Abstract
Despite the development and the wide use of E-Learning, developing an adaptive personalised E-Learning system tailored to the needs of individual learners remains a challenge. In an early work, the authors proposed APELS that extracts freely available resources on the web using an ontology to model the leaning topics and optimise the information extraction process. APELS takes into consideration the leaner’s needs and background. In this paper, we developed an approach to evaluate the topics’ content extracted previously by APELS against a set of learning outcomes as defined by standard curricula. Our validation approach is based on finding patterns in part of speech and grammatical dependencies using the Stanford English Parser. As a case study, we use the computer science field with the IEEE/ACM Computing curriculum as the standard curriculum.
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Aeiad, E., Meziane, F. (2016). Validating Learning Outcomes of an E-Learning System Using NLP. In: Métais, E., Meziane, F., Saraee, M., Sugumaran, V., Vadera, S. (eds) Natural Language Processing and Information Systems. NLDB 2016. Lecture Notes in Computer Science(), vol 9612. Springer, Cham. https://doi.org/10.1007/978-3-319-41754-7_27
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DOI: https://doi.org/10.1007/978-3-319-41754-7_27
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