research-article

Using Visualizations to Improve Assessment in Blended Learning Environments

Authors:

Mikel Villamañe,

Ainhoa Álvarez,

Mikel Larrañaga,

Oscar Hernández-Rivas,

Jessica CaballeroAuthors Info & Claims

TEEM'18: Proceedings of the Sixth International Conference on Technological Ecosystems for Enhancing Multiculturality

Pages 165 - 169

https://doi.org/10.1145/3284179.3284209

Published: 24 October 2018 Publication History

Abstract

A good assessment process entails analyzing students' results in order to detect anomalies and be able to provide adequate feedback to solve them. The analysis of the results becomes harder when the amount of information and its heterogeneity increases. Current education tendencies (for example blended learning), face this kind of problem as they have to deal with a lot of information from many different sources. In this paper, we present a system that helps lecturers to integrate information from different sources and to perform an analysis of the data through the use of visual learning analytics techniques. The acceptance of the system has been satisfactorily evaluated.

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Digital Library

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Cited By

Afzaal MNouri JZia APapapetrou PFors UWu YLi XWeegar R(2021)Explainable AI for Data-Driven Feedback and Intelligent Action Recommendations to Support Students Self-RegulationFrontiers in Artificial Intelligence10.3389/frai.2021.7234474Online publication date: 12-Nov-2021
https://doi.org/10.3389/frai.2021.723447

Index Terms

Using Visualizations to Improve Assessment in Blended Learning Environments
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Visual analytics
2. Social and professional topics
  1. Professional topics
    1. Computing education
      1. Student assessment

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TEEM'18: Proceedings of the Sixth International Conference on Technological Ecosystems for Enhancing Multiculturality

October 2018

1072 pages

ISBN:9781450365185

DOI:10.1145/3284179

Editor:
Francisco José García-Peñalvo
University of Salamanca

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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University of Salamanca: University of Salamanca

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 October 2018

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Research-article
Research
Refereed limited

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Euskal Herriko Unibertsitatea
Vice-Chancellor for Innovation, Social Engagement and Cultural Action of the UPV/EHU
Eusko Jaurlaritza

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TEEM'18

TEEM'18: Sixth International Conference on Technological Ecosystems for Enhancing Multiculturality

October 24 - 26, 2018

Salamanca, Spain

Acceptance Rates

TEEM'18 Paper Acceptance Rate 151 of 243 submissions, 62%;

Overall Acceptance Rate 496 of 705 submissions, 70%

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Cited By

Afzaal MNouri JZia APapapetrou PFors UWu YLi XWeegar R(2021)Explainable AI for Data-Driven Feedback and Intelligent Action Recommendations to Support Students Self-RegulationFrontiers in Artificial Intelligence10.3389/frai.2021.7234474Online publication date: 12-Nov-2021
https://doi.org/10.3389/frai.2021.723447

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