E/R Based Scenario Modeling for Rapid Prototyping of Web Information Services

Feyer, Thomas; Thalheim, Bernhard

doi:10.1007/3-540-48054-4_21

Cited by 13 publications

(6 citation statements)

References 4 publications

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“…Our own work on storyboarding has been reported in [42,5], and lead to the definition of the storyboarding language SiteLang in [8]. The role and use of metaphors in storyboarding was investigated in [43].…”

Section: Related and Previous Workmentioning

confidence: 98%

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Conceptual modelling of web information systems

Schewe

Thalheim

2005

Data & Knowledge Engineering

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Section: Related and Previous Workmentioning

confidence: 98%

“…This includes knowledge about the used access channels and enddevices. At a high level of abstraction this first leads to storyboarding, an activity that addresses the design of an underlying application story [5,6]. As soon as WISs become large, it becomes decisive that such an underlying application story is well designed.…”

Section: Introductionmentioning

confidence: 99%

Conceptual modelling of web information systems

Schewe

Thalheim

2005

Data & Knowledge Engineering

Self Cite

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“…Storyboarding as a means to model information and learning processes has been introduced around 1998/1999 by Feyer and others [8], [9]. One of the first Storyboarding languages called SiteLang [6] was introduced in 2001.…”

Section: The Storyboarding Conceptmentioning

confidence: 99%

Knowledge mining for supporting learning processes

Knauf

Böck

Sakurai

et al. 2008

2008 IEEE International Conference on Systems, Man and Cybernetics

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Abstract-AI technologies for knowledge mining are commonly used in technical environments. Their application for social processes like learning processes, for example, is a quite a new challenge, which is characterized by having "humans in the loop". Humans' desires, preferences and decisions may be unpredictable and thus, not appropriate for modeling -at a first glance. However, in learning processes didactic variants can be anticipated and can become a subject of AI technologies. A semiformal modeling approach called storyboarding, is outlined here. A storyboard represents various opportunities for composing a learning process according to individual circumstances, such as topical prerequisites (educational history), mental prerequisites (preferred learning styles, etc.), performance prerequisites (a requested success level in former learning activities, etc.), and personal aspects (needs, wishes, talents, aims). By storyboarding, various didactic variants can be validated by considering the average learning success associated with the different paths through a storyboard in a case study. Based on validation results, success chances can be derived for the different paths. Here, a concept and an implementation to pre-estimate success chances of intended (future) learning paths through a storyboard are introduced. They are based on a Data Mining technology, and construct a decision tree by analyzing former learners' paths and their degrees of success. Furthermore, this technology generates a supplement to a submitted path, which is optimal according to the success chances. This technology has been tested at a Japanese university, in which students had to compose their individual plan (subject sequences) in advance, and the technology helped them by predicting success chances and suggesting alternatives.

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“…Former Storyboarding concepts to model information and learning processes have been introduced 1998/1999 [10]. The employment of storyboarding approaches for (unfortunately, only) e-learning is characterized by misunderstandings.…”

Section: A Former Storyboarding Conceptsmentioning

confidence: 99%