Recently, research in individual differences and in particular, learning and cognitive style, has been used as a basis to consider learner preferences in a web-based educational context. Modelling style in a web-based learning environment demands that developers build a specific framework describing how to design a variety of options for learners with different approaches to learning. In this p...

In the MOOCs context, learners experience information overload. Thus, it is necessary to improve personalized recommendation algorithms for learners. The current algorithm focuses mainly on learners’ course ratings. However, choice of courses not only based interests and preferences. It also affected by knowledge domains learning capabilities, all which change dynamically over time. Therefore, ...

We present an approach and a system that explores the application of interactive machine learning to a branching program-based boosting algorithm—Martingale Boosting. Typically, its performance is based on the ability of a learner to meet a fixed objective and does not account for preferences (e.g., low FPs) arising from an underlying classification problem. We use user preferences gathered on ...

the present study attempts to investigate parsing preferences (early vs. late closure) of native and l2 learners of both english and persian when they read ambiguous relative clauses. the purpose is to find out if l2 learners process l2 linguistic input in the way monolingual speakers of that language do. the participants took tests including 10 test sentences plus 10 distractors, each followed...