Specification of the Autonomic Cycles of Learning Analytic Tasks for a Smart Classroom

Authors: Jose Aguilar, Jorge Cordero and Omar Buendía

Abstract:
In this article, we propose the concept of ‘‘Autonomic Cycle Of Learning Analysis Tasks’’ (ACOLAT), which defines a set of tasks of learning analysis, whose objective is to improve the learning process. The data analysis has become a fundamental area for the knowledge discovery from data extracted from different sources. In the autonomic cycle, each learning analysis task interacts with each other and has different roles: Some of them must observe the learning process, others must analyze and interpret what happens in it, and finally, others make decisions in order to improve the learning process. In this article, we study the application of the autonomic cycle in a smart classroom, which is composed of a set of intelligent components of hardware (e.g., smart board) and software (e.g., virtual learning environments), which must exploit the knowledge generated by the ACOLAT to improve the learning process in the smart classroom. Moreover, we present the set of ACOLATs present in a smart classroom and the implementation of some of them.

Keywords: Learning Analytics, Smart Classroom, Autonomic Computing, Learning Environments, Knowledge Discovery

Link: http://journals.sagepub.com/doi/abs/10.1177/0735633117727698

Competences as Services in the Autonomic Cycles of Learning Analytic Tasks for a Smart Classroom

Authors: Alexandra González-Eras, Omar Buendia, Jose Aguilar, Jorge Cordero and Taniana Rodriguez

Abstract:
Learning Analytic is a useful tool in the context of the learning process, in order to improve the educational environment. In previous works, we have proposed autonomic cycles of learning Analytic tasks, in order to improve the learning process in smart classrooms. One aspect to be considered by the autonomic cycles is their adaptability to the formation of competences, assuming that a student has competences that must be strengthened during the learning process. In this paper, we propose the utilization of competences to guide the adaptation process of a learning environment. Particularly, we propose the extensions of the autonomic cycles for smart classrooms, using the idea of competences. In this case, we define the competences as a service, to help the autonomic cycles in their processes of adaptation.

Keywords: Learning analytics, Smart classroom, Educational competences, Autonomic cycles

Link: https://link.springer.com/chapter/10.1007/978-3-319-67283-0_16

A general framework for learning analytic in a smart classroom

Authors: Jose Aguilar, Priscila Valdiviezo, Jorge Cordero, Guido Riofrio, and Eduardo Encalada

Abstract. In this paper, we propose the utilization of the “Learning Analytics” paradigm in a Smart Classroom, a classroom that integrates artificial intelligence technology on the educational process. Learning Analytics can extract knowledge from the Smart Classroom platform, to better understand students and his/her learning processes. In this way, a Smart Classroom can understand and optimize
the learning process and the teaching environments proposed. The smart classroom can adapt its components to improve students’ performance, among other aspects. Particularly, this paper proposes a framework about how the Learning Analytics paradigm can be used in a Smart Classroom, in order to provide knowledge about the activities taking place within it. The framework is defined like a closed cycle of Learning Analytics tasks, which generate metrics used like feedback to optimize the pedagogical model proposed by the smart Classroom. The metrics evaluate the learning process and pedagogical practice provided by the smart Classroom. So, our main contribution is about how the Learning Analytics paradigm can be used in a Smart Classroom in order to improve the students’
performance.

Keywords: Learning analytics, Smart classroom,  Ambient intelligence, Data mining

Link: http://link.springer.com/chapter/10.1007/978-3-319-48024-4_17

DOI: 10.1007/978-3-319-48024-4_17

Cloud Computing in Smart Educational Environments: Application in Learning Analytics as Service

Authors: Manuel Sánchez, Jose Aguilar, Jorge Cordero, Priscila Valdiviezo-Díaz, Luis
Barba-Guamán, Luis Chamba-Eras

Abstract
In this paper, we present an extension of a Middleware for Smart Educational Environments based in agents, using the paradigm of Cloud Computing. In that sense, we detail the Middleware components, which enable the process of management of the Cloud Computing. We also present the utilization of this Middleware to provide services on the cloud about task of Learning Analytics that allow processing of data of students and learning environments, to understand and optimize the learning processes.

Keywords
Cloud computing, Smart educational environment, Learning analytics

Link: http://link.springer.com/chapter/10.1007/978-3-319-31232-3_94

DOI: 10.1007/978-3-319-31232-3_94

A business intelligence model for online tutoring process

Authors: Priscila Valdiviezo-Díaz, Jorge Cordero, Ruth Reátegui, Jose Aguilar

Abstract:
This work aims to implement business intelligence strategies in an educational institution based on the distance education, particularly in the online tutoring process. In this paper we propose to use the business intelligence paradigm to analyze the online tutoring process, based on the data collected on the interactions of students and teachers in a virtual learning environment, and the results recorded in the institutional academic system of evaluations. This analysis should answer the following questions: 1) Can we define a model of online tutoring that can adapt to each student profile? 2) Can we predict the success of an online tutoring process for a course and a student given? To this purpose, this paper presents three aspects: characterize and determine the key elements in an online tutoring process, build a descriptive model of the online tutoring process, and build a predictive model of the success of the online tutoring process. The models to be defined will be based on data mining techniques, and will be obtained from the current data stored in transactional databases of the University. This data is preprocessed with ETL techniques to build a multidimensional model, and the key elements are obtained through operations OLAP.

Keywords—Business Intelligence Systems, Data Warehouses, Learning Analytics, Online Tutoring

Published in: Frontiers in Education Conference (FIE), 2015. 32614 2015. IEEE
DOI: 10.1109/FIE.2015.7344385

Link: http://ieeexplore.ieee.org/document/7344385/