A distinct feature of an adaptive e-learning hypermedia system (AEHS) is the learner model it employs, that is, a representation of information about an individual learner. Learner modeling and adaptation are strongly correlated, in the sense that the amount and nature of the information represented in the learner model depend largely on the kind of adaptation effect that the system has to deli...

Natural language processing (NLP) has long been used to automatically analyze language produced by language learners, typically aimed at providing individualized feedback and learner modeling in Intelligent Computer-Assisted Language Learning systems (cf. Heift & Schulze 2007). While much interesting research has been reported, it is difficult to determine the state of the art for the automatic...

This study presents a systematic comparison of methods for individual treatment assignment. We group the various proposed in literature into three general classes algorithms (or metalearners): learning models to predict outcomes (the O-learner), causal effects E-learner), and optimal assignments A-learner). discuss how metalearners differ their level generality objective function, which has cri...

Fill-in-the-blank items are commonly featured in computer-assisted language learning (CALL) systems. An item displays a sentence with a blank, and often proposes a number of choices for filling it. These choices should include one correct answer and several plausible distractors. We describe a system that, given an English corpus, automatically generates distractors to produce items for preposi...

In designing intelligent web based educational systems different student needs and preferences should be taken into consideration. Personalization of a system usually results in an increase in its effectiveness, which can be measured by the degree to which learning outcomes are achieved. However, taking into account the individual requirements of each learner and adjusting the system to their n...

A framework for assisting a learner’s progressive knowledge acquisition in simulation-based learning environments (SLEs) is proposed. In SLE, usually a learner is first given a simple situation to acquire basic knowledge, then given more complicated situation to refine it. Such change of situation often causes the change of the model to be used. Our GMW (graph of microworlds) framework effifien...

Programming a robot to perform tasks requires training that is beyond the skill level of most individuals. To address this issue, we focus on developing a method that identifies keywords used to convey task knowledge among people and a framework that uses these keywords as conditions for knowledge acquisition by the robot learner. The methodology includes generalizing task modeling and providin...

While Educational Data Mining research has traditionally emphasized the practical aspects of learner modeling, such as predictive modeling, estimating students knowledge, and informing adaptive instruction, in the current study, we argue that Educational Data Mining can also be used to test and improve our fundamental theories of human learning. Using the Apprentice Learner architecture, a comp...