SPHERE: Students' performance dataset of conceptual understanding, scientific ability, and learning attitude in physics education research (PER)

The SPHERE is students' performance in physics education research dataset. It is presented as a multi-domain learning dataset of students’ performance on physics that has been collected through several research-based assessments (RBAs) established by the physics education research (PER) community. A total of 497 eleventh-grade students were involved from three large and a small public high school located in a suburban district of a high-populated province in Indonesia. Some variables related to demographics, accessibility to literature resources, and students’ physics identity are also investigated. Some RBAs utilized in this data were selected based on concepts learned by the students in the Indonesian physics curriculum. We commenced the survey of students’ understanding on Newtonian mechanics at the end of the first semester using Force Concept Inventory (FCI) and Force and Motion Conceptual Evaluation (FMCE). In the second semester, we assessed the students’ scientific abilities and learning attitude through Scientific Abilities Assessment Rubrics (SAAR) and the Colorado Learning Attitudes about Science Survey (CLASS) respectively. The conceptual assessments were continued at the second semester measured through Rotational and Rolling Motion Conceptual Survey (RRMCS), Fluid Mechanics Concept Inventory (FMCI), Mechanical Waves Conceptual Survey (MWCS), Thermal Concept Evaluation (TCE), and Survey of Thermodynamic Processes and First and Second Laws (STPFaSL). We expect SPHERE could be a valuable dataset for supporting the advancement of the PER field particularly in quantitative studies. For example, there is a need to help advance research on using machine learning and data mining techniques in PER that might face challenges due to the unavailable dataset for the specific purpose of PER studies. SPHERE can be reused as a students’ performance dataset on physics specifically dedicated for PER scholars which might be willing to implement machine learning techniques in physics education.SPHERE数据集是面向物理教育研究的学生学业表现数据集。该数据集属于多领域学习数据集，收录了学生在物理学科的学业表现数据，数据源自物理教育研究（Physics Education Research, PER）社群制定的多项基于研究的评估（Research-Based Assessments, RBAs）。本次研究共纳入印度尼西亚某人口大省郊区下辖三所大型公立高中与一所小型公立高中的497名十一年级学生。研究同时调查了与学生人口统计学特征、文献资源获取情况以及物理学科自我认同相关的多项变量。本次数据所采用的部分RBAs，是根据印尼物理课程中学生需掌握的知识点筛选而来。第一学期末，我们使用力概念问卷（Force Concept Inventory, FCI）与力与运动概念评估（Force and Motion Conceptual Evaluation, FMCE），对学生的牛顿力学理解水平开展调查。第二学期，我们分别通过科学能力评估量规（Scientific Abilities Assessment Rubrics, SAAR）与科罗拉多科学学习态度调查（Colorado Learning Attitudes about Science Survey, CLASS），评估学生的科学能力与学习态度。第二学期还延续了概念性测评，采用旋转与滚动运动概念调查（Rotational and Rolling Motion Conceptual Survey, RRMCS）、流体力学概念问卷（Fluid Mechanics Concept Inventory, FMCI）、机械波概念调查（Mechanical Waves Conceptual Survey, MWCS）、热学概念评估（Thermal Concept Evaluation, TCE）以及热力学过程与一二定律调查（Survey of Thermodynamic Processes and First and Second Laws, STPFaSL）完成相关测量。我们期望SPHERE数据集能够为物理教育研究领域的发展，尤其是定量研究提供宝贵的数据支持。例如，当前物理教育研究领域在运用机器学习与数据挖掘技术开展相关研究时，常因缺乏适配PER研究场景的专用数据集而面临挑战，而SPHERE可作为面向物理教育研究学者的专用物理学生学业表现数据集，供有意愿将机器学习技术应用于物理教育领域的研究者复用。