Markov solution processes: Modeling human problem solving with procedural knowledge space theory

2021 ◽  
Vol 103 ◽  
pp. 102552
Author(s):  
Luca Stefanutti ◽  
Debora de Chiusole ◽  
Andrea Brancaccio
Keyword(s):  
Problem Solving ◽  
Procedural Knowledge ◽  
Space Theory ◽  
Knowledge Space ◽  
Knowledge Space Theory ◽  
Solution Processes ◽  
Human Problem

Problem solving learning environments and assessment: A knowledge space theory approach

2013 ◽  
Vol 64 ◽  
pp. 183-193 ◽  
Author(s):  
Peter Reimann ◽  
Michael Kickmeier-Rust ◽  
Dietrich Albert
Keyword(s):  
Problem Solving ◽  
Learning Environments ◽  
Theory Approach ◽  
Space Theory ◽  
Knowledge Space ◽  
Knowledge Space Theory

Modeling learning in knowledge space theory through bivariate Markov processes

2021 ◽  
Vol 103 ◽  
pp. 102549
Author(s):  
Pasquale Anselmi ◽  
Luca Stefanutti ◽  
Debora de Chiusole ◽  
Egidio Robusto
Keyword(s):  
Markov Processes ◽  
Space Theory ◽  
Knowledge Space ◽  
Knowledge Space Theory

Recent Developments in Performance-based Knowledge Space Theory

2013 ◽  
pp. 147-192 ◽  
Author(s):  
Ali Ünlü ◽  
Martin Schrepp ◽  
Jürgen Heller ◽  
Cord Hockemeyer ◽  
Gudrun Wesiak ◽  
...  
Keyword(s):  
Space Theory ◽  
Knowledge Space ◽  
Knowledge Space Theory ◽  
Recent Developments

Stat-Knowlab. Assessment and Learning of Statistics with Competence-based Knowledge Space Theory

2020 ◽  
Vol 30 (4) ◽  
pp. 668-700
Author(s):  
Debora de Chiusole ◽  
Luca Stefanutti ◽  
Pasquale Anselmi ◽  
Egidio Robusto
Keyword(s):  
Space Theory ◽  
Knowledge Space ◽  
Knowledge Space Theory

Using knowledge space theory to compare expected and real knowledge spaces in learning stoichiometry

2018 ◽  
Vol 19 (3) ◽  
pp. 670-680
Author(s):  
M. T. Segedinac ◽  
S. Horvat ◽  
D. D. Rodić ◽  
T. N. Rončević ◽  
G. Savić
Keyword(s):  
Cognitive Complexity ◽  
Knowledge Structure ◽  
Space Theory ◽  
Knowledge Space ◽  
Knowledge Space Theory ◽  
The Real ◽  
Knowledge Spaces ◽  
Problem Solving Process ◽  
Constructing Knowledge ◽  
Data Analytic

This paper proposes a novel application of knowledge space theory for identifying discrepancies between the knowledge structure that experts expect students to have and the real knowledge structure that students demonstrate on tests. The proposed approach combines two methods of constructing knowledge spaces. The expected knowledge space is constructed by analysing the problem-solving process, while the real knowledge space is identified by applying a data-analytic method. These two knowledge spaces are compared for graph difference and the discrepancies between the two are analysed. In this paper, the proposed approach is applied to the domain of stoichiometry. Although there was a decent agreement between expected and real knowledge spaces, a number of relations that were not present in the expected one appeared in the real knowledge space. The obtained results led to a general conclusion for teaching stoichiometry and pointed to some potential improvements in the existing methods for evaluating cognitive complexity.

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