로 알려져 있다. 따라서 영어 형용사 타동결과구문과 같은 복잡한 구문을 학습하기 위해서, 한국인 영어 학습자들에게는 목표 구조에 대한 명시적 교수와 교정적 피드백을 포함한 교수 처치가 요구된다. 수백 명의 학습자들에게 교정적 피드백을 제공하기 위해서는 과도한 시간과 노력이 요구되기 때문에, 인간 교사가 적절한 양의 교정적 피드백을 제공한다는 것은 사실상 불가능하다. 교정적 피드백을 제공할 때 직면하는 이러한 실용성 문제를 해결하기 위하여, 외국어 학습자들에게 인간 교사와 유사한 교정 피드백을 제공할 수 있는 수많은 인공 지능(AI) 챗봇이 개발되었다. 유감스럽게도, 현재 사용 가능한 많은 외국어 학습용 인공지능 챗봇은 아직 충분히 개발되지 않은 상태에 남아있으며, 인공지능 챗봇의 교정적 피드백이 갖는 교수효과를 비교 분석한 연구는 현재 이루어지지 않은 상태다. 이러한 선행연구의 한계에 초점을 두어, 본 연구에서는 인공지능 챗봇의 교정적 피드백이 한국 고등학생의 영어 형용사 타동결과구문의 이해와 생성에 미치는 교수 효과를 살펴보았다. 또한 본 연구에서는 이러한 교수 효과가 언어적으로 관련된 다른 영어 구문의 학습에도 영향을 끼치는지를 알아보기 위해 교실에서 직접 가르치지 않았던 구문인 영어 전치사 타동결과구문(English Prepositional Transitive Resultative Construction)의 학습 양상을 알아보았다. 이를 위해, 본 연구에서는 텍스트 메시지 기반의 페이스북 메신저에서 구동되는 인공지능 챗봇을 개발하였다. 인공지능 챗봇의 교수효과 검증을 위해 본 연구에 참여한 학생들은 네 개의 집단으로 구분되었다: 세 개의 교수 집단에는 교수처치가 적용되었고, 한 개의 통제 집단에서는 교수처치가 적용되지 않았다. 교수처치가 적용된 세 개의 집단은 챗봇그룹, 인간그룹, 영상그룹으로 분류되었으며, 이들은 모두 영어로 된 형용사 타동결과구문의 형태와 의미 쌍에 대한 5분 길이의 학습 비디오를 시청함으로써 명시적 교수 처치를 받았다. 또한 비디오를 시청한 후 세 그룹의 학습자들은 교재를 통해 제공되는 언어연습자료를 해결하는 과업에 참여하였다. 다음으로 세 집단(챗봇그룹, 인간그룹, 영상그룹)은 다음과 같은 추가적 교수처치를 받았다: 챗봇그룹 학습자들은 교재 활동과 관련된 텍스트 기반 인공지능 챗봇과의 대화에 참여함으로써 오류에 대한 교정적 피드백을 받았다. 인간그룹 학습자들은 교재활동을 완수한 내용을 인간 교사에게 전송하고, 이에 대한 교정적 피드백을 받았다. 영상그룹 학습자들은 교재활동을 완수한 후 이에 대한 15분의 추가적인 명시적 교수자료를 영상으로 시청하였다. 학습자의 교수효과는 사전시험, 사후시험 및 지연 사후시험으로 검증되었다. 한편 통제 집단 학생들은 교수처치 없이 세 번의 시험에만 참여하였다. 세 차례의 시험에서는 수용성판단과제(AJT)와 유도작문과제(EWT)의 두 가지 과제가 사용되었다. 수용성판단과제를 통하여, 교수된 영어 형용사 타동결과구문과 지시되지 않은 영어 전치사 타동결과구문 대한 참가자의 이해도를 측정하였다. 유도작문과제를 통하여 교수된 영어 형용사 타동결과구문과 지시되지 않은 영어 전치사 타동결과구문을 참여자가 정확하게 산출할 수 있는지를 측정하였다. 시험의 결과는 다음과 같았다. 수용성판단과제의 경우, 교수처치가 적용된 세 집단이 통제 집단보다 형용사 타동결과구문의 이해도 향상에 약간 더 효과적인 것으로 나타났다. 하지만 형용사 타동결과구문에 대한 교수적처치는 교수되지 않은 전치사 타동결과구문으로의 학습에 영향을 주지 못하였다. 유도작문과제의 경우, 인공지능 챗봇이나 인간 교사에 의해 제공되는 교정 피드백 그룹의 참가자가 영상그룹 및 통제집단의 참가자보다 형용사 타동결과구문의 올바른 생성에 더 유의미한 영향을 미치는 것으로 드러났다. 동일한 교수 효과가 전치사 타동결과구문의 학습에서도 관측되어, 형용사 타동결과구문의 학습이 전치사 타동결과구문의 학습에 일반화가 일어났다. 본 연구는 인간 교사가 직면해야 하는 실용성 문제를 극복하고, 인공지능 챗봇이 한국인 고등학교 L2 영어 학습자가 형용사 및 전치사 타동결과구문과 같은 복잡한 언어 구조를 이해하고 생성하는 데에 인간 교사와 비견될 정도로 교정적 피드백을 제공할 수 있을 것임을 시사한다. 