Literaturliste

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Knauf, Rainer; Kinshuk; Takada, Kouhei; Sakurai, Yoshitaka; Kawabe, Takashi; Tsuruta, Setsuo
Personalized and adaptive curriculum optimization based on a performance correlation analysis. - In: Eighth International Conference on Signal Image Technology and Internet Based Systems (SITIS), 2012, ISBN 978-0-7695-4911-8, (2012), S. 655-660

http://dx.doi.org/10.1109/SITIS.2012.99
Knauf, Rainer; Sakurai, Yoshitaka; Takada, Kouhei; Tsuruta, Setsuo
A case study on using personalized data mining for university curricula. - In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2012, ISBN 978-1-4673-1714-6, (2012), S. 3051-3056

In former work, the authors developed a modeling system for university learning processes, which aims at evaluating and refining university curricula to reach an optimum of learning success in terms of a best possible grade point average (GPA). This is performed by applying an Educational Data Mining (EDM) technology to former students curricula and their degree of success (GPA) and thus, uncovering golden didactic knowledge for successful education. We used learner profiles to personalize this technology. After a short introduction to this technology, we discuss the result of a practical application and draw conclusions. In particular, we could not obtain sufficient data to establish this kind of learner profiles. Therefore, we shifted our strategy from an "eager" one of holding an explicit model towards a "lazy" strategy of mining with data, which is really available without making "guesses" what they mean (profiles). In particular, we utilize the educational history of the students and vocational ambitions for student modeling.



http://dx.doi.org/10.1109/ICSMC.2012.6378259
Sakurai, Yoshitaka; Takada, Kouhei; Tsuruta, Setsuo; Knauf, Rainer
A case study on using data mining for university curricula. - In: IEEE 12th International Conference on Advanced Learning Technologies (ICALT), 2012, ISBN 978-0-7695-4702-2, (2012), S. 3-4

In former work, the authors developed a modeling system for university learning processes, which aims at evaluating and refining university curricula to reach an optimum of learning success in terms of best possible best possible grade point average (GPA). This is performed by applying an Educational Data Mining (EDM) technology to former students curricula and their degree of success (GPA) and thus, uncovering golden didactic knowledge for successful education. After a short introduction to this technology, we discuss the result of a practical application and draw conclusions. In particular, we could not obtain sufficient data to establish this kind of learner profiles. Therefore, we shifted strategy from an "eager" strategy of holding an explicit model towards a "lazy" strategy of mining with data, which is really available, holds empirically, and is not a result of "guesses" about the students' general characteristics. In particular, we utilize the educational history of the students and vocational ambitions for student modeling.



http://dx.doi.org/10.1109/ICALT.2012.212
Takada, Kouhei; Sakurai, Yoshitaka; Kinshuk; Knauf, Rainer; Tsuruta, Setsuo
Enriched cyberspace through adaptive multimedia utilization for dependable remote collaboration. - In: IEEE transactions on systems, man, and cybernetics, ISSN 1558-2426, Bd. 42 (2012), 5, S. 1026-1039

Due to the geographical distribution, different cognitive capacity, and different domain competency of workers or learners, many misunderstandings can occur during distributed remote collaboration, leading to inefficient discussions and undesired results. To make remote collaboration more efficient and dependable, enriching cyberspace through adaptively utilizing multimedia information is proposed and evaluated. This assesses situations of remote users through information fusion of multiple biomedical sensors and the related contexts such as user profiles. Transmitting and using such information, the system adaptively supports the distributed remote collaboration by stressing, warning, and presenting keywords/summaries in multimedia. Effects of presenting keywords/summaries adaptively depending on situations and cognitive profiles of remote members are evaluated as to the decrease of not-/misunderstanding possibilities during the explanation on the Cyberspace. The evaluation demonstrates the feasibility and usefulness of the proposed method.



http://dx.doi.org/10.1109/TSMCA.2012.2183588
Jantke, Klaus P.; Knauf, Rainer
Taxonomic concepts for storyboarding digital games for learning in context. - In: Proceedings of 4th International Conference on Computer Supported Education, ISBN 978-989-8565-07-5, (2012), S. 401-409

The design and employment of digital games for serious purposes such as learning has several prerequisites. Designing a game that affects human players effectively requires the anticipation of particular human game playing experiences. Recent digital games taxonomies provide the WHAT and storyboarding is the technology for determining the HOW of planning the manifold of potential affective experiences in digital game playing. Game-based learning needs storyboarding and storyboarding needs concepts of digital games taxonomies. The appropriate consolidation of taxonomies and storyboarding results in explicit media didactics in context.



