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

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. 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
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4.  Recognize and analyse users’ emotions
 Provide adaptation of teaching materials
Motivation
Decission
making

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
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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
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7. edX-CAS: Content Analyzer
System for edX MOOC
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 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. Tool Architecture

9. Features and analyses provided
by the tool

10. Features and analyses provided
by the tool
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No. of sentences, tokens
and characters
Syntactic
analysis
Vector
representation
Main
terms
Lexical
diversity
Subjectivity
Polarity
Graphical representation

11. Input datasets

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 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. Visualizations

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Visualizations – Video Data

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Visualizations – Textual content

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Visualizations – Test content

17. The utilization of the tool and
initial results
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18. Texts provided by the learners
(Learners Data)
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19. Learners’ opinions expressed in
the course's forums (Forum Data)
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20. Contents of the course (Textual
Data, Video Data and Test Data),
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Live analyses

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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