• Journal of The Korean Association For Science Education
• /
• v.43 no.4
• /
• pp.351-368
• /
• 2023

The scientist-led Research and Education (R&E) program aims to strengthen gifted science students' research capabilities under the guidance of scientists. Students' actual research experiences in scientist-led R&E activities range from understanding how scientists conduct research to actively participating in research. To develop R&E that promotes student agency, i.e., student participation, this study aimed to identify the pedagogical characteristics that supported gifted science students' agentic participation in the scientist-led R&E program. We conducted interviews with learners and scientists in three teams undertaking R&E activities every three months. The interview covered their perceptions of R&E activities, student participation, and scientists' support for the activities. The recordings and transcripts of the interviews were used as primary data sources for the analysis. The trajectory of each team's activities, as well as the learners' and scientists' dynamic positioning were identified. Based on this analysis, we inductively identified the pedagogical characteristics that emerged from classes in which the scientists supported the students' learning and engagement in research. Regarding agency, three types of student participation were identified: 1) the sustained exercise of agency, 2) the initial exercise and subsequent discouragement of agency, and 3) the continuous non-exercise of agency. Two pedagogical characteristics that supported the learners' agentic participation were identified: 1) opportunities for students to take part in research management and 2) scientist-student interactions encouraging learners to present expert-level ideas. This study contributes to developing pedagogies that foster gifted science students' agentic participation in scientist-led R&E activities.

• The Mathematical Education
• /
• v.63 no.1
• /
• pp.19-33
• /
• 2024

This study investigates longitudinal patterns in middle school students' mathematics interest and achievement using panel data from the 4th to 6th year of the Gyeonggi Education Panel Study. Results from the multivariate growth mixture model confirmed the existence of heterogeneous characteristics in the longitudinal trajectory of students' mathematics interest and achievement. Students were classified into four latent classes: a low-level class with weak interest and achievement, a high-level class with strong interest and achievement, a middlelevel-increasing class where interest and achievement rise with grade, and a middle-level-decreasing class where interest and achievement decline with grade. Each class exhibited distinct patterns in the change of interest and achievement. Moreover, an examination of the correlation between intercepts and slopes in the multivariate growth mixture model reveals a positive association between interest and achievement with respect to their initial values and growth rates. We further explore predictive variables influencing latent class assignment. The results indicated that students' educational ambition and time spent on private education positively affect mathematics interest and achievement, and the influence of prior learning varies based on its intensity. The perceived instruction method significantly impacts latent class assignment: teacher-centered instruction increases the likelihood of belonging to higher-level classes, while learner-centered instruction increases the likelihood of belonging to lower-level classes. This study has significant implications as it presents a new method for analyzing the longitudinal patterns of students' characteristics in mathematics education through the application of the multivariate growth mixture model.

• Journal of the Korean earth science society
• /
• v.45 no.3
• /
• pp.173-191
• /
• 2024

Typhoons are significant meteorological phenomena that cause interactions among the ocean, atmosphere, and land within Earth's system. In particular, wind speed, a key characteristic of typhoons, is influenced by various factors such as central pressure, trajectory, and sea surface temperature. Therefore, a comprehensive understanding based on actual observational data is essential. In the 2015 revised secondary school textbooks, typhoon wind speed is presented through text and illustrations; hence, exploratory activities that promote a deeper understanding of wind speed are necessary. In this study, we developed a data visualization program with a graphical user interface (GUI) to facilitate the understanding of typhoon wind speeds with simple operations during the teaching-learning process. The program utilizes red-green-blue (RGB) image data of Typhoons Mawar, Guchol, and Bolaven -which occurred in 2023- from the Korean geostationary satellite GEO-KOMPSAT-2A (GK-2A) as the input data. The program is designed to calculate typhoon wind speeds by inputting cloud movement coordinates around the typhoon and visualizes the wind speed distribution by inputting parameters such as central pressure, storm radius, and maximum wind speed. The GUI-based program developed in this study can be applied to typhoons observed by GK-2A without errors and enables scientific exploration based on actual observations beyond the limitations of textbooks. This allows students and teachers to collect, process, analyze, and visualize real observational data without needing a paid program or professional coding knowledge. This approach is expected to foster digital literacy, an essential competency for the future.