또한, 본 연구는 인공지능 챗봇 기반 외국어 교육의 실제적 사례 및 효과를 선도적으로 보여주었다는 점에서 의미가 있다.ABSTRACT i TABLE OF CONTENTS iii LIST OF TABLES v LIST OF FIGURES vii CHAPTER 1. INTRODUCTION 1 1.1. Statement of Problems and Objectives 1 1.2. Scope of the Research 6 1.3. Research Questions 9 1.4. Organization of the Dissertation 10 CHAPTER 2. LITERATURE REVIEW 12 2.1. Syntactic and Semantic Analysis of Korean and English Transitive Resultative Constructions 13 2.1.1. Syntactic Analysis of English Transitive Resultative Construction 13 2.1.2. Syntactic Analysis of Korean Transitive Resultative Constructions 25 2.1.3. Semantic Differences in VtR between Korean and English 46 2.1.4. Previous acquisition study on English adjectival and prepositional VtR 54 2.2. Corrective Feedback 59 2.2.1. Definition of Corrective Feedback 59 2.2.2. Types of Corrective Feedback 61 2.2.3. Noticeability in Corrective Feedback 67 2.2.4. Corrective Recast as a Stepwise Corrective Feedback 69 2.3. The AI Chatbot in Foreign Language Learning 72 2.3.1. Non-communicative Intelligent Computer Assisted Language Learning (ICALL) 73 2.3.2. AI Chatbot without Corrective Feedback 79 2.3.3. AI Chatbot with Corrective Feedback 86 2.4. Summary of the Literature Review 92 CHAPTER 3. METHODOLOGY 98 3.1. Participants 98 3.2. Target Structure 102 3.3. Procedure of the Study 106 3.4. Instructional Material Shared by the Experimental Group 107 3.4.1. General Framework of the Instructional Session 108 3.4.2. Instructional Material Shared by Experimental Groups 111 3.5. Group-specific Instructional Treatments: Post-Written Instructional Material Activities on Corrective Feedback from Chatbot, Human, and Additional Explicit Instruction via Video 121 3.5.1. Corrective Feedback from the AI Chatbot 122 3.5.2. Corrective Feedback from a Human Instructor 136 3.5.3. Additional Instruction via Video Material 139 3.6. Test 142 3.6.1. Acceptability Judgment Task (AJT) 144 3.6.2. Elicited Writing Task (EWT) 150 3.7. Statistical Analysis 152 CHAPTER 4. RESULTS AND DISCUSSIONS 154 4.1. Results of Acceptability Judgment Task (AJT) 154 4.1.1. AJT Results of Instructed Adjectival VtR 155 4.1.2. AJT Results of Uninstructed Prepositional VtR 160 4.1.3. Discussion 164 4.2. Results of Elicited Writing Task (EWT) 175 4.2.1. EWT Results for Instructed Adjectival VtR 176 4.2.2. EWT Results of Uninstructed Prepositional VtR 181 4.2.3. Further Analysis 187 4.2.4. Discussion 199 CHAPTER 5. CONCLUSION 205 5.1. Summary of the Findings and Implications 205 5.2. Limitations and Suggestions for Future Research 213 REFERENCES 217 APPENDICES 246 ABSTRACT IN KOREAN 297박

