Automatic generation of personalized tutorial feedback in e-learning
This event took place on Wednesday 22 September 2010 at 11:30
Ruben Lagatie KU Leuven, Belgium
We are arguably in the midst of a transition from traditional classroom learning (c-learning) to electronic and mostly individual learning (e-learning). One of the problems we are facing today is that feedback given automatically by a computer is much more limited and often less helpful than feedback provided by a teacher. For exercise types with limited input possibilities, like multiple choice questions, the teacher is asked to enter feedback for all possible wrong answers. Once we make use of more open question, such as a translate exercise, this is no longer feasible. The student can make any grammar, spelling, translation or style error and for a number of different reasons. Current state-of-the-art solutions use language specific parsers in combination with spellcheckers to provide corrections and feedback. They are however very hard to construct and although their precision is acceptable, they often lack in recall. What we are planning to do is develop a system that can compare errors and reuse feedback messages from the past. To accomplish this, we make use of natural language processing (such as part-of-speech tagging and corpus linguistics) and machine learning techniques (classification, clustering, etc.). Combining linguistics, statistics, computer science and pedagogy, a truly interdisciplinary undertaking.
(Due to unforeseen circumstances we were unable to record or webcast this event, we apologise to those who were otherwise unable to attend this event in person)
This event took place on Wednesday 22 September 2010 at 11:30
We are arguably in the midst of a transition from traditional classroom learning (c-learning) to electronic and mostly individual learning (e-learning). One of the problems we are facing today is that feedback given automatically by a computer is much more limited and often less helpful than feedback provided by a teacher. For exercise types with limited input possibilities, like multiple choice questions, the teacher is asked to enter feedback for all possible wrong answers. Once we make use of more open question, such as a translate exercise, this is no longer feasible. The student can make any grammar, spelling, translation or style error and for a number of different reasons. Current state-of-the-art solutions use language specific parsers in combination with spellcheckers to provide corrections and feedback. They are however very hard to construct and although their precision is acceptable, they often lack in recall. What we are planning to do is develop a system that can compare errors and reuse feedback messages from the past. To accomplish this, we make use of natural language processing (such as part-of-speech tagging and corpus linguistics) and machine learning techniques (classification, clustering, etc.). Combining linguistics, statistics, computer science and pedagogy, a truly interdisciplinary undertaking.
(Due to unforeseen circumstances we were unable to record or webcast this event, we apologise to those who were otherwise unable to attend this event in person)
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Interacting with other people not only forms the core of human social and psychological experience, but also lies at the centre of what makes the internet such a rich, powerful and exciting collection of knowledge media. We are especially interested in what happens when such interactions take place on a very large scale -- not only because we work regularly with tens of thousands of distance learners at the Open University, but also because it is evident that being part of a crowd in real life possesses a certain 'buzz' of its own, and poses a natural challenge. Different nuances emerge in different user contexts, so we choose to investigate the contexts of work, learning and play to better understand the trade-offs involved in designing effective large-scale social software for multiple purposes.
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