The Value of Multimodal Data in Classification of Social and Emotional Aspects of Tutoring

There are many aspects of tutoring that are associated with social and emotional learning. These are complex processes that involve dynamic combinations of skills, abilities and knowledge. Here, we present the results of our investigation on the particular personal, emotional, and experience traits of tutors who are likely to be successful at social and emotional aspects of tutoring. In particular, we present our approach to measure the social and emotional aspects of tutoring through classification models of 47 candidates’ multimodal data from audio and psychometric measures. Moreover, we compare the accuracy of models with unimodal and multimodal data, and show that multimodal data leads to more accurate classifications of the candidates. We argue that when evaluating the social and emotional aspects of tutoring, multimodal data might be more preferrable.

[1]  Christopher Zou,et al.  Charisma in Everyday Life: Conceptualization and Validation of the General Charisma Inventory , 2018, Journal of personality and social psychology.

[2]  Maria Evagorou,et al.  Argumentation in the Teaching of Science , 2011 .

[3]  Mutlu Cukurova,et al.  Supervised machine learning in multimodal learning analytics for estimating success in project-based learning , 2018, J. Comput. Assist. Learn..

[4]  S. Srivastava,et al.  The Big Five Trait taxonomy: History, measurement, and theoretical perspectives. , 1999 .

[5]  Leslie Loble,et al.  Future frontiers: education for an AI world , 2017 .

[6]  Afsaneh Ghanizadeh,et al.  The Relationship between Iranian EFL Teachers' Emotional Intelligence and Their Self-Efficacy in Language Institutes. , 2009 .

[7]  K. Petrides Psychometric Properties of the Trait Emotional Intelligence Questionnaire (TEIQue) , 2009 .

[8]  Paulo Blikstein,et al.  Multimodal learning analytics , 2013, LAK '13.

[9]  M. Rothbart,et al.  Developing a model for adult temperament , 2007 .

[10]  Louis-Philippe Morency,et al.  Multimodal Machine Learning: A Survey and Taxonomy , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Sidney K. D'Mello,et al.  A Review and Meta-Analysis of Multimodal Affect Detection Systems , 2015, ACM Comput. Surv..

[12]  Rosemary Luckin,et al.  The NISPI framework: Analysing collaborative problem-solving from students' physical interactions , 2018, Comput. Educ..

[13]  Tatyana N. Petrova,et al.  Teachers Professional Competence Assessment Technology in Qualification Improvement Process , 2016 .