A priori evaluation & refinement of curricula by data mining over storyboards. - 6 S. = 146,6 KB, TextDruckausg.: Proceedings of the Twenty-First International Florida Artificial Intelligence Research Society Conference : 15 - 17 May 2008 in Coconut Grove, Florida, USA / ed. by David C. Wilson ... - Menlo Park, Calif. : AAAI Press, 2008. - ISBN 978-1-577-35365-2, S. 335-340
In university studies, there is a flexible but complicated learning system of subject offers, enrollment rules for particular subject combinations, and prerequisites to meet for taking particular subjects, which need to be matched with students' needs and desires. Students need assistance in the jungle of such learning opportunities and limitations at today's universities. To face this problem, we employed our formerly developed storyboard concept and used it to develop, maintain, and evaluate curricula. Storyboarding is based on the idea of formally representing, processing, evaluating and refining didactic knowledge. This concept is utilized to supplement an educational system called Dynamic Learning Needs Reflection System (DLNRS) of the School of Information Environment of Tokyo Denki University, Japan. Didactic knowledge of DLNRS can be represented by storyboarding and used for supporting dynamic learning activities of students. Here, we introduce an additional benefit of storyboarding. By using data mining-like methods to evaluate storyboard paths, we are able to estimate success chances of storyboard paths. Based on this evaluation we will be able to rate planned (future) paths and thus, to prevent students from failing by non-appropriate curricula. Moreover, besides the evaluation, the estimation can be used for computer enforced suggestions to complete a path towards optimal success chances.
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Artificial intelligence through digital games. - In: Tagungsband, (2007), S. 65-68
Using storyboarding and data mining to estimate success chances of curricula. - 9 S. = 166,5 KB, TextPubl. entstand im Rahmen der Veranst.: Proceedings of IADIS International Conference on Cognition and Exploratory Learning in Digital Age (CELDA 2007), 7 - 9 December 2007, Algarve, Portugal / ed. by Kinshuk. - IADIS Press, 2007. - ISBN 978-972-8924-48-5, S. 235-242
In university studies, there is a flexible but complicated learning system of subject offers, enrollment rules for particular subject combinations, and prerequisites to meet for taking particular subjects, which need to be matched with students' needs and desires. Students need assistance in the jungle of such learning opportunities and limitations at today's universities. To face this problem, we employed our formerly developed storyboard concept and used it to develop, maintain, and evaluate curricula. Storyboarding is based on the idea of formally representing, processing, evaluating and refining didactic knowledge. This concept is utilized to supplement an educational system called Dynamic Learning Needs Reflection System (DLNRS) of the School of Information Environment of Tokyo Denki University, Japan. Concretely speaking, didactic knowledge of DLNRS can be represented by storyboarding and used for supporting dynamic learning activities of students. Here, we introduce an additional benefit of the storyboard concept. By using data mining - like methods to evaluate storyboard paths, we are able to estimate success chances of storyboard paths. Based on such an evaluation we will be able to rate planned (future) paths and thus, to prevent students from failing by non-appropriate curricula. Moreover, besides the evaluation, the estimation can be used for computer enforced suggestions to complete a path towards optimal success chances.
http://www.db-thueringen.de/servlets/DocumentServlet?id=9686
Toward making didactics a subject of knowledge engineering. - In: Seventh IEEE International Conference on Advanced Learning Technologies, 2007, ISBN 978-0-7695-2916-5, (2007), S. 788-792
http://dx.doi.org/10.1109/ICALT.2007.259
Compiling experience into knowledge. - In: Proceedings of the Twentieth International Florida Artificial Intelligence Research Society conference, ISBN 978-1-577-35319-5, (2007), S. 550-551
Typical application fields of Knowledge Based Systems are a usually characterized by having human expertise as the only one source to specify their desired behavior. Their design, evaluation and refinement has to make effective use of this valuable source. After sketching the concept of collecting validation experience in a Validation Knowledge Base (VKB), the paper introduces an estimation of the significance of the cases collected in the VKB. A high significance signalizes that a VKB should not longer serve as a case-based source of external knowledge, but compiled into the Knowledge Base instead. A technology to compile well-selected cases into the Knowledge Base of the system under evaluation is shown.
