Assessment of Science Process Skills in Physics Education: A Case Study on Electrostatics Among Grade 11 Students - Pathomphong Boonpratham; Kanyarat Cojorn; Chanat Inkanok (Unnes Science Education Journal, 2026)
Empirical evidence on students’ science process skills (SPS) proficiency within specific physics content areas remains limited in Thai secondary education, particularly in the context of electrostatics. This study is a survey research aimed at assessing the fundamental science process skills (SPS) of Grade 11 students in the context of electrostatics. A validated 30-item multiple-choice test covering eight SPS dimensions -observing, measuring, classifying, using space/time relationships, using numbers, organizing data and communicating, inferring, and predicting - was administered to 36 students enrolled in the Science-Mathematics-Technology-Environment (SMTE) Program at Sarakham Pittayakhom School Thailand, and analyzed using descriptive statistics and quartile distribution. The results revealed an overall mean score of 12.30 out of 30 (41.01%), reflecting a below-average level of SPS proficiency. Classifying was the strongest skill (mean = 2.53, 63.19%), with the majority of students reaching the third quartile (Q3), while using numbers (mean = 0.64, 21.30%) and predicting (mean = 0.97, 24.31%) were the weakest, with most students falling in the first quartile (Q1). The five remaining skills - observing, measuring, using space/time relationships, organizing data and communicating, and inferring-clustered at a moderate level in the second quartile (Q2). The pronounced difficulty in numerical and predictive skills is attributed not solely to general mathematical weakness, but to the abstract and invisible nature of electrostatic phenomena, which requires students to simultaneously apply scientific notation, interpret charge signs, and execute multi-step proportional reasoning without the support of direct sensory experience. These findings suggest that targeted instructional interventions-including problem-based learning, predict-observe-explain strategies, digital simulations, and adaptive learning technologies-are needed to explicitly scaffold the integration of quantitative procedures with conceptual understanding in electrostatics instruction.