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Online change detection for monitoring individual student behavior via clickstream data on E-book system

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Hiroaki OgataAuthors Info & Claims

LAK '18: Proceedings of the 8th International Conference on Learning Analytics and Knowledge

Pages 446 - 450

https://doi.org/10.1145/3170358.3170412

Published: 07 March 2018 Publication History

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Abstract

We propose a new change detection method using clickstream data collected through an e-Book system. Most of the prior work has focused on the batch processing of clickstream data. In contrast, the proposed method is designed for online processing, with the model parameters for change detection updated sequentially based on observations of new click events. More specifically, our method generates a model for an individual student and performs minute-by-minute change detection based on click events during a classroom lecture. We collected clickstream data from four face-to-face lectures, and conducted experiments to demonstrate how the proposed method discovered change points and how such change points correlated with the students' performances.

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Cited By

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Nakamura KIshihara MHorikoshi IOgata H(2024)Uncovering insights from big data: change point detection of classroom engagementSmart Learning Environments10.1186/s40561-024-00317-611:1Online publication date: 1-Jul-2024
https://doi.org/10.1186/s40561-024-00317-6
Ahmad ASchneider JGriffiths DBiedermann DSchiffner DGreller WDrachsler H(2022)Connecting the dots – A literature review on learning analytics indicators from a learning design perspectiveJournal of Computer Assisted Learning10.1111/jcal.1271640:6(2432-2470)Online publication date: 26-Jul-2022
https://doi.org/10.1111/jcal.12716
Zhou JBhat SScheffel MDowell NJoksimovic SSiemens G(2021)Modeling Consistency Using Engagement Patterns in Online CoursesLAK21: 11th International Learning Analytics and Knowledge Conference10.1145/3448139.3448161(226-236)Online publication date: 12-Apr-2021
https://dl.acm.org/doi/10.1145/3448139.3448161
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Index Terms

Online change detection for monitoring individual student behavior via clickstream data on E-book system
1. Applied computing
  1. Education
    1. Computer-assisted instruction
    2. Interactive learning environments
2. Computer systems organization
  1. Real-time systems
    1. Real-time system architecture

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Published In

LAK '18: Proceedings of the 8th International Conference on Learning Analytics and Knowledge

March 2018

489 pages

ISBN:9781450364003

DOI:10.1145/3170358

Conference Chairs:
Abelardo Pardo
The University of Sydney, AUS
,
Kathryn Bartimote-Aufflick
The University of Sydney, AUS
,
Grace Lynch
Society for Learning Analytics Research, AUS
,
Program Chairs:
Simon Buckingham Shum
University of Technology Sydney, AUS
,
Rebecca Ferguson
The Open University, UK
,
Agathe Merceron
Beuth University of Applied Sciences, GER
,
Xavier Ochoa
Escuela Superior Politécnica del Litoral, EC

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 March 2018

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JSPS KAKENHI, Japan
JST PRESTO

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LAK '18

LAK '18: International Conference on Learning Analytics and Knowledge

March 7 - 9, 2018

New South Wales, Sydney, Australia

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LAK '18 Paper Acceptance Rate 35 of 115 submissions, 30%;

Overall Acceptance Rate 236 of 782 submissions, 30%

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Nakamura KIshihara MHorikoshi IOgata H(2024)Uncovering insights from big data: change point detection of classroom engagementSmart Learning Environments10.1186/s40561-024-00317-611:1Online publication date: 1-Jul-2024
https://doi.org/10.1186/s40561-024-00317-6
Ahmad ASchneider JGriffiths DBiedermann DSchiffner DGreller WDrachsler H(2022)Connecting the dots – A literature review on learning analytics indicators from a learning design perspectiveJournal of Computer Assisted Learning10.1111/jcal.1271640:6(2432-2470)Online publication date: 26-Jul-2022
https://doi.org/10.1111/jcal.12716
Zhou JBhat SScheffel MDowell NJoksimovic SSiemens G(2021)Modeling Consistency Using Engagement Patterns in Online CoursesLAK21: 11th International Learning Analytics and Knowledge Conference10.1145/3448139.3448161(226-236)Online publication date: 12-Apr-2021
https://dl.acm.org/doi/10.1145/3448139.3448161
Minematsu TSaguey MShimada ATaniguchi R(2020)Inactive Behavior Analytics In On-Site Lectures2020 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE)10.1109/TALE48869.2020.9368453(708-713)Online publication date: 8-Dec-2020
https://doi.org/10.1109/TALE48869.2020.9368453
Umarji ODay SXu YZargar EYu RConnor C(2020)Opening the black box: user-log analyses of children’s e-Book reading and associations with word knowledgeReading and Writing10.1007/s11145-020-10081-xOnline publication date: 18-Aug-2020
https://doi.org/10.1007/s11145-020-10081-x
Sakai MShuzo MYuasa MMatsui KMaeda E(2020)Biological and Behavioral Information-Based Method of Predicting Listener Emotions Toward Speaker Utterances During Group DiscussionActivity and Behavior Computing10.1007/978-981-15-8944-7_12(189-207)Online publication date: 24-Dec-2020
https://doi.org/10.1007/978-981-15-8944-7_12
Shimada AMinematsu TYamada M(2019)Advanced Tools for Digital Learning Management Systems in University EducationDistributed, Ambient and Pervasive Interactions10.1007/978-3-030-21935-2_32(419-429)Online publication date: 7-Jun-2019
https://doi.org/10.1007/978-3-030-21935-2_32
Shuzo MSakai MMaeda E(2018)Fundamental Concept of University Living Laboratory for Appropriate FeedbackProceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers10.1145/3267305.3267511(1454-1461)Online publication date: 8-Oct-2018
https://dl.acm.org/doi/10.1145/3267305.3267511

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Detecting changes in student behavior from clickstream data

Predicting Learners’ Performance Using MOOC Clickstream

Using coursera clickstream data to improve online education for software engineering

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