Challenge 1: Advance Personalized Learning
Human Learning by Human learning is studied in a variety of ways. Motor learning is often studied separately from verbal learning. Studies may delve into anatomy vs function, may view behavioral outcomes or look discretely at the molecular and cellular level of learning. All have merit but they are dispersed across a wide literature and rarely are the findings integrated and synthesized in a meaningful way. Human Learning: Biology, Brain, and Neuroscience synthesizes findings across these levels and types of learning and memory investigation. Divided into three sections, each section includes a discussion by the editors integrating themes and ideas that emerge across the chapters within each section. Section 1 discusses general topics in human learning and cognition research, including inhibition, short term and long term memory, verbal memory, memory disruption, and scheduling and learning. Section 2 discusses cognitive neuroscience aspects of human learning. Coverage here includes models, skill acquisition, declarative and non declarative memory, age effects on memory, and memory for emotional events. Section 3 focuses on human motor learning. This book is suitable for cognitive neuroscientists, cognitive psychologists, kinesthesiologists, and graduate courses in learning. * Synthesizes research from a variety of disciplines, levels, and content areas * Provides section discussions on common findings between chapters * Covers motor and verbal learning
Recommender Systems for Technology Enhanced Learning: Research Trends and Applications by As an area, Technology Enhanced Learning (TEL) aims to design, develop and test socio-technical innovations that will support and enhance learning practices of individuals and organizations. Information retrieval is a pivotal activity in TEL and the deployment of recommender systems has attracted increased interest during the past years.Recommendation methods, techniques and systems open an interesting new approach to facilitate and support learning and teaching. The goal is to develop, deploy and evaluate systems that provide learners and teachers with meaningful guidance in order to help identify suitable learning resources from a potentially overwhelming variety of choices.Contributions address the following topics: i) user and item data that can be used to support learning recommendation systems and scenarios, ii) innovative methods and techniques for recommendation purposes in educational settings and iii) examples of educational platforms and tools where recommendations are incorporated.Article
M. Á C. González and F. J. García-Peñalvo, "Mobile Personal Learning Applied to a Software Engineering Subject," in IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, vol. 9, no. 3, pp. 114-121, Aug. 2014.
doi: 10.1109/RITA.2014.2340051 URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6866158&isnumber=6895325
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The references below are provided by the National Academy of Engineering. See whether you can find them in the library either in the digital or the print format.
Canales, A. et al. 2007. Adaptive and intelligent Web-based education system: Towards an integral architecture and framework. Expert Systems with Applications 33(4): 1076-1089. DOI: 10.1016/j.eswa.2006.08.034
Hsu, M.H. 2008. A personalized English learning recommender system for ESL students. Expert Systems with Applications 34(1): 683-688.
Huang, M.J. et al. 2007. Constructing a personalized e-learning system based on genetic algorithm and case-based reasoning approach. Expert Systems with Applications 33(3): 551-564. DOI: 10.1016/j.eswa.2006.05.019
Liu, J., C.K. Wong, and K.K. Hui. 2003. An Adaptive User Interface Based on Personalized Learning. IEEE Intelligent Systems 18(2): 52-57. DOI: 10.1109/MIS.2003.1193657
Margaret Martinez, M. 2002. What is Personalized Learning? The e Learning Developers’ Journal May 7: 1-7.
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