Identification of Influential Factors that Affect Students\u27 Behaviors in Traditional Classes Versus Technology-Mediated Learning (TML) Classes

Learning environments are rapidly changing from the traditional setting to include the use of multimedia technology in the classroom. In the past, researchers studied how the use of technology as a learning tool affects students’ learning and performance. There are, however, few studies that report students’ learning behavior in technology based learning environments. The purpose of this study is to find out whether or not there are any unique behaviors exhibited by students that are related to a different learning environment. In this study, two researchers observed two undergraduate elementary statistics classes (traditional class versus Technology-Mediated Learning (TML) class), and documented student behavioral differences between them. The data included quantitative and qualitative observations based on specific behavior categories. The results of the analysis lead to identification of six influential factors that affect students’ learning behaviors in different learning environments. Implications of res ts for both educators and administrators are discussed

Three Essays on Monetary and Fiscal Policy

Monetary and fiscal policies are two main tools to steer the economy. How they are or will be implemented affects the private sector’s behavior and expectation about the future, thus the macroeconomic dynamics. In this thesis, I study the monetary and fiscal policies recently introduced in reality or documented empirically. Chapter 1 studies the nonlinear properties of average inflation targeting(AIT) and explores how those affect the fiscal multiplier. Chapter 2 estimates state-dependent fiscal multipliers. Instead of widely used states, such as business cycles or zero lower bound(ZLB) episodes, I estimate them conditional on the credit cycle. Chapter 3 investigates the effects of negative interest rate policy(NIRP) expectation on the fiscal multiplier.Chapter 1 shows that it matters how to solve the model both with AIT and with occasionally binding ZLB. When the model is solved based on the first-order perturbation method in a piecewise linear fashion, the social welfare increases as the averaging window lengthens, which is in line with the conventional wisdom. On the contrary, if the model is solved fully nonlinearly, the social welfare starts decreasing beyond the medium window length, in our baseline case, a 6-year window, while it rises steadily up to that threshold. With the longer window AIT, the agents expect a much looser policy when ZLB is binding, which results in less possibility of binding constraint through the high inflation expectation. This effect becomes stronger under a longer window AIT so that average inflation gets higher than the target. For the monetary authority to meet the target, it raises the rates in the longer window AIT, unlike the shorter window AIT. These opposite reactions increase social welfare up to a 6-year window but generate lower welfare beyond that. This property also Hyundo Joo June 2022 Economics affects the fiscal multipliers under AITs with different window lengths both outside the ZLB and at the ZLB. Chapter 2 demonstrates that the credit data anticipate the government spending shocks identified without controlling the credit market. To circumvent this problem, the shocks are extracted with real credit data as well as conventional real variables. Using this new series of shocks, I provide new evidence on the effects of unexpected changes in government spending conditional on the credit cycle. The on-impact output multiplier is 1.85 in the credit recession and 0.92 outside the recession. Since the credit cycle defined is not much overlapped with the business cycle, the results seem not driven by business cycle dependency. In addition, the results are robust to alternative model specifications. Finally, Chapter 3 finds that the fiscal multiplier decreases nonlinearly as the private sector anticipates the implementation of NIRP more in the future. While the on-impact fiscal multiplier at the ZLB is 1.5 without any expectation about NIRP, it falls below 1 when the agents expect the economy to switch the NIRP in two quarters, on average. This result is not derived from the fact that the fiscal multiplier under NIRP is smaller than one. In fact, in the baseline simulation, it is still higher than the unity, 1.52. This shows that the possibility of NIRP (or, more broadly speaking, much looser policies) mitigates the size of the fiscal multiplier at the ZLB, and that effect is nontrivial

Three Essays on Monetary and Fiscal Policy

Monetary and fiscal policies are two main tools to steer the economy. How they are or will be implemented affects the private sector’s behavior and expectation about the future, thus the macroeconomic dynamics. In this thesis, I study the monetary and fiscal policies recently introduced in reality or documented empirically. Chapter 1 studies the nonlinear properties of average inflation targeting(AIT) and explores how those affect the fiscal multiplier. Chapter 2 estimates state-dependent fiscal multipliers. Instead of widely used states, such as business cycles or zero lower bound(ZLB) episodes, I estimate them conditional on the credit cycle. Chapter 3 investigates the effects of negative interest rate policy(NIRP) expectation on the fiscal multiplier.Chapter 1 shows that it matters how to solve the model both with AIT and with occasionally binding ZLB. When the model is solved based on the first-order perturbation method in a piecewise linear fashion, the social welfare increases as the averaging window lengthens, which is in line with the conventional wisdom. On the contrary, if the model is solved fully nonlinearly, the social welfare starts decreasing beyond the medium window length, in our baseline case, a 6-year window, while it rises steadily up to that threshold. With the longer window AIT, the agents expect a much looser policy when ZLB is binding, which results in less possibility of binding constraint through the high inflation expectation. This effect becomes stronger under a longer window AIT so that average inflation gets higher than the target. For the monetary authority to meet the target, it raises the rates in the longer window AIT, unlike the shorter window AIT. These opposite reactions increase social welfare up to a 6-year window but generate lower welfare beyond that. This property also Hyundo Joo June 2022 Economics affects the fiscal multipliers under AITs with different window lengths both outside the ZLB and at the ZLB. Chapter 2 demonstrates that the credit data anticipate the government spending shocks identified without controlling the credit market. To circumvent this problem, the shocks are extracted with real credit data as well as conventional real variables. Using this new series of shocks, I provide new evidence on the effects of unexpected changes in government spending conditional on the credit cycle. The on-impact output multiplier is 1.85 in the credit recession and 0.92 outside the recession. Since the credit cycle defined is not much overlapped with the business cycle, the results seem not driven by business cycle dependency. In addition, the results are robust to alternative model specifications. Finally, Chapter 3 finds that the fiscal multiplier decreases nonlinearly as the private sector anticipates the implementation of NIRP more in the future. While the on-impact fiscal multiplier at the ZLB is 1.5 without any expectation about NIRP, it falls below 1 when the agents expect the economy to switch the NIRP in two quarters, on average. This result is not derived from the fact that the fiscal multiplier under NIRP is smaller than one. In fact, in the baseline simulation, it is still higher than the unity, 1.52. This shows that the possibility of NIRP (or, more broadly speaking, much looser policies) mitigates the size of the fiscal multiplier at the ZLB, and that effect is nontrivial

Machine Learning Approach to Predict Physical Properties of Polypropylene Composites: Application of MLR, DNN, and Random Forest to Industrial Data

Manufacturing polypropylene (PP) composites to meet customers’ needs is difficult, time-consuming, and costly, owing to the ever-increasing diversity and complexity of the corresponding specifications and the trial-and-error method currently used to satisfy the required physical properties. To address this issue, we developed three models for predicting the physical properties of PP composites using three machine learning (ML) methods: multiple linear regression (MLR), deep neural network (DNN), and random forest (RF). Further, the industrial data of 811 recipes were acquired to verify the developed models. Data categorization was performed to account for the differences between data and the fact that different recipes require different materials. The three models were then deployed to predict the flexural strength (FS), melting index (MI), and tensile strength (TS) of the PP composites in nine case studies. The predictive performance results differed according to the physical properties of the composites. The FS and MI prediction models with MLR exhibited the highest R2 values of 0.9291 and 0.9406. The TS model with DNN exhibited the highest R2 value of 0.9587. The proposed models and study findings are useful for predicting the physical properties of PP composites for recipes and the development of new recipes with specific physical properties