Developing adaptive learning management systems - An open IMS-based user modelling approach

Boticario, Jesús G.; Santos, Olga C.

doi:10.5334/2007-2

Cited by 19 publications

(2 citation statements)

References 8 publications

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“…ITS or iLMS) (Badaracco, Liu, & Martinez, 2013). The main focus of these works are how to deal with content, data storage and Human Communication Interfaces (HCI) within devices with constrained characteristics and features (Badaracco, Liu, & Martinez, 2013), (Brown, Lee, Salvucci, & Aleven, 2008), (Boticario, & Santos, 2007).…”

Section: Mobility Adaptability and Context Awareness In Learning Systemsmentioning

confidence: 99%

Using a Knapsack Model to Optimize Continuous Building of a Hybrid Intelligent Tutoring System

Khemaja

2016

International Journal of Human Capital and Information Technology Professionals

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Intelligent Tutoring Systems (ITS) provide an alternative to the traditional “one size fits all” approach. Their main aim is to adapt learning content, activities and paths to support learners. Meanwhile, during the last decades, advances in lightweight, portable devices and wireless technologies had drastically impacted Mobile and Ubiquitous environments' development which has driven opportunities towards more personalized, context-aware and dynamic learning processes. Moreover, mobile and hand held devices could be advantageous to incremental learning, based on very short and fine grained activities and resources delivery. However, measuring efficiency and providing the most relevant combination/orchestration of learning activities, resources and paths remains and open and challenging problem especially for enterprises where choices and decisions face several constraints as time, budget, targeted core competencies, etc. This paper, attempts to provide a knapsack based model and solution in order to implement ITS's intelligent decision making about best combination and delivery of e-training activities and resources especially in the context of fast changing Information and Communication Technology (ICT) domain and its required skills. An android and OSGi based prototype is implemented to validate the proposal through some realistic use cases.

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Section: Mobility Adaptability and Context Awareness In Learning Systemsmentioning

confidence: 99%

Using a Knapsack Model to Optimize Continuous Building of a Hybrid Intelligent Tutoring System

Khemaja

2016

International Journal of Human Capital and Information Technology Professionals

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“…According to Ullrich and Melis (2009), they can map the final plan into IMS Content Packaging or SCORM but not the IMS-LD model itself and they do not use standards in the definition of the student profile. Others use state-based planners that implement metrics to measure the adaptation to a specific learning style (Boticario & Santos, 2007; Limongelli et al ., 2008), but without taking into consideration other profile features and different learning styles theories. Finally, others incorporate machine learning techniques to assist content providers when constructing LOs that comply with an ontology, concerning objectives and prerequisites, as can be seen in Camacho et al .…”

Section: Combining E-learning and Artificial Intelligence Planning: Mmentioning

confidence: 99%

On the automatic compilation of e-learning models to planning

Garrido

Fernández

Morales

et al. 2013

The Knowledge Engineering Review

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Cambridge University Press (CUP)Garrido Tejero, A.; Morales, L.; 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. doi:10.1017/S0269888912000380.On the automatic compilation of e-learning models to planning AbstractThis 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 domainindependent 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 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.

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