Visualization of student activity patterns within intelligent tutoring systems

David Hilton Shanabrook, Ivon Arroyo, Beverly Park Woolf, Winslow Burleson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Novel and simplified methods for determining low-level states of student behavior and predicting affective states enable tutors to better respond to students. The Many Eyes Word Tree graphics is used to understand and analyze sequential patterns of student states, categorizing raw quantitative indicators into a limited number of discrete sates. Used in combination with sensor predictors, we demonstrate that a combination of features, automatic pattern discovery and feature selection algorithms can predict and trace higher-level states (emotion) and inform more effective real-time tutor interventions.

Original languageEnglish (US)
Title of host publicationIntelligent Tutoring Systems - 11th International Conference, ITS 2012, Proceedings
Pages46-51
Number of pages6
Volume7315 LNCS
DOIs
StatePublished - 2012
Event11th International Conference on Intelligent Tutoring Systems, ITS 2012 - Chania, Crete, Greece
Duration: Jun 14 2012Jun 18 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7315 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other11th International Conference on Intelligent Tutoring Systems, ITS 2012
CountryGreece
CityChania, Crete
Period6/14/126/18/12

Fingerprint

Intelligent Tutoring Systems
Intelligent systems
Visualization
Students
Pattern Discovery
Sequential Patterns
Feature Selection
Predictors
Trace
Real-time
Predict
Sensor
Feature extraction
Demonstrate
Sensors
Graphics
Emotion

Keywords

  • engagement
  • pattern discovery
  • student emotion
  • user modeling

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Shanabrook, D. H., Arroyo, I., Woolf, B. P., & Burleson, W. (2012). Visualization of student activity patterns within intelligent tutoring systems. In Intelligent Tutoring Systems - 11th International Conference, ITS 2012, Proceedings (Vol. 7315 LNCS, pp. 46-51). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7315 LNCS). https://doi.org/10.1007/978-3-642-30950-2_6

Visualization of student activity patterns within intelligent tutoring systems. / Shanabrook, David Hilton; Arroyo, Ivon; Woolf, Beverly Park; Burleson, Winslow.

Intelligent Tutoring Systems - 11th International Conference, ITS 2012, Proceedings. Vol. 7315 LNCS 2012. p. 46-51 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7315 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Shanabrook, DH, Arroyo, I, Woolf, BP & Burleson, W 2012, Visualization of student activity patterns within intelligent tutoring systems. in Intelligent Tutoring Systems - 11th International Conference, ITS 2012, Proceedings. vol. 7315 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7315 LNCS, pp. 46-51, 11th International Conference on Intelligent Tutoring Systems, ITS 2012, Chania, Crete, Greece, 6/14/12. https://doi.org/10.1007/978-3-642-30950-2_6
Shanabrook DH, Arroyo I, Woolf BP, Burleson W. Visualization of student activity patterns within intelligent tutoring systems. In Intelligent Tutoring Systems - 11th International Conference, ITS 2012, Proceedings. Vol. 7315 LNCS. 2012. p. 46-51. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-30950-2_6
Shanabrook, David Hilton ; Arroyo, Ivon ; Woolf, Beverly Park ; Burleson, Winslow. / Visualization of student activity patterns within intelligent tutoring systems. Intelligent Tutoring Systems - 11th International Conference, ITS 2012, Proceedings. Vol. 7315 LNCS 2012. pp. 46-51 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{eb34c5549dfe4df496522c3d401b468c,
title = "Visualization of student activity patterns within intelligent tutoring systems",
abstract = "Novel and simplified methods for determining low-level states of student behavior and predicting affective states enable tutors to better respond to students. The Many Eyes Word Tree graphics is used to understand and analyze sequential patterns of student states, categorizing raw quantitative indicators into a limited number of discrete sates. Used in combination with sensor predictors, we demonstrate that a combination of features, automatic pattern discovery and feature selection algorithms can predict and trace higher-level states (emotion) and inform more effective real-time tutor interventions.",
keywords = "engagement, pattern discovery, student emotion, user modeling",
author = "Shanabrook, {David Hilton} and Ivon Arroyo and Woolf, {Beverly Park} and Winslow Burleson",
year = "2012",
doi = "10.1007/978-3-642-30950-2_6",
language = "English (US)",
isbn = "9783642309496",
volume = "7315 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "46--51",
booktitle = "Intelligent Tutoring Systems - 11th International Conference, ITS 2012, Proceedings",

}

TY - GEN

T1 - Visualization of student activity patterns within intelligent tutoring systems

AU - Shanabrook, David Hilton

AU - Arroyo, Ivon

AU - Woolf, Beverly Park

AU - Burleson, Winslow

PY - 2012

Y1 - 2012

N2 - Novel and simplified methods for determining low-level states of student behavior and predicting affective states enable tutors to better respond to students. The Many Eyes Word Tree graphics is used to understand and analyze sequential patterns of student states, categorizing raw quantitative indicators into a limited number of discrete sates. Used in combination with sensor predictors, we demonstrate that a combination of features, automatic pattern discovery and feature selection algorithms can predict and trace higher-level states (emotion) and inform more effective real-time tutor interventions.

AB - Novel and simplified methods for determining low-level states of student behavior and predicting affective states enable tutors to better respond to students. The Many Eyes Word Tree graphics is used to understand and analyze sequential patterns of student states, categorizing raw quantitative indicators into a limited number of discrete sates. Used in combination with sensor predictors, we demonstrate that a combination of features, automatic pattern discovery and feature selection algorithms can predict and trace higher-level states (emotion) and inform more effective real-time tutor interventions.

KW - engagement

KW - pattern discovery

KW - student emotion

KW - user modeling

UR - http://www.scopus.com/inward/record.url?scp=84862487194&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84862487194&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-30950-2_6

DO - 10.1007/978-3-642-30950-2_6

M3 - Conference contribution

AN - SCOPUS:84862487194

SN - 9783642309496

VL - 7315 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 46

EP - 51

BT - Intelligent Tutoring Systems - 11th International Conference, ITS 2012, Proceedings

ER -