The Covid-19 pandemic indicates that distance education needs an interactive learning environment that students take part in actively.  PhET software simulation provides the learners such a learning environment on science education.  PhET is a simulation developed by the University of Colorado, a physics, biology, and chemistry simulation. PhET simulation emphasizes the relationship between real-life phenomena and the underlying science, supports learning with interactive and constructivist approaches, provides feedback, and provides a creative workplace (Perkins et at., 2006). The efficient integration of the simulations into a science course contribute to improvement in students outcomes.  One of those outcomes is students science process skills. Science process  skills are commonly defined as scientists' skills and characteristics (Carin  &  Bass,  2001;  Carin  &  Sund,  1989;  Ewers,  2001;  Padilla,  1986;  Rutherford  &  Ahlgren, 1990). They are all skills used to discover and develop science with the scientific method (Ramayanti et al., 2017). Science process skills are classified into basic science process skills and integrated science process skills (Shaw, 1983). The basic one includes ―observing, measuring, inferring, predicting, classifying, and collecting and recording data and the integrated contains ―interpreting data, controlling variables, defining operationally, formulating hypothesis, and experimenting (Shaw, 1983). Although there are some changes between the classifications, the necessary skills are always seen as the prerequisite to integrating skills during the science learning process (Ewers, 2001).

The current study focus on integrated science process skills. According to Okey et al. (1982) categorization, those skills are about identifying variables, identifying and stating a hypothesis, operationally defining, designing investigations, and graphing and interpreting the data.  Identifying variables is about the ability to categorize an experiment's variables as manipulative, responding, and controlled variables while identifying and expressing hypothesis is up to stating the experiment's expected outcomes. Besides, operationally defining refers to explaining how to measure the variable of an experiment. Designing investigations involves setting up an experiment based on a given hypothesis which also requires identifying the variables as manipulative, responding and controlled. Finally, graphing and interpreting the data is about organizing data, identify the graph representing the related data and draw conclusions from graphs about the relationships between variables. Acquiring these skills provides to use those processes in science class and retain them for future use (Padilla, 1990), the long-lasting effect of those skills are essential for gifted education. Therefore, currents study aims to improve gifted students' science process skills through PhET simulations. Science process skills provide gifted students with a vision of a scientist, utilizing the scientific methods to solve the problems encountered in their whole life, question, investigate and connect scientific knowledge with their daily lives. In gifted education, skill development has been emphasized through students' experiences (Day & O'Connor, 2017). Domain-specific activities are suggested for supporting those students' skill development (Taber & Riga, 2016).

The current study sample was a group of middle school gifted students taking science education in Turkey. The design of the study is the one-group pretest-posttest experimental design. The Science Process Skills Test was used in pretest and posttest to test the effectiveness of distance science education based on PhET simulations during eight-weeks. The Science Process Skills Test was originally developed by Okey et al. (1982) and translated and adapted into Turkish by Ozkan, Askar, and Geban, (1989). The original version of the test covers 36 multiple choice questions that measure students' skills related to identifying variables, identifying and stating hypotheses, operationally defining, designing investigations, and graphing and interpreting the data. Each question of the test presents four alternatives with only one correct answer. KR-20 values were calculated as a reliability coefficient, and it was reported as, .86 for original version and .81 for Turkish version. Although the original version developers administered the test to middle and high school students, 36 items were considered excessive for middle school students to complete the test within a class period. Thus, Can(2008) developed the short version by eliminating ten items having lower discrimination indices than .20, which is calculated based on the data collected from 227 seventh grade students from four elementary schools. KR-20 value was found to be .80 for the new 26-item test. So the test was decided to use in the present study.

Descriptive statistics for pretest and posttest will be presented to observe the difference at the end of the eight-weeks distance science education based on PhET simulations. Also, to test the significance of the difference, statistical analysis will be conducted, and results will be reported. A positive effect of the conducted intervention is expected.

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