Publication: Learning outcomes evaluation based on mixed diagnostic tests and cognitive graphic tools
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Issued Date
2018
Resource Type
File Type
application/pdf
ISSN
21945357
Other identifier(s)
2-s2.0-85030631624
Rights Holder(s)
Scopus
Bibliographic Citation
Advances in Intelligent Systems and Computing. Vol 677, (2018), p.81-90
Suggested Citation
Yankovskaya A.E., Dementev Y.N., Lyapunov D.Y., Yamshanov A.V. Learning outcomes evaluation based on mixed diagnostic tests and cognitive graphic tools. Advances in Intelligent Systems and Computing. Vol 677, (2018), p.81-90. doi:10.1007/978-3-319-67843-6_11 Retrieved from: https://hdl.handle.net/20.500.14740/3862
Abstract
In this paper, we discuss the relevance of students’ learning outcomes evaluation using computer-based testing. The learning process is based on mixed diagnostic tests. For the purpose of evaluation, we use the threshold, fuzzy logic and cognitive graphic tools. The construction of mixed diagnostic tests, representing a compromise between unconditional and conditional components, in order to develop students’ knowledge evaluation is proposed for a number of disciplines. We suggest a technique for optimal mixed diagnostic tests construction based on the expert knowledge of the subjects for effective learning. The developed approach is used for a number of both the humanities and technical disciplines. One of useful outcomes of mixed diagnostic tests application is the learning trajectory design for each individual. We construct students’ learning trajectory using the intelligent learning and testing system and suggest defining their inherent approach to the learning process within the problem area. © 2018, Springer International Publishing AG.
Subject(s)
Computer circuits
Computer testing
Fuzzy logic
Intelligent systems
Learning systems
Linguistics
Pattern recognition
Pattern recognition systems
Students
Threshold logic
Trajectories
Cognitive graphic tools
Computer-based testing
Effective learning
Intelligent learning
Knowledge evaluations
Learning trajectories
Mixed diagnostic tests
N simplex
Education
Computer testing
Fuzzy logic
Intelligent systems
Learning systems
Linguistics
Pattern recognition
Pattern recognition systems
Students
Threshold logic
Trajectories
Cognitive graphic tools
Computer-based testing
Effective learning
Intelligent learning
Knowledge evaluations
Learning trajectories
Mixed diagnostic tests
N simplex
Education