An Architecture for a Personalized Mobile Environment to Facilitate Contextual Lifelong Learning

Dimakopoulos, Dionisios; Magoulas, George D.

doi:10.4018/978-1-60566-062-2.ch012

Cited by 10 publications

(3 citation statements)

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“…Mobile computing technology provides learners with unprecedented opportunities for communicating, interacting, sharing, meaning-making, and content and context generation (Cook & Pachler, 2012). Mobile learning, or m-learning, will play a vital role in the rapidly growing electronic education market (Dimakopoulos & Magoulas, 2009;Wang, Wu, & Wang, 2009). RnRMarke-tResearch.com (April 2015) predicted that the worldwide market for m-learning applications (apps) will grow from US$7.98 billion in 2015 to US$37.60 billion by 2020 and mushroom to US$325 billion by 2025, with a compound annual growth rate (CAGR) of over 7%.…”

Section: Introductionmentioning

confidence: 99%

Developing and validating a model for assessing paid mobile learning app success

Wang

Lin

et al. 2018

Interactive Learning Environments

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With the proliferation of paid mobile learning applications (m-learning apps), understanding how to assess their success has become an important issue for academics and practitioners. Based on the information systems (IS) success models and the value-based adoption model, this study developed and validated a multidimensional model for assessing paid m-learning app success. The proposed model describes the interrelationships among seven paid m-learning app success variables: system quality, information quality, perceived enjoyment, perceived fee, user satisfaction, intention to reuse, and learning effectiveness. Data collected from 160 paid m-learning app users were tested against the research model using structural equation modeling (SEM). The empirical findings provide evidence that learning effectiveness is affected by user satisfaction and intention to reuse, which, in turn, are determined by system quality, information quality, perceived enjoyment, and perceived fee. The findings of this study provide several important theoretical and practical implications for the development, implementation, and promotion of paid m-learning apps.

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Section: Introductionmentioning

confidence: 99%

Developing and validating a model for assessing paid mobile learning app success

Wang

Lin

et al. 2018

Interactive Learning Environments

View full text Add to dashboard Cite

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“…It has been argued that personal mobile technologies have a role in sustaining and promoting lifelong learning more generally (e.g. Dimakopoulos and Magoulas, 2009). However, in order to understand how best to support mobile language learners, we need to understand learner practices, across both formal and informal settings.…”

Section: Introductionmentioning

confidence: 99%

Why and how do distance learners use mobile devices for language learning?

et al. 2016

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Most of the literature on mobile language learning is located in classroom contexts, and often concerns the use of resources developed by teachers or researchers. However, we also need to understand learner initiated practices, in informal as well as formal settings, where mobile language learners are increasingly using digital resources. In this paper, we focus on the emerging practices of students learning languages at a distance. Adult students of five languages at different levels were surveyed about using their mobile devices to support their learning as well as their motivations in doing so. We also draw on eight follow-up interviews. We discuss the learners’ preferred activities, the resources and apps that they use, and also their motivations for their mobile language practices. These distance learners are very aware of how their devices provide them with high exposure to their target language and the importance of such exposure. They also use their mobile devices to introduce variety into their learning and to make it enjoyable. This knowledge of self-directed learners’ practices is extremely valuable in informing educators about how mobile devices feature in language learning and can be harnessed in contributing to it.

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“…Another study found that the size of a learner's vocabulary is strongly associated with their reading and writing abilities (Staehr, 2008). The use of technologies in language learning support lifelong and contextual vocabulary learning (Dimakopoulos & Magoulas, 2009) and is becoming more popular among language learners. A review on language learning applications showed that vocabulary instruction was the main focus of 84% of the reviewed language learning applications.…”

Section: Informal Vocabulary Learning Using Technologiesmentioning

confidence: 99%

Learning isolated polysemous words: identifying the intended meaning of language learners in informal ubiquitous language learning environments

Abou-Khalil

Helou

Flanagan

et al. 2019

Smart Learn. Environ.

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A growing number of language learners use ubiquitous language learning applications to learn anytime and anywhere. Learners translate and learn isolated words inspired by their activities and surroundings. However, isolated words may have several meanings that change depending on the context. Since learners don't have the opportunity to indicate the meaning they are looking for in an online learning environment, they risk learning translations that do not correspond to their intended meaning. Identifying the intended meaning of the learner is needed to provide them with an appropriate translation. However, isolated words are difficult to disambiguate due to a lack of text around them. To this end, informal ubiquitous learning environments can offer another type of context, one that is formed by the users' past learning logs. In this work, we propose using the learners' past vocabulary to disambiguate their intended meaning when they look up isolated words. Accordingly, we propose and evaluate three methods. The first method considers that the intended meaning of the learner is the one that is the most semantically similar to the learner's past vocabulary. The second method builds on the first method but gives more weight to the vocabulary that the learner logged shortly before the target word. The third method addresses situations where the semantic similarities between the different meanings of the word and the past vocabulary have similar values. In those cases, the method considers that the intended meaning of the learner is the most common meaning in the target language. The three methods were evaluated using 148 logs of SCROLL, a ubiquitous informal language learning environment. The success rates of the three methods were 72.180%, 75.630%, and 83.050% respectively. This work shows that the past activity of language learners in informal ubiquitous language learning environments could be used to identify their intended meaning when learning a new word.

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An Architecture for a Personalized Mobile Environment to Facilitate Contextual Lifelong Learning

Cited by 10 publications

References 0 publications

Developing and validating a model for assessing paid mobile learning app success

Developing and validating a model for assessing paid mobile learning app success

Why and how do distance learners use mobile devices for language learning?

Learning isolated polysemous words: identifying the intended meaning of language learners in informal ubiquitous language learning environments

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