Optimizing Applied Physics Learning through Computer-Based Practicum Modules

Study Case Optimizing Applied Physics Learning through Computer-Based Practicum Modules at the University of Cincinnati Michael Kurniawan (2018123456) University of Cincinnati, 2600 Clifton Ave, Cincinnati, OH 45221, United States kurniaml@mail.uc.edu Abstract This study aims to develop computer-based Applied Physics modules to improve the efficiency and effectiveness of learning in the Applied Physics laboratory. The modules developed include Free Fall Motion, Mechanical Heat Equivalence, Boyle's Law, Resistor Circuits, Moment of Inertia, and Power Factor in AC Current. The results of the study show that the average score of laboratory reports with computer assistance is significantly higher than laboratory reports on the same module without computer assistance. Efficiency measurements show that processing experimental data using computer software for calculations and graphing can overcome common obstacles, such as delays in report collection, lack of time for feedback from lecturers after data processing, and disruption to the next lecture schedule. Keywords: Applied Physics; Computer-Based Modules; Efficiency; Effectiveness. INTRODUCTION Applied Physics is one of the basic courses in engineering programs at universities, including the University of Cincinnati. As part of science, physics studies natural phenomena empirically, logically, systematically, and rationally with a measurable scientific approach. Practical activities are an important element in Applied Physics learning. Laboratory experiments serve as the main medium for students to understand physics concepts and principles through direct experience. Activity-based learning models and integrated practical activities have been proven to be significantly more effective than theoretical learning without practical activities. However, this method also requires more time for preparation and implementation. MICHAEL KURNIAWAN 1 Study Case Students need to complete pre-laboratory assignments, take preliminary tests, prepare equipment, conduct experiments, record data, process results, and compile written reports. Of the entire process, the data processing stage—such as creating tables, calculations, and graphs—is the most time-consuming part. The use of computer applications such as Microsoft Excel, MATLAB, or Python can significantly speed up this process compared to manual methods. In the 2013–2014 academic year, the Applied Physics Laboratory at the University of Cincinnati held practical activities for engineering students from various study programs as listed in Table 1. Table 1. Applied Physics Practicum Implementation for the 2013–2014 Academic Year The large number of students requires an efficient laboratory management system to ensure that learning continues to be effective and learning outcomes are optimal. The practicum modules used and developed in this study cover topics such as Free Fall Motion, Resistor Circuits, Moment of Inertia, Boyle's Law, Mechanical Heat Equivalence, and AC Current Power Factor. The distribution of the use of these modules in various study programs is shown in Table 2. MICHAEL KURNIAWAN 2 Study Case Table 2. Distribution of Applied Physics Practicum Modules Used in the Research This research is also part of a research roadmap developed by a group of lecturers at the Physics Laboratory, Department of Basic Sciences, University of Cincinnati. In 2012, preliminary research was conducted to improve laboratory capacity through the development of Standard Operating Procedures (SOP) and competency-based practicum modules. The following year, research focused on the development of computer-based modules as an improvement on the results of previous research. MICHAEL KURNIAWAN 3 Study Case Previously, students still processed experimental data manually or with calculators, which often caused delays in report collection, limited time for discussion of results with supervising lecturers, and disruption of subsequent academic activities. Therefore, a practicum method was developed that could speed up the data processing process so that students and lecturers had more time to analyze and provide feedback on the experimental results. This research was conducted over six months with the aim of developing a more effective and efficient method of managing practical learning through computer-based Applied Physics modules. LITERATURE REVIEW In Physics classes, students are introduced to various concepts, theories, principles, and basic laws of physics. In addition, they are also trained to conduct experiments both inside and outside the laboratory as part of the scientific process. During the learning process, students are expected to develop scientific attitudes such as honesty, objectivity, rationality, skepticism, and critical thinking skills. Through experimental experience, students learn to draw conclusions based on data and logical analysis, think systematically, and make decisions based on valid evidence. This trains them to be scientific, calm, and rational in dealing with differences of opinion or conflict (U.S. Physics Network, 2023). According to Sumadi (2008), mastery of physics concepts, scientific attitudes, laboratory skills, academic aspirations, self-efficacy, and creativity have a direct influence on student learning outcomes. Through experimental activities, students are required to cultivate scientific integrity. Without a scientific attitude, practical activities become meaningless because the results cannot be accounted for. Honesty in recording experimental data, accuracy in calculations, and openness to results that do not match theory are important