Decision Support Models for Composing and Navigating through e-Learning Objects

Libraries of learning objects may serve as basis for deriving course offerings that are customized to the needs of different learning communities or even individuals. Several ways of organizing this course composition process are discussed. Course composition needs a clear understanding of the dependencies between the learning objects. Therefore we discuss the metadata for object relationships proposed in different standardization projects and especially those suggested in the Dublin Core Metadata Initiative. Based on these metadata we construct adjacency matrices and graphs. We show how Gozinto-type computations can be used to determine direct and indirect prerequisites for certain learning objects. The metadata may also be used to define integer programming models which can be applied to support the instructor in formulating his specifications for selecting objects or which allow a computer agent to automatically select learning objects. Such decision models could also be helpful for a learner navigating through a library of learning objects. We also sketch a graph-based procedure for manual or automatic sequencing of the learning objects.

Object-Based Customization of Learning Content: Potentials and Pitfalls

Specification consortia and standardization bodies concentrate on e-Learning objects to en-sure reusability of content. Learning objects may be collected in a library and used for deriv-ing course offerings that are customized to the needs of different learning communities. How-ever, customization of courses is possible only if the logical dependencies between the learn-ing objects are known. Metadata for describing object relationships have been proposed in several e-Learning specifications. This paper discusses the customization potential of e-Learning objects but also the pitfalls that exist if content is customized inappropriately.

Comparison of machine learning approaches and statistical methods for estimation of airborne chemical exposures

Accurate quantitative estimation of exposure using retrospective data has been one of the most challenging tasks in the exposure assessment field. To improve these estimates, some models have been developed using published exposure databases with their corresponding exposure determinants. These models are designed to be applied to reported exposure determinants obtained from study subjects or exposure levels assigned by an industrial hygienist, so quantitative exposure estimates can be obtained. ^ In an effort to improve the prediction accuracy and generalizability of these models, and taking into account that the limitations encountered in previous studies might be due to limitations in the applicability of traditional statistical methods and concepts, the use of computer science- derived data analysis methods, predominantly machine learning approaches, were proposed and explored in this study. ^ The goal of this study was to develop a set of models using decision trees/ensemble and neural networks methods to predict occupational outcomes based on literature-derived databases, and compare, using cross-validation and data splitting techniques, the resulting prediction capacity to that of traditional regression models. Two cases were addressed: the categorical case, where the exposure level was measured as an exposure rating following the American Industrial Hygiene Association guidelines and the continuous case, where the result of the exposure is expressed as a concentration value. Previously developed literature-based exposure databases for 1,1,1 trichloroethane, methylene dichloride and, trichloroethylene were used. ^ When compared to regression estimations, results showed better accuracy of decision trees/ensemble techniques for the categorical case while neural networks were better for estimation of continuous exposure values. Overrepresentation of classes and overfitting were the main causes for poor neural network performance and accuracy. Estimations based on literature-based databases using machine learning techniques might provide an advantage when they are applied to other methodologies that combine `expert inputs' with current exposure measurements, like the Bayesian Decision Analysis tool. The use of machine learning techniques to more accurately estimate exposures from literature-based exposure databases might represent the starting point for the independence from the expert judgment.^

The impact of a computer based adult literacy program on literacy and numeracy : evidence from India

With over 700 million illiterate adults in the world, many governments have implemented adult literacy programs across the world, although typically with low rates of success partly because the quality of teaching is low. One solution may lie in the standardization of teaching provided by computer-aided instruction. We present the first rigorous evidence of the effectiveness of a computer-based adult literacy program. A randomized control trial study of TARA Akshar Plus, an Indian adult literacy program, was implemented in the state of Uttar Pradesh in India. We find large, significant impacts of this computer-aided program on literacy and numeracy outcomes. We compare the improvement in learning to that of other traditional adult literacy programs and conclude that TARA Akshar Plus is effective in increasing literacy and numeracy for illiterate adult women.

An improved way for evaluating competences: A different approach to project management learning .;

This paper presents a formal initiative for monitoring the competence acquisition by a team of students with different backgrounds facing the experience of being working by projects and in a project. These students are inexperienced in the project management field and they play this game on a time-shared manner along with other activities. The goal of this experience is to make some improvements in determining the competence levels acquired by means of how the work is being done. The use of this information, which is out of the scope of this particular work, could make possible to bring additional information to the students involved in terms of their individual competencies and the identification of new opportunities of personal improvement.

