Alignment of Standards using WordNet for Assessing K-12 Engineering Practices in a Participatory Learning Environment

DOI

10.2991/icat2e-17.2016.17How to use a DOI?

Keywords

K-12 educational assessment, Next Generation Science Standards, Science and Engineering practices, participatory learning, machine learning, latent semantic analysis, WordNet.

Abstract

Student assessment based on advanced computational methods will play a critical role in 21st century educational practices. In this paper we describe the challenge of automating the instructional assessment of student discourse based on national standards, in the context of an experimental participatory learning platform. Latent Semantic Analysis, machine learning, data mining and natural language processing techniques were used in conjunction with WordNet to create a classification scheme for engineering standards based on the Science and Engineering Practices in the U.S. Next Generation Science Standards. The scheme was applied to interactive student presentations, to assess and report on students' engineering and domain learning.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Sam Shuster
AU  - Erin Shaw
PY  - 2017/03
DA  - 2017/03
TI  - Alignment of Standards using WordNet for Assessing K-12 Engineering Practices in a Participatory Learning Environment
BT  - Proceedings of The 2017 International Conference on Advanced Technologies Enhancing Education (ICAT2E 2017)
PB  - Atlantis Press
SP  - 68
EP  - 72
SN  - 2352-5398
UR  - https://doi.org/10.2991/icat2e-17.2016.17
DO  - 10.2991/icat2e-17.2016.17
ID  - Shuster2017/03
ER  -