Nikolaos Avouris

Bio: Nikolaos Avouris is an academic researcher from University of Patras. The author has contributed to research in topics: Usability & Collaborative learning. The author has an hindex of 30, co-authored 199 publications receiving 3486 citations.

A Review of Mobile Location-based Games for Learning across Physical and Virtual Spaces.

Abstract: In this paper we review mobile location-based games for learning. These games are played in physical space, but at the same time, they are supported by actions and events in an interconnected virtual space. Learning in these games is related to issues like the narrative structure, space and game rules and content that define the virtual game space. First, we introduce the theoretical and empirical considerations of mobile location based games, and then we discuss an analytical framework of their main characteristics through typical examples. In particular, we focus on their narrative structure, the interaction modes that they afford, their use of physical space as prop for action, the way this is linked to virtual space and the possible learning impact the game activities have. Finally we conclude with an outline of future trends and possibilities that these kinds of playful activities can have on learning, especially outside school, like in environmental studies and visits in museums and other sites of cultural and historical value.

Evaluation of classifiers for an uneven class distribution problem

Abstract: Classification problems with uneven class distributions present several difficulties during the training as well as during the evaluation process of classifiers. A classification problem with such characteristics has resulted from a data mining project where the objective was to predict customer insolvency. Using the data set from the customer insolvency problem, we study several alternative methodologies, which have been reported to better suit the specific characteristics of this type of problem. Three different but equally important directions are examined: (a) the performance measures that should be used for problems in this domain; (b) the class distributions that should be used for the training data sets; and (c) the classification algorithms to be used. The final evaluation of the resulting classifiers is based on a study of the economic impact of classification results. This study concludes to a framework that provides the “best” classifiers, identifies the performance measures that should be used...

Context-based design of mobile applications for museums: a survey of existing practices

Abstract: This paper includes a review of mobile applications used in museum environments, focusing on the notion of context and its constituent dimensions Museums are a representative example in which the context influences interaction During a museum visit, the visitors interact with the exhibits through mobile devices We argue that, effective interaction design needs to take into consideration multiple dimensions of the context Since context is often misinterpreted, superficially used or poorly defined, we attempt to analyze a number of existing mobile applications used in museum environments, through this perspective The point of analysis is to evaluate those applications against various context dimensions We argue that these results can be useful in other kinds of applications, in which the impact of context is not taken for granted

Mystery in the museum: collaborative learning activities using handheld devices

Abstract: In this paper, we describe the experience of designing a collaborative learning activity for a traditional historical/cultural museum. The activity, based on a "Mystery in the Museum" story, involves collaboration of small groups of students through mobile handheld devices. An application has been built that permits authoring of such activities, while a usability evaluation study was performed that revealed some of the limitations of the design. The reported findings can be of use to those interested in following similar approaches in cultural and educational settings, and draw conclusions of general interest relating to interaction and collaboration through mobile technology.

Machine learning

Abstract: 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.).

The European commission

Abstract: 2 1 The innovative transport systems and the CityMobil project 10 1.1 The research questions 10 2 The state of art in the field of automatic transport systems 12 2.1 Case studies and demand studies for innovative transport systems 12 3 The design and implementation of surveys 14 3.1 Definition of experimental design 14 3.2 Questionnaire design and delivery 16 3.3 First analyses on the collected sample 18 4 Calibration of Logit Multionomial demand models 21 4.1 Methodology 21 4.2 Calibration of the “full” model. 22 4.3 Calibration of the “final” model 24 4.4 The demand analysis through the final Multinomial Logit model 25 5 The analysis of interaction between the demand and socioeconomic attributes 31 5.1 Methodology 31 5.2 Application of Mixed Logit models to the demand 31 5.3 Analysis of the interactions between demand and socioeconomic attributes through Mixed Logit models 32 5.4 Mixed Logit model and interaction between age and the demand for the CTS 38 5.5 Demand analysis with Mixed Logit model 39 6 Final analyses and conclusions 45 6.1 Comparison between the results of the analyses 45 6.2 Conclusions 48 6.3 Answers to the research questions and future developments 52

Evaluation of Evidence-Based Practices in Online Learning: A Meta-Analysis and Review of Online Learning Studies

Abstract: A systematic search of the research literature from 1996 through July 2008 identified more than a thousand empirical studies of online learning. Analysts screened these studies to find those that (a) contrasted an online to a face-to-face condition, (b) measured student learning outcomes, (c) used a rigorous research design, and (d) provided adequate information to calculate an effect size. As a result of this screening, 51 independent effects were identified that could be subjected to meta-analysis. The meta-analysis found that, on average, students in online learning conditions performed better than those receiving face-to-face instruction. The difference between student outcomes for online and face-to-face classes—measured as the difference between treatment and control means, divided by the pooled standard deviation—was larger in those studies contrasting conditions that blended elements of online and face-to-face instruction with conditions taught entirely face-to-face. Analysts noted that these blended conditions often included additional learning time and instructional elements not received by students in control conditions. This finding suggests that the positive effects associated with blended learning should not be attributed to the media, per se. An unexpected finding was the small number of rigorous published studies contrasting online and face-to-face learning conditions for K–12 students. In light of this small corpus, caution is required in generalizing to the K–12 population because the results are derived for the most part from studies in other settings (e.g., medical training, higher education).