Intelligent computer-assisted language learning

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Intelligent Computer Assisted Language Learning (ICALL), or Intelligent Computer Assisted Language Instruction (ICALI), involves the application of computing technologies to the teaching and learning of second or foreign languages. [1] [2] ICALL combines Artificial intelligence with Computer Assisted Language Learning (CALL) systems to provide software that interacts intelligently with students, responding flexibly and dynamically to student's learning progress. [2] [3] [4]

Contents

Natural language processing (NLP) and Intelligent tutoring systems (ITS) are prominent computing technologies in artificial intelligence that inform and influence ICALL. [5] [6] Other computing technologies applied to ICALL include Knowledge representation (KP), Automatic Speech Recognition (ASR), Neural networks, User modelling, and Expert systems. In relation to language learning, ICALL utilizes linguistic theory and theories of second-language acquisition in its pedagogy. [5] [6]

History

ICALL developed from the field of Computer Assisted Language Learning (CALL) in the late 1970s [1] and early 1980s. [5] ICALL is a smaller field, and not yet fully formed.

Following the pattern of most language learning technologies, English is a prominent language featured in ICALL technology. [7] ICALL programs have also been developed in languages such as German, [8] Japanese, [8] Portuguese, [8] Mandarin Chinese, [9] and Arabic. [7] ICALL systems are also contributing to the learning of languages that are not as accessible to learn (due to a lesser amount of language resources), or less commonly learned languages, such as Cree. [3]

Features

Intelligent CALL is sometimes called parser-based CALL, due to the heavy reliance that ICALL has on parsing. [5] An example of the function of parsing in an ICALL software is a parser detecting errors in the syntax and morphology of sentences freely generated by student users. After using parsing to find any errors, ICALL can provide corrective feedback to students. [5] Parsing is considered a task of natural language processing.

The ability for students to receive feedback on random, uniquely produced sentences places ICALL in a more engaging teacher role. If students are struggling in certain areas, some ICALL systems will invent new sentences or questions in those areas, giving students more practice. [5] Basically, ICALL is meant to intelligently adapt to student learning needs as a student progresses; this often means (partially or wholly) fulfilling a tutor or teacher role. [8] [10] Programs that attempt to fulfill this role are categorized as tutorial ICALL. [1]

Non-tutorial ICALL systems include various language tools and dialogue systems, [1] such as a digital interlocutor. [2] Programs for automatically evaluating student-written essays have also been invented, [5] such as the E-rater. [11]

Limitations

ICALL technology still has many issues and limitations, due to the recency of artificial intelligence being integrated into CALL systems, and the complexity of this enormous task. [1] Artificially intelligent educational software should do its best to encompass the linguistic knowledge and pedagogy of a language teacher in order to resolve these issues. [10] This includes tracking student learning, giving feedback, creating new challenging material in response to student needs, understanding effective teaching strategies, and detecting linguistic errors (grammar, spelling, semantics, morphology, and so on). [5] [10]

Additionally, ICALL systems take a long time to develop, and developers must consult professionals in many disciplines. [10] Programming ICALL software is a necessarily multi-disciplinary project. [8]

Further research and development in ICALL will benefit the fields of applied linguistics, computational linguistics, artificial intelligence, educational technology, to name a few. ICALL will also expand current knowledge about second language acquisition. [5] Despite its limitations, ICALL is a worthwhile field, especially as technology progresses. [8]

See also

Related Research Articles

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Computer-assisted language learning (CALL), British, or Computer-Aided Instruction (CAI)/Computer-Aided Language Instruction (CALI), American, is briefly defined in a seminal work by Levy as "the search for and study of applications of the computer in language teaching and learning". CALL embraces a wide range of information and communications technology applications and approaches to teaching and learning foreign languages, from the "traditional" drill-and-practice programs that characterised CALL in the 1960s and 1970s to more recent manifestations of CALL, e.g. as used in a virtual learning environment and Web-based distance learning. It also extends to the use of corpora and concordancers, interactive whiteboards, computer-mediated communication (CMC), language learning in virtual worlds, and mobile-assisted language learning (MALL).

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An intelligent tutoring system (ITS) is a computer system that aims to provide immediate and customized instruction or feedback to learners, usually without requiring intervention from a human teacher. ITSs have the common goal of enabling learning in a meaningful and effective manner by using a variety of computing technologies. There are many examples of ITSs being used in both formal education and professional settings in which they have demonstrated their capabilities and limitations. There is a close relationship between intelligent tutoring, cognitive learning theories and design; and there is ongoing research to improve the effectiveness of ITS. An ITS typically aims to replicate the demonstrated benefits of one-to-one, personalized tutoring, in contexts where students would otherwise have access to one-to-many instruction from a single teacher, or no teacher at all. ITSs are often designed with the goal of providing access to high quality education to each and every student.

Project LISTEN was a 25-year research project at Carnegie Mellon University to improve children's reading skills. Project LISTEN. The project created a computer-based Reading Tutor that listens to a child reading aloud, corrects errors, helps when the child is stuck or encounters a hard word, provides hints, assesses progress, and presents more advanced text when the child is ready. The Reading Tutor has been used daily by hundreds of children in field tests at schools in the United States, Canada, Ghana, and India. Thousands of hours of usage logged at multiple levels of detail, including millions of words read aloud, have been stored in a database that has been mined to improve the Tutor's interactions with students. An extensive list of publications can be found at Carnegie Mellon University.

