Search results for "e learning"
showing 10 items of 2703 documents
Stability-Based Model Selection for High Throughput Genomic Data: An Algorithmic Paradigm
2012
Clustering is one of the most well known activities in scien- tific investigation and the object of research in many disciplines, ranging from Statistics to Computer Science. In this beautiful area, one of the most difficult challenges is the model selection problem, i.e., the identifi- cation of the correct number of clusters in a dataset. In the last decade, a few novel techniques for model selection, representing a sharp departure from previous ones in statistics, have been proposed and gained promi- nence for microarray data analysis. Among those, the stability-based methods are the most robust and best performing in terms of predic- tion, but the slowest in terms of time. Unfortunately…
Identifying individual differences using log-file analysis: Distributed learning as mediator between conscientiousness and exam grades
2018
Abstract Online learning poses major challenges on students' self-regulated learning. This study investigated the role of learning strategies and individual differences in cognitive abilities, high school GPA and conscientiousness for successful online learning. We used longitudinal log-file data to examine learning strategies of a large cohort (N = 424) of university students taking an online class. Distributed learning, the use of self-tests and a better high school GPA was associated with better exam grades. The positive effect of conscientiousness on exam grades was mediated by distributed learning. Conscientious students distributed their studying over the course of the semester, which…
One-Sided Prototype Selection on Class Imbalanced Dissimilarity Matrices
2012
In the dissimilarity representation paradigm, several prototype selection methods have been used to cope with the topic of how to select a small representation set for generating a low-dimensional dissimilarity space. In addition, these methods have also been used to reduce the size of the dissimilarity matrix. However, these approaches assume a relatively balanced class distribution, which is grossly violated in many real-life problems. Often, the ratios of prior probabilities between classes are extremely skewed. In this paper, we study the use of renowned prototype selection methods adapted to the case of learning from an imbalanced dissimilarity matrix. More specifically, we propose the…
On Duality in Learning and the Selection of Learning Teams
1996
AbstractPrevious work in inductive inference dealt mostly with finding one or several machines (IIMs) that successfully learn collections of functions. Herein we start with a class of functions and considerthe learner setof all IIMs that are successful at learning the given class. Applying this perspective to the case of team inference leads to the notion ofdiversificationfor a class of functions. This enable us to distinguish between several flavours of IIMs all of which must be represented in a team learning the given class.
Teaching and learning in consumer behavior: a class activity supporting real decision-making in cultural management
2010
Abstract Problem-based learning has been suggested as a useful tool to foster student-centered learning and increase student motivation. Students of Business Administration are not familiar with Research on Consumer Behavior, its contents and techniques, since this subject is traditionally linked to Psychology. In order to facilitate the teaching-learning process in this subject, this paper presents an application of problem-based learning in the context of Consumer Behavior. Results of this pilot experience are assessed under a qualitative and a quantitative basis. The evidence obtained allow us to conclude that this collaboration project enable students to obtain and to process informatio…
Variability of Classification Results in Data with High Dimensionality and Small Sample Size
2021
The study focuses on the analysis of biological data containing information on the number of genome sequences of intestinal microbiome bacteria before and after antibiotic use. The data have high dimensionality (bacterial taxa) and a small number of records, which is typical of bioinformatics data. Classification models induced on data sets like this usually are not stable and the accuracy metrics have high variance. The aim of the study is to create a preprocessing workflow and a classification model that can perform the most accurate classification of the microbiome into groups before and after the use of antibiotics and lessen the variability of accuracy measures of the classifier. To ev…
Challenges for Surgical Residents’ Practice-Based Learning
2013
This chapter explores the practice-based learning of surgical residents. We concentrate on the challenges encountered and experienced by the residents during their clinical practice. In line with Billett (2010), we understand learning through practice as a process that arises through the exercise of occupational activities. For the surgical residents this means that they learn through participating in various kinds of hands-on surgical practices and interactions in clinical wards and units.
Transformative words: writing otherness and identities
2019
Juhani Ihanus is a Finnish pioneer of biblio/poetry therapy in Europe. His new insightful and broad-minded work Transformative words: Writing otherness and identities depicts literature, biblio/poe...
SMART: Unique splitting-while-merging framework for gene clustering
2014
© 2014 Fa et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Successful clustering algorithms are highly dependent on parameter settings. The clustering performance degrades significantly unless parameters are properly set, and yet, it is difficult to set these parameters a priori. To address this issue, in this paper, we propose a unique splitting-while-merging clustering framework, named "splitting merging awareness tactics" (SMART), which does not require any a priori knowledge of either the number …
A local complexity based combination method for decision forests trained with high-dimensional data
2012
Accurate machine learning with high-dimensional data is affected by phenomena known as the “curse” of dimensionality. One of the main strategies explored in the last decade to deal with this problem is the use of multi-classifier systems. Several of such approaches are inspired by the Random Subspace Method for the construction of decision forests. Furthermore, other studies rely on estimations of the individual classifiers' competence, to enhance the combination in the multi-classifier and improve the accuracy. We propose a competence estimate which is based on local complexity measurements, to perform a weighted average combination of the decision forest. Experimental results show how thi…