Background. Blended learning (BL) requires a virtual learning and online environment (VLE), which makes available a process for establishing learning communities. The Faculty of Physical Education at the University of Jordan designed several courses which incorporate blended learning with contact classes and online components on e-learning models. Objectives. The present study is to investigat...

This paper aims to study Vygotsky’s (1987) sociocultural theory of learning with respect to how it relates to technology-based second language learning and teaching. The researchers selected their participants from advanced students from Payame Noor University. We divided the participants into two groups- an experimental group and a control group. After teaching the course an experimental group...

Batch learning techniques achieve good performance, but at the cost of many (sometimes even hundreds) of passes over the data. For many tasks, such as web-scale ranking of machine translation hypotheses, making many passes over the data is prohibitively expensive, even in parallel over thousands of machines [1]. Online algorithms, which treat data as a stream of examples, are conceptually appea...

Learning to rank is a supervised learning problem where the output space is the space of rankings but the supervision space is the space of relevance scores. We make theoretical contributions to the learning to rank problem both in the online and batch settings. First, we propose a perceptron-like algorithm for learning a ranking function in an online setting. Our algorithm is an extension of t...

Boosting is a popular ensemble algorithm that generates more powerful learners by linearly combining base models from a simpler hypothesis class. In this work, we investigate the problem of adapting batch gradient boosting for minimizing convex loss functions to online setting where the loss at each iteration is i.i.d sampled from an unknown distribution. To generalize from batch to online, we ...