Feature Analysis for Affect Recognition Supporting Task Sequencing in Adaptive Intelligent Tutoring Systems

Originally, the task sequencing in adaptive intelligent tutoring systems needs information gained from expert and domain knowledge as well as information about former performances. In a former work a new efficient task sequencer based on a performance prediction system was presented, which only needs former performance information but not the expensive expert and domain knowledge. This task sequencer uses the output of the performance prediction to sequence the tasks according to the theory of Vygotsky's Zone of Proximal Development. In this paper we aim to support this sequencer by a further automatically to gain information source, namely speech input from the students interacting with the tutoring system. The proposed approach extracts features from students speech data and applies to that features an automatic affect recognition method. The output of the affect recognition method indicates, if the last task was too easy, too hard or appropriate for the student. Hence, as according to Vygotsky's theory the next task should not be too easy or too hard for the student to neither bore nor frustrate him, obviously the output of our proposed affect recognition is suitable to be used as an input for supporting a sequencer based on the theory of Vygotsky's Zone of Proximal Development. Hence, in this paper we (1) propose a new approach for supporting task sequencing by affect recognition, (2) present an analysis of appropriate features for affect recognition extracted from students speech input and (3) show the suitability of the proposed features for affect recognition for supporting task sequencing in adaptive intelligent tutoring systems.

[1]  Huan Liu,et al.  Exploiting social relations for sentiment analysis in microblogging , 2013, WSDM.

[2]  Björn W. Schuller,et al.  Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge , 2011, Speech Commun..

[3]  Saturnino Luz,et al.  Automatic identification of experts and performance prediction in the multimodal math data corpus through analysis of speech interaction , 2013, ICMI '13.

[4]  Carolyn Penstein Rosé,et al.  The Beginning of a Beautiful Friendship? Intelligent Tutoring Systems and MOOCs , 2015, AIED.

[5]  Inge Molenaar,et al.  The effect of dynamic computerized scaffolding on collaborative discourse , 2011 .

[6]  Andrzej Cichocki,et al.  Nonnegative Matrix and Tensor Factorization T , 2007 .

[7]  Lars Schmidt-Thieme,et al.  Adaptive Content Sequencing without Domain Information , 2014, CSEDU.

[8]  Neil T. Heffernan,et al.  Extending Knowledge Tracing to Allow Partial Credit: Using Continuous versus Binary Nodes , 2013, AIED.

[9]  Beverly Park Woolf,et al.  Affect-aware tutors: recognising and responding to student affect , 2009, Int. J. Learn. Technol..

[10]  Yan Liu,et al.  A novel two-step SVM classifier for voiced/unvoiced/silence classification of speech , 2004, 2004 International Symposium on Chinese Spoken Language Processing.

[11]  Marcelo Worsley,et al.  What's an Expert? Using Learning Analytics to Identify Emergent Markers of Expertise through Automated Speech, Sentiment and Sketch Analysis , 2011, EDM.

[12]  Nadir Weibel,et al.  ICMI 2013 grand challenge workshop on multimodal learning analytics , 2013, ICMI '13.

[13]  Zulaiha Ali Othman,et al.  Opinion Mining and Sentiment Analysis: A Survey , 2012, BIOINFORMATICS 2012.

[14]  Manolis Mavrikis,et al.  Talk, Tutor, Explore, Learn: Intelligent Tutoring and Exploration for Robust Learning , 2015, AIED 2015.

[15]  Arthur C. Graesser,et al.  Automatic detection of learner’s affect from conversational cues , 2008, User Modeling and User-Adapted Interaction.

[16]  Jeff Heflin,et al.  The Semantic Web – ISWC 2012 , 2012, Lecture Notes in Computer Science.

[17]  Lars Schmidt-Thieme,et al.  Active Learning for Technology Enhanced Learning , 2011, LWA.

[18]  L. Vygotsky.  Mind in Society: The Development of Higher Psychological Processes: Harvard University Press , 1978 .

[19]  Arthur C. Graesser,et al.  Toward an Affect-Sensitive AutoTutor , 2007, IEEE Intelligent Systems.

[20]  Harith Alani,et al.  Semantic Sentiment Analysis of Twitter , 2012, SEMWEB.