Adaptive Learning Using an integration of competence model with Knowledge Space Theory

被引:6
|
作者
Sitthisak, Onjira [1 ]
Gilbert, Lester
Albert, Dietrich [2 ]
机构
[1] Thaksin Univ, Fac Sci, Sch Comp & Informat Technol, Songkhla, Thailand
[2] Graz Univ, Graz, Austria
关键词
knowledge space theory; competence; computer adaptive test; Moodle; IMS QTI; e-assessment;
D O I
10.1109/IIAI-AAI.2013.15
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A model for the interactions in an assessment to support learning identifies the need for response options and for contingent feedback, both of which pose problems when computer-aided. Apart from the difficulties of allowing arbitrary student responses, of judging them for correctness or error, and of providing appropriate specific and contingent feedback, explicitly identifying a range of options or alternatives from which a student may make selections remains an unsolved research problem. The "Knowledge Space Theory (KST)" model of the domain "problems" provides some opportunity for response options. The "Competence Based Assessment (COMBA)" model of the required knowledge provides some opportunity for relevant feedback. The paper explores ComKoS, a model which integrates both approaches. We propose to apply ComKoS and IMS QTI in Moodle to instantiate the design and development of one kind of adaptive testing system. This implementation overcomes limitations in adaptability, interoperability, portability, and reusability. Key benefits of this implementation are identified and possibilities suggestion for future work is provided.
引用
收藏
页码:199 / 202
页数:4
相关论文
共 50 条
  • [1] On the assessment of learning in competence based knowledge space theory
    Stefanutti, Luca
    de Chiusole, Debora
    [J]. JOURNAL OF MATHEMATICAL PSYCHOLOGY, 2017, 80 : 22 - 32
  • [2] Learning in Moodle Using Competence-Based Knowledge Space Theory and IMS QTI
    Sitthisak, Onjira
    Gilbert, Lester
    Albert, Dietrich
    [J]. 2013 INTERNATIONAL COMPUTER SCIENCE AND ENGINEERING CONFERENCE (ICSEC), 2013, : 53 - 57
  • [3] The Semantic Annotation of Digital Learning Content Using Competence-Based Knowledge Space Theory
    Savic, Goran Z.
    Segedinac, Milan M.
    [J]. IPSI BGD TRANSACTIONS ON INTERNET RESEARCH, 2013, 9 (01): : 38 - 43
  • [4] Using a competence model to aggregate Learning Knowledge Objects
    Zouaq, Amal
    Nkambou, Roger
    Frasson, Claude
    [J]. 7TH IEEE INTERNATIONAL CONFERENCE ON ADVANCED LEARNING TECHNOLOGIES, PROCEEDINGS, 2007, : 836 - +
  • [5] Stat-Knowlab. Assessment and Learning of Statistics with Competence-based Knowledge Space Theory
    Debora de Chiusole
    Luca Stefanutti
    Pasquale Anselmi
    Egidio Robusto
    [J]. International Journal of Artificial Intelligence in Education, 2020, 30 : 668 - 700
  • [6] Stat-Knowlab. Assessment and Learning of Statistics with Competence-based Knowledge Space Theory
    de Chiusole, Debora
    Stefanutti, Luca
    Anselmi, Pasquale
    Robusto, Egidio
    [J]. INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION, 2020, 30 (04) : 668 - 700
  • [7] E-Learning Based on Metadata, Ontologies and Competence-Based Knowledge Space Theory
    Albert, Dietrich
    Flockemeyer, Cord
    Kickmeier-Rust, Michael D.
    Nussbaumer, Alexander
    Steiner, Christina M.
    [J]. KNOWLEDGE TECHNOLOGY, 2012, 295 : 24 - +
  • [8] Learning, forgetting, and the correlation of knowledge in knowledge space theory
    Matayoshi, Jeffrey
    Uzun, Hasan
    [J]. JOURNAL OF MATHEMATICAL PSYCHOLOGY, 2022, 109
  • [9] Using knowledge space theory to compare expected and real knowledge spaces in learning stoichiometry
    Segedinac, M. T.
    Horvat, S.
    Rodic, D. D.
    Roncevic, T. N.
    Savic, G.
    [J]. CHEMISTRY EDUCATION RESEARCH AND PRACTICE, 2018, 19 (03) : 670 - 680
  • [10] Supporting Self-Regulated Personalised Learning through Competence-Based Knowledge Space Theory
    Steiner, Christina M.
    Nussbaumer, Alexander
    Albert, Dietrich
    [J]. POLICY FUTURES IN EDUCATION, 2009, 7 (06): : 645 - 661