2018
Menchaca, Iratxe; Guenaga, Mariluz; Solabarrieta, J.
Learning analytics for formative assessment in engineering education Journal Article
In: International Journal of Engineering Education, vol. 34, no. 3, pp. 953-967, 2018.
Abstract | Links | BibTeX | Tags: data analytics, Engineering Education, learning
@article{Menchaca2018,
title = {Learning analytics for formative assessment in engineering education},
author = {Iratxe Menchaca and Mariluz Guenaga and J. Solabarrieta},
url = {https://home/learninglabdeust/public_html.researchgate.net/publication/325580658_Learning_analytics_for_formative_assessment_in_engineering_education},
year = {2018},
date = {2018-01-01},
journal = {International Journal of Engineering Education},
volume = {34},
number = {3},
pages = {953-967},
abstract = {The development of skills in the engineering education is one of the issues that generate greater interest at present. Thanks to Learning Analytics, we found an excellent opportunity to offer a quality competence assessment of our engineering students. Research in Learning Analytics currently focuses on applying these techniques to find out how the student learns and to improve teaching/learning processes. A key aspect in improving these processes is the assessment of general competences, which constitutes key learning in engineering students and has thus been identified as a need that can be met by Learning Analytics. This article presents two related studies conducted at the University of Deusto. The first study wants to show that it is possible to carry out an assessment of the project management competence through the analysis of the data that is obtained when the students interact with certain tools for the management of projects. In this sense, in the first study conducted with 93 students in the academic year 2014–2015, it compares the automatic assessment performed with Learning Analytics and the manual assessment carried out by the teacher. Another objective of this first study is to compare the validity at the time to assess the project management competence of the three technological tools used in the study. In the second study conducted with 227 students in the academic year 2015–2016, an assessment model is designed based on analytical data that is extracted from even more complex technological tools. In this second study the objective is to demonstrate that the use of Learning Analytics assessment to carry out continuous monitoring and provide feedback to the students, directly influences their capacity to manage a project and therefore, leads to an improvement in their results. The model designed in both studies for analysis is described in this paper, in addition to the methodology and research carried out.
Learning analytics for formative assessment in engineering education | Request PDF. Available from: https://home/learninglabdeust/public_html.researchgate.net/publication/325580658_Learning_analytics_for_formative_assessment_in_engineering_education [accessed Jul 10 2018].},
keywords = {data analytics, Engineering Education, learning},
pubstate = {published},
tppubtype = {article}
}
The development of skills in the engineering education is one of the issues that generate greater interest at present. Thanks to Learning Analytics, we found an excellent opportunity to offer a quality competence assessment of our engineering students. Research in Learning Analytics currently focuses on applying these techniques to find out how the student learns and to improve teaching/learning processes. A key aspect in improving these processes is the assessment of general competences, which constitutes key learning in engineering students and has thus been identified as a need that can be met by Learning Analytics. This article presents two related studies conducted at the University of Deusto. The first study wants to show that it is possible to carry out an assessment of the project management competence through the analysis of the data that is obtained when the students interact with certain tools for the management of projects. In this sense, in the first study conducted with 93 students in the academic year 2014–2015, it compares the automatic assessment performed with Learning Analytics and the manual assessment carried out by the teacher. Another objective of this first study is to compare the validity at the time to assess the project management competence of the three technological tools used in the study. In the second study conducted with 227 students in the academic year 2015–2016, an assessment model is designed based on analytical data that is extracted from even more complex technological tools. In this second study the objective is to demonstrate that the use of Learning Analytics assessment to carry out continuous monitoring and provide feedback to the students, directly influences their capacity to manage a project and therefore, leads to an improvement in their results. The model designed in both studies for analysis is described in this paper, in addition to the methodology and research carried out.
Learning analytics for formative assessment in engineering education | Request PDF. Available from: https://home/learninglabdeust/public_html.researchgate.net/publication/325580658_Learning_analytics_for_formative_assessment_in_engineering_education [accessed Jul 10 2018].
Learning analytics for formative assessment in engineering education | Request PDF. Available from: https://home/learninglabdeust/public_html.researchgate.net/publication/325580658_Learning_analytics_for_formative_assessment_in_engineering_education [accessed Jul 10 2018].
2016
Guenaga, Mariluz; Menchaca, Iratxe; Solabarrieta, J.
Using learning analytics to assess project management skills on engineering degree courses Journal Article
In: Proceedings of the Fourth International Conference on Technological Ecosystems for Enhancing Multiculturality, pp. 369-376, 2016, ISBN: 978-1-4503-4747-1.
Abstract | Links | BibTeX | Tags: Collaborative Learning, Computational Science and Engineering Education, computing education programs, data analytics, database management system engines, decision support systems, Engineering Education, Formative Assessment, human computer interaction (hci), information systems applications, interaction paradigms
@article{Guenaga2016b,
title = {Using learning analytics to assess project management skills on engineering degree courses},
author = {Mariluz Guenaga and Iratxe Menchaca and J. Solabarrieta},
editor = {Francisco José García-Peñalvo},
url = {https://dl.acm.org/citation.cfm?doid=3012430.3012542},
doi = {10.1145/3012430.3012542},
isbn = {978-1-4503-4747-1},
year = {2016},
date = {2016-11-02},
journal = {Proceedings of the Fourth International Conference on Technological Ecosystems for Enhancing Multiculturality},
pages = {369-376},
abstract = {Learning analytics is a field of study that has been evolving since the outset in attempting to meet various needs. The use of learning analytics techniques has helped us ascertain the level of students' participation and their degree of satisfaction in order to learn how they use resources or identify students at risk. Research currently focuses on applying these techniques to find out how the student learns and to improve teaching/learning processes. A key aspect in improving these processes is the assessment of general competences, which constitutes key learning in engineering students and has thus been identified as a need that can be met by learning analytics. An experiment was conducted on 93 students from different engineering groups at the University of Deusto with a view to assessing the extent to which students have developed the project management competence, using learning analytics techniques. The model designed for analysis is described in this paper, in addition to the methodology and research carried out. Results have shown that by combining an automatic analysis and exploratory learning analytics techniques, conclusions can effectively be drawn about the extent to which a given student has developed a competence based on data obtained via use of a technological tool.},
keywords = {Collaborative Learning, Computational Science and Engineering Education, computing education programs, data analytics, database management system engines, decision support systems, Engineering Education, Formative Assessment, human computer interaction (hci), information systems applications, interaction paradigms},
pubstate = {published},
tppubtype = {article}
}
Learning analytics is a field of study that has been evolving since the outset in attempting to meet various needs. The use of learning analytics techniques has helped us ascertain the level of students' participation and their degree of satisfaction in order to learn how they use resources or identify students at risk. Research currently focuses on applying these techniques to find out how the student learns and to improve teaching/learning processes. A key aspect in improving these processes is the assessment of general competences, which constitutes key learning in engineering students and has thus been identified as a need that can be met by learning analytics. An experiment was conducted on 93 students from different engineering groups at the University of Deusto with a view to assessing the extent to which students have developed the project management competence, using learning analytics techniques. The model designed for analysis is described in this paper, in addition to the methodology and research carried out. Results have shown that by combining an automatic analysis and exploratory learning analytics techniques, conclusions can effectively be drawn about the extent to which a given student has developed a competence based on data obtained via use of a technological tool.