Method for generating expert recommendations to advise students on ubiquitous learning experiences

Durán, Elena Beatriz; Álvarez, Margarita

doi:10.1109/sccc.2017.8405121

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…Contextual aspects should also be considered. Therefore, recommendations are generated by experts for the subject that a student intends to learn by taking their own past experiences with other students into consideration [67]. Keeping in mind the need for developing smart learning environment, the University of Jaen developed a SmartLab called UJAmI based on ambient intelligence to provide an assistive learning environment [68].…”

Section: Iot and Recommender Systemsmentioning

confidence: 99%

RecIoT: A Deep Insight into IoT-Based Smart Recommender Systems

Sharma

Rani

Nuagah

2022

Wireless Communications and Mobile Computing

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Typically, Internet of Things (IoT) is perceived as the technology for connecting people, devices, vehicles, home appliances, etc., and 212 billion heterogenous devices are presumed to be connected by 2020 under its umbrella. This domain is gaining popularity worldwide because of the wave it has brought in both research and industrial sector. Smart applications of this scenario provide assistance and services to the users in smart homes, smart cities, smart retail, agriculture, healthcare, etc. However, researchers are now exploring the potential of IoT systems in other domains, one of them being recommender systems. In recent years, the focus of researchers has drifted towards utilizing recommender systems to improve offered selections to IoT users. Therefore, the aim of this paper is to study the present practices, approaches, and challenges in the aforementioned domain along with possible solutions. Briefly, the contributions of this paper are as follows: (1) providing an overview of the prevailing scenario in a range of application domains associated with IoT based recommender systems, (2) providing a framework of some real-world applications, (3) outlining key areas for the improvement towards context-aware personalized services in the future research, and (4) providing a bibliometric trend analysis prevalent in the concerned domain.

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Section: Iot and Recommender Systemsmentioning

confidence: 99%

RecIoT: A Deep Insight into IoT-Based Smart Recommender Systems

Sharma

Rani

Nuagah

2022

Wireless Communications and Mobile Computing

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“…In [Duran and Alvarez 2017], a method to generate recommendations of experts in the subject that a student wants to learn has been proposed. This method takes into account the experiences of these experts with other students, their availability and their physical closeness with the student for face-to-face consultations.…”

Section: Introductionmentioning

confidence: 99%

“…This model is based on a multi-agent architecture to monitor the student interaction with the system, detect learning problems and offer personalized help. To generate the recommendations, the method proposed at [Duran and Alvarez 2017] is applied.…”

Section: Introductionmentioning

confidence: 99%

Multi-agent system model for tutor recommendation in ubiquitous learning environments

Reuter¹,

Álvarez²,

Gonzalez³

et al. 2018

Anais Do I Workshop on Advanced Virtual Environments and Education (WAVE 2018)

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Ubiquitous learning environments allow to learn anywhere at anytime, enabling people to have better learning experiences in their daily lives. In order to detect learning problems and offer help, a tutor needs to observe the actions of students and to evaluate them, which is not easy to accomplish in a ubiquitous environment. Therefore, this work presents a multi-agent model to generate recommendations of tutors in the topic that a student needs help with, based on the experiences of these tutors with other students, their availability and their physical proximity. The proposed model allows to monitor the student interaction within the learning environment, detect learning problems and offer personalized help.

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