Statistical Machine Learning Techniques for the Prediction of Learning Disabilities in School-Age Children

Learning Disability (LD) is a general term that describes specific kinds of learning problems. It is a neurological condition that affects a child's brain and impairs his ability to carry out one or many specific tasks. The learning disabled children are neither slow nor mentally retarded. This disorder can make it problematic for a child to learn as quickly or in the same way as some child who isn't affected by a learning disability. An affected child can have normal or above average intelligence. They may have difficulty paying attention, with reading or letter recognition, or with mathematics. It does not mean that children who have learning disabilities are less intelligent. In fact, many children who have learning disabilities are more intelligent than an average child. Learning disabilities vary from child to child. One child with LD may not have the same kind of learning problems as another child with LD. There is no cure for learning disabilities and they are life-long. However, children with LD can be high achievers and can be taught ways to get around the learning disability. In this research work, data mining using machine learning techniques are used to analyze the symptoms of LD, establish interrelationships between them and evaluate the relative importance of these symptoms. To increase the diagnostic accuracy of learning disability prediction, a knowledge based tool based on statistical machine learning or data mining techniques, with high accuracy,according to the knowledge obtained from the clinical information, is proposed. The basic idea of the developed knowledge based tool is to increase the accuracy of the learning disability assessment and reduce the time used for the same. Different statistical machine learning techniques in data mining are used in the study. Identifying the important parameters of LD prediction using the data mining techniques, identifying the hidden relationship between the symptoms of LD and estimating the relative significance of each symptoms of LD are also the parts of the objectives of this research work. The developed tool has many advantages compared to the traditional methods of using check lists in determination of learning disabilities. For improving the performance of various classifiers, we developed some preprocessing methods for the LD prediction system. A new system based on fuzzy and rough set models are also developed for LD prediction. Here also the importance of pre-processing is studied. A Graphical User Interface (GUI) is designed for developing an integrated knowledge based tool for prediction of LD as well as its degree. The designed tool stores the details of the children in the student database and retrieves their LD report as and when required. The present study undoubtedly proves the effectiveness of the tool developed based on various machine learning techniques. It also identifies the important parameters of LD and accurately predicts the learning disability in school age children. This thesis makes several major contributions in technical, general and social areas. The results are found very beneficial to the parents, teachers and the institutions. They are able to diagnose the child’s problem at an early stage and can go for the proper treatments/counseling at the correct time so as to avoid the academic and social losses.

Institutional Repositories: New Horizon of Science & Technology Knowledge Transactions

Today higher education system and R&D in science & Technology has undergone tremendous changes from the traditional class room learning system and scholarly communication. Huge volume of Academic output and scientific communications are coming in electronic format. Knowledge management is a key challenge in the current century .Due to the advancement of ICT, Open access movement, Scholarly communications, Institutional repositories, ontology, semantic web, web 2.0 etc has revolutionized knowledge transactions and knowledge management in the field of science & technology. Today higher education has moved into a stage where competitive advantage is gained not just through access of infonnation but more importantly from new Knowledge creations.This paper examines the role of institutional repository in knowledge transactions in current scenario of Higher education.

Attribute reduction and missing value imputing with ANN: prediction of learning disabilities

Learning disability (LD) is a neurological condition that affects a child’s brain and impairs his ability to carry out one or many specific tasks. LD affects about 10% of children enrolled in schools. There is no cure for learning disabilities and they are lifelong. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Just as there are many different types of LDs, there are a variety of tests that may be done to pinpoint the problem The information gained from an evaluation is crucial for finding out how the parents and the school authorities can provide the best possible learning environment for child. This paper proposes a new approach in artificial neural network (ANN) for identifying LD in children at early stages so as to solve the problems faced by them and to get the benefits to the students, their parents and school authorities. In this study, we propose a closest fit algorithm data preprocessing with ANN classification to handle missing attribute values. This algorithm imputes the missing values in the preprocessing stage. Ignoring of missing attribute values is a common trend in all classifying algorithms. But, in this paper, we use an algorithm in a systematic approach for classification, which gives a satisfactory result in the prediction of LD. It acts as a tool for predicting the LD accurately, and good information of the child is made available to the concerned

