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This paper presents how platforms initially designed to enable the construction of personal learning environments can help teachers and learners to aggregate their own MOOCs from resources freely available in the Cloud under Creative Commons licenses. Comp ...
The multitask diffusion LMS algorithm is an efficient strategy to address distributed estimation problems that are multitask-oriented in the sense that the optimum parameter vector may not be the same for every cluster of nodes. In this work, we explore th ...
With the advent of information technology, emails have gained wide acceptability among students as an asynchronous communication tool. According to the current pedagogy literature the overall trend of the use of email communication by university students h ...
This work is an attempt to discover hidden structural configurations in learning activity sequences of students in Massive Open Online Courses (MOOCs). Leveraging combined representations of video click- stream interactions and forum activities, we seek to ...
The ever-growing amount of data available on the Internet calls for personalization. Yet, the most effective personalization schemes, such as those based on collaborative filtering (CF), are notoriously resource greedy. This paper presents HyRec, an online ...
In this work, we explore video lec- ture interaction in Massive Open Online Courses (MOOCs), which is central to stu- dent learning experience on these educa- tional platforms. As a research contribu- tion, we operationalize video lecture click- streams of ...
Massive Open Online Courses (MOOCs) have become an emergent paradigm of large-scale knowledge distribution, have generated wide interest in the higher education community and are anticipated by many to bring impact to the future of higher education. In thi ...
With the general technological advances of the recent years, current learning environments amass an abundance of data. Albeit such data offer the chance of better understand the learning process, stakeholders – learners, teachers and institutions – often n ...
We examine the performance of stochastic-gradient learners over connected networks for global optimization problems involving risk functions that are not necessarily quadratic. We consider two well-studied classes of distributed schemes including consensus ...
We propose a fully-distributed stochastic-gradient strategy based on diffusion adaptation techniques. We show that, for strongly convex risk functions, the excess-risk at every node decays at the rate of O(1/Ni), where N is the number of learners and i is ...
