Abstract
This paper presents a computational model for measuring diversity in terms of variety, balance and disparity. This model is informed by the Stirling’s framework for understanding diversity from social science and underpinned by semantic techniques from computer science. A case study in learning is used to illustrate the application of the model. It is driven by the desire to broaden learners’ perspectives in an increasingly diverse and inclusive society. For example, interpreting body language in a job interview may be influenced by the different background of observers. With the explosion of digital objects on social platforms, selecting the appropriate ones for learning can be challenging and time consuming. The case study uses over 2000 annotated comments from 51 YouTube videos on job interviews. Diversity indicators are produced based on the comments for each video, which in turn facilitate the ranking of the videos according to the degree of diversity in the comments for the selected domain.
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Notes
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A medoid is the most centrally located item in a cluster that has minimal average distances to all the other items in the cluster [22].
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A graph in ViewS shows the entities (classes and instances) of a domain category (super class). The colored (darker) shapes are the entities from annotating the comments on the video and the uncolored ones are the entities not present in the user comments.
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Abolkasim, E., Lau, L., Dimitrova, V. (2016). A Semantic-Driven Model for Ranking Digital Learning Objects Based on Diversity in the User Comments. In: Verbert, K., Sharples, M., Klobučar, T. (eds) Adaptive and Adaptable Learning. EC-TEL 2016. Lecture Notes in Computer Science(), vol 9891. Springer, Cham. https://doi.org/10.1007/978-3-319-45153-4_1
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