Performance Improvement of Fuzzy and Neuro Fuzzy Systems: Prediction of Learning Disabilities in School-age Children

Learning Disability (LD) is a classification including several disorders in which a child has difficulty in learning in a typical manner, usually caused by an unknown factor or factors. LD affects about 15% of children enrolled in schools. The prediction of learning disability is a complicated task since the identification of LD from diverse features or signs is a complicated problem. There is no cure for learning disabilities and they are life-long. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. The aim of this paper is to develop a new algorithm for imputing missing values and to determine the significance of the missing value imputation method and dimensionality reduction method in the performance of fuzzy and neuro fuzzy classifiers with specific emphasis on prediction of learning disabilities in school age children. In the basic assessment method for prediction of LD, checklists are generally used and the data cases thus collected fully depends on the mood of children and may have also contain redundant as well as missing values. Therefore, in this study, we are proposing a new algorithm, viz. the correlation based new algorithm for imputing the missing values and Principal Component Analysis (PCA) for reducing the irrelevant attributes. After the study, it is found that, the preprocessing methods applied by us improves the quality of data and thereby increases the accuracy of the classifiers. The system is implemented in Math works Software Mat Lab 7.10. The results obtained from this study have illustrated that the developed missing value imputation method is very good contribution in prediction system and is capable of improving the performance of a classifier.

Learning achievements of farmers during the transition to market-oriented organic agriculture in rural Uganda

Organic agriculture requires farmers with the ability to develop profitable agro-enterprises on their own. By drawing on four years of experiences with the Enabling Rural Innovation approach in Uganda, we outline how smallholder farmers transition to organic agriculture and, at the same time, increase their entrepreneurial skills and competences through learning. In order to document this learning we operationalised the Kirkpatrick learning evaluation model, which subsequently informed the collection of qualitative data in two study sites. Our analysis suggests that the Enabling Rural Innovation approach helps farmers to develop essential capabilities for identifying organic markets and new organic commodities, for testing these organic commodities under varying organic farm management scenarios, and for negotiating contracts with organic traders. We also observed several obstacles that confront farmers’ transition to organic agriculture when using the Enabling Rural Innovation approach. These include the long duration of agronomic experimentation and seed multiplication, expensive organic certification procedures and the absence of adequate mechanism for farmers to access crop finance services. Despite prevailing obstacles we conclude that the Enabling Rural Innovation approach provides a starting point for farmers to develop entrepreneurial competences and profitable agro-enterprises on their own.

Optimal Unsupervised Learning in Feedforward Neural Networks

We investigate the properties of feedforward neural networks trained with Hebbian learning algorithms. A new unsupervised algorithm is proposed which produces statistically uncorrelated outputs. The algorithm causes the weights of the network to converge to the eigenvectors of the input correlation with largest eigenvalues. The algorithm is closely related to the technique of Self-supervised Backpropagation, as well as other algorithms for unsupervised learning. Applications of the algorithm to texture processing, image coding, and stereo depth edge detection are given. We show that the algorithm can lead to the development of filters qualitatively similar to those found in primate visual cortex.

Online Learning of Non-stationary Sequences

We consider an online learning scenario in which the learner can make predictions on the basis of a fixed set of experts. The performance of each expert may change over time in a manner unknown to the learner. We formulate a class of universal learning algorithms for this problem by expressing them as simple Bayesian algorithms operating on models analogous to Hidden Markov Models (HMMs). We derive a new performance bound for such algorithms which is considerably simpler than existing bounds. The bound provides the basis for learning the rate at which the identity of the optimal expert switches over time. We find an analytic expression for the a priori resolution at which we need to learn the rate parameter. We extend our scalar switching-rate result to models of the switching-rate that are governed by a matrix of parameters, i.e. arbitrary homogeneous HMMs. We apply and examine our algorithm in the context of the problem of energy management in wireless networks. We analyze the new results in the framework of Information Theory.

