Odin: a service for gamification of learning activities

Existing gamification services have features that preclude their use by e-learning tools. Odin is a gamification service that mimics the API of state-of-the-art services without these limitations. This paper describes Odin, its role in an e-learning system architecture requiring gamification, and details its implementation. The validation of Odin involved the creation of a small e-learning game, integrated in a Learning Management System (LMS) using the Learning Tools Interoperability (LTI) specification.

Implicit and explicit functions in the acquisition of a phonics rule in the word recognition of learning disabled and non learning disabled students /

One group of 12 non learning disabled students and two groups of 12 learning disabled students between the ges of 10 and 12 were measured on implicit and explicit knowledge cquisition. Students in each group implicitly cquired knowledge bout I of 2 vocabulary rules. The vocabulary rules governed the pronunciation of 2 types of pseudowords. After completing the implicit acquisition phase, all groups were administered a test of implicit knowledge. The non learning disabled group and I learning disabled group were then asked to verbalize the knowledge acquired during the initial phase. This was a test of explicit knowledge. All 3 groups were then given a postlest of implicit knowledge. This tcst was a measure of the effectiveness of the employment of the verbalization technique. Results indicate that implicit knowledge capabilities for both the learning disabled and non learning disabled groups were intact. However. there were significant differences between groups on explicit knowledge capabilities. This led to the conclusion that implicit functions show little individual differences, and that explicit functions are affected by ability difference. Furthermore, the employment of the verbalization technique significantly increased POStlest scores for learning disabled students. This suggested that the use of metacognitive techniques was a beneficial learning tool for learning disabled students.

An examination of learning resulting from a transition from a directed to a self-directed work environment

~ This study focuses on the process of self-directed learning that individuals go through as they adapt to new work situations. This is a study of how one critical incident, specifically the transition from a traditional office structure to a home office structure, affected employees and what their learning process was as they adapted to the new environment. This study has 3 educational foundations: adult learning, self-directed learning, and the social context from which the learning will occur. Six women and 2 men were interviewed approximately 1 year following the transition. Analysis of the data revealed 5 themes of: impacts of the self-directed environment on participants' personal lives, their roles, skill set, productivity, and the physical environment; support offered by the organization, family, and office administration; personal development, specific learning needs, and personal skills; boundaries as they relate to family and work; and skill set and orientation requirements of new home office employees. The findings revealed the learning processes of the 8 participants. The learning processes of these participants were discussed within a theoretical framework of the learners, their immediate surroundings, and the larger social environment. The results indicated that the transition from a directed work environment to a self directed work environment is a complex, interrelated process. An element found throughout the theoretical framework is that of control. A second critical element is the need for participants to have a clearly defined work role and an opportunity to engage in discussion with peers and the community. Further findings reinforced the importance of climate and found that the physical environment is a key factor in a successful selfdirected work environment. The findings of this study revealed that no one factor makes an individual function successfully in a self-directed work environment, but that it is a complex interplay among the leamer, their immediate surroundings, and the social environment that will have the greatest impact on success. Recommendations are made which can be used to guide organizational leaders in facilitating employees' transition from a directed to a self-directed work environment. Additionally, recommendations are made for further research in the area of self-directed work environments.

The role of learning and growth hormone-releasing factor in the control of protein intake in rats /

Both learning and basic biological mechanisms have been shown to play a role in the control of protein int^e. It has previously been shown that rats can adapt their dietary selection patterns successfully in the face of changing macronutrient requirements and availability. In particular, it has been demonstrated that when access to dietary protein is restricted for a period of time, rats selectively increase their consumption of a proteincontaining diet when it becomes available. Furthermore, it has been shown that animals are able to associate various orosensory cues with a food's nutrient content. In addition to the role that learning plays in food intake, there are also various biological mechanisms that have been shown to be involved in the control of feeding behaviour. Numerous studies have documented that various hormones and neurotransmitter substances mediate food intake. One such hormone is growth hormone-releasing factor (GRF), a peptide that induces the release of growth hormone (GH) from the anterior pituitary gland. Recent research by Vaccarino and Dickson ( 1 994) suggests that GRF may stimulate food intake by acting as a neurotransmitter in the suprachiasmatic nucleus (SCN) and the adjacent medial preoptic area (MPOA). In particular, when GRF is injected directly into the SCN/MPOA, it has been shown to selectively enhance the intake of protein in both fooddeprived and sated rats. Thus, GRF may play a role in activating protein consumption generally, and when animals have a need for protein, GRF may serve to trigger proteinseeking behaviour. Although researchers have separately examined the role of learning and the central mechanisms involved in the control of protein selection, no one has yet attempted to bring together these two lines of study. Thus, the purpose of this study is to join these two parallel lines of research in order to further our understanding of mechanisms controlling protein selection. In order to ascertain the combined effects that GRF and learning have on protein intake several hypothesis were examined. One major hypothesis was that rats would successfully alter their dietary selection patterns in response to protein restriction. It was speculated that rats kept on a nutritionally complete maintenance diet (NCMD) would consume equal amount of the intermittently presented high protein conditioning diet (HPCD) and protein-free conditioning diet (PFCD). However, it was hypothesized that rats kept on a protein-free maintenance diet (PFMD) would selectively increase their intake of the HPCD. Another hypothesis was that rats would learn to associate a distinct marker flavour with the nutritional content of the diets. If an animal is able to make the association between a marker flavour and the nutrient content of the food, then it is hypothesized that they will consume more of a mixed diet (equal portion HPCD and PFCD) with the marker flavour that was previously paired with the HPCD (Mixednp-f) when kept on the PFMD. In addition, it was hypothesized that intracranial injection of GRF into the SCN/MPOA would result in a selective increase in HPCD as well as Mixednp-t consumption. Results demonstrated that rats did in fact selectively increase their consumption of the flavoured HPCD and Mixednp-f when kept on the NCMD. These findings indicate that the rats successfully learned about the nutrient content of the conditioning diets and were able to associate a distinct marker flavour with the nutrient content of the diets. However, the results failed to support previous findings that GRF increases protein intake. In contrast, the administration of GRF significantly reduced consumption of HPCD during the first hour of testing as compared to the no injection condition. In addition, no differences in the intake of the HPCD were found between the GRF and vehicle condition. Because GRF did not selectively increase HPCD consumption, it was not surprising that GRF also did not increase MixedHP-rintake. What was interesting was that administration of GRF and vehicle did not reduc^Mixednp-f consumption as it had decreased HPCD consumption.

