Title

Semi-Supervised Classification Of Realtime Physiological Sensor Datastreams For Student Affect Assessment In Intelligent Tutoring

Keywords

Affective Computing; Datastream Mining; Intelligent Tutoring

Abstract

Famously, individual expert tutoring holds the promise of two standard deviations of improvement over classroom-based instruction. Current content-scaling techniques have been able to prove one standard deviation of improvement. However, just as expert tutors take the motivation and emotional state of the student into account for instruction, so too must computer instructors. Differences between individuals and individual baselines make this difficult, but this information is known across one training session. The construction of assessing modules in realtime, from the available performance and sensor datastreams, skirts these problems, but is technically difficult. This research investigates automated student model construction in realtime from datastreams as a solution from which to base pedagogical strategy recommendations. © 2012 Springer-Verlag.

Publication Date

6-22-2012

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

7315 LNCS

Number of Pages

582-584

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/978-3-642-30950-2_77

Socpus ID

84862491746 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84862491746

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