Interactive multimedia education: Using Authorware as an instructional tool to enhance teaching and learning in the Malaysian classroom

The infusion of multimedia technology into the teaching and learning process is changing the way teachers teach and students learn. It is providing powerful tools for accessing, storing and disseminating information and re-shaping the delivery methodology of our educational content. This paper discusses the use of multimedia as an enabler for educators to become developers of their educational content, focussing on the creation of an interactive multimedia learning (IML) module using Authorware. A survey was carried out to assess students' response toward the module. Results showed a favourable trend towards using authoring technology in the classroom.The infusion of multimedia technology into the teaching and learning process is changing the way teachers teach and students learn. It is providing powerful tools for accessing, storing and disseminating information and re-shaping the delivery methodology of our educational content. This paper discusses the use of multimedia as an enabler for educators to become developers of their educational content, focussing on the creation of an interactive multimedia learning (IML) module using Authorware. A survey was carried out to assess students' response toward the module. Results showed a favourable trend towards using authoring technology in the classroom

Reduced pattern training based on task decomposition using pattern distributor

Task Decomposition with Pattern Distributor (PD) is a new task decomposition method for multilayered feedforward neural networks. Pattern distributor network is proposed that implements this new task decomposition method. We propose a theoretical model to analyze the performance of pattern distributor network. A method named Reduced Pattern Training is also introduced, aiming to improve the performance of pattern distribution. Our analysis and the experimental results show that reduced pattern training improves the performance of pattern distributor network significantly. The distributor module’s classification accuracy dominates the whole network’s performance. Two combination methods, namely Cross-talk based combination and Genetic Algorithm based combination, are presented to find suitable grouping for the distributor module. Experimental results show that this new method can reduce training time and improve network generalization accuracy when compared to a conventional method such as constructive backpropagation or a task decomposition method such as Output Parallelism

Interactive multimedia education: Using Authorware as an instructional tool to enhance teaching and learning in the Malaysian classroom

The infusion of multimedia technology into the teaching and learning process is changing the way teachers teach and students learn. It is providing powerful tools for accessing, storing and disseminating information and re-shaping the delivery methodology of our educational content. This paper discusses the use of multimedia as an enabler for educators to become developers of their educational content, focussing on the creation of an interactive multimedia learning (IML) module using Authorware. A survey was carried out to assess students' response toward the module. Results showed a favourable trend towards using authoring technology in the classroom.The infusion of multimedia technology into the teaching and learning process is changing the way teachers teach and students learn. It is providing powerful tools for accessing, storing and disseminating information and re-shaping the delivery methodology of our educational content. This paper discusses the use of multimedia as an enabler for educators to become developers of their educational content, focussing on the creation of an interactive multimedia learning (IML) module using Authorware. A survey was carried out to assess students' response toward the module. Results showed a favourable trend towards using authoring technology in the classroom

Task decomposition using pattern distributor

In this paper, we propose a new task decomposition method for multilayered feedforward neural networks, namely Task Decomposition with Pattern Distributor in order to shorten the training time and improve the generalization accuracy of a network under training. This new method uses the combination of modules (small-size feedforward network) in parallel and series, to produce the overall solution for a complex problem. Based on a “divide-and-conquer” technique, the original problem is decomposed into several simpler sub-problems by a pattern distributor module in the network, where each sub-problem is composed of the whole input vector and a fraction of the output vector of the original problem. These sub-problems are then solved by the corresponding groups of modules, where each group of modules is connected in series with the pattern distributor module and the modules in each group are connected in parallel. The design details and implementation of this new method are introduced in this paper. Several benchmark classification problems are used to test this new method. The analysis and experimental results show that this new method could reduce training time and improve generalization accuracy

Use of waste optical disc in concrete to promote Sustainable development in construction industry

In recent years, happening of few catastrophic natural disasters have raise the awareness of public on environmental issue that the Earth is getting sick. Preservation and conservation of environment started to be given concern by people all over the world. Sustainable development is becoming the trench of construction industry nowadays and many countries start emphasizing on application of green practices in building construction

Jaccard/Tanimoto similarity test and estimation methods

Binary data are used in a broad area of biological sciences. Using binary presence-absence data, we can evaluate species co-occurrences that help elucidate relationships among organisms and environments. To summarize similarity between occurrences of species, we routinely use the Jaccard/Tanimoto coefficient, which is the ratio of their intersection to their union. It is natural, then, to identify statistically significant Jaccard/Tanimoto coefficients, which suggest non-random co-occurrences of species. However, statistical hypothesis testing using this similarity coefficient has been seldom used or studied. We introduce a hypothesis test for similarity for biological presence-absence data, using the Jaccard/Tanimoto coefficient. Several key improvements are presented including unbiased estimation of expectation and centered Jaccard/Tanimoto coefficients, that account for occurrence probabilities. We derived the exact and asymptotic solutions and developed the bootstrap and measurement concentration algorithms to compute statistical significance of binary similarity. Comprehensive simulation studies demonstrate that our proposed methods produce accurate p-values and false discovery rates. The proposed estimation methods are orders of magnitude faster than the exact solution. The proposed methods are implemented in an open source R package called jaccard (https://cran.r-project.org/package=jaccard). We introduce a suite of statistical methods for the Jaccard/Tanimoto similarity coefficient, that enable straightforward incorporation of probabilistic measures in analysis for species co-occurrences. Due to their generality, the proposed methods and implementations are applicable to a wide range of binary data arising from genomics, biochemistry, and other areas of science

Resolvability of topological groups

A research project submitted in partial fulfilment of the requirements for the degree of Master of Science School of Mathematics, University Of Witwatersrand 18 May 2016A topological group is called resolvable (ω-resolvable) if it can be partitioned into two (into ω) dense subsets and absolutely resolvable (absolutely ω-resolvable) if it can be partitioned into two (into ω) subsets dense in every nondiscrete group topology. These notions have been intensively studied over the past 20 years. In this dissertation some major results in the field are presented. In particular, it is shown that (a) every countable nondiscrete topological group containing no open Boolean subgroup is ω-resolvable, and (b) every infinite Abelian group containing no infinite Boolean subgroup is absolutely ω-resolvable.M T 201