Goal: The use of mobile game-based learning with a mobile app in higher education aims to provide an interesting learning method which is acceptable and workable for university students in different majors and different gender to improve their English vocabulary acquisition. Objective: To analyze how, and to what extent, mobile game-based learning influences the participants’ learning performance and learning motivational effects. Experimental Setting: A mobile application game was employed to learn English vocabulary for a hundred university freshmen students from one private university in Northern Taiwan during the spring semester, 2016. Method: A pre-questionnaire and pre-test were carried out before the participants started using the app game for learning. A post-test and post-questionnaire were completed and analyzed after the 2-week experiment. Results: This paper reveals that mobile game-based learning is a workable and acceptable learning method for both female and male university students from different faculties. The results indicate that language teachers could benefit from collecting mobile educational applications and implementing the teaching via technology model into ubiquitous learning activities. The improvement between the pre-test and post-test showed the students’ positive learning performance.
The wide implementation of social networking sites (SNS) in many fields, for instance, government, celebrities, schools, social groups, events promoted by national organizations or private enterprises, NPOs, businesses, etc., attests to its popularity. Currently more and more educators and students have integrated these online social communication platforms into their learning environment. Learning Management Systems (LMS) also have great features and functions that may serve as a bridge between learners and educators leading to better communication, in addition to helping students increase their learning engagement and teachers in evaluating learning performance. Although many studies have investigated the effects of adopting Facebook as an additional learning management system (LMS), its functions and benefits differing from the LMS have hardly been systematically analyzed. Besides, due to the increasing popularity of mobile devices among the younger generation, university students are obviously likely to use their smart phones to access both Facebook Mobile App and school LMS. Therefore, the first purpose of this study is to determine students’ opinions on mobile Facebook course groups and a mobile LMS course group, through a survey conducted after a 2-semester experiment. The other objective of this study is to evaluate the benefits of each function of mobile FB course groups that may strengthen the current weaknesses of LMS. In addition, regression analysis and t-test were used to reveal the relationships among variables: the FB functions and its benefits for FB course group. The findings might provide a clear notion for teachers regarding the functions and advantages contributed by a mobile FB course group which can be implemented as a supplemental learning system. The results of the research will provide university administrators with more detailed information for improving the LMS’ features or developing new LMS Apps.
Classification rules should be open for public inspection to ensure fairness. These rules can be originally induced from some dataset. If induced classification rules are supported only by a small number of objects in the dataset, publication can lead to identification of objects supporting the rule, given their speciality. Eventually, it is possible to retrieve information about the identified objects. This identifiability is not desirable in terms of data privacy. In this paper, to avoid such privacy breaches, we propose rule clustering for achieving k-anonymity of all induced rules, i.e., the induced rules are supported by at least k objects in the dataset. The proposed approach merges similar rules to satisfy k-anonymity while aiming to maintain the classification accuracy. Two numerical experiments were executed to verify both the accuracy of the classifier with the rules obtained by the proposed method and the ratio of decision classes revealed from leaked information about objects. The experimental results show the usefulness of the proposed method.
A robust and sparse Lp-norm support vector regression (Lp-RSVR) is proposed in this paper. The implementation of feature selection in our Lp-RSVR not only preserves the performance of regression but also improves its robustness. The main characteristics of Lp-RSVR are as follows: (i) By using the absolute constraint, Lp-RSVR performs robustly against outliers. (ii) Lp-RSVR ensures that useful features are selected based on theoretical analysis. (iii) Based on the feature-selection results, nonlinear Lp-RSVR can be used when data is structurally nonlinear. Experimental results demonstrate the superiorities of the proposed Lp-RSVR in both feature selection and regression performance as well as its robustness.
Wine consumption is gaining popularity, and significant attention has been given to its quality. In the present paper, an objective evaluation model along with a reliability test via Lasso and nonlinear effect test via support vector regression (SVR) is proposed. The digital simulation is finished with the experimental data obtained from the A problem of CUMCM-2012 (China Undergraduate Mathematical Contest in Modeling in 2012). The results of Lasso regression show that the wine quality mainly depends upon eight physicochemical indicators. Further research results of SVR imply that with several training samples, a good evaluation can be realized, denoting that our model based on Lasso SVR can significantly reduce the costs of measurement and appraisal. Compared to other relevant articles, this paper builds an objective and credible wine evaluation system where the physicochemical indicators and the latent nonlinear effect are considered. Moreover, the evaluation costs are taken into account.
