el 16(9): e4

Research Article

E-learning project assessment: A new approach through the analysis of learners’ posts on social media

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  • @ARTICLE{10.4108/eai.10-3-2016.151121,
        author={A. Caione and A.L. Guido and R. Paiano and A. Pandurino and S. Pasanisi},
        title={E-learning project assessment: A new approach through the analysis of learners’ posts on social media},
        journal={EAI Endorsed Transactions on e-Learning},
        volume={3},
        number={9},
        publisher={EAI},
        journal_a={EL},
        year={2016},
        month={3},
        keywords={Assessment, Social and organizational perspectives, Best Practices},
        doi={10.4108/eai.10-3-2016.151121}
    }
    
  • A. Caione
    A.L. Guido
    R. Paiano
    A. Pandurino
    S. Pasanisi
    Year: 2016
    E-learning project assessment: A new approach through the analysis of learners’ posts on social media
    EL
    EAI
    DOI: 10.4108/eai.10-3-2016.151121
A. Caione1, A.L. Guido1,*, R. Paiano1, A. Pandurino1, S. Pasanisi1
  • 1: Department of Engineering for Innovation, Via per Monteroni, Lecce Italy
*Contact email: annalisa.guido@unisalento.it

Abstract

E-learning assessment is a key aspect in the overall e-learning process. There are several parameters to consider during the assessment. In recent years, several sets of factors, called Critical Success Factors, have been defined to provide a structural approach to assessment. They focus on many aspects but, in our view, they do not properly consider student satisfaction with courses. In e-learning applications, student opinion must be examined where it is expressed: on e learning course social pages and/or social pages outside the platform but specific to the e-learning course. The problem is that these resources are unstructured and thus it is important to structure these resources before using them for assessment. In this paper, we discuss a proposal that can capture student opinion from social pages, combining several techniques, such as Natural Language Processing, Information Extraction; ontologies that help us to understand what and how students discuss about e-learning courses.