Real-Time Data Logging and Online Curve Fitting Using Raspberry Pi in Physics Laboratories

Real-Time Data Logging and Online Curve Fitting Using Raspberry Pi in Physics Laboratories

Wing-Kwong Wong (Department of Electronic Engineering, National Yunlin University of Science and Technology, Douliu, Taiwan), Kai-Ping Chen (Department of Electronic Engineering, National Yunlin University of Science and Technology, Douliu, Taiwan) and Jia-Wei Lin (Department of Electronic Engineering, National Yunlin University of Science and Technology, Douliu, Taiwan)
Copyright: © 2020 |Pages: 21
DOI: 10.4018/IJDET.2020070104


The results of PISA 2015 indicate that Taiwanese students have excellent mathematical and scientific knowledge but are weak in applying such knowledge and in conducting practical experiments in the laboratory. To support students conducting practical experiments in physics laboratories, a real-time data logging system and an online tool for fitting experimental data were developed. During data logging in an experiment, the data was immediately plotted, which enabled students to observe the characteristics of the plot. The online curve fitting system, which employed Internet of Things technologies, allowed students to fit experimental data to various mathematical functions and plot a function curve superimposed on the data. Two empirical studies were conducted involving first-year university students and secondary school teachers. The results indicated that these developed tools improved students' understanding of an experiment's mathematical characteristics. The average curve fitting error rates of students and teachers were 4.62% and 1.4%, respectively.
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To keep pace with industrial development, Taiwan’s recent 12-year national education curriculum has emphasized science, technology, engineering, and mathematics (STEM) education. At present, however, education in Taiwan focuses on paper-and-pencil examinations. This focus drives students to study hard for higher test scores and deemphasizes the conduct of scientific experiments. Many laboratory classes in school, for subjects such as life science and technology, have been replaced by textbook-based classes.

This constitutes a hidden crisis in STEM education. Although Taiwan has ostensibly achieved impressive results in scientific literacy in PISA 2015, the results of the PISA teacher questionnaire indicated that almost 56% of teachers require students to perform only one experiment and less than one student-designed experiment per semester. The student questionnaire yielded similar results: more than half the students stated that they had almost never designed their own experiments. This indicates a lack of practical experiments in school. By contrast, in the United States, educators believe that training professionals in subject areas is important, and that STEM education considerably improves the student’s ability to integrate and apply their knowledge (The White House, 2013; US Department of Education, 2014). The Next Generation Science Standards (NGSS) in the United States emphasizes three dimensions, one of which is science and engineering practice.

The emphasis on STEM education implies that technology is becoming increasingly important in the school curriculum (Bybee, 2010). Due to advances in hardware and software, large quantities of experimental data can be collected using microprocessors and advanced sensors at a fast sampling rate and with high precision. For example, PASCO Scientific and the MINDSTORMS series of robotic kits of LEGO have introduced the use of various data loggers and modular sensors for experimental data collection. However, in response to the high price of such commercial tools, some researchers have developed low-cost experimental data logging kits (Bermudez-Ortega, Besada-Portas, López-Orozco, Bonache-Seco, & De la Cruz, 2015; Chen et al., 2012; Church, Ford, Perova, & Rogers, 2010; Kuhn & Vogt, 2013; Liu, Wu, Wong, Lien, & Chao, 2017).

The development of science teaching tools is important for STEM education. Widiyatmoko and Nurmasitah (2013) believed that the teaching process must be paired with practical tools compatible with the school environment; hence, they developed tools for teaching science and obtained positive responses from teachers and students. Liu et al. (2017) developed low-cost loggers with open-source Arduino boards. Although empirical studies applying the loggers in schools have demonstrated the loggers’ overall effectiveness, two concerns remain. First, in these studies, data were collected from the data loggers on a personal computer (PC), in real time and through Bluetooth. However, PCs in schools may not be well-maintained and using them for data visualization can be a challenge. Thus, in this study, instead of solely relying on PCs, smartphones and tablets were used for data visualization during and after the experiment. Second, in the aforementioned studies, the logged data were not plotted in real time during data collection; students often plotted the data using Excel only after data collection. If a plot indicated mistakes in the data collection process, students had to rerun the experiment, which considerably slows down laboratory work. Thornton (2008) proposed that using real-time data logging tools in research-based curricula can improve students’ spatial ability and enable students to obtain answers directly from observation. Rather than appealing to authority, such as the teacher, for the “correct answer,” students can collect data empirically and display them in a pattern that can be remembered, apprehended, and manipulated. In this study, a data logger was developed. The data logger could visualize the logged data in real time through a web browser, whether on a smartphone, tablet, or PC. Moreover, a curve fitting tool, which could also be run on a web browser, was developed to help students obtain a model, among models of good fit, that best fits the data.

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