The fuzzy autocorrelation model is a fuzzified autoregressive (AR) model. The aim of the fuzzy autocorrelation model is to describe the possible states of the system with high accuracy. This model uses autocorrelation similar to the Box–Jenkins model. The fuzzy autocorrelation model occasionally increases the vagueness. Although the problem can be mitigated using fuzzy confidence intervals instead of fuzzy time-series data, the unnatural estimations do not improve. Subsequently, an alternate method was used to fuzzify the time-series data and mitigate the unnatural estimation problem. This method also improved the model prediction accuracy. This paper focuses on fuzzification method, and discusses the prediction accuracy of the model and fuzzification of the time-series data. The analysis of the Nikkei stock average shows a high prediction accuracy and manageability of a fuzzy autocorrelation model. In this pape, a quartile is employed as an alternate fuzzification method. The model prediction accuracy and estimation behavior are verified through an analysis. Finally, the proposed method was found to be successful in mitigating the problems.
In this study, we focus on the feature selection problem in regression, and propose a new version of L1 support vector regression (L1-SVR), known as L1-norm least squares support vector regression (L1-LSSVR). The alternating direction method of multipliers (ADMM), a method from the augmented Lagrangian family, is used to solve L1-LSSVR. The sparse solution of L1-LSSVR can realize feature selection effectively. Furthermore, L1-LSSVR is decomposed into a sequence of simpler problems by the ADMM algorithm, resulting in faster training speed. The experimental results demonstrate that L1-LSSVR is not only as effective as L1-SVR, LSSVR, and SVR in both feature selection and regression, but also much faster than L1-SVR and SVR.
Rough set theory was proposed by Z. Pawlak in 1982. This theory enables the mining of knowledge granules as decision rules from a database, the web, and other sources. This decision rule set can then be used for data analysis. We can apply the decision rule set to reason, estimate, evaluate, or forecast an unknown object. In this paper, rough set theory is used for the analysis of time-series data. We propose a method to acquire rules from time-series data using regression. The trend of the regression line can be used as a condition attribute. We predict the future slope of the time-series data as decision attributes. We also use merging rules to further analyze the time series data.
We study production competition between two electricity producers, where one of them is subject to a nationalization decision and the other is a private producer that chooses managerial incentives to counter governmental actions. The government wants to maximize a modified form of social welfare and chooses partial nationalization, which still has a serious impact on the rival private producer. We find, that by offering managerial incentives the private producer recovers its lost profit and induces even less nationalization. We also find that such equilibrium might produce the same level of social welfare than one without incentives.
Based on the purchase price data of new real estate markets three cities in China, Beijing, Shanghai, and Guangzhou, including architectural features, neighborhood property features, and location features, in this study a boosting regression tree model was built to study the factors and the influence path of housing prices from the microcosmic perspective. First, a classical hedonic price model was constructed to analyze and compare the significant effect factors on housing prices in the market segments of the three cities. Second, the gradient boosting regression tree method that is proposed in this paper was applied to the three markets in combination to analyze the influence paths and factors and the importance of the type of housing hedonic price. The influence paths of housing hedonic prices and decision tree rules are visualized. The significant housing features are effectively extracted. Finally, we present three main conclusions and several suggestions for policy makers to improve urban functions while stabilizing real estate prices.
The economy is facing transformation and technological upgrading, which is particularly imperative to upgrading the real economy in China. It is well known that manufacturing is the core of the real economy. Therefore, insights into the innovation path of manufacturing enterprises are helpful in understanding problems that exist, and they suggest future strategies in manufacturing innovation. In this paper, based on historical data from 2000 to 2014, Bayesian methods and time-varying parameter dynamic regression models are used to identify the innovation path of manufacturing enterprises. This paper shows that the relevant research departments within firms need to improve the efficiency of research and development (R&D) institutions and mobilize the enthusiasm of full-time R&D personnel to provide a solid foundation for innovation, and transformation, and upgrading of the manufacturing industry. The paper also shows the need to improve the efficiency of R&D investment and new product R&D investments. In addition to independent R&D, manufacturing enterprises should also increase the adoption of foreign technology into their own manufacturing processes. Finally, some future strategies with regards to transformation and upgrading are discussed.
Although the effects of agents’ bounded rationality and stock mispricing on corporate investment is becoming a frontier research field in corporate finance, little research has been devoted to different channels of managers catering to agents’ bounded rationality and stock mispricing. With a sample of 2003–2010 Chinese listed companies, we investigate how firms cater to stock mispricing in their investment decision-making. The empirical study results support the view that managers do cater to investors’ perceived bias for investment in intangible assets and/or fixed assets and that firms’ financial constraints, market characteristics, and the myopia of investors are important factors in catering for such investment. Moreover, fixed asset investment may be a more important channel than intangible asset investment for managers when catering to stock mispricing.
