Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

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U.s. agency for international development

Panel Data in Econometrics

Prior studies on e-learning service quality were conducted mainly in developed countries; however, little effort has been made in emerging countries. This study examines the relationships among e-learning service quality attributes, overall e-learning service quality, e-learning student satisfaction, and e-learning student loyalty in the context of Vietnam, an emerging country. Survey data collected from 1232 college students were analyzed by means of exploratory factor analysis, confirmatory factor analysis, and structural equation modeling using SPSS 25 and SmartPLS 3.0. The results indicated that e-learning service quality was a second-order construct comprising of three factors, namely, e-learning system quality, e-learning instructor and course materials quality, and e-learning administrative and support service quality. The e-learning system quality was the most important dimension of overall e-learning service quality, followed by e-learning instructor and course materials quality, and e-learning administrative and support service quality. In addition, the overall e-learning service quality was positively related to e-learning student satisfaction, which in turn positively influences e-learning student loyalty. Also, overall e-learning service quality has a direct effect on e-learning student loyalty. Implications for colleges and universities are discussed.

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Does e-learning service quality influence e-learning student satisfaction and loyalty? Evidence from Vietnam

The notion and measurement of destination competitiveness have received increasing attention in the economics literature on tourism. The reason for this emerges from both the growing economic importance of the tourist sector and the increasing competition in the tourist market resulting from the transition from mass tourism to a new age of tourism that calls for a tailor-made approach to the specific attitudes and needs of tourists. The central subject of this paper – inspired by the conceptual competitiveness model developed by Crouch and Ritchie – concerns the efficiency of tourist site destinations. Using a data set of 103 Italian regions for 2001, an economic efficiency analysis is carried out, based on a production frontier approach. The study deploys a measure of tourist site competitiveness in terms of its technical efficiency using parametric and non-parametric methods, a stochastic production function and data envelopment analysis, respectively.

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Assessment of tourism competitiveness by analysing destination efficiency

An application of DEA for comparative analysis and ranking of regions in Serbia with regards to social-economic development

The aim of this paper is to present new ideas in evaluating Shanghai University’s Academic Ranking of World Universities (ARWU). In particular, this paper shall try to determine whether the normalization of data affects University ranks. In accordance with this, both the normalized and original (raw) data for each of the six variables has been obtained. Based on a sample containing the 54 US universities which are placed in the ARWU top 100, the statistical I-distance method was performed. The results showed great inconsistencies between university ranks obtained for the original and normalized data. These findings were then analyzed and the universities that had the greatest fluctuation in their ranks were noted.

How does the normalization of data affect the ARWU ranking

The growing risk of climate change caused by the emission of greenhouse gases poses new challenges to contemporary countries. The development of economies is usually related to increasing levels of greenhouse gas emissions. Therefore, the question arises whether it is possible to achieve sustainable economic and financial development and simultaneously reduce greenhouse gas emissions. This paper assumes it is possible if energy efficiency is increased. The aim of the paper is to show the link between energy efficiency and sustainable economic and financial development in Organisation for Economic Co-operation and Development (OECD) countries for the period 2000–2018 by using data envelopment analysis (DEA) and regression analysis. The results show a slight upward trend of total factor energy efficiency (TFEE) in OECD countries for the analysed period; however, there is a difference in TFEE levels. Developed OECD countries have higher TFEE levels than developing OECD countries. The links between total factor energy efficiency and sustainable economic and financial development reveal different impacts depending on the variables taken into consideration.

https://www.mdpi.com/1996-1073/13/22/5898/pdf

Link between Energy Efficiency and Sustainable Economic and Financial Development in OECD Countries

Students’ evaluations of teaching are increasingly used by universities to evaluate teaching performance. However, these evaluations are controversial mainly due to fact that students value various aspects of excellent teaching differently. Therefore, in this paper we propose a new approach to students’ evaluations of university teaching based on data from conjoint analysis. Conjoint analysis is a multivariate technique used to analyze the structure of individuals’ preference. In particular, our approach accounts for different importance students attach to various aspects of teaching. Moreover, it accounts explicitly for heterogeneity arising from students’ preferences, and incorporates it to form comprehensive teaching evaluation score. We have conducted survey and confirmed applicability and efficiency of the proposed approach.

A new approach to evaluation of university teaching considering heterogeneity of students’ preferences

In recent years, the need for greater accountability and improvement in teaching's quality has become a major issue in higher education. The results of student evaluation of teaching are increasingly used to make judgments about teaching quality, career advancement, and the funding of teaching itself. An important component of any such student evaluation is the communication of rating results in a manner that allows for fair and meaningful interpretations and comparisons of results by a wide range of evaluation users. In this paper, an approach for a more objective evaluation of university teachers is proposed, one which is based on previously obtained conjoint analysis data concerning criteria importance from a student's point of view. Therein, an illustrative example is given to demonstrate the efficiency and practical usage of such a proposed approach.

Gordana Savić

Papers

An application of DEA for comparative analysis and ranking of regions in Serbia with regards to social-economic development

How does the normalization of data affect the ARWU ranking

Link between Energy Efficiency and Sustainable Economic and Financial Development in OECD Countries

A new approach to evaluation of university teaching considering heterogeneity of students’ preferences

A Conjoint-based approach to student evaluations of teaching performance