PERCEPOLIS: Pervasive Cyberinfrastructure for Personalized Learning and Instructional Support

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PERCEPOLIS: Pervasive Cyberinfrastructure for Personalized Learning and Instructional Support Faculty Advisors: Dr. Sahra Sedigh, Department of Electrical and Computer Engineering and Dr. Ali Hurson, Department of Computer Science Student: Amir Bahmani, PhD student, Department of Computer Science • PROPOSED APPROACH • One goal of PERCEPOLIS is to facilitate selection of a personalized trajectory (topics and artifacts) for each student, based on his or her background, interests, and needs. • The following information is utilized in personalization: • A detailed profile of each student. • Detailed information about each course topic and artifact. • Course objectives and curricular requirements. • PERCEPOLIS serves as middleware that links the databases containing this information. • From the computing point of view: • Each artifact is tagged with metadata. • The Summary Schemas Model (SSM) is utilized to create a curricular taxonomy, that represents the relationships among various topics in the curriculum. • SSMhas been developed to support automatic identification of semantically similar/ dissimilar data that have different/same names and representations. • Agent-based software uses SSM to identify appropriate topics/artifacts for each student. • INTRODUCTION • The overarching objective of the PERCEPOLIS project is to develop educational cyberinfrastructure that facilitates resource sharing, collaboration, and personalized learning in higher education. • We leverage advances in agent-based software engineering, databases, global information sharing processes, and pervasive computing to create this cyberinfrastructure. • PERCEPOLIS promotes and enables three key changes to currently dominant pedagogy: • Modular course development and offering • Blended learning • Networked curricula • The modular approach increases the resolution of the curriculum and allows for finer-grained personalization of learning processes and associated data collection. • Each course is decomposed of into several topics, some of which are mandatory, per course/curriculum objectives. • Additional, elective topics that can be provided to reinforce prerequisites, or present more advanced topics. • Associated with each topic are teaching (intended for instructors) and learning (intended for students) artifacts. • Examples include lectures’ slides, assignments, and experiential artifacts such as individual and group projects. • In blended learning, online, computer-mediated instruction supplements traditional classrooms. • This allows class time to be used for active learning, interactive problem-solving, and reflective instructional tasks, rather than traditional lectures. • In a networked curriculum the components form a cohesive and strongly interconnected whole, where learning in one area reinforces and supports learning in other areas. • MAIN RESEARCH TASKS • This doctoral research started in Sept. 2010 and is in the exploratory phase. • Near-term research tasks include the following: • Literature review (FS 2010 - SP 2011) • Prototyping (SP 2011) • Developing the multi-agent system with IBM Aglets framework • Refining the Summary Schemas Model • Designing a distributed database of learning artifacts for select CS/CpE courses (CpE 111, CpE 213, CS 301) • These tasks will enable refinement of the research focus. • RELATED WORK • Research on training and learning in virtual environments, e.g., Medulla [FKP09], MASCARET[BQD04], and HERA [ALB08]. • Research on multi-agent architectures with focus on distance education systems and intelligent tutoring systems, e.g., [DLF03] and [WBP01]. • Frameworks focused on knowledge creation and course management processes, e.g., [VSM07] and [PIR05]. • Research on pervasive computing, mobile agent technology, and distributed, heterogeneous multi-database systems. Overview of proposed cyberinfrastructure • CONCLUSIONS • PERCEPOLIS supports a shift from teaching technical skills to fostering the professional expertise required for a student to succeed as a practicing engineer. • The development of PERCEPOLIS will yield advances in pervasive computing, multi-agent systems, and distributed databases. Partial Taxonomy of CS /CpE Curricula • REFERENCES • [FKP09] M. R. FOX, H. KELLY, S. PATIL: “Medulla: A cyberinfrastructure-enabled framework for research, teaching, and learning with virtual worlds”, In Online Worlds: Convergence of the Real and the Virtual, Springer-Verlag, pp. 87–100, 2009. • [DLF03] F.A. Dorça, C.R. Lopes, and M.A. Fernandes: “A Multiagent Architecture for Distance Education Systems”, Proceedings of the 3rd IEEE International Conference on Advanced Learning Technologies (ICALT 2003), pp. 368-369, 2003. • [PIR05] S. Piramuthu: “Knowledge-based web-enabled agents and intelligent tutoring systems”, IEEE Trans. Educ., vol. 48, no. 4, pp. 750–756, Nov. 2005. • [BQD04] C. Buche, R. Querrec, P. De Loor, and P. Chevaillier. MASCARET: “ A  Pedagogical Multi-agent System for Virtual Environment for Training”, International Journal of Distance Education Technologies (JDET), pp.41-61, November 2004. • [ALB08] K. Amokrane, D. Lourdeaux, and J. Burkhardt, “Hera: Learner tracking in a virtual environment”, IJVR : International Journal of Virtual Reality, vol. 7, no. 3, pp. 23–30, September 2008. • [WBP01] C. Webber, L. Bergia, S. Pesty, and N. Balacheff, “The Baghera project: a multi-agent architecture for human learning”. In J.I. Vassileva, ed., Workshop on Multi-Agent Architectures for Distributed Learning Environments, pp. 12–17, San Antonio, USA, 2001. • [VSM07] Y. Vovides, S. Sanchez-Alonso, V. Mitropoulou, and G. Nickmans, “The use of e-learning course management system to support learning strategies and to improve self-regulated learning”, in Educational Research Review, Vol 2, Issue 1, pp. 64-74, 2007. • ACKNOWLEDGEMENTS • This research was supported in part by the Missouri S&T Intelligent Systems Center. Sample environment for a course on design and analysis of algorithms