It's just a word : CALL, French verbs and mixed-ability pupils

This thesis follows the trail of a perennial problem in the written work of pupils studying GCSE French, and suggests a CALL solution. The motivation for the research lies in the decline of grammatical accuracy, particularly in verb use, in the French produced by mixed-ability pupils and university students alike.Theories of language acquisition are assessed and a limited amount of guidance emerges. French GCSE Examiners' Reports then provide a firm foundation for research with their suggestion that the rise in oral work has affected written standards. A review of the literature reveals a wide range of barriers to verb learning. These can be classified as linguistic, psycholinguistic and pedagogic.One of the most impenetrable barriers is the redundancy of many verb endings. Empirical evidence from written and interview data is presented to show the startling kinds of misconceptions held by many pupils about verbs,and the complex of systems learners devise to solve problems.The thesis then proposes an explicit grammar-teaching approach based on principles of pedagogical grammar. Current Computer Assisted Language Learning (CALL) approaches to verb teaching offer admirable formal practice for able pupils but do not cater for the difficulties experienced by less able learners, who may therefore be disenfranchised. Detailed proposals are given for the creation of 'mixed-ability CALL' for verb learning, followed by a description of the design and production processes of three new programs aimed at less able pupils. Further empirical work is undertaken with GCSE pupils in order to assess the effects of tutorial, game and 'cognitive' CALL approaches. The quantitative data show that written performance can improve after using these programs. However, the most striking result of CALL intervention is the transformation of weak pupils' spoken metalanguage from restricted grammatical expression to accurate verb articulation within a short space of time

English for science and technology: a computer corpus-based analysis of English science and technology texts for application in higher education