Branding is an important resource in a technical standards alliance. As a kind of essential resource utilization pattern, joint branding is beneficial for the enterprises in an alliance in realizing the increment of value. The selection of a cooperative partner is the first step in co-branding, and plays a significant role. This paper emphasizes the critical significance of alliance member selection for co-branding, and regards it as a breakthrough point in analyzing the key influential factors and causal correlation of such branding. Through a combination of a fuzzy cognitive map and the non-linear Hebbian learning algorithm, this research establishes a fuzzy evaluation model, realizes the dynamic simulation of a complex network system with multiple causal correlations, and obtains a final steady state of co-branding for a technical standards alliance. Thus, it allows a better understanding of the mutual relations among the different influencing factors of co-branding and their effect, as well as the proposal of a reference policy for an improvement of such influencing factors and the conversion efficiency of the optimal results.
Entrepreneurial Marketing has a significant effect on new ventures’ performance. However, the findings have been mixed and conflicting. There is still little in-depth exploration of its specific working mechanism based on two distinct literatures streams from ambidextrous innovation and entrepreneurial marketing. We present an integrated framework for analyzing entrepreneurial marketing, ambidextrous innovation and new venture performance (NVP). By conducting an empirical study on a sample of 883 new ventures (NVs) in Anhui province in China, the study found that: (a) EM is an important driver of NVP and only five dimensions of EM have positively effects on NVP ,including proactiveness, opportunity-focus, innovations, risk-taking and resource leveraging. (b) Both exploration innovation and exploitation innovation advance NVP. (c) The ambidextrous innovation did not affect NVP significantly. (d) Exploration innovation and exploitation innovation partly mediate the relationship between EM and NVP.
The People’s Bank of China in 2013 released a report revealing that the balance of non-performing loans of Chinese banking financial institutions had rebounded for the first time since 2005. In this situation, establishing early warning models – to recognize the factors that influence non-performing loans, and take effective measures to prevent defaults and control the banks’ credit assets – has become a major new issue. This paper examines the determinants of the non-performing loans (NPL) ratio in the Chinese banking sector from 2005 to 2011 using a panel data model. This model incorporates a new factor called distance to default (DD). The results show that the rates of change of total asset size, commercial loan ratio, and distance to default correlate negatively with NPL. There are positive correlations between capital return ratio, net interest margin, and single-lag NPL with NPL. However, there is no significant correlation between the proportion of shareholders’ equity, or the proportion of total loans, and NPL. In conclusion, this study suggests that regulators should consider and pay more attention to all these banks’ operational indicators to control NPL.
The balance of non-performing loans (NPLs) of Chinese banking financial institutions rebounded for the first time after 2005, and credit risk has emerged as one of the rapidly rising risks in today’s financial markets. In this study, we focus on the NPLs of financial institutions. In particular, the factors affecting their rate are studied. A dynamic control theory model is used to set up a Hamilton function for describing the effect of these factors. Moreover, the path of NPLs under the influence of various factors is obtained. It was found that improved risk control and macroeconomy factors reduced the number of NPLs. In particular, to reduce the number of NPLs, the capacity of banks to manage loans should be strengthened.
What is the level of non-performing loans in China’s banking sector and in different countries? Has the relationship between economic growth and the non-performing loan ratio changed? Is there a difference in the effect of the economic growth of different economies on the rate of non-performing loans in the banking sector? This study analyzes the relationship between economic growth and the non-performing loan ratios and characteristics of 13 countries from 2005-2014 based on quantile regression models with panel data. The results showed that the relationship between economic growth and the non-performing loan ratio was positive before the financial crisis in 2008 but was negative after 2008. The non-performing loan ratio in Canada, Mexico, and the US was low before 2008 and high after 2008. The impact of economic growth on the non-performing loan ratio was more significant for countries with a high non-performing loan ratio than for countries with a low non-performing loan ratio.
This study explores the relationship between the performance of corporate environmental protection responsibility (CEP) and corporate value. Because CEP is part of corporate social responsibility (CSR), we also investigated the impacts of other parts of CSR on corporate value. From a stakeholder perspective and with considering the unique aspects of Chinese corporations, we establish a CSR index evaluation system based on the framework of ISO26000. We subdivided the CSR index into four types: NCEPX (qualitative indices of non-CEP performance), NCEPL (quantitative indices of non-CEP performance), CEPX (qualitative indices of CEP performance), and CEPL (quantitative indices of CEP performance). We selected 122 listed manufacturing companies in mainland China to conduct CSR performance evaluations and run multiple linear fixed effect panel models. The results show that enterprises can increase corporate value by fulfilling the non-CEP, and NCEPL is more significant than NCEPX; Conversely, fulfilling CEP decreases corporate value. We also find that the coefficient of integrated CSR index is not significant.
In the operation of networked control systems, signal quantization is a fundamental problem. Because a network can be shared, another important problem that affects the system performance is the network’s traffic congestion. The event-triggered quantizer is developed to reduce the effects of such problems. Its design problem is formulated, and it is solved using the differential evolution (DE) metaheuristic algorithm. The effectiveness of the event-triggered quantizer is verified through numerical examples.