Learning Commonsense Categorical Knowledge in a Thread Memory System

If we are to understand how we can build machines capable of broad purpose learning and reasoning, we must first aim to build systems that can represent, acquire, and reason about the kinds of commonsense knowledge that we humans have about the world. This endeavor suggests steps such as identifying the kinds of knowledge people commonly have about the world, constructing suitable knowledge representations, and exploring the mechanisms that people use to make judgments about the everyday world. In this work, I contribute to these goals by proposing an architecture for a system that can learn commonsense knowledge about the properties and behavior of objects in the world. The architecture described here augments previous machine learning systems in four ways: (1) it relies on a seven dimensional notion of context, built from information recently given to the system, to learn and reason about objects' properties; (2) it has multiple methods that it can use to reason about objects, so that when one method fails, it can fall back on others; (3) it illustrates the usefulness of reasoning about objects by thinking about their similarity to other, better known objects, and by inferring properties of objects from the categories that they belong to; and (4) it represents an attempt to build an autonomous learner and reasoner, that sets its own goals for learning about the world and deduces new facts by reflecting on its acquired knowledge. This thesis describes this architecture, as well as a first implementation, that can learn from sentences such as ``A blue bird flew to the tree'' and ``The small bird flew to the cage'' that birds can fly. One of the main contributions of this work lies in suggesting a further set of salient ideas about how we can build broader purpose commonsense artificial learners and reasoners.

Learning-Based Approach to Real Time Tracking and Analysis of Faces

This paper describes a trainable system capable of tracking faces and facialsfeatures like eyes and nostrils and estimating basic mouth features such as sdegrees of openness and smile in real time. In developing this system, we have addressed the twin issues of image representation and algorithms for learning. We have used the invariance properties of image representations based on Haar wavelets to robustly capture various facial features. Similarly, unlike previous approaches this system is entirely trained using examples and does not rely on a priori (hand-crafted) models of facial features based on optical flow or facial musculature. The system works in several stages that begin with face detection, followed by localization of facial features and estimation of mouth parameters. Each of these stages is formulated as a problem in supervised learning from examples. We apply the new and robust technique of support vector machines (SVM) for classification in the stage of skin segmentation, face detection and eye detection. Estimation of mouth parameters is modeled as a regression from a sparse subset of coefficients (basis functions) of an overcomplete dictionary of Haar wavelets.

Enhancing learning beyond the classroom: blogging and other social networking tools

Blogging has become one of the key ingredients of the so-called socials networks. This phenomenon has indeed invaded the world of education. Connections between people, comments on each other posts, and assessment of innovation are usually interesting characteristics of blogs related to students and scholars. Blogs have become a kind of new form of authority, bringing about (divergent) discussions which lead to creation of knowledge. The use of blogs as an innovative, educational tool is not at all new. However, their use in universities is not very widespread yet. Blogging for personal affairs is rather commonplace, but blogging for professional affairs – teaching, research and service, is scarce, despite the availability of ready-to-use, free tools. Unfortunately, Information Society has not reached yet enough some universities: not only are (student) blogs scarcely used as an educational tool, but it is quite rare to find a blog written by University professors. The Institute of Computational Chemistry of the University of Girona and the Department of Chemistry of the Universitat Autònoma de Barcelona has joined forces to create “InnoCiència”, a new Group on Digital Science Communitation. This group, formed by ca. ten researchers, has promoted the use of blogs, twitters. wikis and other tools of Web 2.0 in activities in Catalonia concerning the dissemination of Science, like Science Week, Open Day or Researchers’ Night. Likewise, its members promote use of social networking tools in chemistry- and communication-related courses. This communication explains the outcome of social-network experiences with teaching undergraduate students and organizing research communication events. We provide live, hands-on examples and interactive ground to show how blogs and twitters can be used to enhance the yield of teaching and research. Impact of blogging and other social networking tools on the outcome of the learning process is very depending on the target audience and the environmental conditions. A few examples are provided and some proposals to use these techniques efficiently to help students are hinted

