11_04_2019 EDUCON eMadrid special session on "Moods in MOOCs: analysing emotions in the content of online courses with edX-CAS"
Moods in MOOCs:
Analysing Emotions in the
Content of Online Courses
Universidad Autónoma de Madrid
Ruth.Cobos@uam.es | Francisco.Jurado@uam.es | Alvaro.Villen@estudiante.uam.es
Red eMadrid, www.emadridnet.org
Introduction to the proposed tool: edX-CAS
Features and analyses provided by the tool
Visualizations provided by the tool
The utilization of the tool and initial results
Conclusions and future work
Recognize and analyse users’ emotions
Provide adaptation of teaching materials
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.)
Techniques and resources
The most widely used classification techniques:
Naive Bayes, SVM, Latent Dirichlet Allocation, Random Forest,
etc. like other ML areas.
Forum of MOOCs and social networks are the most used
resources to perform the Sentiment Analysis
edX-CAS: Content Analyzer
System for edX MOOC
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
Features and analyses provided
by the tool
No. of sentences, tokens
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.
The materials of online courses are charged with
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.
Tested with seven MOOCs at UAM.
Positive polarity along most of the courses.
Negative polarity detected at the beginning of certain
Intended use in collaboration with courses instructors.