TY - GEN
T1 - Free response evaluation via neural network for an IMathAS system
AU - Wiggins, Nathanial
AU - Smith, Milton
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - A fully interactive class with mixed reality and simulation learning should provide many free response types for students to learn beyond numerical answers and multiple choice. Essay and string responses in the IMathAS homework system have to be manually graded, making the free response questions difficult to generate instant feedback. The ability to write questions with automatic feedback during active lecture offer improvements to the current systems and provide an opportunity for critical thinking to occur. The following study provides framework for an interpretive neural network to be implemented into any IMathAS system. These responses can be in the form of equations, words and sentences, or pictures. Findings show that correctly trained networks using manually graded artifacts can be more than 90% accurate in providing feedback to a correct answer in student practice, allowing for lessons that guide students towards correct and well-phrased answers using their own words, and can even assign partial credit. The findings imply that Marzano's taxonomy level of analysis can be reached using the IMathAS system and that critical thinking methods can be directly applied for scoring. When integrated into the existing system, simulation-based or mixed reality homework can have free responses and the grades can be transferred via learning tool interoperability connection into the institutional learning management system for direct scoring in the gradebook.
AB - A fully interactive class with mixed reality and simulation learning should provide many free response types for students to learn beyond numerical answers and multiple choice. Essay and string responses in the IMathAS homework system have to be manually graded, making the free response questions difficult to generate instant feedback. The ability to write questions with automatic feedback during active lecture offer improvements to the current systems and provide an opportunity for critical thinking to occur. The following study provides framework for an interpretive neural network to be implemented into any IMathAS system. These responses can be in the form of equations, words and sentences, or pictures. Findings show that correctly trained networks using manually graded artifacts can be more than 90% accurate in providing feedback to a correct answer in student practice, allowing for lessons that guide students towards correct and well-phrased answers using their own words, and can even assign partial credit. The findings imply that Marzano's taxonomy level of analysis can be reached using the IMathAS system and that critical thinking methods can be directly applied for scoring. When integrated into the existing system, simulation-based or mixed reality homework can have free responses and the grades can be transferred via learning tool interoperability connection into the institutional learning management system for direct scoring in the gradebook.
KW - critical thinking
KW - mixed reality education application
KW - neural networks
KW - undergraduate education
UR - http://www.scopus.com/inward/record.url?scp=85078844214&partnerID=8YFLogxK
U2 - 10.1109/ISMCR47492.2019.8955695
DO - 10.1109/ISMCR47492.2019.8955695
M3 - Conference contribution
AN - SCOPUS:85078844214
T3 - 2019 22nd IEEE International Symposium on Measurement and Control in Robotics: Robotics for the Benefit of Humanity, ISMCR 2019
BT - 2019 22nd IEEE International Symposium on Measurement and Control in Robotics
A2 - Harman, Thomas L.
A2 - Taqvi, Zafar
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd IEEE International Symposium on Measurement and Control in Robotics, ISMCR 2019
Y2 - 19 September 2019 through 21 September 2019
ER -