Using Machine Learning to Score Multidimensional Assessments of Students' Skill Levels in Mathematics.
This research aims to establish a mathematical skill measurement model to examine seventhgrade students' mathematical skills in two aspects: their understanding of mathematical processes and the concept and structure. The researchers surveyed the mathematical skills of 521 seventh-grade students fro...
| Published in: | Pertanika Journal of Social Sciences & Humanities Vol. 32; no. 1; pp. 217 - 236 |
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| Main Authors: | , , , |
| Format: | Article |
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Universiti Putra Malaysia
Mar2024
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=176160867&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 176160867 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 01287702 TKQ jtl: Pertanika Journal of Social Sciences & Humanities issn: 01287702 maglogo: N pubinfo: dt: Mar2024 vid: 32 iid: 1 pid: 20751 pub: Universiti Putra Malaysia artinfo: ui: 176160867 10.47836/pjssh.32.1.10 ppf: 217 ppct: 19 formats: fmt: @attributes: type: P size: 3.1MB tig: atl: Using Machine Learning to Score Multidimensional Assessments of Students' Skill Levels in Mathematics. aug: au: Doungruethai Chitaree Putcharee Junpeng Suphachoke Sonsilphong Keow Ngang Tang affil: Faculty of Education, Khon Kaen University, 40002 Khon Kaen, Thailand Faculty of Medicine, Khon Kaen University, 40002 Khon Kaen, Thailand Faculty of Business, Hospitality and Humanities, Nilai University, 71800 Nilai, Negeri Sembilan, Malaysia su: Machine learning Mathematical ability Seventh grade (Education) Multilevel models Rasch models sug: subj: Machine learning Mathematical ability Seventh grade (Education) Multilevel models Rasch models keyword: Construct modeling approach machine learning mathematical skill measurement model Rasch model analysis seventh-grade students ab: This research aims to establish a mathematical skill measurement model to examine seventhgrade students' mathematical skills in two aspects: their understanding of mathematical processes and the concept and structure. The researchers surveyed the mathematical skills of 521 seventh-grade students from the northeastern province of Thailand. Their test results were used to prototype a mathematical skill measurement model using machine learning. It involved a design-based approach that included four stages: a construct map, item design, a Wright Map, and outcome space, the so-called Multidimensional Random Coefficient Multinomial Logit Model, to verify its quality. The initial findings revealed the creation of a construct map consisting of five levels. The researchers determined the cut-off point in the form of the threshold level after considering the Wright Map criteria area for each aspect. Lastly, the measurement model was examined to provide adequate evidence of the internal structure's validity and reliability. In conclusion, students' skill levels can be measured accurately using multidimensional assessments, even though the levels of mathematical capabilities of the students varied from low to moderate to high. Therefore, it provides significant evidence of the mathematical skill measurement model to diagnose seventh-grade students' learning. The significant implications contributed to educational measurement and evaluation are that machine learning algorithms can provide more accurate and consistent scoring of assessments compared to human graders. With accurate assessment using machine learning, teachers can gain deeper insights into individual students' mathematical skills across multiple dimensions. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Copyright of Pertanika Journal of Social Sciences & Humanities is the property of Universiti Putra Malaysia and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. item: Pertanika Journal of Social Sciences & Humanities holder: Universiti Putra Malaysia dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
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