Doutoramento em LinguísticaThis thesis presents two analyses: first the analysis of computer corpora from undergraduate textbooks to isolate the (American) English language of science and technology they present; secondly an analysis of the English language competence of undergraduates starting their university studies in science and technology. These two analyses are contrasted in order to apply the results to the design of an English language syllabus for first year undergraduates. A frequency and range word list was produced using a large baseline corpus to contrast with the main corpora taken from physics and chemistry textbooks on the students’ bibliographies as a resource for syllabus design. Secondly, four corpora, two main and two sub-corpora produced from the physics and chemistry textbooks on the bibliographies of the undergraduates were analysed using Biber’s (1988) algorithms and functions for variation across speech and writing. The student intake was tested over five years and the results of those tests analysed. It was found that there was considerable variation in the students’ levels of language competence. However, there was a close correlation between the students’ competence and the number of years they had studied English in secondary school. Nevertheless there were students with extremely advanced competence and some with little or no competence in English amongst the undergraduates. Comprehension of scientific texts was generally found to correlate with more advanced competence and more years of study. The frequency and range word list showed the contexts which are appropriate for materials to be used with these students and demonstrated variation from many of the accepted views of the language of science and technology. The computer corpora analyses varied from Biber’s academic prose category. The sub-corpora demonstrated greatest variation which is believed to be as a result of specific cultural and/or literary material in the analogies used in the textbooks. The heavy load of cultural background knowledge which the reader would need in order to work with the textbooks adequately was also found in the exercises the students were supposed to use for practice on the topic presented in the chapter. This and the interpretation of visuals in the textbooks were considered to be two principle factors that needed to be emphasised in a syllabus for first year undergraduates. However, given the time constraints on language teaching for science and technology students, a methodology which would lead to greater student autonomy is suggested using computer corpus-based studies - data- viii driven learning and computer-supported distance communications and learning.Esta tese apresenta duas análises: primeiro uma análise de corpora computadorizados, criados a partir de livros dos estudantes de licenciaturas, para isolar a linguagem Inglesa (Americana) das ciências e tecnologias que apresentam; segundo uma análise dos conhecimentos da língua Inglesa que estes alunos apresentam ao iniciar os seus estudos universitários em ciências e tecnologias. Estas duas análises são postas em contraste para se aplicar os resultados obtidos ao desenho de um programa de língua Inglesa para os alunos do primeiro ano. Foi criada uma lista com a abrangência e a frequência das palavras de um corpus de larga base, para ser contrastada com os principais corpora compilados dos livros de física e química constantes das bibliografias dos estudantes, como uma fonte para o desenho de programas. Seguidamente, quatro corpora, dois principais e dois subordinados, produzidos a partir dos livros de física e química referidos nas bibliografias dos estudantes, foram analisados usando os algoritmos e funções de Biber (1988) para variações entre linguagem falada e escrita. Durante cinco anos, à entrada para a Universidade, os estudantes foram submetidos a testes e os resultados analisados. Constatou-se que havia variações consideráveis no nível de conhecimentos da língua por parte dos estudantes. Contudo, havia uma correlação apertada entre as competências dos estudantes e o número de anos que tinham estudado Inglês nas escolas secundárias. Todavia, havia estudantes com competências extremamente avançadas e outros com competências reduzidas, ou quase nulas, em Inglês. A compreensão de textos científicos estava geralmente correlacionada com os níveis mais avançados de competências e maior número de anos de estudo. A lista com a abrangência e a frequência das palavras mostrou os contextos apropriados dos materiais a utilizar com estes estudantes e demonstrou que havia diferenças em relação a muitos dos pontos de vista aceites em relação à linguagem das ciências e tecnologias. A análise dos corpora computadorizados varia das categorias da linguagem da prosa académica de Biber. Os corpora subordinados mostram uma maior variação, que se julga ser devida a materiais específicos, culturais e/ou literário, usados nas analogias dos livros de estudo. O grande peso dos conhecimentos de fundo de que os estudantes necessitam para trabalhar adequadamente com os livros de estudo foi, também, encontrado nos exercícios que necessitam de fazer para praticarem o que está referido nos tópicos dos capítulos. Isto, juntamente com a interpretação das imagens dos livros, foram considerados os dois principais factores a precisarem de ser relevados no programa para o primeiro ano dos estudantes. Contudo, atendendo às restrições de tempo x para o ensino de línguas a estudante de ciências e tecnologias, a metodologia que conduziria a maior autonomia dos alunos será baseada na utilização de corpora computadorizados (data-driven learning) e aprendizagem à distância assistida por computador

Students´ language in computer-assisted tutoring of mathematical proofs

Truth and proof are central to mathematics. Proving (or disproving) seemingly simple statements often turns out to be one of the hardest mathematical tasks. Yet, doing proofs is rarely taught in the classroom. Studies on cognitive difficulties in learning to do proofs have shown that pupils and students not only often do not understand or cannot apply basic formal reasoning techniques and do not know how to use formal mathematical language, but, at a far more fundamental level, they also do not understand what it means to prove a statement or even do not see the purpose of proof at all. Since insight into the importance of proof and doing proofs as such cannot be learnt other than by practice, learning support through individualised tutoring is in demand. This volume presents a part of an interdisciplinary project, set at the intersection of pedagogical science, artificial intelligence, and (computational) linguistics, which investigated issues involved in provisioning computer-based tutoring of mathematical proofs through dialogue in natural language. The ultimate goal in this context, addressing the above-mentioned need for learning support, is to build intelligent automated tutoring systems for mathematical proofs. The research presented here has been focused on the language that students use while interacting with such a system: its linguistic propeties and computational modelling. Contribution is made at three levels: first, an analysis of language phenomena found in students´ input to a (simulated) proof tutoring system is conducted and the variety of students´ verbalisations is quantitatively assessed, second, a general computational processing strategy for informal mathematical language and methods of modelling prominent language phenomena are proposed, and third, the prospects for natural language as an input modality for proof tutoring systems is evaluated based on collected corpora

