Automated analysis of lecture video engagement using student posts

Nicholas R. Stepanek, Brian Dorn

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

Abstract

This work explores the feasibility of a learning analytic that would provide high level engagement data to instructors based on students’ text artifacts in online learning systems. Student posts from an online lecture video system were collected and manually coded by engagement using the ICAP framework. Analyses show what features are most indicative of engagement and the performance of using a neural network to classify posts by engagement.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence in Education - 18th International Conference, AIED 2017, Proceedings
EditorsElisabeth Andre, Xiangen Hu, Ma. Mercedes T. Rodrigo, Benedict du Boulay, Ryan Baker
PublisherSpringer Verlag
Pages565-569
Number of pages5
ISBN (Print)9783319614243
DOIs
StatePublished - 2017
Event18th International Conference on Artificial Intelligence in Education, AIED 2017 - Wuhan, China
Duration: Jun 28 2017Jul 1 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10331 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Conference on Artificial Intelligence in Education, AIED 2017
Country/TerritoryChina
CityWuhan
Period6/28/177/1/17

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

Fingerprint

Dive into the research topics of 'Automated analysis of lecture video engagement using student posts'. Together they form a unique fingerprint.

Cite this