Thanyanan Somnam*, Phanuphat Srisukhawasu
Department of Physics, Mahidol Wittayanusorn School
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The International Science Olympiads, a group of worldwide annual competitions in various areas of the formal sciences, natural sciences, and social sciences originally inspired our work. These competitions have garnered significant interest from young students. They aim to challenge the brightest students from participating countries and to develop curricula and teaching methods that meet international standards. In Thailand, the Promotion of Academic Olympiads and Development of Science Education (POSN) foundation has been in charge of selecting students through the POSN centers scattered all over the country and organizing national Olympiad competitions to recruit highly talented representatives of Thailand.
Mahidol Wittayanusorn School (MWIT), a specialized science high school, has been assigned to provide advanced academic content and practical skills in eight subjects for students by organizing Olympiad camps and selecting representatives for national Olympiad competitions. Last year, the 21st Thailand Astronomy Olympiad (TAO 21), consisting of three main parts: theory, observation, and data analysis, was held in May 2024. To prepare our students, we analyzed previous feedback indicating that the summative assessments of our students’ data analysis skills were unsatisfactory. This could be due to a lack of effective practice within a limited timeframe. To address this issue, we aimed to develop a team-based learning approach in data analysis in astronomy to prepare representative students from the MWIT Astronomy Olympiad Center for TAO 21.
Learning design
This section introduces the learning design, incorporating the cognitive domain in Bloom’s taxonomy and the 21st-century essential skills. The original Taxonomy of Educational Objectives, commonly referred to as Bloom’s Taxonomy, was created by Benjamin Bloom in 1956, and later revised in 2001 (Anderson and Krathwohl, 2001). Bloom classified the cognitive domain of learning into various levels according to complexity. The complexity increases when traveling up the pyramid as shown in Figure 1. This framework is essential for instructors when designing a meaningful learning experience. In this study, team-based learning was implemented to guide students from simply remembering facts to creating new knowledge or outcomes. In addition, our approach fosters 21st-century essential skills, especially the four core learning skills: critical thinking, creativity, collaboration, and communication.
Methodology
This section outlines the methodology for improving data analysis skills through a team-based learning approach as illustrated in Figure 2. In the academic year 2023, we organized two Astronomy Olympiad camps and selected eight representative students of the MWIT Astronomy Olympiad Center. We conducted a One-Group Pretest-Posttest Design. We prepared two parallel practice exams in data analysis as pretest and posttest assessments. The students were given one hour to complete the pretest exam. They were then divided into two groups of four to share and discuss their processes and results. After corrections, the students completed the posttest exam under the same conditions and repeated the same methodology. Finally, we analyzed data from the study using related-samples Wilcoxon signed-rank test.
To prepare the parallel practice exams, each with a total score of 75 points, we took into account the following data analysis skills: presenting data using significant figures, estimating data uncertainty, data analysis using various types of graph plotting, interpreting data from images or experimental data graphs, organizing experimental data, and presenting data in an appropriate format. Both exams assess similar data analysis skills to ensure consistency in evaluation. Importantly, the content validity of the parallel exams was assessed by three experts. Only qualified questions with an Item-Objective Congruence (IOC) greater than 0.5 were selected for the exams.
The team-based learning activity was conducted following the pretest exam session (Figure 3). The students were divided into two groups of four to share and discuss their processes and results. Each group selected the best solutions for all questions to present to the class. This session allowed students to learn correct methods and alternative approaches to data analysis from one another while reflecting on and learning from their mistakes.
To analyze the data from this study, we applied descriptive statistics (mean, standard deviation, frequency, and skewness) to summarize the pretest and posttest exam scores. Additionally, we applied a nonparametric statistical method, the related-samples Wilcoxon signed-rank test, to test the study’s hypothesis. This method is well-suited for small sample sizes, continuous but non-normally distributed data, and two dependent samples (before-and-after measurements).
Results and Discussion
Figure 4 represents the analysis of the pretest exam results, showing a left-skewed distribution, while the analysis of the posttest exam results tends towards a normal distribution. Moreover, we analyzed the differences between posttest and pretest scores. Some negative differences were observed, which were attributed to students’ calculation errors rather than misconceptions. Overall, the mean score of the posttest (67.88 points) was higher than that of the pretest (58.95 points), with a smaller standard deviation. The related-samples Wilcoxon signed-rank test indicated a statistically significant improvement at the 0.05 significance level. In summary, our study highlights an alternative approach to improve data analysis skills and could pave the way for better preparation of representative students in other Astronomy Olympiad Centers in Thailand.
References
Anderson, L. W., & Krathwohl, D. R. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives (Complete edition). New York: Longman.
