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（Physics Education Research, PER）的学生表现数据集。该数据集作为多领域的学生物理表现学习数据集发布，其数据源自物理教育研究社区构建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稠密省份郊区行政区内的3所大型公立高中与1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、文献资源获取途径及物理身份认同相关的多项变量。本次数据所采用的部分基于研究的评估工具，均依据印尼物理课程中学生所学的物理概念进行选取。本研究于第一学期末通过力概念问卷（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。第二学期中，研究者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。第二学期的概念性评估还涵盖了转动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）。本团队期望SPHERE数据集能够为物理教育研究领域的发展，尤其是定量研究领域，提供有价值的数据支撑。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，常因缺乏适配物理教育研究特定场景的数据集而面临挑战，SPHERE数据集可为此类研究提供有效支持。SPHERE数据集可作为专门面向物理教育研究学者的学生物理表现数据集重复使用，助力其在物理教育领域应用机器学习相关技术。