Automatic generation of temporal planning domains for e-learning problems

Castillo, Luis; Morales, Lluvia; González-Ferrer, Arturo; Fdez-Olivares, Juan; Borrajo, Daniel; Onaindía, Eva

doi:10.1007/s10951-009-0140-x

Cited by 23 publications

(20 citation statements)

References 8 publications

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“…Several application areas like intelligent tutoring systems (ITS), 15,16 adaptive e-learning systems, [17][18][19] artificial intelligence (AI) planning and scheduling, 9,11,12,[20][21][22][23][24] learning management systems, 25 ontology-based systems, [26][27][28][29][30] recommender systems 16 , and expert systems 12,31,32 have studied the problem of generating learning paths through learning materials (ie, coursework sequencing).…”

Section: Related Workmentioning

confidence: 99%

Design and evaluation of planning and mathematical models for generating learning paths

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Martínez-Salazar

et al. 2017

Computational Intelligence

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Education research has emphasized the need to develop instructional design tools to facilitate the generation of learning paths for students. Learning paths are important because they enable the personalization and optimization of the learning process. In this work, we present a flexible conceptual framework that allows the representation of curricula information as Artificial Intelligence Planning and Mathematical Programming models to facilitate the generation of learning paths by domain independent algorithms. The resulting models consider a rich set of properties from the education domain, like hierarchical learning structures, enabling conditions, temporal actions, mandatory activities, quality accumulation functions, and metric information. We show that the proposed mathematical models return optimal solutions very efficiently if we relax the total ordering constraints of learning paths. These relaxations allow evaluating greedy planning algorithms to identify the properties from the models that increase the complexity of solution synthesis. We expect that the results of this research can be helpful to education researchers and computer scientists in the quest of scalable systems that capture more flexible standards to model learning and compute more informed learning paths for students.KEYWORDS adaptive e-learning models, artificial intelligence planning, authoring tools and methods, learning object representation, learning path optimization In the last decade, educational research has emphasized the need to study in greater detail the learning paths (ie, series of learning activities or learning trajectories) undertaken by students during their Computational Intelligence. 2018;34:821-838.wileyonlinelibrary.com/journal/coin

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Section: Related Workmentioning

confidence: 99%

Design and evaluation of planning and mathematical models for generating learning paths

Nigenda

Padrón

Martínez-Salazar

et al. 2017

Computational Intelligence

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“…In [3], Fikes and Nils designed STRIPS system to introduce definitions of STRIPS operators, which made significant difference in the research of automated planning. In [4], the authors designed the first nonlinear planning system SNLP of the world. In [5], Kautz converted planning into SAT problem, which effectively solved partial planning problem and showed new direction of automated planning.…”

Section: Related Workmentioning

confidence: 99%

An Automatic Software Requirement Analysis Model based on Planning and Machine Learning Techniques

Zhang¹

2015

IJFGCN

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“…Several authors have used planning for generating learning routes based on students' preferences [15], [19]- [21], but these have some limitations: i) they do not use the learning standards extensively, ii) the routes are not displayed and integrated in a common LMS, and iii) they are limited to a specific ontology, course, and/or planning paradigm. In contrast, by using our approach it is possible to use any standard planning to find the best learning route, with the idea of offering the right content to the right student.…”

Section: B Motivation What Is Intelligent Planning and Why Use It Imentioning

confidence: 99%

E-Learning and Intelligent Planning: Improving Content Personalization

Garrido

Morales

2014

IEEE R. Iberoamericana Tecnologias Aprendizaje

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Abstract-Combining learning objects is a challenging topic because of its direct application to curriculum generation, tailored to the students' profiles and preferences. Intelligent planning allows us to adapt learning routes (i.e. sequences of learning objects), thus highly improving the personalization of contents, the pedagogical requirements and specific necessities of each student. This paper presents a general and effective approach to extract metadata information from the e-learning contents, a form of reusable learning objects, to generate a planning domain in a simple, automated way. Such a domain is used by an intelligent planner that provides an integrated recommendation system, which adapts, stores and reuses the best learning routes according to the students' profiles and course objectives. If any inconsistency happens during the route execution, e.g. the student fails to pass an assessment test which prevents him/her from continuing the natural course of the route, the system adapts and/or repairs the course to meet the new objectives.

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Automatic generation of temporal planning domains for e-learning problems

Cited by 23 publications

References 8 publications

Design and evaluation of planning and mathematical models for generating learning paths

Design and evaluation of planning and mathematical models for generating learning paths

An Automatic Software Requirement Analysis Model based on Planning and Machine Learning Techniques

E-Learning and Intelligent Planning: Improving Content Personalization

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