There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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

A wide variety of media can be used in learning, including distance learning, such as print, lectures, conference sections, tutors, pictures, video, sound, and computers. Any one instance of distance learning will make choices among these media, perhaps using several.

Multimedia in Learning

https://www.healio.com/psychiatry/journals/psycann/1976-12-6-12/{806b8887-c02e-40c8-a228-57f1cdb2bdba}/the-stress-of-life.pdf

The stress of life

Multi-Criteria Decision Aid (MCDA) or Multi-Criteria Decision Making (MCDM) methods have received much attention from researchers and practitioners in evaluating, assessing and ranking alternatives across diverse industries. Among numerous MCDA/MCDM methods developed to solve real-world decision problems, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) continues to work satisfactorily across different application areas. In this paper, we conduct a state-of-the-art literature survey to taxonomize the research on TOPSIS applications and methodologies. The classification scheme for this review contains 266 scholarly papers from 103 journals since the year 2000, separated into nine application areas: (1) Supply Chain Management and Logistics, (2) Design, Engineering and Manufacturing Systems, (3) Business and Marketing Management, (4) Health, Safety and Environment Management, (5) Human Resources Management, (6) Energy Management, (7) Chemical Engineering, (8) Water Resources Management and (9) Other topics. Scholarly papers in the TOPSIS discipline are further interpreted based on (1) publication year, (2) publication journal, (3) authors' nationality and (4) other methods combined or compared with TOPSIS. We end our review paper with recommendations for future research in TOPSIS decision-making that is both forward-looking and practically oriented. This paper provides useful insights into the TOPSIS method and suggests a framework for future attempts in this area for academic researchers and practitioners.

Review: A state-of the-art survey of TOPSIS applications

Computer games are very popular among children and adolescents. In this respect, they could be exploited by educational software designers to render educational software more attractive and motivating. However, it remains to be explored what the educational scope of educational software games is. In this paper, we explore several issues concerning the educational effectiveness, appeal and scope of educational software games through an evaluation study of an Intelligent Tutoring System (ITS) that operates as a virtual reality educational game. The results of the evaluation show that educational virtual reality games can be very motivating while retaining or even improving the educational effects on students. Moreover, one important finding of the study was that the educational effectiveness of the game was particularly high for students who used to have poor performance in the domain taught prior to their learning experience with the game.

Combining Software Games with Education: Evaluation of its Educational Effectiveness

Educational software games aim at increasing the students' motivation and engagement while they learn. However, if software games are targeted to school classrooms they have to be usable and likeable by all students. Usability of virtual reality games may be a problem because these games tend to have complex user interfaces so that they are more attractive. Moreover, if the games acquire an educational content they may lose the attractiveness and appeal that they have on users who are familiar with commercial games. Consequently, likeability may also be questioned. In this paper, we address the issue of usability and likeability of a virtual reality game that is meant to teach students geography. We describe the evaluation experiments conducted, which involved three categories of students in terms of their level of game-playing expertise: novice, intermediate and expert game players. The evaluation results showed that the game was indeed usable and likeable but there was scope for usability and likeability improvement so that the educational benefits may be maximised for all categories of students. The evaluation studies reported in this paper, revealed important issues about further research on virtual reality educational games.

On the usability and likeability of virtual reality games for education: The case of VR-ENGAGE

This paper constitutes a literature review on student modeling for the last decade. The review aims at answering three basic questions on student modeling: what to model, how and why. The prevailing student modeling approaches that have been used in the past 10years are described, the aspects of students' characteristics that were taken into consideration are presented and how a student model can be used in order to provide adaptivity and personalisation in computer-based educational software is highlighted. This paper aims to provide important information to researchers, educators and software developers of computer-based educational software ranging from e-learning and mobile learning systems to educational games including stand alone educational applications and intelligent tutoring systems. In addition, this paper can be used as a guide for making decisions about the techniques that should be adopted when designing a student model for an adaptive tutoring system. One significant conclusion is that the most preferred technique for representing the student's mastery of knowledge is the overlay approach. Also, stereotyping seems to be ideal for modeling students' learning styles and preferences. Furthermore, affective student modeling has had a rapid growth over the past years, while it has been noticed an increase in the adoption of fuzzy techniques and Bayesian networks in order to deal the uncertainty of student modeling.

Review: Student modeling approaches: A literature review for the last decade

One important field where mobile technology can make significant contributions is Education. In the fast pace of modern life, students and instructors would appreciate using constructively some spare time that they may have, in order to work on lessons at any place, even when away from offices, classrooms and labs where computers are usually located. In this paper, we describe a mobile authoring tool that we have developed and is called Mobile Author. Mobile Author can be used by human instructors either from a computer or a mobile phone to create their own Intelligent Tutoring Systems (ITSs) and to distribute them to their students. After the ITSs have been created, students can also use any computer or mobile phone to have access to theory and tests. The tutoring systems can assess the students performance, inform the databases that record the students progress and provide advice adapted to the needs of individual students. Finally, instructors can monitor their students progress and communicate with their students during the course. The mobile features of both the authoring tool itself and the resulting ITSs from it have been evaluated by instructors and students, respectively. The results of the evaluation showed that mobile features are indeed considered useful.

/pdf/mobile-educational-features-in-authoring-tools-for-22ocfe61sr.pdf

Mobile educational features in authoring tools for personalised tutoring

This paper describes the evaluation of the persona effect of a speech-driven anthropomorphic agent that has been embodied in the interface of an intelligent tutoring system (ITS). This agent is responsible for guiding the student in the environment and communicating the system's feedback messages. The agent was evaluated in terms of the effect that it could have on students' learning, behaviour and experience. The participants in the experiment were divided into two groups: half of them worked with a version of the ITS which embodied the agent and the rest worked with an agent-less version. The results from this study confirm the hypothesis that a pedagogical agent incorporated in an ITS can enhance students' learning experience. On the other hand, the hypothesis that the presence of the agent improves short-term learning effects was rejected.

Maria Virvou

Papers

Combining Software Games with Education: Evaluation of its Educational Effectiveness

On the usability and likeability of virtual reality games for education: The case of VR-ENGAGE

Review: Student modeling approaches: A literature review for the last decade

Mobile educational features in authoring tools for personalised tutoring

Evaluating the persona effect of an interface agent in a tutoring system