Assembling Learning Objects for Personalized Learning: An AI Planning Perspective

Garrido, António; Onaindía, Eva

doi:10.1109/mis.2011.36

Cited by 34 publications

(22 citation statements)

References 9 publications

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“…This paper builds on the general work of (Garrido & Onaindia, 2013) and extends the results presented in (Caputi & Garrido, 2013) to offer now a thorough design, development, implementation and testing of intelligent personalization in Moodle. Particularly, in this paper the personalization of an e-learning path is faced from the point of view of AI planning through the automated compilation of e-learning models.…”

Section: Objectives Of the Papermentioning

confidence: 73%

“…It is important to note that each student has his/her own characteristics (profile, learning style, prior background and learning objectives). These individual traits are very useful to provide each student the most adequate learning path to attain his/her learning outcomes (Garrido & Onaindia, 2013;Papanikolaoum, Grigoriadou, Magoulas, & Kornilakis, 2002). In other words, it is not enough to plan a general learning path for all students but to personalize as much as possible each learning path.…”

Section: Learning Management Systemsmentioning

confidence: 99%

“…Particularly, in this paper the personalization of an e-learning path is faced from the point of view of AI planning through the automated compilation of e-learning models. We have fully adapted the knowledge engineering planning mapping introduced in (Garrido & Onaindia, 2013) to be directly used in Moodle, while trying to minimize the modifications in Moodle. It is important to highlight that our general idea of applying planning to e-learning personalization does not depend on any specific LMS.…”

Section: Objectives Of the Papermentioning

confidence: 99%

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Student-oriented planning of e-learning contents for Moodle

Caputi

Garrido

2015

Journal of Network and Computer Applications

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We present a way to automatically plan student-oriented learning contents in Moodle. Rather than offering the same contents for all students, we provide personalized contents according to the students' background and learning objectives. Although curriculum personalization can be faced in several ways, we focus on Artificial Intelligence (AI) planning as a very useful formalism for mapping actions, i.e. learning contents, in terms of preconditions (precedence relationships) and causal effects to find plans, i.e. learning paths that best fit the needs of each student. A key feature is that the learning path is generated and shown in Moodle in a seamless way for both the teacher and student, respectively. We also include some experimental results to demonstrate the scalability and viability of our approach.

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Section: Objectives Of the Papermentioning

confidence: 73%

Section: Learning Management Systemsmentioning

confidence: 99%

Section: Objectives Of the Papermentioning

confidence: 99%

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Student-oriented planning of e-learning contents for Moodle

Caputi

Garrido

2015

Journal of Network and Computer Applications

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“…In paper [19], the author illustrates five-step scheme to personalized learning object in e-learning (Fig.1). These steps could illustrate the process of adapting the learning path on online learning system: 1.…”

Section: B Adaptation Processmentioning

confidence: 99%

“…The same way in the learning path adaptation, during minimizing each time phase of students attending learning objects, each student attempts a personal learning path by minimizing the relevance loss between consecutive learning objects along the path. The common method to formulate learning path problem in literature is to formulate as a constraint satisfaction problem (CSP) [10,19,21,25]. Constraint satisfaction problem consists of a set of precedence constraints (rules) and objective criteria.…”

Section: Problem Formulationmentioning

confidence: 99%

Learning path adaptation in online learning systems

Muhammad

Zhou

Beydoun

et al. 2016

2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD)

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Learning path in online learning systems refers to a sequence of learning objects which are designated to help the students in improving their knowledge or skill in particular subjects or degree courses. In this paper, we review the recent research on learning path adaptation to pursue two goals, first is to organize and analyze the parameter of adaptation in learning path; the second is to discuss the challenges in implementing learning path adaptation. The survey covers the state of the art and aims at providing a comprehensive introduction to the learning path adaptation for researchers and practitioners. Abstract-Learning path in online learning systems refers to a sequence of learning objects which are designated to help the students in improving their knowledge or skill in particular subjects or degree courses. In this paper, we review the recent research on learning path adaptation to pursue two goals, first is to organize and analyze the parameter of adaptation in learning path; the second is to discuss the challenges in implementing learning path adaptation. The survey covers the state of the art and aims at providing a comprehensive introduction to the learning path adaptation for researchers and practitioners.

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Prerequisite knowledge‐based automated course planning with semantics consideration

Yoo,

Balachandranath,

Saboury

2024

Comp Applic In Engineering

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The knowledge‐based prerequisite framework (KPF) is an alternative to the course‐based prerequisite framework (CPF), which is widely used for curriculum design. The KPF is more flexible because it only requires essential prerequisite knowledge, while the CPF is more rigid and requires students to take all prerequisite courses. Since the number of prerequisite knowledge terms is, in general, much greater than the number of prerequisite courses, flexibility can cause additional complexity. Furthermore, the KPF inevitably requires handling semantics of defined knowledge terms. This work presents a novel Artificial Intelligence (AI) Planning mathematical model that enables the KPF by automatically verifying prerequisite knowledge and incorporating hierarchical semantic relationships among knowledge terms into the model. The proposed model significantly improves the quality of course planning solutions by finding hidden or better solutions that could not be obtained without semantics consideration. The results of the comprehensive experiments show the optimality of the solutions obtained by the mathematical model and demonstrate the outperformance of incorporation of the semantics into the mathematical model, in terms of the quality of solutions. Finally, the experimental results on scalability show the necessity of the development of efficient heuristic algorithms.

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Assembling Learning Objects for Personalized Learning: An AI Planning Perspective

Cited by 34 publications

References 9 publications

Student-oriented planning of e-learning contents for Moodle

Student-oriented planning of e-learning contents for Moodle

Learning path adaptation in online learning systems

Prerequisite knowledge‐based automated course planning with semantics consideration

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