Automatic Student Affective State Detection from Plain Text

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dc.contributor.authorDan, O. Anne
dc.contributor.authorChepkemoi, Agnes
dc.contributor.authorMaina, Elizaphan
dc.date.accessioned2021-09-27T06:55:47Z
dc.date.available2021-09-27T06:55:47Z
dc.date.issued2021
dc.descriptionA research article published in International Journal of Formal Sciences: Current and Future Research Trendsen_US
dc.description.abstractWe explore the concept of automatic detection of affective state of a learner in an e learning environment. We propose a model to detect the emotion from learners’ text. We employ machine learning algorithms with ISEAR data and twitter data from Kaggle data repository. We follow the conventional steps of natural language processing; text preparation, feature extraction and emotion detection and classification. For text preparation we use processes of tokenization and segmentation, noise removal and segmentation. We extract features using count vectors and term frequency -inverse document frequency. For classification compare varied machine leaning algorithms. Results show that Linear SVM using Count Vectors accuracy gave an accuracy of 79% which is encouraging. We deduce that we can extract the affective states of the learners automatically from text during their interaction with e-learning environment. This will help in understanding the learners needs and help in enhancing adaptabilityen_US
dc.identifier.citationAnne , D. O., Chepkemoi, A., & Maina, E. (2021). Automatic Student Affective State Detection from Plain Text. International Journal of Formal Sciences: Current and Future Research Trends, 10(01), 41-60. Retrieved from https://ijfscfrtjournal.isrra.org/index.php/Formal_Sciences_Journal/article/view/554en_US
dc.identifier.urihttps://ijfscfrtjournal.isrra.org/index.php/Formal_Sciences_Journal/article/view/554/17
dc.identifier.urihttp://ir-library.ku.ac.ke/handle/123456789/22663
dc.language.isoenen_US
dc.publisherInternational Scientific Research and Researchers Associationen_US
dc.subjectAffective stateen_US
dc.subjectE-learningen_US
dc.subjectISEAR dataen_US
dc.subjectMachine Learningen_US
dc.titleAutomatic Student Affective State Detection from Plain Texten_US
dc.typeArticleen_US
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