Many of us are guilty of harboring an unfair stereotype – that, once we grow old, our best days are behind us and that we can no longer fully realize our dreams. As photographer Vladimir Yakovlev has discovered, however, that's just not true. His Age Of Happiness photo project introduces us to people who, at 70, 80 or even 100 years of age, are still following their dreams and trying new things.
Via Frederic Serriere
East China's Jiangsu province will establish a day care center for the elderly in each of its communities over the next five years to ease the growing pressure of supporting and caring for a quickly aging population.
Via Frederic Serriere
“ Not too long ago, the NMC Horizon Report: 2014 Higher Education Edition was released, with the aim of examining emerging technologies for their potential impact on and use in teaching and learning within higher education settings.”
“Educators need to adapt, or become extinct Dailyrecord.com Knowledge is growing more participatory and collaborative by the digital minute. Our children grow up on the Internet. They are addicted to the screen and instant information.”
AbstractThis study examined the prediction of dropouts through data mining approaches in an online program. The subject of the study was selected from a total of 189 students who registered to the online Information Technologies Certificate Program in 2007-2009. The data was collected through online questionnaires (Demographic Survey, Online Technologies Self-Efficacy Scale, Readiness for Online Learning Questionnaire, Locus of Control Scale, and Prior Knowledge Questionnaire). The collected data included 10 variables, which were gender, age, educational level, previous online experience, occupation, self efficacy, readiness, prior knowledge, locus of control, and the dropout status as the class label (dropout/not). In order to classify dropout students, four data mining approaches were applied based on k-Nearest Neighbour (k-NN), Decision Tree (DT), Naive Bayes (NB) and Neural Network (NN). These methods were trained and tested using 10-fold cross validation. The detection sensitivities of 3-NN, DT, NN and NB classifiers were 87%, 79.7%, 76.8% and 73.9% respectively. Also, using Genetic Algorithm (GA) based feature selection method, online technologies self-efficacy, online learning readiness, and previous online experience were found as the most important factors in predicting the dropouts.Keywords: Educational data mining, student dropout prediction, k-nearest neighbour, decision tree, Naive Bayes, neural network.
Via Dennis T OConnor
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