• Journal of Intelligence and Information Systems
• /
• v.17 no.1
• /
• pp.53-69
• /
• 2011

Vision and voice-based technologies are commonly utilized for human-robot interaction. But it is widely recognized that the performance of vision and voice-based interaction systems is deteriorated by a large margin in the real-world situations due to environmental and user variances. Human users need to be very cooperative to get reasonable performance, which significantly limits the usability of the vision and voice-based human-robot interaction technologies. As a result, touch screens are still the major medium of human-robot interaction for the real-world applications. To empower the usability of robots for various services, alternative interaction technologies should be developed to complement the problems of vision and voice-based technologies. In this paper, we propose the use of accelerometer-based gesture interface as one of the alternative technologies, because accelerometers are effective in detecting the movements of human body, while their performance is not limited by environmental contexts such as lighting conditions or camera's field-of-view. Moreover, accelerometers are widely available nowadays in many mobile devices. We tackle the problem of classifying acceleration signal patterns of 26 English alphabets, which is one of the essential repertoires for the realization of education services based on robots. Recognizing 26 English handwriting patterns based on accelerometers is a very difficult task to take over because of its large scale of pattern classes and the complexity of each pattern. The most difficult problem that has been undertaken which is similar to our problem was recognizing acceleration signal patterns of 10 handwritten digits. Most previous studies dealt with pattern sets of 8~10 simple and easily distinguishable gestures that are useful for controlling home appliances, computer applications, robots etc. Good features are essential for the success of pattern recognition. To promote the discriminative power upon complex English alphabet patterns, we extracted 'motion trajectories' out of input acceleration signal and used them as the main feature. Investigative experiments showed that classifiers based on trajectory performed 3%~5% better than those with raw features e.g. acceleration signal itself or statistical figures. To minimize the distortion of trajectories, we applied a simple but effective set of smoothing filters and band-pass filters. It is well known that acceleration patterns for the same gesture is very different among different performers. To tackle the problem, online incremental learning is applied for our system to make it adaptive to the users' distinctive motion properties. Our system is based on instance-based learning (IBL) where each training sample is memorized as a reference pattern. Brute-force incremental learning in IBL continuously accumulates reference patterns, which is a problem because it not only slows down the classification but also downgrades the recall performance. Regarding the latter phenomenon, we observed a tendency that as the number of reference patterns grows, some reference patterns contribute more to the false positive classification. Thus, we devised an algorithm for optimizing the reference pattern set based on the positive and negative contribution of each reference pattern. The algorithm is performed periodically to remove reference patterns that have a very low positive contribution or a high negative contribution. Experiments were performed on 6500 gesture patterns collected from 50 adults of 30~50 years old. Each alphabet was performed 5 times per participant using $Nintendo{(R)}$ $Wii^{TM}$ remote. Acceleration signal was sampled in 100hz on 3 axes. Mean recall rate for all the alphabets was 95.48%. Some alphabets recorded very low recall rate and exhibited very high pairwise confusion rate. Major confusion pairs are D(88%) and P(74%), I(81%) and U(75%), N(88%) and W(100%). Though W was recalled perfectly, it contributed much to the false positive classification of N. By comparison with major previous results from VTT (96% for 8 control gestures), CMU (97% for 10 control gestures) and Samsung Electronics(97% for 10 digits and a control gesture), we could find that the performance of our system is superior regarding the number of pattern classes and the complexity of patterns. Using our gesture interaction system, we conducted 2 case studies of robot-based edutainment services. The services were implemented on various robot platforms and mobile devices including $iPhone^{TM}$. The participating children exhibited improved concentration and active reaction on the service with our gesture interface. To prove the effectiveness of our gesture interface, a test was taken by the children after experiencing an English teaching service. The test result showed that those who played with the gesture interface-based robot content marked 10% better score than those with conventional teaching. We conclude that the accelerometer-based gesture interface is a promising technology for flourishing real-world robot-based services and content by complementing the limits of today's conventional interfaces e.g. touch screen, vision and voice.