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Cross-Domain Polarity Models to Evaluate User eXperience in E-learning

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Abstract

Virtual learning environments are growing in importance as fast as e-learning is becoming highly demanded by universities and students all over the world. This paper investigates how to automatically evaluate User eXperience in this domain using sentiment analysis techniques. For this purpose, a corpus with the opinions given by a total of 583 users (107 English speakers and 476 Spanish speakers) about three learning management systems in different courses has been built. All the collected opinions were manually labeled with polarity information (positive, negative or neutral) by three human annotators, both at the whole opinion and sentence levels. We have applied our state-of-the-art sentiment analysis models, trained with a corpus of a different semantic domain (a Twitter corpus), to study the use of cross-domain models for this task. Cross-domain models based on deep neural networks (convolutional neural networks, transformer encoders and attentional BLSTM models) have been tested. In order to contrast our results, three commercial systems for the same task (MeaningCloud, Microsoft Text Analytics and Google Cloud) were also tested. The obtained results are very promising and they give an insight to keep going the research of applying sentiment analysis tools on User eXperience evaluation. This is a pioneering idea to provide a better and accurate understanding on human needs in the interaction with virtual learning environments and a step towards the development of automatic tools that capture the feed-back of user perception for designing virtual learning environments centered in user’s emotions, beliefs, preferences, perceptions, responses, behaviors and accomplishments that occur before, during and after the interaction.

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Notes

  1. https://julielab.de/econlp/2019/.

  2. https://www.aclweb.org/portal/content/first-international-workshop-e-commerce-and-nlp.

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  4. https://medicinagenomica.com/eugmygo/.

  5. https://www.upvx.es/.

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Acknowledgements

Special thanks to the following biomedical organizations: Fundación IVI and Medigene Press S.L.; both have provided data from their Master and Posgraduate Courses through the academic stay research of Rosario Sanchis-Font, during 2017 and 2018. Many thanks to Carlos Turró-Ribalta and Ignacio Despujol-Zabala for supporting this research with data from UPV MOOCs.

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  1. VRAIN: Valencian Research Institute for Artificial Intelligence, Universitat Politècnica de València, Valencia, Spain

    Rosario Sanchis-Font, Maria Jose Castro-Bleda, José-Ángel González, Ferran Pla & Lluís-F. Hurtado

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  1. Rosario Sanchis-Font
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Correspondence to Maria Jose Castro-Bleda.

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Partially supported by the Spanish MINECO and FEDER founds under Project TIN2017-85854-C4-2-R. Work of J.A. González is financed under Grant PAID-01-17.

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Sanchis-Font, R., Castro-Bleda, M.J., González, JÁ. et al. Cross-Domain Polarity Models to Evaluate User eXperience in E-learning. Neural Process Lett 53, 3199–3215 (2021). https://doi.org/10.1007/s11063-020-10260-5

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