Browsing by Author "König, Jemma Lynette"

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  • Automating vocabulary tests and enriching online courses for language learners

    König, Jemma Lynette (The University of Waikato, 2019)
    The past decade has seen a massive growth in online academic courses, most of which are offered in the English language. However, although more people speak English as their second language than as their first, online ...
  • F-Lingo: Integrating lexical feature identification into MOOC platforms for learning professional and academic English

    Fitzgerald, Alannah; König, Jemma Lynette; Witten, Ian H. (IEEE, 2019)
    F-Lingo is a chrome extension that works on top of the FutureLearn MOOC platform to support content-based language learning of domain-specific terminology for professional and academic purposes.
  • Learning collocations with FLAX apps

    Yu, Alex; Wu, Shaoqun; Witten, Ian H.; König, Jemma Lynette (The University of Technology, Sydney, 2016)
    The rise of Mobile Assisted Language Learning has brought a new dimension and dynamic into language classes. Game-like apps have become a particularly effective way to promote self-learning to young learners outside ...
  • A mobile reader for language learners

    König, Jemma Lynette; Witten, Ian H.; Wu, Shaoqun (The University of Technology, Sydney, 2016)
    This paper describes a new approach to mobile language learning; a mobile reader that aids learners in extending the breadth of their existing vocabulary knowledge. The FLAX Reader supports L2 (second language) learners ...
  • Using character-grams to automatically generate pseudowords and how to evaluate them

    König, Jemma Lynette; Calude, Andreea S.; Coxhead, Averil (Oxford University Press, 2019)
    This paper provides a practical solution to the problem of generating (good) pseudowords, which are commonly used in vocabulary testing and experimental research in applied linguistics, and introduces an empirically founded ...

Jemma Lynette König has 8 co-authors in Research Commons.

Showing up to 5 theses - most recently added to Research Commons first.

  • Utilizing IoT in hazardous work environments

    Exton, Dylan Tylor (The University of Waikato, 2021)
    In New Zealand, the forestry industry is one of the most dangerous industries to work in. Workers in the forestry industry are three times more likely to be killed while at work than any other industry in New Zealand ...