• Journal of Elementary Mathematics Education in Korea
• /
• v.17 no.2
• /
• pp.225-243
• /
• 2013

This study was to analyze the effects of basic computational abilities using G-learning contents, which was developed for mathematically slow learners. The participant students were 146 mathematically slow learners in grade 3-6 in Seoul. The researchers analyzed the difference between pre and post test scores to check their effectiveness. Also, teachers and parents as well as students responded survey items to check dispositions and satisfactions towards the program. The research results showed that the application of the G-learning contents on basic computation areas was effective to develop students' basic computational skills. In addition, students also showed that they were satisfied studying basic computations with the G-learning contents. They had increased beliefs about and decreased difficulties in mathematics. Parents and teachers also had satisfactions in using the G-learning programs in spite of some negative effects such as errors in the contents, use of computers, and concentration on the game itself. For the improvement of G-learning contents, we need to keep studying on G-learning contents with wide range of areas and long term studies.

• The Mathematical Education
• /
• v.63 no.2
• /
• pp.139-163
• /
• 2024

The purpose of this study is to examine the capabilities of ChatGPT as a tool for supporting students in generating mathematical arguments that can be considered proofs. To examine this, we engaged students enrolled in a mathematics pathways course in evaluating and revising their original arguments using ChatGPT feedback. Students attempted to find and prove a method for the area of a triangle given its side lengths. Instead of directly asking students to prove a formula, we asked them to explore a method to find the area of a triangle given the lengths of its sides and justify why their methods work. Students completed these ChatGPT-embedded proving activities as class homework. To investigate the capabilities of ChatGPT as a proof tutor, we used these student homework responses as data for this study. We analyzed and compared original and revised arguments students constructed with and without ChatGPT assistance. We also analyzed student-written responses about their perspectives on mathematical proof and proving and their thoughts on using ChatGPT as a proof assistant. Our analysis shows that our participants' approaches to constructing, evaluating, and revising their arguments aligned with their perspectives on proof and proving. They saw ChatGPT's evaluations of their arguments as similar to how they usually evaluate arguments of themselves and others. Mostly, they agreed with ChatGPT's suggestions to make their original arguments more proof-like. They, therefore, revised their original arguments following ChatGPT's suggestions, focusing on improving clarity, providing additional justifications, and showing the generality of their arguments. Further investigation is needed to explore how ChatGPT can be effectively used as a tool in teaching and learning mathematical proof and proof-writing.

• Journal of The Korean Association For Science Education
• /
• v.34 no.7
• /
• pp.693-701
• /
• 2014

Based on social cognitive theory, self-efficacy in the context of learning has been steadily emphasized as an indicator of students' motivation and performance. The premise for developing such an instrument was that a specific measure of Physics self-efficacy was deemed to be an important predictor of the change processes necessary to improve students' physics understanding. In this study we described the process of developing and validating an instrument to measure students' beliefs in their abilities to perform essential tasks in physics and then investigated high school students' self-efficacy about physics learning and performance. Validity and reliability of PSEI were tested using various statistical techniques including the Cronbach alpha coefficient, exploratory factor analysis. The result of factor analysis supported the contention that the Physics Self-Efficacy Inventory (PSEI) was a multidimensional construct consisting of at least four dimensions: understanding and application of Physics concepts, achievement motivation, confidence for physics laboratory, confidence for Mathematics. The result showed that Kroean high schools students have low Physics self-efficacy for the all four dimensions. Therefore, researchers should focus on development of students' Physics self-efficacy. In addition, the instrument may lead to further understanding of student behavior, which in turn can facilitate the development of strategies that may increase students' aspiration to understand and study Physics. More specifically, by using the PSEI as a pre- and post-test indicator, instructors can gain insight into whether students' confidence levels increase as they engage in learning Physics, and, in addition, what type of teaching strategies are most effective in building deeper understanding of Physics concepts.where they freely exchanged opinions and feedback for constructing better collective ideas.

• Asia pacific journal of information systems
• /
• v.24 no.2
• /
• pp.233-253
• /
• 2014

Can the stock market really be predicted? Stock market prediction has attracted much attention from many fields including business, economics, statistics, and mathematics. Early research on stock market prediction was based on random walk theory (RWT) and the efficient market hypothesis (EMH). According to the EMH, stock market are largely driven by new information rather than present and past prices. Since it is unpredictable, stock market will follow a random walk. Even though these theories, Schumaker [2010] asserted that people keep trying to predict the stock market by using artificial intelligence, statistical estimates, and mathematical models. Mathematical approaches include Percolation Methods, Log-Periodic Oscillations and Wavelet Transforms to model future prices. Examples of artificial intelligence approaches that deals with optimization and machine learning are Genetic Algorithms, Support Vector Machines (SVM) and Neural Networks. Statistical approaches typically predicts the future by using past stock market data. Recently, financial engineers have started to predict the stock prices movement pattern by using the SNS data. SNS is the place where peoples opinions and ideas are freely flow and affect others' beliefs on certain things. Through word-of-mouth in SNS, people share product usage experiences, subjective feelings, and commonly accompanying sentiment or mood with others. An increasing number of empirical analyses of sentiment and mood are based on textual collections of public user generated data on the web. The Opinion mining is one domain of the data mining fields extracting public opinions exposed in SNS by utilizing data mining. There have been many studies on the issues of opinion mining from Web sources such as product reviews, forum posts and blogs. In relation to this literatures, we are trying to understand the effects of SNS exposures of firms on stock prices in Korea. Similarly to Bollen et al. [2011], we empirically analyze the impact of SNS exposures on stock return rates. We use Social Metrics by Daum Soft, an SNS big data analysis company in Korea. Social Metrics provides trends and public opinions in Twitter and blogs by using natural language process and analysis tools. It collects the sentences circulated in the Twitter in real time, and breaks down these sentences into the word units and then extracts keywords. In this study, we classify firms' exposures in SNS into two groups: positive and negative. To test the correlation and causation relationship between SNS exposures and stock price returns, we first collect 252 firms' stock prices and KRX100 index in the Korea Stock Exchange (KRX) from May 25, 2012 to September 1, 2012. We also gather the public attitudes (positive, negative) about these firms from Social Metrics over the same period of time. We conduct regression analysis between stock prices and the number of SNS exposures. Having checked the correlation between the two variables, we perform Granger causality test to see the causation direction between the two variables. The research result is that the number of total SNS exposures is positively related with stock market returns. The number of positive mentions of has also positive relationship with stock market returns. Contrarily, the number of negative mentions has negative relationship with stock market returns, but this relationship is statistically not significant. This means that the impact of positive mentions is statistically bigger than the impact of negative mentions. We also investigate whether the impacts are moderated by industry type and firm's size. We find that the SNS exposures impacts are bigger for IT firms than for non-IT firms, and bigger for small sized firms than for large sized firms. The results of Granger causality test shows change of stock price return is caused by SNS exposures, while the causation of the other way round is not significant. Therefore the correlation relationship between SNS exposures and stock prices has uni-direction causality. The more a firm is exposed in SNS, the more is the stock price likely to increase, while stock price changes may not cause more SNS mentions.