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)
Future Internet
KnowledgeManagementMultimedia &
Information SystemsNarrative
HypermediaNew Media SystemsSemantic Web &
Knowledge ServicesSocial Software
Semantic Web and Knowledge Services is...
Our research in the Semantic Web area looks at the potentials of fusing together advances in a range of disciplines, and applying them in a systemic way to simplify the development of intelligent, knowledge-based web services and to facilitate human access and use of knowledge available on the web. For instance, we are exploring ways in which tnatural language interfaces can be used to facilitate access to data distributed over different repositories. We are also developing infrastructures to support rapid development and deployment of semantic web services, which can be used to create web applications on-the-fly. We are also investigating ways in which semantic technology can support learning on the web, through a combination of knowledge representation support, pedagogical theories and intelligent content aggregation mechanisms. Finally, we are also investigating the Semantic Web itself as a domain of analysis and performing large scale empirical studies to uncover data about the concrete epistemologies which can be found on the Semantic Web. This exciting new area of research gives us concrete insights on the different conceptualizations that are present on the Semantic Web by giving us the possibility to discover which are the most common viewpoints, which viewpoints are mutually inconsistent, to what extent different models agree or disagree, etc...
Our aim is to be at the forefront of both theoretical and practical developments on the Semantic Web not only by developing theories and models, but also by building concrete applications, for a variety of domains and user communities, including KMi and the Open University itself.
List all Semantic Web and Knowledge Services Projects
Check out these Hot Semantic Web and Knowledge Services Technologies:
List all Semantic Web and Knowledge Services Technologies
