Instructors as MALL engineers: adapting, modifying, and creating mobile materials for listening practice

Barcomb, Mike; Grimshaw, Jennica; Cardoso, Walcir

doi:10.14705/rpnet.2018.26.805

Cited by 2 publications

(1 citation statement)

References 2 publications

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“…Through this approach, students can freely select various learning resources to efficiently deploy according to their learning style and most convenient time. For those reasons, developing language learning apps should meet the following criteria (Barcomb et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Most-Wanted English Learning Mobile Apps

Nizzam¹

2020

AJPGMI

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Covid-19 pandemic has contributed a great encouragement for autonomous learners in using mobile learning to learn foreign languages, including English. Geared under qualitative design, the present study aims to find out the availability of mobile apps that most people use in learning English. It concerns to review and make comparisons among the top three most popular apps gathered on the Google Playstore list to investigate what they teach, how they teach, and what technology they use in order to figure out on which aspects they are distinct to each other and on which they are similar, where are they lacking, and what they do well. The results point out that the apps were developed in such easy and fun ways of learning with various materials, ie daily life situations, and stories. Though enriched with vast exercises in receptive skills, they are commonly offered limited practice in productive skills.

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Section: Introductionmentioning

confidence: 99%

Most-Wanted English Learning Mobile Apps

Nizzam¹

2020

AJPGMI

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Joining the blocks together – an NLP pipeline for CALL development

Ward¹

2019

CALL and Complexity – Short Papers From EUROCALL 2019

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Intelligent Computer-Assisted Language Learning (ICALL) involves using tools and techniques from computational linguistics and Natural Language Processing (NLP) in the language learning process. It is an inherently complex endeavour and is multi-, inter-, and trans-disciplinary in nature. Often these tools and techniques are designed for tasks and purposes other than language learning, and this makes their adaptation and use in the CALL domain difficult. It can be even more challenging for Less-Resourced Languages (LRLs) for CALL researchers to adapt or incorporate NLP into CALL artefacts. This paper reports on how two existing NLP resources for Irish, a morphological analyser and a parser, were used to develop an app for Irish. The app, Irish Word Bricks (IWB), was adapted from an existing CALL app – Word Bricks (Mozgovoy & Efimov, 2013). Without this ‘joining the blocks together’ approach, the development of the IWB app would certainly have taken longer, may not have been as efficient or effective, and may not even have been accomplished at all.

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Instructors as MALL engineers: adapting, modifying, and creating mobile materials for listening practice

Cited by 2 publications

References 2 publications

Most-Wanted English Learning Mobile Apps

Most-Wanted English Learning Mobile Apps

Joining the blocks together – an NLP pipeline for CALL development

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