Sakurai, Yoshitaka; Takada, Kouhei; Knauf, Rainer; Tsuruta, Setsuo
A retrieval method adaptively reducing user's subjective impression gap. - In: Multimedia tools and applications, ISSN 1573-7721, Bd. 59 (2012), 1, S. 25-40

As an approach to search/retrieve such objects as pictures, music, perfumes and apparels on the Internet, sensitivity-vectors or kansei-vectors are useful since textual keywords are not sufficient to find objects that users want. The sensitivity-vector is an array of values. Each value indicates a degree of feeling or impression represented by a sensitivity word or kansei word. However, due to the gap between user's subjective sensitivity (impression, image and feeling) degree and the corresponding value in the database. Also, such an approach is not enough to retrieve what users want. This paper proposes a retrieval method to automatically and dynamically reduce such gaps by estimating a subjective criterion deviation (we call "SCD") using the user's retrieval history and fuzzy modeling. Additionally, the proposed method can avoid users' burden caused by conventional methods such as completing required questionnaires. This method can also reflect the dynamic change of user's preference which cannot be accomplished by using questionnaires. For the evaluation, an experiment was performed by building and using a perfume retrieval system. Through observing the transition of the deviation reduction degree, it was clarified that the proposed method is effective. In the experiment, the machine could learn users' subjective criteria deviation as well as its dynamic change caused by factors such as user's preference, if the learning rate is well adjusted.



http://dx.doi.org/10.1007/s11042-010-0690-0
akurai, Yoshitaka; Tsuruta, Setsuo; Knauf, Rainer
Success chances estimation of university curricula based on educational history, self-estimated intellectual traits and vocational ambitions. - In: 11th IEEE International Conference on Advanced Learning Technologies (ICALT), 2011, ISBN 978-1-61284-209-7, (2011), S. 476-478

http://dx.doi.org/10.1109/ICALT.2011.148
Knauf, Rainer; Sakurai, Yoshitaka; Tsuruta, Setsuo
A data mining method to optimize individually personalised curricula in university education. - In: Sustaining student-centric higher education, (2011), S. 33

Modelling, processing, evaluating and refining processes with humans involved is widely deployed in fields like marketing and social sciences, but is becoming more and more of a trend in learning systems. The more such processes can be formalised, the more powerful knowledge engineering methods such as data mining can be applied to reveal "hidden knowledge" within the data. Here, we introduce a concept to optimise university learning processes. In particular, individual university curricula are subject to a so-called "lazy" data mining method of students' data. A formerly developed concept called storyboarding has been applied to model the various ways to study at this university. Along with this storyboard, we developed a form of data mining technology to estimate the chances of success for the students following each curricular path offered. Here, we discuss chances to improve these results by implementing a student profiling concept that represents the students' individual educational history and a self-estimation of each student's intellectual traits and vocational ambitions.



Sakurai, Yoshitaka; Knauf, Rainer; Kawabe, Takashi; Tsuruta, Setsuo
Adaptive kansei search method using user's subjective criterion deviation. - In: International journal of computer vision and image processing, ISSN 2155-6997, Bd. 1 (2011), 1, S. 14-26

Sensibility-vectors (kansei-vectors) are useful for retrieving objects like pictures, music, perfumes, and apparels on the Internet. The sensibility-vector is an array of values, each indicating a degree of feeling or impression represented as sensibility word or kansei word. However, even such an approach leaves a gap between user's subjective sensibility (image, feeling) value and the corresponding one stored in the database. This paper proposes a search method to automatically and adaptively decrease such gaps by estimating a subjective criterion deviation (SCD) of the user's search histories and fuzzy modeling. Conventional methods need tests and questionnaires beforehand to infer user's individual sensibility to his or her instinct or impression. The proposed method automatically decreases such gaps without users' burden caused by such conventional methods as requiring questionnaires. Moreover, this method reflects the dynamic changes in user's preferences. Namely, this method does not need to know user's preferences beforehand with questionnaires. An experiment was conducted by building and using a perfume search system. Experimental data results showed that the proposed method is effective.



Sakurai, Yoshitaka; Takada, Kouhei; Tsukamoto, Natsuki; Onoyama, Takashi; Knauf, Rainer; Tsuruta, Setsuo
Ensuring diversity in a backtrack and GA optimization method for delivery schedule. - In: Seventh International Conference on Signal-Image Technology and Internet-Based Systems (SITIS), 2011, ISBN 978-1-4673-0431-3, (2011), S. 201-208

Modeling, processing, evaluating and refining social processes is a widely deployed in fields like marketing, but becomes more and more a trend in learning systems, too. The higher the degree for formalization in such models, the better the chances to employ Knowledge Engineering methods such as data mining to it to reveal "hidden knowledge" within the data. Here, we introduce a concept to optimize university learning processes. In particular, individual university curricula are subject to a so called "lazy" Data Mining method on students' data and replaces a formerly developed explicit modeling approach ("eager" Data Mining). A formerly developed concept called storyboarding has been applied at a university to model the various ways to study at this university. Along with this storyboard, we formerly developed a data mining technology to estimate success chances of curricula. Here, we discuss chances to improve these results by implementing a student profiling concept that represents the students' individual educational history and a self estimation about intellectual traits and vocational ambitions.



http://dx.doi.org/10.1109/SITIS.2011.15