scholarly journals Authoring a Web-enhanced interface for a new language-learning environment

2011 ◽  
Vol 8 (1) ◽  
Author(s):  
Dominique Hémard ◽  
Steve Cushion

Computer-based applications in second language teaching have now been used for a protracted period of time, evolving from a deductive approach relying on grammatical progression to inductive methods and, more recently, exploratory interaction better suited to the constructivist mode. However, despite the initial adoption of a traditional learning environment, the first, albeit influential, generation of software design was poorly recognized, or worse, even met with scepticism by academics inasmuch as it did not seem to represent or, indeed, symbolize good teaching practices (Laurillard, 1991). As a result, original CALL programmes, such as gap-filling or substituting exercises, were often only considered appropriate as supplementary teaching material and, as such, referred to or introduced within courses as convenient adjuncts providing students with greater practical experience. Equally, students as users were never consulted on the use of CALL or, indeed, implicated beyond the designed interaction. Indeed, it was generally assumed that, since computer-based learning was a new concept, it would be, by itself, attractive and generate increased enthusiasm within the language-learning context. This situation was made even worse by a developmental process, dominated by self-taught, in-house authoring, which was too often amateurish, task-based in approach and empirical. Unfortunately, despite recent development in multimedia and hypermedia, this CALL legacy has been affecting CALL in design, practice and projected use.DOI: 10.1080/0968776000080105

1993 ◽  
Vol 9 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Jing-Fong J. Hsu ◽  
Carol A. Chapelle ◽  
Ann D. Thompson

Computer-based learning environments have been defined as settings where students use software to facilitate active, exploratory learning. We distinguish between illocutionary and non-illocutionary learning environments: the computer interprets the intent of students in the former but not in the latter. We apply these terms to language learning environments, and report descriptive research on ESL students' use of a non-illocutionary learning environment. Results indicated students “explored” the learning environment in a routine way, but failed to explore creatively the program's morphosyntactic possibilities; routine exploration was positively related to attitudes for part of the group, but negatively related to attitudes for students who may have found the software environment too easy. Implications for illocutionary and non- illocutionary learning environments are discussed.


Author(s):  
Maria A. Perifanou

Mobile devices can motivate learners through moving language learning from predominantly classroom–based contexts into contexts that are free from time and space. The increasing development of new applications can offer valuable support to the language learning process and can provide a basis for a new self regulated and personal approach to learning. A key challenge for language teachers is to actively explore the potential of mobile technologies in their own learning so that they can support students in using them. The aim of this paper is first to describe the basic theoretical framework of Mobile Learning and Personal Learning Environments. Secondly, it intends to assist language teachers and learners in building their own Mobile Personal Learning Environment providing a useful classification of iPhone applications with a description and examples. The paper concludes with the proposal of ideas for practical, personal language learning scenarios, piloted in an Italian language learning context.


2019 ◽  
Vol 47 (2) ◽  
pp. 67-75 ◽  
Author(s):  
Youngjin Lee

Purpose The purpose of this paper is to investigate an efficient means of estimating the ability of students solving problems in the computer-based learning environment. Design/methodology/approach Item response theory (IRT) and TrueSkill were applied to simulated and real problem solving data to estimate the ability of students solving homework problems in the massive open online course (MOOC). Based on the estimated ability, data mining models predicting whether students can correctly solve homework and quiz problems in the MOOC were developed. The predictive power of IRT- and TrueSkill-based data mining models was compared in terms of Area Under the receiver operating characteristic Curve. Findings The correlation between students’ ability estimated from IRT and TrueSkill was strong. In addition, IRT- and TrueSkill-based data mining models showed a comparable predictive power when the data included a large number of students. While IRT failed to estimate students’ ability and could not predict their problem solving performance when the data included a small number of students, TrueSkill did not experience such problems. Originality/value Estimating students’ ability is critical to determine the most appropriate time for providing instructional scaffolding in the computer-based learning environment. The findings of this study suggest that TrueSkill can be an efficient means for estimating the ability of students solving problems in the computer-based learning environment regardless of the number of students.


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