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篇名
物理空间中的智能学伴系统: 感知数据驱动的学习分析技术——访柏林洪堡大学教育技术专家Niels Pinkwart教授
並列篇名
Intelligent Learning Companion System in Physical Spaces: Perception Data-Driven Learning Analytics Technologies —An Interview with Professor Niels Pinkwart from Humboldt University of Berlin
作者 劉智劉三枒康令雲
英文摘要
Under the educational development of “human-technology” co-evolution, the learning support service in physical spaces will profound transformations. In recent years, with the advent of “Big Data” and wearable technologies, physical space oriented learning analytics technologies has emerged as the times require. One of the research trends is to use sensor technologies to explore individual learning states and to adopt perception data to establish learner-centric adaptive and autonomous learning environments. Internationally, relevant experts and scholars in the field of educational technology have begun to focus on this research in order to build mobile intelligent learning companion systems to offer learners personalized learning feedback, recommendations, interventions, etc. Thus, how to acquire and use learners’ physiological data, how to infer their learning states (emotion, cognition, attention, etc.) and what learning support service can be offered based on these data have become the realistic problems researchers face. Leaning Analytics for Sensor-based Adaptive Learning (LISA) is a joint R&D project funded by German Federal Ministry of Education and Research that aims at detecting the individual learning states as well as supporting adaptive learning and augmented learning experience in physical spaces. The core of this project is to establish a “mobile learning companion” that helps learners conduct reflective learning and assists teachers to conduct real-time monitoring. With some emerged questions, such as “what are the background and significance of this project?”, “what information can the intelligent learning companion detect?”, “what learning service can this system offer?”, “how to evaluate its effect?”, “what are the future trends and limitations?”, we interviewed Professor Niels Pinkwart, the principal investigator of this project. Professor Niels Pinkwart is an internationally recognized scholar in educational technology. He is currently the deputy director of the Department of Computer Science of Humboldt University of Berlin (HU Berlin) in Germany, the leader of the research group “Computer Science Education/Computer Science and Society” and the center of technology enhanced learning located in the Professional School of Education of HU Berlin, the co-chair of the working group on Learning Analytics and e-learning working group within the German Computer Science Association. His research interests include learning analytics, adaptive learning technology, computer-assisted collaborative learning and multimodal human-computer interaction systems. He has published over 220 papers with a total citations of more than 2360 and been serving on the editorial board for International Journal of Artificial Intelligence in Education (IJAIED) and the Program Committees of International Conference on Intelligent Tutoring Systems (ITS), IEEE International Conference on Advanced Learning Technologies (ICALT), International Conference on Learning Analytics & Knowledge (LAK). Recently, Prof. Pinkwart’ team has focused on the empirical study of learning system design and interactive learning with multidisciplinary approaches (computer science, cognitive science and learning science) and has hosted several projects funded by German National Science Foundation (DFG), the Federal Ministry of Education and Research (BMBF), and some joint R&D projects on intelligent tutoring and human-computer interaction in industry. His project on “Learning Feedback in Intelligent Tutoring Systems (FIT)” was awarded by DFG as the priority programme proposal in the direction “Learning Methods in Artificial Systems”.
起訖頁 067-072
關鍵詞 物理空间感知数据学习同伴学习分析Physical SpacesPerception DataLearning CompanionLearning AnalyticsCSSCI
刊名 中國電化教育  
期數 201807 (378期)
出版單位 中國電化教育雜誌社
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該期刊-下一篇 “AI”时代未来教师专业发展途径探究
 

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