Conceptualizing Student Models for ICALL

Amaral, Luiz; Meurers, Detmar

doi:10.1007/978-3-540-73078-1_41

Cited by 21 publications

(12 citation statements)

References 5 publications

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“…Examples of such language learning applications, so far, are rare (Heift & Schulze, 2007;Nerbonne, 2003; but cf. for example Dokter, Nerbonne, Schurcks-Grozeva, & Smit, 1998;Amaral & Meurers, 2007). However, events such as the two recent Interfaces of ICALL conferences (see http://www.ling.ohio-state.edu/icall/iicall/ and http://purl.org/i-call/) and the ICALL workshop during the CALICO 2008 conference (http://purl.org/net/calicoworkshop) show that this is an area of active research and development.…”

Section: Introductionmentioning

confidence: 98%

Developing ICALL tools using GATE

Wood

2008

Computer Assisted Language Learning

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This article discusses the use of the General Architecture for Text Engineering (GATE) as a tool for the development of ICALL and NLP applications. It outlines a paradigm shift in software development, which is mainly influenced by projects such as the Free Software Foundation. It looks at standards that have been proposed to facilitate the compatibility and reusability of resources in the area of NLP, e-learning and ICALL. A pragmatic solution to the problem of missing standards for the development of ICALL related software will be presented and a concrete project based on this model of development is briefly discussed at the end of the paper.

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Section: Introductionmentioning

confidence: 98%

Developing ICALL tools using GATE

Wood

2008

Computer Assisted Language Learning

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“…TAGARELA is an individualized instruction program, presented in [1], which analyzes student input for different activities and provides individual feedback.…”

Section: B Computer Assisted Language Learning (Call)mentioning

confidence: 99%

Multiple parameter cluster analysis in a multiple language learning system

2013

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In this paper, we present our multiple parameter cluster analysis in our multiple language learning system. Towards this direction, we have used algorithmic approaches residing in the field of machine learning. Multiple parameter cluster analysis is conducted by the kmeans clustering algorithm which takes as input seven important users' characteristics in order to initialize the process. The clustering is conducted by k-means clustering algorithm, which takes as input multiple user characteristics. The incorporation of k-means clustering is used to address several barriers posed by the heterogeneous learning audience of educational systems. After determining in which cluster each new student belongs, the system can reason about this specific student, adapting its behavior to the student's needs, performance and preferences.

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“…In (Dickinson et al 2008), the authors designed a paper-based system that provides feedback on particle usage for first-year Korean learners, who learn a second language. TAGARELA is an individualized instruction program, implemented by (Amaral & Meurers 2007), which analyzes student input for different activities, provides individual feedback, motivates a broader perspective of student models for Intelligent Computer-Assisted Language Learning and incorporates insights from current research on second language acquisition and language testing. In (Tsiriga & Virvou 2004), the authors presented a framework for the initialization of student models in web-based educational applications.…”

Section: Related Workmentioning

confidence: 99%

Comulang: towards a collaborative e-learning system that supports student group modeling

2013

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This paper describes an e-learning system that is expected to further enhance the educational process in computer-based tutoring systems by incorporating collaboration between students and work in groups. The resulting system is called “Comulang” while as a test bed for its effectiveness a multiple language learning system is used. Collaboration is supported by a user modeling module that is responsible for the initial creation of student clusters, where, as a next step, working groups of students are created. A machine learning clustering algorithm works towards group formatting, so that co-operations between students from different clusters are attained. One of the resulting system’s basic aims is to provide efficient student groups whose limitations and capabilities are well balanced.

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Conceptualizing Student Models for ICALL

Cited by 21 publications

References 5 publications

Developing ICALL tools using GATE

Developing ICALL tools using GATE

Multiple parameter cluster analysis in a multiple language learning system

Comulang: towards a collaborative e-learning system that supports student group modeling

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