The Effectiveness of Learning Portfolios: A Study of Quality Assurance Programs of Selected Health Regulatory Colleges in Ontario

Health regulatory colleges promote quality practice and continued competence through Quality Assurance (QA) programs. For many colleges, a QA program includes the use of portfolios that incorporate self-directed learning. The purpose of this study was to determine some of the issues surrounding the effectiveness of QA portfolio programs. The literature review revealed that portfolios are valuable tools, but gaps in knowledge include a comparative analysis of QA programs and the perspective of regulatory college administrators. Data were collected through interviews with 6 administrators and a review of 14 portfolio models described on college websites. The results from the two data sources were applied to Robert Stake's responsive evaluation framework to identify issues related to the portfolio's effectiveness (Stake, 1967). The learning components of portfolios were analyzed through the humanist and constructivist lenses. All 14 portfolio models were found to have 3 main components: self-diagnosis, learning plan and activities, and self-evaluation. However, differences were uncovered in learners' autonomy in selecting learning activities, methods of portfolio evaluation, and the relationship between the portfolio and other QA components. The results revealed a dual philosophy of learning in portfolio models and an apparent contradiction between the needs of the individual learner and the organization. Paths for future research include the tenuous relationship between competence and learning, and the impact of technical approaches on selfdirected learning initiatives. A key recommendation is to acknowledge the unique identity of each profession so that health regulatory colleges can address legislative demands and learner needs.

Using First Nations Children's Literature in the Classroom: Portfolio of Learning

A portfolio was developed to encourage teachers of Aboriginal children to include First Nations mentor texts into their daily teaching practices. The artifacts within the portfolio have been produced in accordance with guiding beliefs about how students, specifically First Nations students, learn. The portfolio supports the notion that Aboriginal children need to encounter representations of their own culture, histories and beliefs within the literature in order to be successful in school. The use of First Nations children’s literature in the classroom was explored with an emphasis on how using this literature will assist in improving literacy levels and the self-esteem of First Nations students.

New method of learning to read write and speak the spanish language; with an appendix : y M. Velazquez and T. Simonne

UANL

A new practical and easy method of learning after the system

UANL

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.

Significance Of Classification Techniques In Prediction Of Learning Disabilities

The aim of this study is to show the importance of two classification techniques, viz. decision tree and clustering, in prediction of learning disabilities (LD) of school-age children. LDs affect about 10 percent of all children enrolled in schools. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Decision trees and clustering are powerful and popular tools used for classification and prediction in Data mining. Different rules extracted from the decision tree are used for prediction of learning disabilities. Clustering is the assignment of a set of observations into subsets, called clusters, which are useful in finding the different signs and symptoms (attributes) present in the LD affected child. In this paper, J48 algorithm is used for constructing the decision tree and K-means algorithm is used for creating the clusters. By applying these classification techniques, LD in any child can be identified

Prediction of Key Symptoms of Learning Disabilities in School-Age Children Using Rough Sets

This paper highlights the prediction of learning disabilities (LD) in school-age children using rough set theory (RST) with an emphasis on application of data mining. In rough sets, data analysis start from a data table called an information system, which contains data about objects of interest, characterized in terms of attributes. These attributes consist of the properties of learning disabilities. By finding the relationship between these attributes, the redundant attributes can be eliminated and core attributes determined. Also, rule mining is performed in rough sets using the algorithm LEM1. The prediction of LD is accurately done by using Rosetta, the rough set tool kit for analysis of data. The result obtained from this study is compared with the output of a similar study conducted by us using Support Vector Machine (SVM) with Sequential Minimal Optimisation (SMO) algorithm. It is found that, using the concepts of reduct and global covering, we can easily predict the learning disabilities in children

Prediction of Learning Disabilities in School Age Children using SVM and Decision Tree

This paper highlights the prediction of Learning Disabilities (LD) in school-age children using two classification methods, Support Vector Machine (SVM) and Decision Tree (DT), with an emphasis on applications of data mining. About 10% of children enrolled in school have a learning disability. Learning disability prediction in school age children is a very complicated task because it tends to be identified in elementary school where there is no one sign to be identified. By using any of the two classification methods, SVM and DT, we can easily and accurately predict LD in any child. Also, we can determine the merits and demerits of these two classifiers and the best one can be selected for the use in the relevant field. In this study, Sequential Minimal Optimization (SMO) algorithm is used in performing SVM and J48 algorithm is used in constructing decision trees.

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.

Explorations of the Practical Issues of Learning Prediction-Control Tasks Using Temporal Difference Learning Methods

There has been recent interest in using temporal difference learning methods to attack problems of prediction and control. While these algorithms have been brought to bear on many problems, they remain poorly understood. It is the purpose of this thesis to further explore these algorithms, presenting a framework for viewing them and raising a number of practical issues and exploring those issues in the context of several case studies. This includes applying the TD(lambda) algorithm to: 1) learning to play tic-tac-toe from the outcome of self-play and of play against a perfectly-playing opponent and 2) learning simple one-dimensional segmentation tasks.