ANALYZING THE PRELUDES AND DETERMINANTS OF ARTIFICIAL INTELLIGENCE AND LEARNING IN THE 21st CENTURY: A CRITICAL ANALYSIS

In the coming years, there will be a transformation of learning systems around the globe, which will be aided by new technologies and information science. With the advent of artificial intelligence and mobile devices with cutting-edge capabilities, the learning process will undergo the most significant changes in over a century. The use of mobile learning methods is becoming increasingly popular as new technology advances. According to the authors' literature review, as the field of research advances, more and more researchers are investigating the effects of artificial intelligence on learning, teaching methods, and teacher evaluations. The paper also discusses the benefits of using artificial intelligence to facilitate independent learning. Rather than being seen as an adjunct to learning and teaching, Artificial Intelligence helps to enable a fundamental shift in thinking about what should be taught and how it should be taught. It is both an exciting opportunity and a serious responsibility for a company that are of big size. Toward meeting this challenge, this article examines several key precursors and determinants of learning and artificial intelligence. Recently, a number of terms have been introduced into the field of learning, including portal connectivity, artificial intelligence, big data, machine learning, mobile technologies, and intelligent learning patterns. Due to these changes, society and education have undergone unprecedented transformations. The authors of the study conclude that the use of artificial intelligence in learning is likely to follow the trend that shows a rapid increase after a period of relative stability. As artificial intelligence develops rapidly in the field of learning, the way knowledge is conveyed and the ability to learn new things will be greatly altered

AI Education: Open-Access Educational Resources on AI

Open-access AI educational resources are vital to the quality of the AI education we offer. Avoiding the reinvention of wheels is especially important to us because of the special challenges of AI Education. AI could be said to be “the really interesting miscellaneous pile of Computer Science”. While “artificial” is well-understood to encompass engineered artifacts, “intelligence” could be said to encompass any sufficiently difficult problem as would require an intelligent approach and yet does not fall neatly into established Computer Science subdisciplines. Thus AI consists of so many diverse topics that we would be hard-pressed to individually create quality learning experiences for each topic from scratch. In this column, we focus on a few online resources that we would recommend to AI Educators looking to find good starting points for course development. [excerpt

Progressor: Personalized visual access to programming problems

This paper presents Progressor, a visualization of open student models intended to increase the student's motivation to progress on educational content. The system visualizes not only the user's own model, but also the peers' models. It allows sorting the peers' models using a number of criteria, including the overall progress and the progress on a specific topic. Also, in this paper we present results of a classroom study confirming our hypothesis that by showing a student the peers' models and ranking them by progress it is possible to increase the student's motivation to compete and progress in e-learning systems. © 2011 IEEE

COLLABORATIVE PORTAL MODEL FOR INTERCULTURAL TEAMS KNOWLEDGE MANAGEMENT

In the multinational organizations, more groups of individuals are being involved in the process of knowledge creation in a collaborative manner, of sharing knowledge and learning from it. These individuals can have heterogeneous cultures and they must use a common language. IT has created and is developing the infrastructure for cross-cultural communications and intercultural knowledge management. Nowadays, intercultural knowledge management can be realized with support of Collaborative Technologies and Knowledge Management Support Systems (KMSS). In this respect Collaborative Technologies and Intercultural Knowledge Management Support Systems (IKMSS) will be the appropriate way for supporting intercultural communication, learning and collaborative knowledge management in organizations. In this paper we present a conceptual model of a collaborative portal for Intercultural Team Knowledge Management as a powerful support for increasing team’s performance.: collaborative support, collaboration, knowledge management, intercultural teams, intercultural knowledge management, intercultural knowledge management portal

Aligning Community Colleges to Their Local Labor Markets

Examines ways to better align community college curricula with employer needs, including analyzing online job ads to gather data on occupation and skill demands; examples of use of labor market information; and the potential and limitations of such data

Pros and cons gamification and gaming in classroom

The aim of the current work is to assess the challenges that gamification in education are facing nowadays. Benefits and disadvantages of using gamification in classroom are both discussed to offer a clearer view on the impact of using gamification within learning process. Exploratory study cases are provided to investigate the relation between motivation and engagement of the students and gamification in training. Following this idea, a survey was conducted to assess how students behavior and motivation is affected by introducing a single, specific gamification element during a semester learning process. To stimulate competition among students, a ranking type plugin was introduced within the university learning management system used for extramural education. The results prove that motivation decreases by comparison to the previous semester.Comment: 7 pages, 3 figure

Methodologies for the Automatic Location of Academic and Educational Texts on the Internet

Traditionally online databases of web resources have been compiled by a human editor, or though the submissions of authors or interested parties. Considerable resources are needed to maintain a constant level of input and relevance in the face of increasing material quantity and quality, and much of what is in databases is of an ephemeral nature. These pressures dictate that many databases stagnate after an initial period of enthusiastic data entry. The solution to this problem would seem to be the automatic harvesting of resources, however, this process necessitates the automatic classification of resources as ‘appropriate’ to a given database, a problem only solved by complex text content analysis. This paper outlines the component methodologies necessary to construct such an automated harvesting system, including a number of novel approaches. In particular this paper looks at the specific problems of automatically identifying academic research work and Higher Education pedagogic materials. Where appropriate, experimental data is presented from searches in the field of Geography as well as the Earth and Environmental Sciences. In addition, appropriate software is reviewed where it exists, and future directions are outlined

University Knowledge Management Tool for Academic Research Activity Evaluation

The implementation of an efficient university knowledge management system involves the de-velopment of several software tools that assist the decision making process for the three main activities of a university: teaching, research, and management. Artificial intelligence provides a variety of techniques that can be used by such tools: machine learning, data mining, text mining, knowledge based systems, expert systems, case-based reasoning, decision support systems, intelligent agents etc. In this paper it is proposed a generic structure of a university knowledge management system, and it is presented an expert system, ACDI_UPG, developed for academic research activity evaluation, that can be used as a decision support tool by the university knowledge management system for planning future research activities according to the main objectives of the university and of the national / international academic research funding organizations.University Knowledge Management, Research Activity Evaluation, Artificial Intelligence, Expert Systems, Decision Support System

Methodologies for the Automatic Location of Academic and Educational Texts on the Internet

Traditionally online databases of web resources have been compiled by a human editor, or though the submissions of authors or interested parties. Considerable resources are needed to maintain a constant level of input and relevance in the face of increasing material quantity and quality, and much of what is in databases is of an ephemeral nature. These pressures dictate that many databases stagnate after an initial period of enthusiastic data entry. The solution to this problem would seem to be the automatic harvesting of resources, however, this process necessitates the automatic classification of resources as ‘appropriate’ to a given database, a problem only solved by complex text content analysis. This paper outlines the component methodologies necessary to construct such an automated harvesting system, including a number of novel approaches. In particular this paper looks at the specific problems of automatically identifying academic research work and Higher Education pedagogic materials. Where appropriate, experimental data is presented from searches in the field of Geography as well as the Earth and Environmental Sciences. In addition, appropriate software is reviewed where it exists, and future directions are outlined