![]() Serina, Using ai planning to enhance e-learning processes, 2012. Castillo, On the automatic compilation of e-learning models to planning, The Knowledge Engineering Review, 28 (2013) 121-136. Serina, Plan stability: Replanning versus plan repair, AAAI Press, 2006. Silverman, Learning and teaching styles in engineering education, Engineering Education, 78 (1988) 674-681. Graf, Kinshuk, Generalized metrics for the analysis of e-learning personalization strategies, Computers in Human Behavior, 48 (2015) 310-322. Oliveira, Recommendation of programming activities by multi-label classification for a formative assessment of students, Expert Systems with Applications, 40 (2013) 6641-6651. Sarne, Using semantic negotiation for ontology enrichment in e-learning multi agent systems, 2015. Sarne, Forming homogeneous classes for e-learning in a social network scenario, 2015. de Antonio, A proposal for student modeling based on ontologies and diagnosis rules, Expert Systems with Applications, 38 (2011) 8066-8078. Virvou, Student modeling approaches: a literature review for the last decade, Expert Systems with Applications, 40 (2013) 4715-4729. Hsu, Temporal planning using subgoal partitioning and resolution in SGPlan, Journal of Artificial Intelligence Research, 26 (2006) 323-369. Salim, A systematic review of scholar context-aware recommender systems, Expert Systems with Applications, 42 (2015) 1743-1758. Onaindia, Automatic generation of temporal planning domains for e-learning, Journal of Scheduling, 13 (2010) 347-362. Garrido, Student-oriented planning of e-learning contents for moodle, Journal of Network and Computer Applications, 53 (2015) 115-127. Verri, Data mining models for student careers, Expert Systems with Applications, 42 (2015) 5508-5521. Vassileva, Course sequencing techniques for large-scale web-based education, International Journal of Continuing Engineering Education and Lifelong Learning, 13 (2003) 75-94. ![]() Serina, Progress in case-based planning, ACM Computing Surveys, 47 (2015) 1-39. Serina, Effective plan retrieval in case-based planning for metric-temporal problems, Journal of Experimental & Theoretical Artificial Intelligence, 27 (2015) 603-647. Fabregat, Linking educational specifications and standards for dynamic modelling in ADAPTAPlan, 2007. Nebel, Complexity results for SAS+ planning, Computational Intelligence, 11 (1995) 625-655. GarcĂa-Saiz, Recommender system in collaborative learning environment using an influence diagram, Expert Systems with Applications, 40 (2013) 7193-7202. Virvou, Automatic generation of emotions in tutoring agents for affective e-learning in medical education, Expert Systems with Applications, 38 (2011) 9840-9847. It is also of practical significance for repairing unexpected discrepancies (while the students are executing their learning routes) by using a Case-Based Planning adaptation process that reduces the differences between the original and the new route, thus enhancing the learning process. Therefore, it is perfectly valid for schools, high schools and universities, especially if they already use Moodle, on top of which we have implemented myPTutor. ![]() Our experiments demonstrate that we can solve scenarios with large courses and a high number of students. In this paper we propose myPTutor, a general and effective approach which uses AI planning techniques to create fully tailored learning routes, as sequences of Learning Objects (LOs) that fit the pedagogical and students' requirements.myPTutor has a potential applicability to support e-learning personalization by producing, and automatically solving, a planning model from (and to) e-learning standards in a vast number of real scenarios, from small to medium/large e-learning communities. Selection+sequencing of contents for e-learning personalization by using AI planning.Automated compilation of PDDL files, making our approach solver independent.Case-based planning to adapt and repair routes to meet new requirements.Integration on top of Moodle Learning Management System.Good performance from short to long courses and up to 100 students.
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