34,302 research outputs found

    On the automatic compilation of e-learning models to planning

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    [EN] This paper presents a general approach to automatically compile e-learning models to planning, allowing us to easily generate plans, in the form of learning designs, by using existing domain-independent planners. The idea is to compile, first, a course defined in a standard e-learning language into a planning domain, and, second, a file containing students learning information into a planning problem. We provide a common compilation and extend it to three particular approaches that cover a full spectrum of planning paradigms, which increases the possibilities of using current planners: (i) hierarchical, (ii) including PDDL (Planning Domain Definition Language) actions with conditional effects and (iii) including PDDL durative actions. The learning designs are automatically generated from the plans and can be uploaded, and subsequently executed, by learning management platforms. We also provide an extensive analysis of the e-learning metadata specification required for planning, and the pros and cons on the knowledge engineering procedures used in each of the three compilations. Finally, we include some qualitative and quantitative experimentation of the compilations in several domain-independent planners to measure its scalability and applicability.This work has been supported by the Spanish MICINN under projects TIN2008-06701-C03 and Consolider Ingenio 2010 CSD2007-00022, by the Mexican National Council of Science and Technology and the regional projects CCG08-UC3M/TIC-4141 and Prometeo GVA 2008/051.Garrido Tejero, A.; Fernandez, S.; Onaindia De La Rivaherrera, E.; Morales, L.; Borrajo, D.; Castillo, L. (2013). On the automatic compilation of e-learning models to planning. Knowledge Engineering Review. 28(2):121-136. https://doi.org/10.1017/S0269888912000380S121136282Garrido A. , Onaindía E. 2010. On the application of planning and scheduling techniques to E-learning. In Proceedings of the 23rd International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA-AIE 2010)—Lecture Notes in Computer Science 6096, 244–253. Springer.Ullrich C 2008. Pedagogically founded courseware generation for web-based learning, No. 5260, Lecture Notes in Artificial Intelligence 5260, Springer.Sicilia M.A. , Sánchez-Alonso S. , García-Barriocanal E. 2006. On supporting the process of learning design through planners. CEUR Workshop Proceedings: Virtual Campus 2006 Post-Proceedings. Barcelona, Spain, 186(1), 81–89.IMSLD 2003. IMS Learning Design Specification. Version 1.0 (February, 2003). Retrieved December, 2012, from http://www.imsglobal.org/learningdesign.Sharable Content Object Reference Model (SCORM) 2004. Retrieved December, 2012, from http://scorm.com.Garrido A. , Onaindia E. , Morales L. , Castillo L. , Fernandez S. , Borrajo D. 2009. Modeling E-learning activities in automated planning. In Proceedings of the 3rd International Competition on Knowledge Engineering for Planning and Scheduling (ICKEPS-2009), Thessaloniki, Greece, 18–27.Essalmi, F., Ayed, L. J. B., Jemni, M., Kinshuk, & Graf, S. (2010). A fully personalization strategy of E-learning scenarios. Computers in Human Behavior, 26(4), 581-591. doi:10.1016/j.chb.2009.12.010Camacho D. , R-Moreno M.D. , Obieta U. 2007. CAMOU: a simple integrated e-learning and planning techniques tool. In 4th International Workshop on Constraints and Language Processing, Roskilde University, Denmark, 1–11.Fox, M., & Long, D. (2003). PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains. Journal of Artificial Intelligence Research, 20, 61-124. doi:10.1613/jair.1129KONTOPOULOS, E., VRAKAS, D., KOKKORAS, F., BASSILIADES, N., & VLAHAVAS, I. (2008). An ontology-based planning system for e-course generation. Expert Systems with Applications, 35(1-2), 398-406. doi:10.1016/j.eswa.2007.07.034Fuentetaja R. , Borrajo D. , Linares López C. 2009. A look-ahead B&B search for cost-based planning. In Proceedings of CAEPIA'09, Murcia, Spain, 105–114.Limongelli C. , Sciarrone F. , Vaste G. 2008. LS-plan: an effective combination of dynamic courseware generation and learning styles in web-based education. In Adaptive Hypermedia and Adaptive Web-Based Systems, 5th International Conference, AH 2008, Nejdl, W., Kay, J., Pu, P. & Herder, E. (eds.)., 133–142. Springer.Castillo L. , Fdez.-Olivares J. , García-Perez O. Palao F. 2006. Efficiently handling temporal knowledge in an HTN planner. In Proceedings of 16th International Conference on Automated Planning and Scheduling (ICAPS 2006), Borrajo, D. & McCluskey, L. (eds.). AAAI, 63–72.Castillo, L., Morales, L., González-Ferrer, A., Fdez-Olivares, J., Borrajo, D., & Onaindía, E. (2009). Automatic generation of temporal planning domains for e-learning problems. Journal of Scheduling, 13(4), 347-362. doi:10.1007/s10951-009-0140-xUllrich, C., & Melis, E. (2009). Pedagogically founded courseware generation based on HTN-planning. Expert Systems with Applications, 36(5), 9319-9332. doi:10.1016/j.eswa.2008.12.043Boticario J. , Santos O. 2007. A dynamic assistance approach to support the development and modelling of adaptive learning scenarion based on educational standards. In Proceedings of Workshop on Authoring of Adaptive and Adaptable Hypermedia, International Conference on User Modelling, Corfu, Greece, 1–8.IMSMD 2003. IMS Learning Resource Meta-data Specification. Version 1.3 (August, 2006). Retrieved December, 2012, from http://www.imsglobal.org/metadata.Mohan P. , Greer J. , McCalla G. 2003. Instructional planning with learning objects. In IJCAI-03 Workshop Knowledge Representation and Automated Reasoning for E-Learning Systems, Acapulco, Mexico, 52–58.Alonso C. , Honey P. 2002. Honey-alonso Learning Style Theoretical Basis (in Spanish). Retrieved December 2012, from http://www.estilosdeaprendizaje.es/menuprinc2.htm

