Evaluation and selection of group recommendation strategies for collaborative searching of learning objects

Zapata, Alfredo; Domínguez, Víctor Hugo Menéndez; Prieto, Manuel E.; Romero, Cristóbal

doi:10.1016/j.ijhcs.2014.12.002

Cited by 54 publications

(46 citation statements)

References 39 publications

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“…2015 [45] General development approach for the selection of LOs: application of collaborative searching to assist users in the search for LOs in repositories. [48] General development approach for selection of LOs: application of collaborative filtering for "recommender systems" to predict the utility items and LOs for users based on their preferences.…”

Section: Theory Of Instructional Design Of Losmentioning

confidence: 99%

MODELI: An Emotion-Based Software Engineering Methodology for the Development of Digital Learning Objects for the Preservation of the Mixtec Language

Allende-Hernández

Caballero‐Morales

2015

Sustainability

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In this paper, a methodology termed MODELI (methodology for the design of educational digital objects for indigenous languages) is presented for the development of digital learning objects (DLOs) for the Mixtec language, which is an indigenous Mexican language. MODELI is based on the spiral model of software development and integrates three important aspects for the analysis and design of DLOs: pedagogical, affective-emotional and technological-functional. The premise of MODELI is that the emotional aspect with the inclusion of cultural factors has an important effect on the learning motivation of indigenous users when interacting with the DLO. Principles of the visual, auditory (or aural), read/write, kinesthetic (VARK) model and Kansei engineering were considered for the inclusion of the pedagogical, emotional and technological-functional aspects within the spiral model for the development of MODELI. The methodology was validated with the development of a DLO for a previously unknown variant of the Mixtec language. Usability tests of the DLO built with MODELI evidenced an improvement on the learning motivation and the value of cultural identity of indigenous children. These results are important for the preservation of indigenous languages in Mexico, because most of them are partially documented, and there is social rejection of indigenous culture caused by discrimination of ethnic communities.

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Section: Theory Of Instructional Design Of Losmentioning

confidence: 99%

MODELI: An Emotion-Based Software Engineering Methodology for the Development of Digital Learning Objects for the Preservation of the Mixtec Language

Allende-Hernández

Caballero‐Morales

2015

Sustainability

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“…In this case, grouping is a smart choice considering that students in the same group may have similar characteristics, interests, preferences, or abilities, while the ones in different groups may not have much in common. Besides, grouping makes special sense to collaborative learning space [5], along with recommendations [6] for future research.…”

Section: Introductionmentioning

confidence: 99%

A behavioral sequence analyzing framework for grouping students in an e-learning system

Xie

Zheng

Zhang

2016

Knowledge-Based Systems

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Grouping of students benefits the formation of virtual learning communities, and contributes to collaborative learning space and recommendation. However, the existed grouping criteria are mainly limited in the learning portfolios, profiles, and social attributes etc. In this paper, we aim to build a unified framework for grouping students based on the behavioral sequences and further predicting which group a newcomer will be. The sequences are represented as a series of behavioral trajectories. We discuss a shape descriptor to approximately express the geometrical information of trajectories, and then capture the structural, micro, and hybrid similarities. A weighted undirected graph, using the sequence as a node, the relation as an edge, and the similarity as the weight, is constructed, on which we perform an extended spectral clustering algorithm to find fair groups. In the phase of prediction, an indexing and retrieval scheme is proposed to assign a newcomer to the corresponding group. We conduct some preliminary experiments on a real dataset to test the availability of the framework and to determine the parameterized conditions for an optimal grouping. Additionally, we also experiment on the grouping prediction with a synthetic data generator. Our proposed method outperforms the counterparts and makes grouping more meaningful.

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“…An example could be the application of meta-learning techniques, such as the multi-label classification proposed in [45] or the group recommendation strategy explained in [46], to be used in helping instructors to select on-the-fly the best classification algorithms to analyze particular novel and unseen students' data subsets, by means of proper recommendations. This is especially important in the educational scenario, since common users may not be familiar with data mining and machine learning techniques, and the usage of opportune techniques, especially if comprehensible and interpretable, such as decision trees and rule-based algorithms, can lead to better knowledge of the underlying educational phenomena with respect to black-box models.…”

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confidence: 99%

A Virtual Learning Architecture Enhanced by Fog Computing and Big Data Streams

Pecori

2018

Future Internet

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Abstract:In recent years, virtual learning environments are gaining more and more momentum, considering both the technologies deployed in their support and the sheer number of terminals directly or indirectly interacting with them. This essentially means that every day, more and more smart devices play an active role in this exemplary Web of Things scenario. This digital revolution, affecting education, appears clearly intertwined with the earliest forecasts of the Internet of Things, envisioning around 50 billions heterogeneous devices and gadgets to be active by 2020, considering also the deployment of the fog computing paradigm, which moves part of the computational power to the edge of the network. Moreover, these interconnected objects are expected to produce more and more significant streams of data, themselves generated at unprecedented rates, sometimes to be analyzed almost in real time. Concerning educational environments, this translates to a new type of big data stream, which can be labeled as educational big data streams. Here, pieces of information coming from different sources (such as communications between students and instructors, as well as students' tests, etc.) require accurate analysis and mining techniques in order to retrieve fruitful and well-timed insights from them. This article presents an overview of the current state of the art of virtual learning environments and their limitations; then, it explains the main ideas behind the paradigms of big data streams and of fog computing, in order to introduce an e-learning architecture integrating both of them. Such an action aims to enhance the ability of virtual learning environments to be closer to the needs of all the actors in an educational scenario, as demonstrated by a preliminary implementation of the envisioned architecture. We believe that the proposed big stream and fog-based educational framework may pave the way towards a better understanding of students' educational behaviors and foster new research directions in the field.

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Evaluation and selection of group recommendation strategies for collaborative searching of learning objects

Cited by 54 publications

References 39 publications

MODELI: An Emotion-Based Software Engineering Methodology for the Development of Digital Learning Objects for the Preservation of the Mixtec Language

MODELI: An Emotion-Based Software Engineering Methodology for the Development of Digital Learning Objects for the Preservation of the Mixtec Language

A behavioral sequence analyzing framework for grouping students in an e-learning system

A Virtual Learning Architecture Enhanced by Fog Computing and Big Data Streams

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