A1 Journal article (refereed)
Information Extraction from Binary Skill Assessment Data with Machine Learning (2021)

Jauhiainen, S., Krosshaug, T., Petushek, E., Kauppi, J.-P., & Äyrämö, S. (2021). Information Extraction from Binary Skill Assessment Data with Machine Learning. International Journal of Learning Analytics and Artificial Intelligence for Education, 3(1), 20-35. https://doi.org/10.3991/ijai.v3i1.24295

JYU authors or editors

Publication details

All authors or editors: Jauhiainen, Susanne; Krosshaug, Tron; Petushek, Erich; Kauppi, Jukka-Pekka; Äyrämö, Sami

Journal or series: International Journal of Learning Analytics and Artificial Intelligence for Education

eISSN: 2706-7564

Publication year: 2021

Publication date: 12/08/2021

Volume: 3

Issue number: 1

Pages range: 20-35

Publisher: International Association of Online Engineering (IAOE)

Publication country: Austria

Publication language: English

DOI: https://doi.org/10.3991/ijai.v3i1.24295

Publication open access: Openly available

Publication channel open access: Open Access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/77421

Abstract

Strength training exercises are essential for rehabilitation, improving our health as well as in sports. For optimal and safe training, educators and trainers in the industry should comprehend exercise form or technique. Currently, there is a lack of tools measuring in-depth skills of strength training experts. In this study, we investigate how data mining methods can be used to identify novel and useful skill patterns from a binary multiple choice questionnaire test designed to measure the knowledge level of strength training experts. A skill test assessing exercise technique expertise and comprehension was answered by 507 fitness professionals with varying backgrounds. A triangulated approach of clustering and non-negative matrix factorization (NMF) was used to discover skill patterns among participants and patterns in test questions. Four distinct participant subgroups were identified in data with clustering and further question patterns with NMF. The results can be used to, for example, identify missing skills and knowledge in participants and subgroups of participants and form general and personalized or background specific guidelines for future education. In addition, the test can be optimized based on, for example, if some questions can be answered correct even without the required skill or if they seem to be measuring overlapping skills. Finally, this approach can be utilized with other multiple choice test data in future educational research.

Keywords: data mining; machine learning; cluster analysis; strength training; motor skills (sports); measuring instruments (indicators)

Free keywords: data mining; clustering; non-negative matrix factorization; strength training skill test; binary data

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2021

JUFO rating: 1