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Mining online learner profile through learning behavior analysis

Published: 26 October 2018 Publication History

Abstract

User profile is an effective model to describe the user's interests and preferences. In the learning field, learner profile should meet the demand of learning and teaching such as learning patterns recognition or performance prediction. Analysis of user's behaviors is the common way to build user profile. Statistics show that online learning activities are incontinuous and diverse. By taking a closer look at the learning activity data, we found back accessing behavior is a frequent activity and reveals the truth of learners' intention. In this study, we make use of Shanghai Open University's learning platform as the data source for our research, adopt machine learning method to find the hidden patterns of learning activities and build the online learner profile. Statistics show that 15.68% of the accessing activities are back accessing. We found three learning patterns with different amount of back accessing behaviors and learning paths. Meanwhile, they relate to many factors including demographics, major type and area where learners join in learning. Through learner profile, we can predict learner's learning pattern which we found in this study. In the conclusion of our study, we suggest that learning path should be taken into consideration of learning engagement.

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Cited By

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  • (2023)Early Prediction of Students’ Performance Using a Deep Neural Network Based on Online Learning Activity SequenceApplied Sciences10.3390/app1315893313:15(8933)Online publication date: 3-Aug-2023
  • (2023)Educational Game Quality Assessment Based on The User's Persona Profile: A Systematic Literature ReviewProceedings of the Asian HCI Symposium 202310.1145/3604571.3604587(89-98)Online publication date: 28-Apr-2023
  • (2023)A duplex adaptation mechanism in the personalized learning environmentJournal of Computers in Education10.1007/s40692-023-00292-w11:4(1111-1131)Online publication date: 10-Aug-2023
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  1. Mining online learner profile through learning behavior analysis

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    cover image ACM Other conferences
    ICETC '18: Proceedings of the 10th International Conference on Education Technology and Computers
    October 2018
    391 pages
    ISBN:9781450365178
    DOI:10.1145/3290511
    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: 26 October 2018

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    Author Tags

    1. clustering
    2. learner profile
    3. learning pattern prediction
    4. machine learning
    5. online learning

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    • Research-article

    Funding Sources

    • China's National General Project granted by China National Office for Education Sciences Planning, The Construction and Application of Online Learners' Persona based on Big Data Analysis

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    ICETC 2018

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    Cited By

    View all
    • (2023)Early Prediction of Students’ Performance Using a Deep Neural Network Based on Online Learning Activity SequenceApplied Sciences10.3390/app1315893313:15(8933)Online publication date: 3-Aug-2023
    • (2023)Educational Game Quality Assessment Based on The User's Persona Profile: A Systematic Literature ReviewProceedings of the Asian HCI Symposium 202310.1145/3604571.3604587(89-98)Online publication date: 28-Apr-2023
    • (2023)A duplex adaptation mechanism in the personalized learning environmentJournal of Computers in Education10.1007/s40692-023-00292-w11:4(1111-1131)Online publication date: 10-Aug-2023
    • (2022)On the Cognitive Load of Online Learners With Multi-Level Data MiningInternational Journal of Information and Communication Technology Education10.4018/ijicte.31422518:2(1-15)Online publication date: 2-Dec-2022
    • (2019)Incorporating Scenarios and States Definitions on Real-Time Entity Monitoring in PAbMM2019 XLV Latin American Computing Conference (CLEI)10.1109/CLEI47609.2019.235072(1-10)Online publication date: Sep-2019

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