Project Management Learning in a Collaborative Distant Learning Context - An Actual On-going Experience

The goal of this paper is to show the results of an on-going experience on teaching project management to grade students by following a development scheme of management related competencies on an individual basis. In order to achieve that goal, the students are organized in teams that must solve a problem and manage the development of a feasible solution to satisfy the needs of a client. The innovative component advocated in this paper is the formal introduction of negotiating and virtual team management aspects, as different teams from different universities at different locations and comprising students with different backgrounds must collaborate and compete amongst them. The different learning aspects are identified and the improvement levels are reflected in a rubric that has been designed ad hoc for this experience. Finally, the effort frameworks for the student and instructor have been established according to the requirements of the Bologna paradigms. This experience is developed through a software-based support system allowing blended learning for the theoretical and individual?s work aspects, blogs, wikis, etc., as well as project management tools based on WWW that allow the monitoring of not only the expected deliverables and the achievement of the goals but also the progress made on learning as established in the defined rubric

Self-learning of fault diagnosis identification

A good and early fault detection and isolation system along with efficient alarm management and fine sensor validation systems are very important in today¿s complex process plants, specially in terms of safety enhancement and costs reduction. This paper presents a methodology for fault characterization. This is a self-learning approach developed in two phases. An initial, learning phase, where the simulation of process units, without and with different faults, will let the system (in an automated way) to detect the key variables that characterize the faults. This will be used in a second (on line) phase, where these key variables will be monitored in order to diagnose possible faults. Using this scheme the faults will be diagnosed and isolated in an early stage where the fault still has not turned into a failure.

Interactive Graphic Simulation: An Advanced Methodology to Improve the Teaching-Learning Process in Nuclear Engineering Education and Training

Nowadays, computer simulators are becoming basic tools for education and training in many engineering fields. In the nuclear industry, the role of simulation for training of operators of nuclear power plants is also recognized of the utmost relevance. As an example, the International Atomic Energy Agency sponsors the development of nuclear reactor simulators for education, and arranges the supply of such simulation programs. Aware of this, in 2008 Gas Natural Fenosa, a Spanish gas and electric utility that owns and operate nuclear power plants and promotes university education in the nuclear technology field, provided the Department of Nuclear Engineering of Universidad Politécnica de Madrid with the Interactive Graphic Simulator (IGS) of “José Cabrera” (Zorita) nuclear power plant, an industrial facility whose commercial operation ceased definitively in April 2006. It is a state-of-the-art full-scope real-time simulator that was used for training and qualification of the operators of the plant control room, as well as to understand and analyses the plant dynamics, and to develop, qualify and validate its emergency operating procedures.

New Methodology for Integrating Teams into Multidisciplinary Project Based Learning

This paper describes the collaboration among students and professors in four different subjects, to develop multidisciplinary projects. The objective is to simulate the conditions in a company environment. A new methodology based on student interaction and content development in a Wiki environment has been developed. The collaborative server created an ‘out of the classroom’ discussion forum for students of different subjects, and allowed them to compile a ‘project work’ portfolio. Students and professors participated with enthusiasm, due to the correct well-distributed work and the easiness of use of the selected platform in which only an internet connected computer is needed to create and to discuss the multidisciplinary projects. Quality of developed projects has been dramatically improved due to integration of results provided from the different teams.

GLMP for automatic assessment of DFS algorithm learning

We describe how to use a Granular Linguistic Model of a Phenomenon (GLMP) to assess e-learning processes. We apply this technique to evaluate algorithm learning using the GRAPHs learning environment.

An example of Learning based on competences: Use of Maxima in Linear Algebra for Engineers

This paper analyzes the role of Computer Algebra Systems (CAS) in a model of learning based on competences. The proposal is an e-learning model Linear Algebra course for Engineering, which includes the use of a CAS (Maxima) and focuses on problem solving. A reference model has been taken from the Spanish Open University. The proper use of CAS is defined as an indicator of the generic ompetence: Use of Technology. Additionally, we show that using CAS could help to enhance the following generic competences: Self Learning, Planning and Organization, Communication and Writing, Mathematical and Technical Writing, Information Management and Critical Thinking.