    Learning STRIPS Action Models with Classical Planning

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    This paper presents a novel approach for learning STRIPS action models from examples that compiles this inductive learning task into a classical planning task. Interestingly, the compilation approach is flexible to different amounts of available input knowledge; the learning examples can range from a set of plans (with their corresponding initial and final states) to just a pair of initial and final states (no intermediate action or state is given). Moreover, the compilation accepts partially specified action models and it can be used to validate whether the observation of a plan execution follows a given STRIPS action model, even if this model is not fully specified.Comment: 8+1 pages, 4 figures, 6 table

    The 1990 progress report and future plans

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    This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers

    Instructional strategies and tactics for the design of introductory computer programming courses in high school

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    This article offers an examination of instructional strategies and tactics for the design of introductory computer programming courses in high school. We distinguish the Expert, Spiral and Reading approach as groups of instructional strategies that mainly differ in their general design plan to control students' processing load. In order, they emphasize topdown program design, incremental learning, and program modification and amplification. In contrast, tactics are specific design plans that prescribe methods to reach desired learning outcomes under given circumstances. Based on ACT* (Anderson, 1983) and relevant research, we distinguish between declarative and procedural instruction and present six tactics which can be used both to design courses and to evaluate strategies. Three tactics for declarative instruction involve concrete computer models, programming plans and design diagrams; three tactics for procedural instruction involve worked-out examples, practice of basic cognitive skills and task variation. In our evaluation of groups of instructional strategies, the Reading approach has been found to be superior to the Expert and Spiral approaches

    Automation and schema acquisition in learning elementary computer programming: Implications for the design of practice

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    Two complementary processes may be distinguished in learning a complex cognitive skill such as computer programming. First, automation offers task-specific procedures that may directly control programming behavior, second, schema acquisition offers cognitive structures that provide analogies in new problem situations. The goal of this paper is to explore what the nature of these processes can teach us for a more effective design of practice. The authors argue that conventional training strategies in elementary programming provide little guidance to the learner and offer little opportunities for mindful abstraction, which results in suboptimal automation and schema acquisition. Practice is considered to be most beneficial to learning outcomes and transfer under strict conditions, in particular, a heavy emphasis on the use of worked examples during practice and the assignment of programming tasks that demand mindful abstraction from these examples
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