Scholarlink

<p>There is a need to help advance research on using machine learning and data mining techniques in physics education research (PER), which might still be difficult due to the unavailable dataset for the specific purpose of PER. The SPHERE (Students’ Performance Dataset in Physics Education Research) is presented as an educational dataset of physics learning collected through research-based assessments (RBAs) established by&#xa0;the PER scholars. In this study, students’ performance in physics at four public high schools was probed in three learning domains. It encompassed students’ conceptual understanding, scientific ability, and learning attitude toward physics. The employed RBAs were identified based on the curriculum of physics contents taught to the eleventh-grade students in the ongoing academic year. In this paper, we provide an example that SPHERE could be insightful for training machine learning models to predict students’ performance at the end of the learning process. We also revealed that its predictive performance was superior to the former method of students’ performance prediction as labeled by the physics teachers.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Students’ performance dataset for using machine learning technique in physics education research

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月12日

查看全文
  • Purwoko Haryadi Santoso,
  • Bayu Setiaji,
  • Yohanes Kurniawan,
  • Wahyudi,
  • Syamsul Bahri,
  • Fathurrahman,
  • Mobinta Kusuma,
  • Indah Urwatin Wusqo,
  • Nuri Dewi Muldayanti,
  • Arif Didik Kurniawan,
  • Johan Syahbrudin

摘要

There is a need to help advance research on using machine learning and data mining techniques in physics education research (PER), which might still be difficult due to the unavailable dataset for the specific purpose of PER. The SPHERE (Students’ Performance Dataset in Physics Education Research) is presented as an educational dataset of physics learning collected through research-based assessments (RBAs) established by the PER scholars. In this study, students’ performance in physics at four public high schools was probed in three learning domains. It encompassed students’ conceptual understanding, scientific ability, and learning attitude toward physics. The employed RBAs were identified based on the curriculum of physics contents taught to the eleventh-grade students in the ongoing academic year. In this paper, we provide an example that SPHERE could be insightful for training machine learning models to predict students’ performance at the end of the learning process. We also revealed that its predictive performance was superior to the former method of students’ performance prediction as labeled by the physics teachers.