Alle Publikationen
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2014
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(2014): An adaptation algorithm for an intelligent natural language tutoring system. In: Computers & Education 71, S. 97-110. DOI: 10.1016/j.compedu.2013.09.014
DOI: https://doi.org/10.1016/j.compedu.2013.09.014 Abstract: The focus of computerised learning has shifted from content delivery towards personalised online learning with Intelligent Tutoring Systems (ITS). Oscar Conversational ITS (CITS) is a sophisticated ITS that uses a natural language interface to enable learners to construct their own knowledge through discussion. Oscar CITS aims to mimic a human tutor by dynamically detecting and adapting to an individual's learning styles whilst directing the conversational tutorial. Oscar CITS is currently live and being successfully used to support learning by university students. The major contribution of this paper is the development of the novel Oscar CITS adaptation algorithm and its application to the Felder–Silverman learning styles model. The generic Oscar CITS adaptation algorithm uniquely combines the strength of an individual's learning style preference with the available adaptive tutoring material for each tutorial question to decide the best fitting adaptation. A case study is described, where Oscar CITS is implemented to deliver an adaptive SQL tutorial. Two experiments are reported which empirically test the Oscar CITS adaptation algorithm with students in a real teaching/learning environment. The results show that learners experiencing a conversational tutorial personalised to their learning styles performed significantly better during the tutorial than those with an unmatched tutorial.
2012
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(2012): Employing Textual and Facial Emotion Recognition to Design an Affective Tutoring System. In: Turkish Online Journal of Educational Technology - TOJET 11 (4), S. 418-426. Online verfügbar unter https://eric.ed.gov/?id=EJ989317, zuletzt geprüft am 22.11.2019
Abstract: Emotional expression in Artificial Intelligence has gained lots of attention in recent years, people applied its affective computing not only in enhancing and realizing the interaction between computers and human, it also makes computer more humane. In this study, emotional expressions were applied into intelligent tutoring system, where learners’ emotional expression in learning process was observed in order to give an appropriate feedback. Emotional intelligent not only gives high flexibility to the interaction of tutoring system, it also to deepen its level of human interaction. This study uses dual-mode operation: facial expression recognition, and text semantics as the main elements in affective computing to understand users’ emotions. Text semantics are used to understand learners’ learning status, and the results would contribute to course management agents in order to choose the most appropriate teaching strategies and feedback to the users. Facial expression recognition allows interactive agents to provide users a complete sound and animation feedback. (Contains 5 tables and 3 figures.)
Keywords: Affective Behavior, Animation, Artificial intelligence, Bedienung & Handhabung, Computer Assisted Instruction, Computer Software Evaluation, Computer System Design, Educational Technology, Feedback (Response), Focus Groups, Foreign Countries, Grounded Theory, Intelligent tutoring systems, Man machine systems, Mixed Methods Research, Multimedia Instruction, Natural Language Processing, Nonverbal communication, Programming, Psychological Patterns, Rating Scales, Teaching Methods, usability, Use Studies 2011
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(2011): Polite web-based intelligent tutors: Can they improve learning in classrooms?. In: Computers & Education 56 (3), S. 574-584. DOI: 10.1016/j.compedu.2010.09.019
DOI: https://doi.org/10.1016/j.compedu.2010.09.019 Abstract: Should an intelligent software tutor be polite, in an effort to motivate and cajole students to learn, or should it use more direct language? If it should be polite, under what conditions? In a series of studies in different contexts (e.g., lab versus classroom) with a variety of students (e.g., low prior knowledge versus high prior knowledge), the politeness effect was investigated in the context of web-based intelligent tutoring systems, software that runs on the Internet and employs artificial intelligence and learning science techniques to help students learn. The goal was to pinpoint the appropriate conditions for having the web-based tutors provide polite feedback and hints (e.g., ’Let’s convert the units of the first item’) versus direct feedback and hints (e.g., ’Convert the units of the first item now’). In the study presented in this paper, 132 high school students in a classroom setting, grouped as low and high prior knowledge learners according to a pre-intervention knowledge questionnaire, did not benefit more from polite feedback and hints than direct feedback and hints on either an immediate or delayed posttest, both of which contained near transfer and conceptual test items. Of particular interest and contrary to an earlier lab study, low prior knowledge students did not benefit more from using the polite version of a tutor. On the other hand, a politeness effect was observed for the students who made the most errors during the intervention, a different proxy for low prior knowledge, hinting that even in a classroom setting, politeness may be beneficial for more needy students. This article presents and discusses these results, as well as discussing the politeness effect more generally, its theoretical underpinnings, and future directions. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
2005
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Looi, Chee-Kit (2005): Artificial intelligence in education. Supporting learning through intelligent and socially informed technology. International Conference on Artificial Intelligence in Education. Washington, DC: IOS Press. Online verfügbar unter http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=164000
Abstract: The field of Artificial Intelligence in Education includes research and researchers from many areas of technology and social science. This study aims to open opportunities for the cross-fertilization of information and ideas from researchers in the many fields that make up this interdisciplinary research area
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