Macro data for micro learning: Developing the FUN! Tool for automated assessment of learning. Martin, T., Brasiel, S., Jeong, S., Close, K., Lawanto, K., & Janisciewcz, P. In L@S 2016 - Proceedings of the 3rd 2016 ACM Conference on Learning at Scale, pages 233-236, 4, 2016. Association for Computing Machinery, Inc. abstract bibtex Digital learning environments are becoming more common for students to engage in during and outside of school. With the immense amount of data now available from these environments, researchers need tools to process, manage, and analyze the data. Current methods used by many education researchers are inefficient; however, without data science experience tools used in other professions are not accessible. In this paper, we share about a tool we created called the Functional Understanding Navigator! (FUN! Tool). We have used this tool for different research projects which has allowed us the opportunity to (1) organize our workflow process from start to finish, (2) record log data of all of our analyses, and (3) provide a platform to share our analyses with others through GitHub. This paper extends and improves existing work in educational data mining and learning analytics.
@inProceedings{
title = {Macro data for micro learning: Developing the FUN! Tool for automated assessment of learning},
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abstract = {Digital learning environments are becoming more common for students to engage in during and outside of school. With the immense amount of data now available from these environments, researchers need tools to process, manage, and analyze the data. Current methods used by many education researchers are inefficient; however, without data science experience tools used in other professions are not accessible. In this paper, we share about a tool we created called the Functional Understanding Navigator! (FUN! Tool). We have used this tool for different research projects which has allowed us the opportunity to (1) organize our workflow process from start to finish, (2) record log data of all of our analyses, and (3) provide a platform to share our analyses with others through GitHub. This paper extends and improves existing work in educational data mining and learning analytics.},
bibtype = {inProceedings},
author = {Martin, Taylor and Brasiel, Sarah and Jeong, Soojeong and Close, Kevin and Lawanto, Kevin and Janisciewcz, Phil},
booktitle = {L@S 2016 - Proceedings of the 3rd 2016 ACM Conference on Learning at Scale}
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