Search results

The global expansion of higher education has brought about more ambitious educational goals that require new approaches to curriculum, teaching, and learning. While higher education in East Asia is no exception to this trend, it has been observed that both teachers and learners in the region have adhered to a strong tradition of lecture‐based instruction. An underlying research question concerned the responsiveness of East Asian students to learner‐centered education. The purpose of this paper is to examine the extent to which learner‐centered education can be implemented successfully in the East Asian higher education context.

This study presents a quantitative study informed by a description of the context for implementation. It adopts a quasi‐experimental, multiple time series design and examines the process and effects of change in teaching and learning at a graduate school of business (GSB) in Thailand. The GSB implemented a variety of active learning methods that were explicitly designed to increase student engagement. Descriptive statistics, as well as mixed effects models, were used to analyze student course evaluation data over a several year period.

Active learning methods could be implemented in the context of an East Asian high education institution and they entailed positive change in student engagement over time.

The paper's results support assertions that Asian students respond positively to well‐designed instructional methods that seek to foster active learning.

Proponents have argued that simulation-based learning (SBL) offers capabilities that respond to persisting critiques of management education. This research intended to provide additional empirical evidence for the instructional effectiveness of SBL. This paper aims to discuss these issues.

This research adopted a quasi-experimental, multiple time series design to examine the instructional effectiveness of courses that incorporated computer simulations in a Master of Management program at a business school in Thailand. It compared student perceptions of three SBL courses with courses that used a variety of other instructional approaches over a period of seven years.

Results revealed that students rated the SBL courses significantly higher on overall perceived instructional effectiveness, as manifested by action-directed learning, student engagement, quality of assessment and feedback, and instructor effectiveness.

The consistency of significant results for a large number of course sections over a substantial period of time suggests that the SBL courses created a more active, productive environment in which to learn management theory and practice.

The results support assertions that simulations offer potential for enhancing the quality of university-based management education.

First, the research provides empirical insights into the implementation of SBL in management education; second, many instructors remain skeptical as to whether active learning methods imported from western contexts are suitable for Asian learners. The study addresses this issue in the light of data that describe one institution's sustained attempt to employ computer simulations in its graduate management education program.

Present data suggest that planned price increases should be carried out gradually in small systematic increments. However, an identical approach would not seem necessary for price reductions. This is the third in a series of related experiments on brand choice behaviour, the major interest of which lies in the effects of introducing and withdrawing a price increase/reduction over a protracted period. The effects of each price change are assessed through the use of an interrupted multiple time‐series design with an equivalent no‐treatment control group. Various extensions to previous research are offered, and it is found that the effects of reduction extend beyond the promotional period. Price increases appear to have an effect on penetration only, but a stronger effect is measured than for a decrease.

This chapter analyzes the exchange rate pass-through (ERPT) into different prices for 12 euro area (EA) countries. We provide new up-to-date estimates of ERPT by paying attention to either the time-series properties of data and variables endogeneity. Using VECM framework, we examine the pass-through at different stages along the distribution chain, that is, import prices, producer prices, and consumer prices. When carrying out impulse response functions analysis, we find a higher pass-through to import prices with a complete pass-through (after one year) detected for roughly half of EA countries. These estimates are relatively large compared to single-equation literature. We denote that the magnitude of the pass-through of exchange rate shocks declines along the distribution chain of pricing, with the modest effect recorded for consumer prices. When assessing for the determinant of cross-country differences in the ERPT, we find that inflation level, inflation volatility, and exchange rate persistence are the main macroeconomic factors influencing the pass-through almost along the pricing chain. Thereafter, we have tested for the decline of the response of consumer prices across EA countries. According to multivariate time-series Chow test, the stability of ERPT coefficients was rejected, and the impulse responses of consumer prices over 1990–2010 provide an evidence of general decline in rates of pass-through in most of the EA countries. Finally, using the historical decompositions, our results reveal that external factors, that is, exchange rate and import prices shocks, have had important inflationary impacts on inflation since 1999 compared to the pre-EMU period.

We examined how Conservation of Resources (COR) theory has been applied to work and stress in organizational settings. COR theory has drawn increasing interest in the organizational literature. It is both a stress and motivational theory that outlines how individuals and organizations are likely to be impacted by stressful circumstances, what those stressful circumstances are likely to be, and how individuals and organizations act in order to garner and protect their resources. To date, individual studies and meta-analyses have found COR theory to be a major explanatory model for understanding the stress process at work. Applications of COR theory to burnout, respite, and preventive intervention were detailed. Studies have shown that resource loss is a critical component of the stress process in organizations and that limiting resource loss is a key to successful prevention and post-stress intervention. Applications for future work, moving COR theory to the study of the acquisition, maintenance, fostering, and protection of key resources was discussed.

