Categorizing students into groups according to their learning attitudes based on cluster analysis

Authors

  • Janchiv Shinebayar School of Educational Studies, Mongolian National University of Education, Ulaanbaatar, Mongolia
  • Badarch Jadamba School of Educational Studies, Mongolian National University of Education, Ulaanbaatar, Mongolia
  • Ochirbat Altangoo School of mathematics and natural sciences, Mongolian National University of Education, Ulaanbaatar, Mongolia
  • Raash Namjildagva School of Educational Studies, Mongolian National University of Education, Ulaanbaatar, Mongolia
  • Tumurbaatar Ganbaatar School of mathematics and natural sciences, Mongolian National University of Education, Ulaanbaatar, Mongolia
  • Ravdandorj Togoo Laboratory of theory and high energy physics, Institute of Physics and Technology, Mongolian Academy of Sciences, Ulaanbaatar, Mongolia
  • Sukhbaatar Batchuluun Secondary school of Baruun-Urt soum, School No. 2, Sukhbaatar, Mongolia

DOI:

https://doi.org/10.5564/lavai.v18i27.2493

Keywords:

Classical test theory, cluster analysis, attitude to learning, evaluation, item response theory

Abstract

Tests are frequently used in any field of science education to assess students’ knowledge and skills. In this paper, we briefly introduce the results of the analysis on the test data of twelve grade students in Mongolia. These analyses are based on two approaches- classical test theories and cluster analysis. It is less important to measure learners’ attitudes through only one subject. We believe that students’ attitudes towards learning academic subjects and acquiring scientific education can be defined through their achievement data on their knowledge and skills on multiple subjects. According to learning attitudes, most researchers analyze the data using a survey that includes “Likert” scale statements and questions. In this paper, we have categorized students' learning attitudes based on their results of academic performances that assess only students’ knowledge and skills. Students are categorized into five groups according to their learning attitudes based on the two-step clustering components.

Кластер анализ ашиглан суралцагчдыг сурах хандлагаар ангилсан нь

Хураангуй
Суралцагчдын мэдлэг, чадварыг үнэлэхэд ихэвчлэн тестийг ашигладаг. Уг өгүүлэлд Сүхбаатар аймгийн II сургуулийн 12-р ангийн суралцагчдын улсын шалгалтын үр дүнгийн өгөгдөлд классик тестийн онол, кластер анализ зэрэг аргыг хэрэглэн анализ хийсэн үр дүнг толилуулж байна. Ангийн суралцагчдын сурах хандлагын хэв маягийг зөвхөн нэг хичээлийн эцсийн дүнгээр хэмжих нь ач холбогдол багатай. Ихэнх судлаачид суралцагчдыг сурах хандлагаар ангилахдаа лайкертын хэмжээс бүхий өгүүлбэрүүд болон асуултуудыг агуулсан судалгааны асуулгаар цуглуулсан өгөгдөлд анализ хийдэг. Харин, бид бүхэн зөвхөн суралцагчдын мэдлэг, чадварыг үнэлэх академик гүйцэтгэлийн үр дүнгээр суралцагчдыг сурах хандлагаар ангилсан нь онцлогтой. Шинжлэх ухааны буюу академик боловсролтой холбоотой сурах хандлагыг олон хичээлийн мэдлэг, чадварын цогц байдлаар авч үзэх нь зүйтэй гэдгийг баталж, кластерчлалын хоёр алхамт аргад тулгуурлан суралцагчдыг сурах хандлагаар 5 бүлэгт ангиллаа.

Түлхүүр үг: Классик тестийн онол, кластер анализ, сурах хандлага, үнэлгээ, даалгаврын хариултын онол

Abstract
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Published

2022-12-29

How to Cite

Shinebayar, J., Jadamba, B., Altangoo, O., Namjildagva, R., Ganbaatar, T., Togoo, R., & Batchuluun, S. (2022). Categorizing students into groups according to their learning attitudes based on cluster analysis. Lavai - International Journal of Education, 18(27), 2–13. https://doi.org/10.5564/lavai.v18i27.2493

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