Technology-enabled Learning (TEL): YouTube as a Ubiquitous Learning Aid.

The use of social networks such as Facebook, Twitter, and YouTube in the society has become ubiquitous. The advent of communication technologies alongside other unification trends and notions such as media convergence and digital content allow the users of the social network to integrate these networks in their everyday life. There have been several attempts in the literature to investigate and explain the use of social networks such as Facebook and WhatsApp by university students in the Arab region. However, little research has been done on how university students utilise online audiovisual materials in their academic activities in the UAE. This research aims to elucidate the use of YouTube as a learning aid for university students in the UAE. We adopt the technology acceptance model (TAM) as the theoretical framework for this investigation. A quantitative methodology is employed to answer the research question. Primary data consisting of 221 correspondents were analysed, covering patterns of using YouTube as an academic audiovisual learning aid. Statistical techniques including descriptive, correlations, regression tests were used to analyse the data. The study concluded that students use YouTube as a learning tool for their academic studies and enriching their general knowledge; and there is a positive relationship between the use of YouTube videos in academic settings and the students’ overall performance. This study can shed light for teachers, curriculum designers, government entities, and other stakeholders on how to best utilise and integrate the online technology — YouTube — as a learning aid

The shape of online meetings

Live videoconferencing has become an integral part of international virtual learning and working with professionals, educators and students using online meetings to enhance their collaboration from different parts of the world. This paper explores the visualization of a set of different online meetings produced by the FlashMeeting' videoconferencing system. Our polar area visualization analysis reveals interesting patterns in participant dominance in online meetings: seminars, interviews, moderated project meetings, peer-to-peer meetings, web-casts and video lectures. Visualizing patterns in the use of foreground and background communication channels is a promising way to help us to start to explore individual user roles in different communities and in different meeting types

Fuzzy ART

Adaptive Resonance Theory (ART) models are real-time neural networks for category learning, pattern recognition, and prediction. Unsupervised fuzzy ART and supervised fuzzy ARTMAP synthesize fuzzy logic and ART networks by exploiting the formal similarity between the computations of fuzzy subsethood and the dynamics of ART category choice, search, and learning. Fuzzy ART self-organizes stable recognition categories in response to arbitrary sequences of analog or binary input patterns. It generalizes the binary ART 1 model, replacing the set-theoretic: intersection (∩) with the fuzzy intersection (∧), or component-wise minimum. A normalization procedure called complement coding leads to a symmetric: theory in which the fuzzy inter:>ec:tion and the fuzzy union (∨), or component-wise maximum, play complementary roles. Complement coding preserves individual feature amplitudes while normalizing the input vector, and prevents a potential category proliferation problem. Adaptive weights :otart equal to one and can only decrease in time. A geometric interpretation of fuzzy AHT represents each category as a box that increases in size as weights decrease. A matching criterion controls search, determining how close an input and a learned representation must be for a category to accept the input as a new exemplar. A vigilance parameter (p) sets the matching criterion and determines how finely or coarsely an ART system will partition inputs. High vigilance creates fine categories, represented by small boxes. Learning stops when boxes cover the input space. With fast learning, fixed vigilance, and an arbitrary input set, learning stabilizes after just one presentation of each input. A fast-commit slow-recode option allows rapid learning of rare events yet buffers memories against recoding by noisy inputs. Fuzzy ARTMAP unites two fuzzy ART networks to solve supervised learning and prediction problems. A Minimax Learning Rule controls ARTMAP category structure, conjointly minimizing predictive error and maximizing code compression. Low vigilance maximizes compression but may therefore cause very different inputs to make the same prediction. When this coarse grouping strategy causes a predictive error, an internal match tracking control process increases vigilance just enough to correct the error. ARTMAP automatically constructs a minimal number of recognition categories, or "hidden units," to meet accuracy criteria. An ARTMAP voting strategy improves prediction by training the system several times using different orderings of the input set. Voting assigns confidence estimates to competing predictions given small, noisy, or incomplete training sets. ARPA benchmark simulations illustrate fuzzy ARTMAP dynamics. The chapter also compares fuzzy ARTMAP to Salzberg's Nested Generalized Exemplar (NGE) and to Simpson's Fuzzy Min-Max Classifier (FMMC); and concludes with a summary of ART and ARTMAP applications.Advanced Research Projects Agency (ONR N00014-92-J-4015); National Science Foundation (IRI-90-00530); Office of Naval Research (N00014-91-J-4100

Challenges of E-Learning Management Within the Croatian Higher Education System

For the past few years, e-learning has become synonymous with different learning and teaching techniques based on information and communication technologies. Generally speaking, elearning has been increasingly present in the Croatian higher education system, gradually changing its traditional character. However, this modern learning and teaching concept has not been equally accepted throughout student population. There are numerous reasons for this state of affairs, one of the most important ones being disproportion, i.e. unequal pace of its introduction at different university and vocational studies in Croatia. These discrepancies cannot be eliminated without active support by all the actors participating in the education process. The greatest responsibility, nevertheless, lies with the people directly in charge of the e-learning process. To fulfil its task more efficiently, e-learning management requires relevant information on different aspects of its usage, as well as its acceptance among students. With this aim in mind, we conducted a survey of student attitudes at Josip Juraj Strossmayer University of Osijek. This paper presents the results of this research, which are based on application of various statistical methods, primarily cluster analysis.e-learning management, attitudes of students, relevant information, cluster analysis