Regularization for sparsity in statistical analysis and machine learning

Pragmatism is the leading motivation of regularization. We can understand regularization as a modification of the maximum-likelihood estimator so that a reasonable answer could be given in an unstable or ill-posed situation. To mention some typical examples, this happens when fitting parametric or non-parametric models with more parameters than data or when estimating large covariance matrices. Regularization is usually used, in addition, to improve the bias-variance tradeoff of an estimation. Then, the definition of regularization is quite general, and, although the introduction of a penalty is probably the most popular type, it is just one out of multiple forms of regularization. In this dissertation, we focus on the applications of regularization for obtaining sparse or parsimonious representations, where only a subset of the inputs is used. A particular form of regularization, L1-regularization, plays a key role for reaching sparsity. Most of the contributions presented here revolve around L1-regularization, although other forms of regularization are explored (also pursuing sparsity in some sense). In addition to present a compact review of L1-regularization and its applications in statistical and machine learning, we devise methodology for regression, supervised classification and structure induction of graphical models. Within the regression paradigm, we focus on kernel smoothing learning, proposing techniques for kernel design that are suitable for high dimensional settings and sparse regression functions. We also present an application of regularized regression techniques for modeling the response of biological neurons. Supervised classification advances deal, on the one hand, with the application of regularization for obtaining a na¨ıve Bayes classifier and, on the other hand, with a novel algorithm for brain-computer interface design that uses group regularization in an efficient manner. Finally, we present a heuristic for inducing structures of Gaussian Bayesian networks using L1-regularization as a filter. El pragmatismo es la principal motivación de la regularización. Podemos entender la regularización como una modificación del estimador de máxima verosimilitud, de tal manera que se pueda dar una respuesta cuando la configuración del problema es inestable. A modo de ejemplo, podemos mencionar el ajuste de modelos paramétricos o no paramétricos cuando hay más parámetros que casos en el conjunto de datos, o la estimación de grandes matrices de covarianzas. Se suele recurrir a la regularización, además, para mejorar el compromiso sesgo-varianza en una estimación. Por tanto, la definición de regularización es muy general y, aunque la introducción de una función de penalización es probablemente el método más popular, éste es sólo uno de entre varias posibilidades. En esta tesis se ha trabajado en aplicaciones de regularización para obtener representaciones dispersas, donde sólo se usa un subconjunto de las entradas. En particular, la regularización L1 juega un papel clave en la búsqueda de dicha dispersión. La mayor parte de las contribuciones presentadas en la tesis giran alrededor de la regularización L1, aunque también se exploran otras formas de regularización (que igualmente persiguen un modelo disperso). Además de presentar una revisión de la regularización L1 y sus aplicaciones en estadística y aprendizaje de máquina, se ha desarrollado metodología para regresión, clasificación supervisada y aprendizaje de estructura en modelos gráficos. Dentro de la regresión, se ha trabajado principalmente en métodos de regresión local, proponiendo técnicas de diseño del kernel que sean adecuadas a configuraciones de alta dimensionalidad y funciones de regresión dispersas. También se presenta una aplicación de las técnicas de regresión regularizada para modelar la respuesta de neuronas reales. Los avances en clasificación supervisada tratan, por una parte, con el uso de regularización para obtener un clasificador naive Bayes y, por otra parte, con el desarrollo de un algoritmo que usa regularización por grupos de una manera eficiente y que se ha aplicado al diseño de interfaces cerebromáquina. Finalmente, se presenta una heurística para inducir la estructura de redes Bayesianas Gaussianas usando regularización L1 a modo de filtro.

Learning curves of basic laparoscopic psychomotor skills in SINERGIA VR simulator

Purpose: Surgical simulators are currently essential within any laparoscopic training program because they provide a low-stakes, reproducible and reliable environment to acquire basic skills. The purpose of this study is to determine the training learning curve based on different metrics corresponding to five tasks included in SINERGIA laparoscopic virtual reality simulator. Methods: Thirty medical students without surgical experience participated in the study. Five tasks of SINERGIA were included: Coordination, Navigation, Navigation and touch, Accurate grasping and Coordinated pulling. Each participant was trained in SINERGIA. This training consisted of eight sessions (R1–R8) of the five mentioned tasks and was carried out in two consecutive days with four sessions per day. A statistical analysis was made, and the results of R1, R4 and R8 were pair-wise compared with Wilcoxon signed-rank test. Significance is considered at P value <0.005. Results: In total, 84.38% of the metrics provided by SINERGIA and included in this study show significant differences when comparing R1 and R8. Metrics are mostly improved in the first session of training (75.00% when R1 and R4 are compared vs. 37.50% when R4 and R8 are compared). In tasks Coordination and Navigation and touch, all metrics are improved. On the other hand, Navigation just improves 60% of the analyzed metrics. Most learning curves show an improvement with better results in the fulfillment of the different tasks. Conclusions: Learning curves of metrics that assess the basic psychomotor laparoscopic skills acquired in SINERGIA virtual reality simulator show a faster learning rate during the first part of the training. Nevertheless, eight repetitions of the tasks are not enough to acquire all psychomotor skills that can be trained in SINERGIA. Therefore, and based on these results together with previous works, SINERGIA could be used as training tool with a properly designed training program.

Improving web learning through model optimization using bootstrap for a tour-guide robot

We perform a review of Web Mining techniques and we describe a Bootstrap Statistics methodology applied to pattern model classifier optimization and verification for Supervised Learning for Tour-Guide Robot knowledge repository management. It is virtually impossible to test thoroughly Web Page Classifiers and many other Internet Applications with pure empirical data, due to the need for human intervention to generate training sets and test sets. We propose using the computer-based Bootstrap paradigm to design a test environment where they are checked with better reliability.