An Intentional Model for Learning Process Guidance in Adaptive Learning System

被引:0
|
作者
Bayounes, Walid [1 ]
Saadi, Ines Bayoudh [1 ]
Kinshuk [2 ]
Ben Ghezala, Henda [1 ]
机构
[1] ENSI Manouba Univ, RIADI Res Lab, Manouba, Tunisia
[2] Athabasca Univ, Sch Comp & Informat Syst, Athabasca, AB, Canada
关键词
Learning Process Model; Adaptation; Map and Guidelines;
D O I
暂无
中图分类号
F [经济];
学科分类号
02 ;
摘要
The boom of educational technology has resulted in an increased number of systems that provide adaptive learning. These systems are used by diverse learners to identify the most appropriate learning resource. However, research into adaptive learning has concentrated primarily on suggestion for teaching material to be used in particular situation, and studies have focused on improving the technique underlying the adaptation of learning process without focusing on the guidance of process to achieve the individual learning goal. To tackle these problems this research proposes an intentional model that adopts Map formalism to support personalized learning guidance by an adaptive learning system. The model couples the learner's intention with the learning strategies, and provides a multitude of paths between learner intentions. In fact, based on the corresponding learning mode, the individual learning style and the current electronic media, the learning system can adaptively support the learner to achieve his/her intention through the selected strategy. Preliminary evaluation results shows that the proposed model can successfully guide the adaptive and dynamic learning process construction according to the learning situation and the learners' preferences.
引用
收藏
页码:1476 / +
页数:2
相关论文
共 50 条
  • [21] A model reference adaptive iterative learning control system
    Madady, Ali
    IECON 2006 - 32ND ANNUAL CONFERENCE ON IEEE INDUSTRIAL ELECTRONICS, VOLS 1-11, 2006, : 426 - 431
  • [22] The Construction and Evolution of Learner Model in Adaptive Learning System
    Jia, Bing
    Zhong, Shaochun
    Wang, Wei
    Yang, Bin
    PROCEEDINGS OF THE 2009 INTERNATIONAL CONFERENCE ON COMPUTER TECHNOLOGY AND DEVELOPMENT, VOL 1, 2009, : 148 - 152
  • [23] A Model for an Adaptive e-Learning Hypermedia System
    Mahnane, Lamia
    Tayeb, Laskri Mohamed
    Trigano, Philippe
    INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGY EDUCATION, 2013, 9 (04) : 21 - 39
  • [24] Bayesian model for optimization adaptive e-Learning process
    Universidad de Sonora, Departamento de Matematicas, Bvld. Rosales y Luis Donaldo Colosio, Hermosillo, Sonora, 83000, Mexico
    不详
    Int. J. Emerg. Technol. Learn., 2008, SPEC. ISSUE 2 (38-52): : 38 - 52
  • [25] MeL: a dynamic adaptive model of the learning process in eLearning.
    Sanchez-Santillan, Miguel
    Paule-Ruiz, MPuerto
    Cerezo, Rebeca
    Alvarez-Garcia, Victor
    ANALES DE PSICOLOGIA, 2016, 32 (01): : 106 - 114
  • [26] Intentional Deep Overfit Learning (IDOL): A Novel Deep Learning Strategy for Adaptive Radiation Therapy
    Chun, J.
    Park, J.
    Olberg, S.
    Zhang, Y.
    Nguyen, D.
    Wang, J.
    Kim, J.
    Jiang, S.
    MEDICAL PHYSICS, 2021, 48 (06)
  • [27] Intentional deep overfit learning (IDOL): A novel deep learning strategy for adaptive radiation therapy
    Chun, Jaehee
    Park, Justin C.
    Olberg, Sven
    Zhang, You
    Nguyen, Dan
    Wang, Jing
    Kim, Jin Sung
    Jiang, Steve
    MEDICAL PHYSICS, 2022, 49 (01) : 488 - 496
  • [28] Adaptive Model Learning method for Reinforcement Learning
    Hwang, Kao-Shing
    Jiang, Wei-Cheng
    Chen, Yu-Jen
    2012 PROCEEDINGS OF SICE ANNUAL CONFERENCE (SICE), 2012, : 1277 - 1280
  • [29] On organizational learning system of enterprise under the guidance of learning capability
    Jiang Tianying
    Zhang Yiqing
    PROCEEDINGS OF THE 4TH INTERNATIONAL CONFERENCE ON INNOVATION & MANAGEMENT, VOLS I AND II, 2007, : 2009 - 2014
  • [30] Environment Adaptive Deep Learning Classification System Based on One-shot Guidance
    Jin, Guanghao
    Pei, Chunmei
    Zhao, Na
    Li, Hengguang
    Song, Qingzeng
    Yu, Jing
    CMC-COMPUTERS MATERIALS & CONTINUA, 2022, 73 (03): : 5185 - 5196