Natural-language programming (NLP) is an ontology-assisted way of programming in terms of natural-language sentences, e.g. English. A structured document with Content, sections and subsections for explanations of sentences forms a NLP document, which is actually a computer program. Natural language programming is not to be mixed up with natural language interfacing or voice control where a program is first written and then communicated with through natural language using an interface added on. In NLP the functionality of a program is organised only for the definition of the meaning of sentences. For instance, NLP can be used to represent all the knowledge of an autonomous robot. Having done so, its tasks can be scripted by its users so that the robot can execute them autonomously while keeping to prescribed rules of behaviour as determined by the robot's user. Such robots are called transparent robots as their reasoning is transparent to users and this develops trust in robots. Natural language use and natural-language user interfaces include Inform 7, a natural programming language for making interactive fiction, Shakespeare, an esoteric natural programming language in the style of the plays of William Shakespeare, and Wolfram Alpha, a computational knowledge engine, using natural-language input. Some methods for program synthesis are based on natural-language programming.

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<span class="mw-page-title-main">Pedagogical agent</span>

A pedagogical agent is a concept borrowed from computer science and artificial intelligence and applied to education, usually as part of an intelligent tutoring system (ITS). It is a simulated human-like interface between the learner and the content, in an educational environment. A pedagogical agent is designed to model the type of interactions between a student and another person. Mabanza and de Wet define it as "a character enacted by a computer that interacts with the user in a socially engaging manner". A pedagogical agent can be assigned different roles in the learning environment, such as tutor or co-learner, depending on the desired purpose of the agent. "A tutor agent plays the role of a teacher, while a co-learner agent plays the role of a learning companion".

Vincent Aleven is a professor of human-computer interaction and director of the undergraduate program at Carnegie Mellon University's Human–Computer Interaction Institute.

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<span class="mw-page-title-main">Bruce M. McLaren</span> American researcher, academic and author (born 1959)

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<span class="mw-page-title-main">Learning engineering</span>

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References

  1. 1 2 3 4 5 Contemporary computer-assisted language learning. Thomas, Michael, 1969-, Reinders, Hayo., Warschauer, Mark. London: Bloomsbury Academic. 2012. ISBN   978-1-4411-1300-9. OCLC   820029337.{{cite book}}: CS1 maint: others (link)
  2. 1 2 3 Gamper, Johann; Knapp, Judith (2002). "A Review of Intelligent CALL Systems". Computer Assisted Language Learning. 15 (4): 329–342. doi:10.1076/call.15.4.329.8270. ISSN   0958-8221. S2CID   11814439.
  3. 1 2 Bontogon, Megan; Arppe, Antti; Antonsen, Lene; Thunder, Dorothy; Lachler, Jordan (2018). "Intelligent Computer Assisted Language Learning (ICALL) for nêhiyawêwin : An In-Depth User-Experience Evaluation". Canadian Modern Language Review. 74 (3): 337–362. doi:10.3138/cmlr.4054. ISSN   0008-4506. S2CID   149711542.
  4. ↑ Sentance, Susan (1993). Recognising and responding to English article usage errors : an ICALL based approach. ed.ac.uk (PhD thesis). University of Edinburgh. hdl:1842/20176. OCLC   605993412. EThOS   uk.bl.ethos.661745. Lock-green.svg
  5. 1 2 3 4 5 6 7 8 9 Heift, Trude. (2007). Errors and intelligence in computer-assisted language learning : parsers and pedagogues. Schulze, Mathias, 1962-. New York: Routledge. ISBN   978-0-203-01221-5. OCLC   191541349.
  6. 1 2 Matthews, Clive (1993). "Grammar Frameworks in Intelligent CALL". CALICO Journal. 11 (1): 5–27. doi:10.1558/cj.v11i1.5-27. S2CID   60842088.
  7. 1 2 Shaalan 1, Khaled F (2005). "An Intelligent Computer Assisted Language Learning System for Arabic Learners". Computer Assisted Language Learning. 18 (1–2): 81–109. doi:10.1080/09588220500132399. ISSN   0958-8221.
  8. 1 2 3 4 5 6 Höhn, Sviatlana (2019-06-21). Artificial companion for second language conversation : chatbots support practice using conversation analysis. Cham, Switzerland. ISBN   978-3-030-15504-9. OCLC   1105896323.
  9. ↑ Chen, Nancy F.; Wee, Darren; Tong, Rong; Ma, Bin; Li, Haizhou (2016-11-01). "Large-scale characterization of non-native Mandarin Chinese spoken by speakers of European origin: Analysis on iCALL". Speech Communication. 84: 46–56. doi: 10.1016/j.specom.2016.07.005 . ISSN   0167-6393.
  10. 1 2 3 4 Bailin, Alan; Levin, Lori (1989). "Introduction: Intelligent Computer-Assisted Language Instruction". Computers and the Humanities. 23 (1): 3–11. doi:10.1007/BF00058765. ISSN   0010-4817. JSTOR   30204410. S2CID   64307424.
  11. ↑ Attali, Yigal; Burstein, Jill (2006-02-01). "Automated Essay Scoring With e-rater® V.2". The Journal of Technology, Learning and Assessment. 4 (3). ISSN   1540-2525.
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