Publikationen

Chiang, Feng-Kuang; Wuttke, Heinz-Dietrich; Knauf, Rainer; Sun, Chung-Shan; Tso, Tai-Cheng
Students' attitudes toward using innovative information technology for learning based on theory of planned behavior. - In: International journal of advanced corporate learning, ISSN 1867-5565, Bd. 2 (2009), 4, S. 9-14

The main purpose of this study was to explore how IT is integrated into teaching and learning in a German bachelor curriculum, and to explore the university students' attitude toward the use of integration innovative information technology for learning in Germany. The data were collected by document collection and questionnaires. Multiple Regression Analysis was used to determine the three variables of behavioral intention. The results show that both attitude and subject norm are good predictors of IT for students' learning intention. Finally, results indicated that the theory of reasoned action predicts the use of innovative IT for learning intention well, though its impact is greater than the impact of the theory of planned behavior.

http://dx.doi.org/10.3991/ijac.v2i4.969

Knauf, Rainer; Böck, Ronald; Sakurai, Yoshitaka; Tsuruta, Setsuo
Knowledge engineering with didactic knowledge : first steps towards an ultimate goal. - Online-Ressource (PDF-Datei: 2 S., 860,3 KB)Druckausg.: Proceedings of the Twenty-Second International Florida Artificial Intelligence Research Society Conference : 19 - 21 May 2009, Sanibel Island, Florida, USA / ed. by H. Chad Lane ... - Menlo Park, Calif. : AAAI Press, 2009. - ISBN 978-1-577-35419-2, S. 418-419

Generally, learning systems suffer from a lack of an explicit and adaptable didactic design. A previously introduced modeling approach called storyboarding is setting the stage to apply Knowledge Engineering Technologies to verify and validate the didactics behind a learning process. Moreover, didactics can be refined according to revealed weaknesses and proven excellence. Successful didactic patterns can be explored by applying mining techniques to the various ways students went through the storyboard and their associated level of success.

http://www.db-thueringen.de/servlets/DocumentServlet?id=13147

Knauf, Rainer; Böck, Ronald; Sakurai, Yoshitaka; Dohi, Shinichi; Tsuruta, Setsuo
Knowledge mining for supporting learning processes. - In: IEEE International Conference on Systems, Man and Cybernetics, 2008, (2008), S. 2615-2621

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 semi-formal 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.

http://dx.doi.org/10.1109/ICSMC.2008.4811690

Chiang, Feng-Kuang; Wuttke, Heinz-Dietrich; Knauf, Rainer; Shih, Chun-Ming
Information technology for learning: a European students' perspective. - Ilmenau : Univ.-Bibliothek. - Online-Ressource (PDF-Datei: 6 S., 549,1 KB)Druckausg.: E-Learn 2008 : World Conference on E-Learning in Corporate, Government, Healthcare & Higher Education ; November 17 - 21, 2008, Las Vegas, Nevada, USA / AACE, Association for the Advancement of Computing in Education. Ed. by Curtis J. Bonk ... ; Vol. 4. - Chesapeake, VA : Assoc. for the Advancement of Computing in Education (AACE), 2008. - S. 2566-2571

This study was mainly focused on European university students, in particular on their thinking about IT for learning. Participators came from different European countries and took part in a summer school this year. A total of 21 international students participated in this summer school program. They came from different European countries. The data collection includes a questionnaire survey and interview data. The data were analyzed and common opinions have been extracted. Finally, the authors of the present paper provide six conclusions and refer to university education in European countries to implement an IT integrated instruction. Researchers hope to promote the instructional quality by a well-performed way of IT integration into instruction in high education.

http://www.db-thueringen.de/servlets/DocumentServlet?id=11910

Knauf, Rainer; Sakurai, Yoshitaka; Tsuruta, Setsuo
Applying knowledge engineering methods to didactic knowledge : first steps towards an ultimate goal. - In: IEEE International Joint Conference on Neural Networks, 2008, ISBN 978-1-4244-1820-6, (2008), S. 38-45

Generally, learning systems suffer from a lack of an explicit and adaptable didactic design. Since E-Learning systems are digital by their very nature, their introduction rises the issue of modeling the didactic design in a way that implies the chance to apply Knowledge Engineering Techniques (like Machine Learning and Data Mining). A modeling approach called storyboarding, is outlined here. Storyboarding is setting the stage to apply Knowledge Engineering Technologies to verify and validate the didactics behind a learning process. Moreover, didactics can be refined according to revealed weaknesses and proven excellence and successful didactic patterns can be inductively inferred by analyzing the particular knowledge processing and its alleged contribution to learning success.

http://dx.doi.org/10.1109/IJCNN.2008.4633764