Nikolaos Tselios

Bio: Nikolaos Tselios is an academic researcher from University of Patras. The author has contributed to research in topics: Usability & System usability scale. The author has an hindex of 19, co-authored 75 publications receiving 1447 citations. Previous affiliations of Nikolaos Tselios include Hellenic Open University.

Assessing the Acceptance of a Blended Learning University Course

Abstract: Usefulness and ease of use proved to be key determinants of the acceptance and usage of e-learning. On the contrary, little is known about students’ perceptions in a blended learning setting. In this paper, the Technology Acceptance Model (TAM) was utilised, in order to investigate Greek university students’ attitudes toward blended learning. The goal of the study was twofold. First, to investigate whether the students’ perceptions in a blended learning setting were comparable with other studies reporting perceptions in the context of distant learning. Second, to investigate variation in students’ perceptions before and after actual system use. A sample of 130 students before actual system use and 102 students after the end of the semester was used. As derived from the model analysis using partial least squares, the e-learning system was well accepted and the majority of our hypotheses were confirmed. The most notable difference between pre- and post- use scenario was that perceived usefulness did not prove to have a significant effect on behavioral intention before system use, whereas, in the end, it appeared to be significant. The results are compared with similar studies focused on elearning acceptance. The implications, both for the designer of a blended learning course as well as for the educational community, are also discussed.

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

Perceived usability evaluation of learning management systems: Empirical evaluation of the System Usability Scale

Abstract: Perceived usability affects greatly student’s learning effectiveness and overall learning experience, and thus is an important requirement of educational software. The System Usability Scale (SUS) is a well-researched and widely used questionnaire for perceived usability evaluation. However, surprisingly few studies have used SUS to evaluate the perceived usability of learning management systems (LMSs). This paper presents an empirical evaluation of the SUS questionnaire in the context of LMSs’ perceived usability evaluation. Eleven studies involving 769 students were conducted, in which participants evaluated the usability of two LMSs (eClass and Moodle) used within courses of their curriculum. It was found that the perceived usability of the evaluated LMSs is at a satisfactory level (mean SUS score 76.27). Analysis of the results also demonstrated the validity and reliability of SUS for LMSs’ evaluation, and that it remains robust even for small sample sizes. Moreover, the following SUS attributes were investigated in the context of LMSs evaluation: gender, age, prior experience with the LMS, Internet self-efficacy, attitude towards the Internet and usage frequency of the LMS.

Does size matter?: investigating the impact of mobile phone screen size on users' perceived usability, effectiveness and efficiency.

Abstract: Given the wide adoption of smartphones, an interesting debate is taking place regarding their optimal screen size and specifically whether possible portability issues counterbalance the obvious benefits of a larger screen. Moreover, the lack of scientific evidence about the concrete impact of mobile phones' screen size on usability raises questions both to practitioners and researchers. In this paper, we investigate the impact of a mobile phone's screen size on users' effectiveness, efficiency and perceived usability as measured using System Usability Scale (SUS). An experiment was conducted with 60 participants, which interacted with the same information seeking application on three different devices of the same brand that differed on their screen size. A significant effect of screen size on efficiency was derived, leading to an important finding that users who interact with larger than 4.3in screens are more efficient during information seeking tasks.

Extension of technology acceptance model by using system usability scale to assess behavioral intention to use e-learning

Abstract: This study examines the acceptance of technology and behavioral intention to use learning management systems (LMS). In specific, the aim of the research reported in this paper is to examine whether students ultimately accept LMSs such as eClass and the impact of behavioral intention on their decision to use them. An extended version of technology acceptance model has been proposed and used by employing one of the most reliable measures of perceived eased of use, the System Usability Scale. 345 university students participated in the study. The data analysis was based on partial least squares method. The majority of the research hypotheses were confirmed. In particular, social norm, system access and self-efficacy were found to significantly affect behavioral intention to use. As a result, it is suggested that e-learning developers and stakeholders should focus on these factors to increase acceptance and effectiveness of learning management systems.

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.).

Childhood and Society.

Abstract: Erik Eriksen is a remarkable individual. He has no college degrees yet is Professor of Human Development at Harvard University. He came to psychology via art, which explains why the reader will find him painting contexts and backgrounds rather than stating dull facts and concepts. He has been a training psychoanalyst for many years as well as a perceptive observer of cultural and social settings and their effect on growing up. This is not just a book on childhood. It is a panorama of our society. Anxiety in young children, apathy in American Indians, confusion in veterans of war, and arrogance in young Nazis are scrutinized under the psychoanalytic magnifying glass. The material is well written and devoid of technical jargon. The theme of the volume is that it is human to have a long childhood which will leave a lifelong residue of emotional immaturity in man. Primitive groups and

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).

Multimedia in Learning

Abstract: 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.

国际刊物《Scientometrics》文献计量研究

Abstract: 本文对国际科学计量学杂志《Ｓｃｉｅｎｔｏｍｅｔｒｉｃｓ》１９７９－１９９１年的研究论文内容、栏目、作者及国别和编委及国别作了计量分析，揭示出科学计量学研究的重点、活动的中心及发展趋势，说明了学科带头人在发展科学计量学这门新兴学科中的作用。