This paper tries to examine the effects of Internet on educational opportunity. Internet, as the most useful technology of modern times which helps us not only in our daily lives but also in professional lives. For educational purposes, it is widely used to gather information and to do research or add to the knowledge of various subjects.  The theoretical framework considered with some theories...

Data derived from 72 students in online English course showed that online self-efficacy was a moderating factor in relation between learning strategy and online performance. In high online self-efficacy group, online performance was predicted by learning strategy positively, which explained 23% of the performance variance. In low online self-efficacy group, online performance was not predicted ...

Abstract Learning evaluation is an effective way to ensure the quality of online education. However, most of online learning evaluation models are not fully consider the characteristics of online learning. This paper, from the view of the characteristics of online learning, establishes an index system of evaluation of the online learning’s effect. Besides, weight of every factor affecting onlin...

Background: Cooperative and Collaborative Learning approach with online programs is useful in response to rising demand for university educational system. This study evaluates the use of internet and computer in collaborative teaching and learning process for post graduate nursing students. Method: This is a correlation descriptive study. The population in the study is based on census sampling ...

This paper presents a design framework for online learning. The framework is based upon the taxonomy devised by Dettmer (2006). In a learner-centered focus, it draws together the cognitive, affective, social, and sensorimotor domains of learning, and is situated with the concept of online personal learning spaces and environments. It is at this intersection of learning domains that the graduate...

In a Bayesian approach to online learning a simple approximate parametric form for posterior is updated in each online learning step. Usually in online learning only an estimate of the solution is updated. The Bayesian online approach is applied to two simple learning scenarios, learning a perceptron rule with respectively a spherical and a binary weight prior. In the rst case we rederive the r...

On-line learning algorithms are particularly suitable for developing interactive computational agents. These algorithm can be used to teach the agents the abilities needed for engaging in social interactions with humans. If humans are used as teachers in the context of on-line learning algorithms a serious challenge arises: their lack of commitment and availability during the required extensive...

In this work we propose a random field approach to unsupervised machine learning, classifier training and pattern classification. The proposed method treats each sample as a random field and attempts to assign an optimal cluster label to it so as to partition the samples into clusters without a priori knowledge about the number of clusters and the initial centroids. To start with, the algorithm...