TechNews digests: Jan - Mar 2010

TechNews is a technology, news and analysis service aimed at anyone in the education sector keen to stay informed about technology developments, trends and issues. TechNews focuses on emerging technologies and other technology news. TechNews service : digests september 2004 till May 2010 Analysis pieces and News combined publish every 2 to 3 month

Effect of Feature Selection to Improve Accuracy and Decrease Execution Time with Predicating Learning Disabilities in School Going Children

Learning disability in school children is the representation of brain disorder which includes several disorders in which school going child faces the difficulties. The evaluation of learning disability is a crucial and important task in the field of educational field. This process can be accomplished by using data mining approaches. The efficiency of this approach is based on the feature selection while performing the prediction of the learning disabilities. In paper mainly aims on the efficient method of feature selection to improve the accuracy of prediction and classification in school going children. Feature selection is a process to collect the small subset of the features from huge dataset. A commonly used approach in feature selection is ranking the individual features according to some criteria and then search for an optimal feature subset based on evaluation criterion to test the optimality. In the Wrapper model we use some predetermined learning algorithm to find out the relevant features and test them. It requires more computations, so if there are large numbers of features we prefer to filter. In this paper first we have used feature selection attribute algorithms Chi-square. Info Gain, and Gain Ratio to predict the relevant features. Then we have applied fast correlation base filter algorithm on given features. Later classification is done using KNN and SVM. Results showed reduction in computational cost and time and increase in predictive accuracy for the student model. The objective of this work is to predict the presence of Learning Disability (LD) in school-aged children more accurately and help them to develop a bright future according to his choice by predicting the success at the earliest

A New Approach to Speeding Up Topic Modeling

Latent Dirichlet allocation (LDA) is a widely-used probabilistic topic modeling paradigm, and recently finds many applications in computer vision and computational biology. In this paper, we propose a fast and accurate batch algorithm, active belief propagation (ABP), for training LDA. Usually batch LDA algorithms require repeated scanning of the entire corpus and searching the complete topic space. To process massive corpora having a large number of topics, the training iteration of batch LDA algorithms is often inefficient and time-consuming. To accelerate the training speed, ABP actively scans the subset of corpus and searches the subset of topic space for topic modeling, therefore saves enormous training time in each iteration. To ensure accuracy, ABP selects only those documents and topics that contribute to the largest residuals within the residual belief propagation (RBP) framework. On four real-world corpora, ABP performs around $10$ to $100$ times faster than state-of-the-art batch LDA algorithms with a comparable topic modeling accuracy.Comment: 14 pages, 12 figure

Technology-enhanced Personalised Learning: Untangling the Evidence

Technology-enhanced personalised learning is not yet common in Germany, which is why we have tasked scientists with summarising the current status of international research on the matter. This study demonstrates the great potential of technology in implementing effective personalised learning. Nevertheless, it has not been assessed yet whether the practical implementation actually works: Even in countries such as the U.S., which lead the way in using techology in classroom settings, hardly any evaluation studies have been done to prove the effectiveness of technology-enhanced personalised learning. In the light of the above, the authors make recommendations for actions to be taken in Germany to make best use of the potential of technology in providing individual support and guidance to students