Exploring Adaptive Strategies for Providing Learning Activities

被引:1
|
作者
Chen, Xingliang [1 ]
Mitrovic, Antonija [1 ]
Mathews, Moffat [1 ]
机构
[1] Univ Canterbury, Intelligent Comp Tutoring Grp, Christchurch, New Zealand
关键词
Worked Examples; Erroneous Examples; Problem Solving; Adaptive Learning; Adaptive Strategy; SQL-Tutor; Intelligent Tutoring System; WORKED EXAMPLES; PRINCIPLES; EXPERTISE; IMPROVE;
D O I
10.1145/3209219.3209221
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Research shows that Worked Examples (WE) and Erroneous Examples (ErrEx) provide learning benefits, particularly when presented alternatively with problems to solve. We previously proposed an adaptive strategy for selecting WE, ErrEx, and Problem Solving (PS) adaptively based on the student's problem solving score and found that the adaptive strategy was beneficial for students in comparison to learning from a fixed sequence of alternating WE/PS pairs and ErrEx/PS pairs [1]. Students who received learning activities adaptively achieved the same learning outcomes as their peers in a fixed condition, but with fewer learning activities [2]. In this paper, we investigate a different adaptive strategy, which provides WEs and ErrExs to novices, and ErrEx and PS to advanced students. We found that the original adaptive strategy [2] is more effective than the new adaptive strategy. Furthermore, both novices and advanced students who learned with the original adaptive strategy demonstrated better performance on the post-test.
引用
收藏
页码:139 / 145
页数:7
相关论文
共 50 条
  • [41] Learning Analytics: A case study of Adaptive Video Activities
    Nicolaidou, Despo
    Nicolaidou, Iolie
    PROCEEDINGS OF THE 21ST EUROPEAN CONFERENCE ON E- LEARNING, ECEL, 2021, : 484 - 488
  • [42] Exploring proactive niche market strategies in the steel industry: Activities and implications
    Ottosson, Mikael
    Kindstrom, Daniel
    INDUSTRIAL MARKETING MANAGEMENT, 2016, 55 : 119 - 130
  • [43] Individualizing Learning Pathways with Adaptive Learning Strategies: Design, Implementation and Scale
    Donevska-Todorova, Ana
    Dziergwa, Katrin
    Simbeck, Katharina
    CSEDU: PROCEEDINGS OF THE 14TH INTERNATIONAL CONFERENCE ON COMPUTER SUPPORTED EDUCATION - VOL 2, 2022, : 575 - 585
  • [44] TutorGen: A Carnegie Mellon Start-Up for Providing Adaptive Learning at Scale
    Carmichael, Ted
    Blink, Mary Jean
    Stamper, John C.
    ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2015, 2015, 9112 : 893 - 894
  • [45] Providing QoS through machine-learning-driven adaptive multimedia applications
    Ruiz, PM
    Botía, JA
    Gómez-Skarmeta, A
    IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2004, 34 (03): : 1398 - 1411
  • [46] Adaptive Educational Games: Providing Non-invasive Personalised Learning Experiences
    Peirce, Neil
    Conlan, Owen
    Wade, Vincent
    DIGITEL 2008: SECOND IEEE INTERNATIONAL CONFERENCE ON DIGITAL GAME AND INTELLIGENT TOY ENHANCED LEARNING, PROCEEDINGS, 2008, : 28 - 35
  • [47] Supporting Metacognitive Learning Strategies Through an Adaptive Application
    Van Campenhout, Rachel
    ADAPTIVE INSTRUCTIONAL SYSTEMS, AIS 2020, 2020, 12214 : 218 - 227
  • [48] Exploring students’ self-directed learning strategies and satisfaction in online learning
    Meina Zhu
    Sarah Berri
    Rose Koda
    Yi-jung Wu
    Education and Information Technologies, 2024, 29 : 2787 - 2803
  • [49] ADAPTIVE SELECTION OF QUERY EXECUTION STRATEGIES BY LEARNING AUTOMATA
    TOMPA, FWM
    ICAZA, JI
    INFORMATION SCIENCES, 1990, 50 (03) : 219 - 240
  • [50] Learning Adaptive Differential Evolution by Natural Evolution Strategies
    Zhang, Haotian
    Sun, Jianyong
    Tan, Kay Chen
    Xu, Zongben
    IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE, 2023, 7 (03): : 872 - 886