The purpose of this paper is to describe a real‐world system developed for a large food distribution company which requires forecasting demand for thousands of products across multiple warehouses. The number of different time series that the system must model and predict is on the order of 10⁵. The study details the system's forecasting algorithm which efficiently handles several difficult requirements including the prediction of multiple time series, the need for a continuously self‐updating model, and the desire to automatically identify and analyze various time series characteristics such as seasonal spikes and unprecedented events.

The forecasting algorithm makes use of a hybrid model consisting of both statistical and heuristic techniques to fulfill these requirements and to satisfy a variety of business constraints/rules related to over‐ and under‐stocking.

The robustness of the system has been proven by its heavy and sustained use since being adopted in November 2009 by a company that serves 91 percent of the combined populations of Australia and New Zealand.

This paper provides a case study of a real‐world system that employs a novel hybrid model to forecast multiple time series in a non‐static environment. The value of the model lies in its ability to accurately capture and forecast a very large and constantly changing portfolio of time series efficiently and without human intervention.

The purpose of this paper is to propose a data prediction framework for scenarios which require forecasting demand for large-scale data sources, e.g., sensor networks, securities exchange, electric power secondary system, etc. Concretely, the proposed framework should handle several difficult requirements including the management of gigantic data sources, the need for a fast self-adaptive algorithm, the relatively accurate prediction of multiple time series, and the real-time demand.

First, the autoregressive integrated moving average-based prediction algorithm is introduced. Second, the processing framework is designed, which includes a time-series data storage model based on the HBase, and a real-time distributed prediction platform based on Storm. Then, the work principle of this platform is described. Finally, a proof-of-concept testbed is illustrated to verify the proposed framework.

Several tests based on Power Grid monitoring data are provided for the proposed framework. The experimental results indicate that prediction data are basically consistent with actual data, processing efficiency is relatively high, and resources consumption is reasonable.

This paper provides a distributed real-time data prediction framework for large-scale time-series data, which can exactly achieve the requirement of the effective management, prediction efficiency, accuracy, and high concurrency for massive data sources.

The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.

Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.

Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.

The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.

This study aims to propose a learning pattern analysis method which can improve a predictive model’s performance, as well as discover hidden insights into micro-level learning pattern. Analyzing student’s learning patterns can help instructors understand how their course design or activities shape learning behaviors; depict students’ beliefs about learning and their motivation; and predict learning performance by analyzing individual students’ learning patterns. Although time-series analysis is one of the most feasible predictive methods for learning pattern analysis, literature-indicated current approaches cannot provide holistic insights about learning patterns for personalized intervention. This study identified at-risk students by micro-level learning pattern analysis and detected pattern types, especially at-risk patterns that existed in the case study. The connections among students’ learning patterns, corresponding self-regulated learning (SRL) strategies and learning performance were finally revealed.

The method used long short-term memory (LSTM)-encoder to process micro-level behavioral patterns for feature extraction and compression, thus the students’ behavior pattern information were saved into encoded series. The encoded time-series data were then used for pattern analysis and performance prediction. Time series clustering were performed to interpret the unique strength of proposed method.

Successful students showed consistent participation levels and balanced behavioral frequency distributions. The successful students also adjusted learning behaviors to meet with course requirements accordingly. The three at-risk patten types showed the low-engagement (R1) the low-interaction (R2) and the non-persistent characteristics (R3). Successful students showed more complete SRL strategies than failed students. Political Science had higher at-risk chances in all three at-risk types. Computer Science, Earth Science and Economics showed higher chances of having R3 students.

The study identified multiple learning patterns which can lead to the at-risk situation. However, more studies are needed to validate whether the same at-risk types can be found in other educational settings. In addition, this case study found the distributions of at-risk types were vary in different subjects. The relationship between subjects and at-risk types is worth further investigation.

This study found the proposed method can effectively extract micro-level behavioral information to generate better prediction outcomes and depict student’s SRL learning strategies in online learning. The authors confirm that the research in their work is original, and that all the data given in the paper are real and authentic. The study has not been submitted to peer review and not has been accepted for publishing in another journal.