Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cichocki, A., Zdunek, R., Phan, A.H., Amari, S.I.: Nonnegative matrix and tensor factorizations: applications to exploratory multi-way data analysis and blind source separation. Wiley (2009)
Hu, X., Tang, L., Tang, J., Liu, H.: Exploiting Social Relations for Sentiment Analysis in Microblogging. In: Proceedings of the Sixth ACM WSDM Conference (WSDM 2013) (2013)
Talk, Tutor, Explore, Learn: Intelligent Tutoring and Exploration for Robust Learning (iTalk2Learn), http://www.italk2learn.eu
Krohn-Grimberghe, A., Busche, A., Nanopoulos, A., Schmidt-Thieme, L.: Active learning for technology enhanced learning. In: Kloos, C.D., Gillet, D., Crespo García, R.M., Wild, F., Wolpers, M. (eds.) EC-TEL 2011. LNCS, vol. 6964, pp. 512–518. Springer, Heidelberg (2011)
Luz, S.: Automatic Identification of Experts and Performance Prediction in the Multimodal Math Data Corpus through Analysis of Speech Interaction. In: Second International Workshop on Multimodal Learning Analytics, Sydney, Australia (2013)
DMello, S., Picard, R., Graesser, A.: Towards An Affect-Sensitive AutoTutor. IEEE Intelligent Systems 22(4), 53–61 (2007)
DMello, S.K., Craig, S.D., Witherspoon, A., McDaniel, B., Graesser, A.: Automatic detection of learners affect from conversational cues. User Model User-Adap. Inter. (2008), doi:10.1007/s11257-007-9037-6
Morency, L.P., Oviatt, S., Scherer, S., Weibel, N., Worsley, M.: ICMI 2013 grand challenge workshop on multimodal learning analytics. In: Proceedings of the 15th ACM on International Conference on Multimodal Interaction (ICMI 2013), pp. 373–378 (2013)
Qi, F., Bao, C., Liu, Y.: A novel two-step SVM classifier for voiced/unvoiced/silence classification of speech. In: International Symposium on Chinese Spoken Language Processing, pp. 77–80 (2004)
Sadegh, M., Ibrahim, R., Othman, Z.A.: Opinion Mining and Sentiment Analysis: A Survey. International Journal of Computers & Technology 2(3) (2012)
Saif, H., He, Y., Alani, H.: Semantic Sentiment Analysis of Twitter. In: Cudré-Mauroux, P., et al. (eds.) ISWC 2012, Part I. LNCS, vol. 7649, pp. 508–524. Springer, Heidelberg (2012)
Schatten, C., Schmidt-Thieme, L.: Adaptive Content Sequencing without Domain Information. In: Proceedings of the Conference on Computer Supported Education (CSEDU 2014) (2014)
Schuller, B., Batliner, A., Steidl, S., Seppi, D.: Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge. Speech Communication (2011)
Vygotsky, L.L.S.: Mind in society: The development of higher psychological processes. Harvard University Press (1978)
Wang, Y., Heffernan, N.: Extending Knowledge Tracing to allow Partial Credit: Using Continuous versus Binary Nodes. In: Lane, H.C., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS, vol. 7926, pp. 181–188. Springer, Heidelberg (2013)
Woolf, B., Burleson, W., Arroyo, I., Dragon, T., Cooper, D., Picard, R.: Affect-aware tutors: recognising and responding to student affect. Int. J. of Learning Technology 4(3/4), 129–164 (2009)
Worsley, M., Blikstein, P.: What’s an Expert? Using Learning Analytics to Identify Emergent Markers of Expertise through Automated Speech, Sentiment and Sketch Analysis. In: Proceedings of the 4th International Conference on Educational Data Mining (EDM 2011), pp. 235–240 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Janning, R., Schatten, C., Schmidt-Thieme, L. (2014). Feature Analysis for Affect Recognition Supporting Task Sequencing in Adaptive Intelligent Tutoring Systems. In: Rensing, C., de Freitas, S., Ley, T., Muñoz-Merino, P.J. (eds) Open Learning and Teaching in Educational Communities. EC-TEL 2014. Lecture Notes in Computer Science, vol 8719. Springer, Cham. https://doi.org/10.1007/978-3-319-11200-8_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-11200-8_14
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11199-5
Online ISBN: 978-3-319-11200-8
eBook Packages: Computer ScienceComputer Science (R0)