Modeling second language learners' interlanguage and its variability: a computer-based dynamic assessment approach to distinguishing between errors and mistakes

Despite a long history, interlanguage variability research is a debatable topic as most paradigms do not distinguish between competence and performance. While interlanguage performance has been proven to be variable, determining whether interlanguage competence is exposed to random and/or systematic variations is complex, given the fact that distinction between competence-dependent errors and performance-related mistakes should be established to best represent the interlanguage competence. This thesis suggests a dynamic assessment model grounded in sociocultural theory to distinguish between errors and mistakes in texts written by learners of French, to then investigate the extent to which interlanguage competence varies across time, text types, and students. The key outcomes include: 1. An expanded model based on dynamic assessment principles to distinguish between errors and mistakes, which also provides the structure to create and observe learners’ zone of proximal development; 2. A method to increase the accuracy of the part-of-speech tagging procedure whose reliability correlates with the number of incorrect words contained in learners’ texts; 3. A sociocultural insight into interlanguage variability research. Results demonstrate that interlanguage competence is as variable as performance. The main finding shows that knowledge over time is subject to not only systematic, but also unsystematic variations

Supporting Collocation Learning

Collocations are of great importance for second language learners. Knowledge of them plays a key role in producing language accurately and fluently. But such knowledge is difficult to acquire, simply because there is so much of it. Collocation resources for learners are limited. Printed dictionaries are restricted in size, and only provide rudimentary search and retrieval options. Free online resources are rare, and learners find the language data they offer hard to interpret. Online collocation exercises are inadequate and scattered, making it difficult to acquire collocations in a systematic way. This thesis makes two claims: (1) corpus data can be presented in different ways to facilitate effective collocation learning, and (2) a computer system can be constructed to help learners systematically strengthen and enhance their collocation knowledge. To investigate the first claim, an enormous Web-derived corpus was processed, filtered, and organized into three searchable digital library collections that support different aspects of collocation learning. Each of these constitutes a vast concordance whose entries are presented in ways that help students use collocations more effectively in their writing. To provide extended context, concordance data is linked to illustrative sample sentences, both on the live Web and in the British National Corpus. Two evaluations were conducted, both of which suggest that these collections can and do help improve student writing. For the second claim, a system was built that automatically identifies collocations in texts that teachers or students provide, using natural language processing techniques. Students study, collect and store collocations of interest while reading. Teachers construct collocation exercises to consolidate what students have learned and amplify their knowledge. The system was evaluated with teachers and students in classroom settings, and positive outcomes were demonstrated. We believe that the deployment of computer-based collocation learning systems is an exciting development that will transform language learning

An intelligent computer- based tutoring approach for the management of negative transfer

This research addresses how a prototype of a language tutoring system, the Chinese Tutor, tackles the practical problem of negative transfer (i.e. mother tongue influence) in the learning of Chinese grammar by English-speaking students. The design of the Chinese Tutor has been based on the results of empirical studies carried out as part of this research. The results of the data analysis show that negative transfer can be used to account for almost 80% of the errors observed in the linguistic output of students in their study of Chinese. If the students can be helped to overcome these errors, the standard of their Chinese will be greatly improved. In this research, an approach of Intelligent Language Tutoring Systems (ILTSs) has been adopted for handling negative transfer. This is because there are several advantages of ILTSs, including interactive learning, highly individualised instruction and student-centred instruction [Wyatt 1984 .The Chinese Tutor contains five main components: the Expert Model, which contains all the linguistic knowledge for tutoring and serves as a standard for evaluating the student's performance; the Student Model, which collects information on the student's performance; the Diagnoser, which detects different types of error made by the student; the Tutor Model, which plans student learning, makes didactic decisions and chooses an appropriate tutorial strategy based on the student’s performance; and the Interface Module, which communicates between the student and the system. A general and robust solution to the treatment of negative transfer, i.e. the technique of Mixed Grammar has been devised. The rules in this grammar can be applied to detect arbitrary transfer errors by using a general set of rules. A number of students in the Department of East Asian Studies at the University of Durham have used the Chinese Tutor with positive results