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Effects of Competitive E-Learning Tools on Higher Education Students: A Case Study

Published: 01 May 2009 Publication History

Abstract

Over the last few years, most of the attempts to introduce active learning methodologies in the classroom have made use of Information and Communication Technology (ICT). Many of these efforts have been directed to collaborative scenarios used in remote, blended or face-to-face experiences, in order to take advantage of the flexibility provided by ICT. However competitive learning could also have some positive effects on the success of the learning process, to be added to those of collaborative virtual environments. This paper explores the effects of competitive learning on the satisfaction and the academic achievement of telecommunications students. A tool for active and competitive learning (called QUEST) has been used in the ldquoCommunications Networksrdquo undergraduate course. Significant results on the use of competitive e-learning tools in university students' outcomes and satisfaction are presented.

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  • (2023)Identifying Student Profiles Within Online Judge Systems Using Explainable Artificial IntelligenceIEEE Transactions on Learning Technologies10.1109/TLT.2023.323911016:6(955-969)Online publication date: 1-Dec-2023
  • (2022)RETRACTED ARTICLE: Online education satisfaction assessment based on cloud model and fuzzy TOPSISApplied Intelligence10.1007/s10489-022-03289-752:12(13659-13674)Online publication date: 1-Sep-2022
  • (2020)Developing (Almost) Free Distributed System Labs Using Container-based TechniqueProceedings of the ACM Turing Celebration Conference - China10.1145/3393527.3393545(101-106)Online publication date: 22-May-2020
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  1. Effects of Competitive E-Learning Tools on Higher Education Students: A Case Study

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    Information & Contributors

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    Published In

    cover image IEEE Transactions on Education
    IEEE Transactions on Education  Volume 52, Issue 2
    May 2009
    81 pages

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    IEEE Press

    Publication History

    Published: 01 May 2009

    Author Tags

    1. Active learning
    2. collaborative learning
    3. competitive learning
    4. e-learning
    5. higher education
    6. teaching/learning strategies

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    Cited By

    View all
    • (2023)Identifying Student Profiles Within Online Judge Systems Using Explainable Artificial IntelligenceIEEE Transactions on Learning Technologies10.1109/TLT.2023.323911016:6(955-969)Online publication date: 1-Dec-2023
    • (2022)RETRACTED ARTICLE: Online education satisfaction assessment based on cloud model and fuzzy TOPSISApplied Intelligence10.1007/s10489-022-03289-752:12(13659-13674)Online publication date: 1-Sep-2022
    • (2020)Developing (Almost) Free Distributed System Labs Using Container-based TechniqueProceedings of the ACM Turing Celebration Conference - China10.1145/3393527.3393545(101-106)Online publication date: 22-May-2020
    • (2020)Challengr, a Classroom Response System for Competency Based Assessment and Real-Time Feedback with Micro-Contests2020 IEEE Frontiers in Education Conference (FIE)10.1109/FIE44824.2020.9273992(1-4)Online publication date: 21-Oct-2020
    • (2019)Hybrid learning environment: Collaborative or competitive learning?Virtual Reality10.1007/s10055-018-0358-z23:4(411-423)Online publication date: 1-Dec-2019
    • (2018)The Effect of Different Features for Educational Computer-Based Competition EnvironmentsIEEE Transactions on Learning Technologies10.1109/TLT.2017.276359511:4(468-477)Online publication date: 1-Oct-2018
    • (2017)A recommendation approach for programming online judges supported by data preprocessing techniquesApplied Intelligence10.1007/s10489-016-0892-x47:2(277-290)Online publication date: 1-Sep-2017
    • (2015)Teaching Computer VisionACM Transactions on Computing Education10.1145/259762714:4(1-17)Online publication date: 24-Feb-2015
    • (2015)Ant colony optimization for competency based learning objects sequencing in e-learningApplied Mathematics and Computation10.1016/j.amc.2015.04.067263:C(332-341)Online publication date: 15-Jul-2015
    • (2014)Teaching assistance and automatic difficulty estimation in converting first order logic to clause formArtificial Intelligence Review10.1007/s10462-013-9417-842:3(347-367)Online publication date: 1-Oct-2014
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