2014
Jerez, Alex Rayón; Guenaga, Mariluz; Núñez, Asier
Supporting competency-assessment through a learning analytics approach using enriched rubrics Conference
2014, ISBN: 978-1-4503-2896-8.
Abstract | Links | BibTeX | Tags: data integration, Education, large-scale interoperability, learning analytics, learning dashboard
@conference{Jerez2014,
title = {Supporting competency-assessment through a learning analytics approach using enriched rubrics},
author = {Alex Rayón Jerez and Mariluz Guenaga and Asier Núñez},
doi = {10.1145/2669711.2669913},
isbn = {978-1-4503-2896-8},
year = {2014},
date = {2014-10-03},
pages = {291-298},
abstract = {Universities have increasingly emphasized competencies as central elements of students' development. However, the assessment of these competencies is not an easy task. The availability of data that learners generate in computer mediated learning offers great potential to study how learning takes place, and thus, to gather evidences for competency-assessment using enriched rubrics. The lack of data interoperability and the decentralization of those educational applications set out a challenge to exploit trace data. To face these problems we have designed and developed SCALA (Scalable Competence Assessment through a Learning Analytics approach), an analytics system that integrates usage -how the user interacts with resources-and social -how students and teachers interact among them-trace data to support competency assessment. The case study of SCALA presents teachers a dashboard with enriched rubrics of blended datasets obtained from six assessment learning activities, performed with a group of 28 students working teamwork competency. In terms of knowledge discovery, we obtain results applying clustering and association rule mining algorithms. Thus, we provide a visual analytics tool ready to support competency-assessment.
Supporting Competency-Assessment through a Learning Analytics Approach Using Enriched Rubrics (PDF Download Available). Available from: https://home/learninglabdeust/public_html.researchgate.net/publication/267266928_Supporting_Competency-Assessment_through_a_Learning_Analytics_Approach_Using_Enriched_Rubrics [accessed Mar 08 2018].},
keywords = {data integration, Education, large-scale interoperability, learning analytics, learning dashboard},
pubstate = {published},
tppubtype = {conference}
}
Universities have increasingly emphasized competencies as central elements of students' development. However, the assessment of these competencies is not an easy task. The availability of data that learners generate in computer mediated learning offers great potential to study how learning takes place, and thus, to gather evidences for competency-assessment using enriched rubrics. The lack of data interoperability and the decentralization of those educational applications set out a challenge to exploit trace data. To face these problems we have designed and developed SCALA (Scalable Competence Assessment through a Learning Analytics approach), an analytics system that integrates usage -how the user interacts with resources-and social -how students and teachers interact among them-trace data to support competency assessment. The case study of SCALA presents teachers a dashboard with enriched rubrics of blended datasets obtained from six assessment learning activities, performed with a group of 28 students working teamwork competency. In terms of knowledge discovery, we obtain results applying clustering and association rule mining algorithms. Thus, we provide a visual analytics tool ready to support competency-assessment.
Supporting Competency-Assessment through a Learning Analytics Approach Using Enriched Rubrics (PDF Download Available). Available from: https://home/learninglabdeust/public_html.researchgate.net/publication/267266928_Supporting_Competency-Assessment_through_a_Learning_Analytics_Approach_Using_Enriched_Rubrics [accessed Mar 08 2018].
Supporting Competency-Assessment through a Learning Analytics Approach Using Enriched Rubrics (PDF Download Available). Available from: https://home/learninglabdeust/public_html.researchgate.net/publication/267266928_Supporting_Competency-Assessment_through_a_Learning_Analytics_Approach_Using_Enriched_Rubrics [accessed Mar 08 2018].
2009
Jerez, Alex Rayón; Guenaga, Mariluz; Núñez, Asier
Integrating and visualizing learner and social data to elicit higher-order indicators in SCALA dashboard Journal Article
In: i-KNOW '14 Proceedings of the 14th International Conference on Knowledge Technologies and Data-driven Business, no. 28, 2009, ISBN: 978-1-4503-2769-5.
Abstract | Links | BibTeX | Tags: computer-assisted instruction, dashboard, data integration, information retrieval, large-scale interoperability, learning analytics, visual analytics
@article{Jerez2009,
title = {Integrating and visualizing learner and social data to elicit higher-order indicators in SCALA dashboard},
author = {Alex Rayón Jerez and Mariluz Guenaga and Asier Núñez},
editor = {ACM New York, NY, USA ©2014},
doi = {10.1145/2637748.2638435},
isbn = { 978-1-4503-2769-5},
year = {2009},
date = {2009-09-16},
journal = {i-KNOW '14 Proceedings of the 14th International Conference on Knowledge Technologies and Data-driven Business},
number = {28},
abstract = {The assessment of competencies is a difficult task; on one hand due to its subjective nature, and, on the other one, because of the difficulties to make it scalable and simple. Since ICT are becoming increasingly important learning mediating tools, data stored in learning tools could yield a wealth of information that could serve as an indicator to measure students' progress and the development of competencies. However, the lack of data interoperability among different educational applications imposes a challenge to data mining and analytics that rely on diverse and distributed data. Besides, these educational technologies do neither usually provide a statistics module in which the teacher can obtain specific reports about students' performance, nor visualization tools to summarize student usage data. In response to this weakness, and based on the limitations encountered in existing tools, we have developed an integrated and extensible web tool called SCALA (Scalable Competency Assessment through a Learning Analytics approach) that not only shows but also mines using analytics techniques for the discovery of student patterns and metric relations in web-based educational systems.},
keywords = {computer-assisted instruction, dashboard, data integration, information retrieval, large-scale interoperability, learning analytics, visual analytics},
pubstate = {published},
tppubtype = {article}
}
The assessment of competencies is a difficult task; on one hand due to its subjective nature, and, on the other one, because of the difficulties to make it scalable and simple. Since ICT are becoming increasingly important learning mediating tools, data stored in learning tools could yield a wealth of information that could serve as an indicator to measure students' progress and the development of competencies. However, the lack of data interoperability among different educational applications imposes a challenge to data mining and analytics that rely on diverse and distributed data. Besides, these educational technologies do neither usually provide a statistics module in which the teacher can obtain specific reports about students' performance, nor visualization tools to summarize student usage data. In response to this weakness, and based on the limitations encountered in existing tools, we have developed an integrated and extensible web tool called SCALA (Scalable Competency Assessment through a Learning Analytics approach) that not only shows but also mines using analytics techniques for the discovery of student patterns and metric relations in web-based educational systems.