Supporting graduate development through work based learning

The genesis of this innovation lies in the commitment of a national Irish business enterprise to the professional development of its staff in general, and to the enhancement of its Information Technologies (IT) staff specifically, in collaboration with a national Higher Education (HE) provider. A postgraduate degree, awarded by the HE provider, seeks to bring coherence and cohesion to the education and training provision for newly recruited IT graduate staff of the business enterprise, simultaneously acting both as an induction process for new staff and as a professional capacity building exercise, thereby enhancing the enterprise’s organisational learning and collective competence in the areas of information technologies, IT security and technical service management. The curriculum was designed by the HE provider in collaboration with the business enterprise to offer it to circa sixteen IT staff per cycle of delivery through a model known generally as the new apprenticeship for professional practice which uses a combination of college-based, block release taught elements, regular day release seminars and substantial work-based learning, supported by the academic staff of the HE provider and work-based support staff/mentors of the business enterprise. Academic quality assurance, pedagogical, assessment and accreditation responsibilities remain with the HE provider. (...)

A behavior-based scheme using reinforcement learning for autonomous underwater vehicles

This paper presents a hybrid behavior-based scheme using reinforcement learning for high-level control of autonomous underwater vehicles (AUVs). Two main features of the presented approach are hybrid behavior coordination and semi on-line neural-Q_learning (SONQL). Hybrid behavior coordination takes advantages of robustness and modularity in the competitive approach as well as efficient trajectories in the cooperative approach. SONQL, a new continuous approach of the Q_learning algorithm with a multilayer neural network is used to learn behavior state/action mapping online. Experimental results show the feasibility of the presented approach for AUVs

Adaptive intelligent agent approach to guide the web navigation on the PLAN-G distance learning platform

Hypermedia systems based on the Web for open distance education are becoming increasingly popular as tools for user-driven access learning information. Adaptive hypermedia is a new direction in research within the area of user-adaptive systems, to increase its functionality by making it personalized [Eklu 961. This paper sketches a general agents architecture to include navigational adaptability and user-friendly processes which would guide and accompany the student during hislher learning on the PLAN-G hypermedia system (New Generation Telematics Platform to Support Open and Distance Learning), with the aid of computer networks and specifically WWW technology [Marz 98-1] [Marz 98-2]. The PLAN-G actual prototype is successfully used with some informatics courses (the current version has no agents yet). The propased multi-agent system, contains two different types of adaptive autonomous software agents: Personal Digital Agents {Interface), to interacl directly with the student when necessary; and Information Agents (Intermediaries), to filtrate and discover information to learn and to adapt navigation space to a specific student

The UK Professional Standards Framework for teaching and supporting learning in higher education

This is a part of a collection of materials developed by the HEAcademy Subject Centre for Languages, linguistics and area studies. The materials provide reflective activities designed to engage teachers with some of the key issues in working with international students and practical ideas for ways in which these can be addressed. They will be of particular interest to new staff or anyone new to working with international students.

The New Web Literacy

Web 2.0 is sometimes described as the read/write web, giving everyday users the chance to create and share information as well as to consume information created by others. Social media systems are built on this foundation of participation and sharing, but what is the mindset of these users, and are they quite so everyday as we might suppose? The skills and attitudes held by users can be described as their literacy, and there has been a lot of debate over the last few years about how to describe these literacies, and design for them. One field that has been changed radically by this notion is Technology Enhanced Learning (TEL) where a fierce debate has raged about the potential of a new generation of highly literate digital natives, and Edupunks have argued for open and personal systems that challenge traditional models of institutional control. In this session we look at the arguments surrounding digital literacy and examine TEL as an example of how social media can change an application domain.