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11_04_2019 EDUCON eMadrid special session on "Moods in MOOCs: analysing emotions in the content of online courses with edX-CAS"

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Authors: Ruth Cobos, Francisco Jurado y Álvaro Villén / Universidad Autónoma de Madrid (UAM)

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11_04_2019 EDUCON eMadrid special session on "Moods in MOOCs: analysing emotions in the content of online courses with edX-CAS"

  1. 1. Moods in MOOCs: Analysing Emotions in the Content of Online Courses with edX-CAS Ruth Cobos Francisco Jurado Álvaro Villén Universidad Autónoma de Madrid Ruth.Cobos@uam.es | Francisco.Jurado@uam.es | Alvaro.Villen@estudiante.uam.es Red eMadrid, www.emadridnet.org
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  3. 3. Outline  Motivation  Introduction to the proposed tool: edX-CAS  Tool architecture  Features and analyses provided by the tool  Input datasets  Visualizations provided by the tool  The utilization of the tool and initial results  Conclusions and future work 3
  4. 4.  Recognize and analyse users’ emotions  Provide adaptation of teaching materials Motivation Decission making
  5. 5. Kind of analysys  Subjectivity analysis allows classifying a given text into subjective or objective  Sentiment Analysis or Opinion Mining is the computational treatment of opinion, sentiment and subjectivity in text  Affective Computing attempts to identify the emotional charge (happiness, sadness, fear, anger-passion, etc.) Madrid, 2013-06-13 5
  6. 6. Techniques and resources  Techniques:  The most widely used classification techniques:  Naive Bayes, SVM, Latent Dirichlet Allocation, Random Forest, etc. like other ML areas.  Resources:  Forum of MOOCs and social networks are the most used resources to perform the Sentiment Analysis Madrid, 2013-06-13 6
  7. 7. edX-CAS: Content Analyzer System for edX MOOC 7  This tool was created for textual content analysis in edX MOOCs (in Spanish) at UAM.  It provides information related with the field of Natural Language Processing (NLP), focusing the analysis on emotions recognition  The analyses are applied to all the text contents of the online courses  In the case of videos: their transcriptions are used in the analyses
  8. 8. Tool Architecture
  9. 9. Features and analyses provided by the tool
  10. 10. Features and analyses provided by the tool 10 No. of sentences, tokens and characters Syntactic analysis Vector representation Main terms Lexical diversity Subjectivity Polarity Graphical representation
  11. 11. Input datasets
  12. 12. 12  Learners Data: provided by learners in their sign up.  Textual Data: text-format files.  Video Data: transcriptions.  Test Data: each assignment’s questions and answers.  Forum Data: posts in forums.  Certification Data: whether a student passed or not. Input datasets
  13. 13. Visualizations
  14. 14. 14 Visualizations – Video Data
  15. 15. 15 Visualizations – Textual content
  16. 16. 16 Visualizations – Test content
  17. 17. The utilization of the tool and initial results 17
  18. 18. Texts provided by the learners (Learners Data) 18
  19. 19. Learners’ opinions expressed in the course's forums (Forum Data) 19
  20. 20. Contents of the course (Textual Data, Video Data and Test Data), 20
  21. 21. 21 Live analyses
  22. 22. Conclusions  The materials of online courses are charged with emotions.  edX-CAS: Content Analyser System for edX MOOCs.  Polarity analysis: to detect if the opinion revealed in any text is positive, negative, neutral.  Subjectivity analysis: to detect if any text expresses subjectivity of objectivity.  Syntactic analysis  Tested with seven MOOCs at UAM.  Positive polarity along most of the courses.  Negative polarity detected at the beginning of certain courses.  Intended use in collaboration with courses instructors.
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