The engineering of rule base refinement. - In: International MultiConference of Engineers and Computer Scientists, (2007), insges. 6 S.
The pros and cons of formal methods are the subject of many discussions in Artificial Intelligence (AI). Here, a formal method for the refinement of an AI system is considered against the background of a quite cultural question in system engineering, the shift from technology-driven towards knowledge-driven system design and adjustment. The objective of the considered refinement technique is to overcome particular invalidities revealed by the application of a case-oriented validation technology. So it has to be set into the context of learning by examples. Classical AI approaches are often not useful for a system refinement that is really up to an appropriate modeling of the domain knowledge. Furthermore, they often lead to a knowledge base which is difficult to read and to interpret, because it is too far from a natural way to express domain knowledge. The refinement process here is characterized by (1) using human expertise that also is a product of the validation technique and (2) keeping as much as possible of the original human-made knowledge base. At least the second principle is pretty much adopted from Software Engineering.
Toward knowledge engineering with didactic knowledge. - In: , (2007), S. 119-125
Learning systems suffer from a lack of an explicit and adaptable didactic design. A way to overcome such deficiencies is (semi-) formally representing the didactic design. A modeling approach, storyboarding, is outlined here. Storyboarding is setting the stage to apply Knowledge Engineering Technologies to verify, validate the didactics behind a learning process. As a vision, didactics can be refined according to revealed weaknesses and proven excellence. Furthermore, successful didactic patterns can be inductively inferred by analyzing the particular knowledge processing and its alleged contribution to learning success.
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Compiling experience into knowledge. - In: , (2007), S. 113-118
Typical application fields of Knowledge Based Systems are usually characterized by having human expertise as the only one source to specify their desired behavior. Therefore, their design, evaluation and refinement has to make effective use of this valuable source. After an introduction to the concept of collecting validation experience in a Validation Knowledge Base (VKB), the paper introduces an estimation of the significance of the cases collected in the VKB. A high significance signalizes that a VKB should not longer serve as a case-based source of external (outside the Knowledge Base) knowledge, but compiled into the Knowledge Base instead. Based on this significance estimation, a technology to compile well selected cases into the Knowledge Base of the system under evaluation is presented.
http://www.db-thueringen.de/servlets/DocumentServlet?id=8735
Toward reducing human involvement in validation of knowledge-based systems. - In: IEEE transactions on systems, man, and cybernetics, ISSN 1558-2426, Bd. 37 (2007), 1, S. 120-131
Human experts employed in validation exercises for knowledge-based systems often have limited time and availability. Furthermore, they often have different opinions from each other as well as from themselves over time. We address this situation by introducing the use of validation knowledge used in prior validation exercises for the same knowledge-based system. We present a Validation Knowledge Base (VKB) that is the collective best experience of several human experts. The VKB is constructed and maintained across various validation exercises, and its primary benefits are as follows: (a) more reliable validation results by incorporating external knowledge and (b) decreasing the experts' workload. We also present the concept of Validation Expert Software Agents (VESA) that represent a particular expert's knowledge. VESA is a software agent corresponding to a specific human expert. It models the validation knowledge and behavior of its human counterpart by analyzing similarities with the responses of other experts. After a learning period, it can be used to temporarily substitute for its corresponding human expert. We also describe experiments with a small prototype system to evaluate the usefulness of these concepts.
http://dx.doi.org/10.1109/TSMCA.2006.886365
Storyboarding - an AI technology to represent, process, evaluate, and refine didactic knowledge. - In: Knowledge media technologies, (2006), S. 170-179
The current state of affair in learning systems in general and in e-learning in particular suffers from a lack of an explicit and adaptable didactic design. Students complain about the insufficient adaptability of e-learning to the learners' needs. Learning content and services need to reach their audience properly. That is, according to their different prerequisites, needs, and different learning conditions. After a short introduction to the storyboard concept, which is a way to address these concerns, we present an example of using storyboards for the didactic design of a university course on Intelligent Systems. In particular, we show the way to express didactic variants and didactic intentions within storyboards. Finally, we sketch ideas to for a machine supported knowledge processing, knowledge evaluation, knowledge refinement, and knowledge engineering with storyboards.
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