competencies to be achieved in physics learning (Intan, 2012). In addition, practical work also trains students to be disciplined and responsible in completing tasks, managing time, and collaborating with teammates. This attitude is part of the professional skills needed in the modern world of engineering and science (U.S. Physics Network, 2023). Activity-based and integrated experiment-based applied physics learning has been proven to increase learning motivation because it creates cognitive conflict that encourages curiosity (Kaniawati, 2009). MICHAEL KURNIAWAN 4 Study Case In general, physics practicum activities in higher education are conducted using the Cook Book or experiment recipe method, where the instructor provides predetermined guidelines and equipment, and then students carry out the practicum steps and report the results (Ellianawati, 2010). The same pattern is also applied at the University of Cincinnati. Practical work at this university uses a journal-based reporting system, in which all practical activities and reports must be completed within approximately four academic hours. A common problem is time constraints, which often cause students to submit their reports late. Based on interview results, students spend most of their time on manual calculations, especially when the data being processed is quite large. To maintain efficiency without reducing data quality, the amount of data cannot be reduced because it is related to uncertainty calculations. Repeated measurements provide more accurate results than single measurements (Sardjito, 1996). The main emphasis in the practicum is not only on psychomotor skills, but also on analytical and critical thinking skills. Students are expected to spend most of their time interpreting data and deepening their understanding of concepts, rather than simply performing calculations. The book Experiments in Physics (2011) explains that the process of calculation and graphing can be shortened with the help of software without reducing the essence of the data. Several software programs, such as Mathematica, MAPLE, and Microsoft Excel, are designed to speed up repetitive mathematical calculations (Kutzler, 1996). Several universities in the United States, including Columbia University, have integrated laboratory report formats with computational software so that students can perform calculations and visualize data more easily and accurately. The ease of processing and presenting data in an attractive form in tables and graphs is expected to increase student motivation to learn, which in turn will have a positive impact on learning outcomes. Previous studies have also shown that computer-based learning can significantly improve student academic performance. METHOD The research method applied in this study is the experimental method. The experimental approach is used to conduct objective, systematic, and controlled studies, with the aim of predicting and controlling a phenomenon. The main objective of this MICHAEL KURNIAWAN 5 Study Case experimental study is to investigate cause-and-effect relationships by treating one or more experimental groups under certain conditions, then comparing the results with a control group that did not receive treatment (Danim, 2002). The research procedure was carried out in several stages. The first stage involved evaluating the effectiveness and time efficiency of processing practical module data, both manually and digitally, along with preparing parameters and research instruments. The second stage included developing a computer-assisted physics practical module, creating student worksheets in Microsoft Excel, and preparing research instruments for data collection. The next stage was a trial involving University of Cincinnati students in laboratory practice, analysis of the effectiveness and efficiency of report preparation, followed by feedback and module revision. The research data were analyzed using comparative statistical techniques, correlation, and other descriptive analyses. The research steps are generally described in the following flowchart: Figure 2. Research Process Flowchart MICHAEL KURNIAWAN 6 Study Case RESULTS AND DISCUSSION The research population, as shown in Table 1, consisted of all University of Cincinnati students who took the Applied Physics Laboratory course in the 2013 academic year. Based on the Slovin method, a sample size of 184 students was obtained. The student sample was divided into two independent groups: 1. The group that used computer-assisted modules, and 2. The group that did not use computer assistance. For example, students who conducted the “Free Fall Motion” experiment using computers were different from students who conducted the same experiment manually. The sample distribution is shown in Table 3. Table 3. Research Sample Distribution Of the six experiment modules developed, only four modules were tested due to time constraints (6 months). Data collection was carried out in the first and second rounds of the Applied Physics laboratory sessions. Measurement of the Effectiveness of Using Computer-Assisted Modules 1. Average Practical Report Scores The average practical report score was used as an indicator of the effectiveness of using computer-assisted modules. Figure 3 shows a comparison of the average scores between the class that used computers and the class that did not. MICHAEL KURNIAWAN 7 Study Case Figure 3. Comparison of Average Practical Report Scores To determine whether there was a significant difference between the two groups, an independent two-sample statistical test was conducted. Hypotheses used: • H₀: μ₁ = μ₂ (There is no significant difference in the average report scores) • Hₐ: μ₁ ≠ μ₂ (There is a significant difference in the average report scores) Testing rules: If: −tₜabel ≤ tₕitung ≤ tₜabel → H₀ is accepted If: |tₕitung| ≥ tₜabel → H₀ rejected The comparative test results are presented in the following tables: Table 4. Comparative Test Results for the Free Fall Module MICHAEL KURNIAWAN 8 Study Case Table 5. Comparative Test Results for the Mechanical Heat Module Table 6. Comparative Test Results for the Boyle's Law Module Table 7. Comparative Test Results for the Resistor Circuit Module MICHAEL KURNIAWAN 9 Study Case Table 8. Summary of Comparative Test Results Table 8 shows that for the first three modules (Free Fall Motion, Mechanical Heat Equivalence, and Boyle's Law), the use of computers significantly improved the practical report scores compared to the manual method. Conversely, for the Resistor Circuit module, the difference in scores was not significant because the data analysis process in this module only involved simple calculations, which did not benefit greatly from the use of computers. The use of software (such as Excel) helped students create graphs, perform error analysis, and understand concepts more deeply. The improvement in the quality of answers in the analysis and conclusion sections showed a clear increase in conceptual understanding. 2. Achievement of Applied Physics Practicum Objectives The achievement of practicum objectives is measured based on the achievement of competencies in each module. Table 9 shows the core competencies for each practicum topic. A correlation analysis was conducted to determine the relationship between the average practicum score (X) and the competency assessment score (Y) using the Pearson correlation coefficient (r). MICHAEL KURNIAWAN 10 Study Case Table 9. Competencies Based on Applied Physics Practicum Module Topics Table 10. Correlation Results of Practicum Scores and Competencies MICHAEL KURNIAWAN 11 Study Case Table 11. Correlation Level and Strength of Relationship The calculation results obtained r = 0.824, which according to Table 11 indicates a very strong relationship. This means that students with high report scores also demonstrate a high level of competency mastery. Measurement of Computer-Assisted Module Efficiency Learning efficiency was measured through four main parameters: 1. Ease of data processing, 2. Timeliness of practicum completion, 3. Availability of time for feedback, and 4. Timeliness of report collection. The results of the efficiency parameter analysis are visualized in Figure 4. Figure 4. Comparison of Efficiency Parameter Achievement The results show that with the use of computer-based modules, 100% of students stated that data processing was easy and that they completed the practicum on time. However, only 53% used the feedback time optimally, even though 90% of reports were submitted on time. Conversely, for the group without computer assistance, although 100% of the practicums were completed on time, only 18% of students found data processing easy. MICHAEL KURNIAWAN 12 Study Case This limitation had an impact on feedback efficiency (only 35%) and timeliness of report submission (41%). It can be concluded that computer-assisted modules significantly improve the efficiency of applied physics learning. Student Questionnaire Results To determine students' perceptions of the use of computer-assisted modules, a survey was conducted using a Likert scale questionnaire. The results are presented in Table 12 (criteria) and Table 13 (response summary). Table 12. Questionnaire Criteria and Answer Options Table 13. Student Response Summary Of all the modules, an average of 54% of students in the computer-assisted group agreed that the experiment objectives were easy to achieve. A total of 64.5% stated that they understood the concepts better, and 55.5% felt that they were able to interpret them well. MICHAEL KURNIAWAN 13 Study Case Most (42.1%) considered data processing to be easy, while 61.6% stated that they had the opportunity to analyze the experiment data in greater depth. In addition, 47% of students felt that their interest and curiosity had increased, and 46.6% admitted to being more active and creative during the practicum. In contrast, students without computer assistance tended to give a “fair” rating on most aspects, indicating that the response to learning was more positive in the computer-assisted group. CONCLUSION Based on the research results, the following conclusions were drawn: 1. The use of computer-assisted modules significantly improved the practical report scores for the Free Fall Motion, Mechanical Heat Capacity, and Boyle's Law modules. For the Resistor Circuit module, the difference was not significant. 2. Computer assistance is effective, especially for experiments involving complex calculations and graphing, but has less effect on simple experiments. 3. There is a very strong relationship (r = 0.824) between report scores and student competency achievement. 4. In terms of efficiency, the computer-assisted module showed better results in the four main parameters: ease of data processing, accuracy of practicum completion time, feedback, and accuracy of report collection. 5. Based on the questionnaire results, students with the computer-assisted module: • Achieve experimental objectives more easily, • Understand physics concepts better, • Are more skilled at data processing, • Do not experience difficulties in calculations, • Are more active in analyzing data, • Do not face obstacles in answering questions, • Have higher interest and curiosity, • And become more active, independent, and creative learners. MICHAEL KURNIAWAN 14 Study Case REFERENCE MICHAEL KURNIAWAN 15 Study Case MICHAEL KURNIAWAN 16