Information Extraction from Binary Skill Assessment Data with Machine Learning
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
Julkaistu sarjassa
International Journal of Learning Analytics and Artificial Intelligence for EducationTekijät
Päivämäärä
2021Tekijänoikeudet
© 2021 the Authors
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.
...
Julkaisija
International Association of Online Engineering (IAOE)ISSN Hae Julkaisufoorumista
2706-7564Asiasanat
Julkaisu tutkimustietojärjestelmässä
https://converis.jyu.fi/converis/portal/detail/Publication/99332798
Metadata
Näytä kaikki kuvailutiedotKokoelmat
Lisätietoja rahoituksesta
Susanne Jauhiainen was funded by the Jenny and Antti Wihuri Foundation (grant 00190110) and by the Emil Aaltonen Foundation (grant 180063 KO).Lisenssi
Samankaltainen aineisto
Näytetään aineistoja, joilla on samankaltainen nimeke tai asiasanat.
-
Improvements and applications of the elements of prototype-based clustering
Hämäläinen, Joonas (Jyväskylän yliopisto, 2018) -
Intelligent solutions for real-life data-driven applications
Ivannikova, Elena (University of Jyväskylä, 2017)The subject of this thesis belongs to the topic of machine learning or, specifically, to the development of advanced methods for regression analysis, clustering, and anomaly detection. Industry is constantly seeking ... -
Assessment of nonnegative matrix factorization algorithms for electroencephalography spectral analysis
Hu, Guoqiang; Zhou, Tianyi; Luo, Siwen; Mahini, Reza; Xu, Jing; Chang, Yi; Cong, Fengyu (BioMed Central, 2020)Background Nonnegative matrix factorization (NMF) has been successfully used for electroencephalography (EEG) spectral analysis. Since NMF was proposed in the 1990s, many adaptive algorithms have been developed. However, ... -
On data mining applications in mobile networking and network security
Zolotukhin, Mikhail (University of Jyväskylä, 2014) -
Surrogate Modelling for Oxygen Uptake Prediction Using LSTM Neural Network
Davidson, Pavel; Trinh, Huy; Vekki, Sakari; Müller, Philipp (MDPI AG, 2023)Oxygen uptake (V̇O2) is an important metric in any exercise test including walking and running. It can be measured using portable spirometers or metabolic analyzers. Those devices are, however, not suitable for constant ...
Ellei toisin mainittu, julkisesti saatavilla olevia JYX-metatietoja (poislukien tiivistelmät) saa vapaasti uudelleenkäyttää CC0-lisenssillä.