Categorising student responses for feedback based on multiple choice and text responses

Samuel Cunningham-Nelson; Andrea Goncher; Wageeh Boles

doi:10.1109/TALE.2018.8615325

Categorising student responses for feedback based on multiple choice and text responses

Samuel Cunningham-Nelson, Andrea Goncher, Wageeh Boles

CSU Engineering

Research output: Book chapter/Published conference paper › Conference paper › peer-review

Abstract

Being able to provide fast and useful feedback to students is an essential part of the learning process. Multiple-choice questionnaires are tool often used, due to the ease of marking, and therefore rapid feedback to students. Since these however lack detail and confidence when checking understanding, an added textual component can help to verify students' understanding. In this paper we discuss a methodology for evaluating and categorizing students multiple-choice and text justification. This is applied to three examples of students comments and shows the usefulness of the added textual field.

Original language	English
Title of host publication	Proceedings of 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018
Editors	Mark J.W. Lee, Sasha Nikolic, Gary K.W. Wong, Jun Shen, Montserrat Ros, Leon C. U. Lei, Neelakantam Venkatarayalu
Place of Publication	United States
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	1179-1182
Number of pages	4
ISBN (Electronic)	9781538665220
DOIs	https://doi.org/10.1109/TALE.2018.8615325
Publication status	Published - 16 Jan 2019
Event	2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018 - Novotel Northbeach Wollongong, Wollongong, Australia Duration: 04 Dec 2018 → 07 Dec 2018 https://www.tale2018.org/ (conference website) https://1775b349-31b3-42ae-85cf-8678bf2f3e88.filesusr.com/ugd/fc5d2b_662d818506f94c73958122d27caf8104.pdf (program)

Publication series

Name	Proceedings of 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018

Conference

Conference	2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018
Abbreviated title	Engineering next-generation learning
Country	Australia
City	Wollongong
Period	04/12/18 → 07/12/18
Internet address	https://www.tale2018.org/ (conference website) https://1775b349-31b3-42ae-85cf-8678bf2f3e88.filesusr.com/ugd/fc5d2b_662d818506f94c73958122d27caf8104.pdf (program)

Access to Document

10.1109/TALE.2018.8615325

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Cunningham-Nelson, S., Goncher, A., & Boles, W. (2019). Categorising student responses for feedback based on multiple choice and text responses. In M. J. W. Lee, S. Nikolic, G. K. W. Wong, J. Shen, M. Ros, L. C. U. Lei, & N. Venkatarayalu (Eds.), Proceedings of 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018 (pp. 1179-1182). [8615325] (Proceedings of 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/TALE.2018.8615325

Cunningham-Nelson, Samuel ; Goncher, Andrea ; Boles, Wageeh. / Categorising student responses for feedback based on multiple choice and text responses. Proceedings of 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018. editor / Mark J.W. Lee ; Sasha Nikolic ; Gary K.W. Wong ; Jun Shen ; Montserrat Ros ; Leon C. U. Lei ; Neelakantam Venkatarayalu. United States : IEEE, Institute of Electrical and Electronics Engineers, 2019. pp. 1179-1182 (Proceedings of 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018).

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abstract = "Being able to provide fast and useful feedback to students is an essential part of the learning process. Multiple-choice questionnaires are tool often used, due to the ease of marking, and therefore rapid feedback to students. Since these however lack detail and confidence when checking understanding, an added textual component can help to verify students' understanding. In this paper we discuss a methodology for evaluating and categorizing students multiple-choice and text justification. This is applied to three examples of students comments and shows the usefulness of the added textual field.",

keywords = "Conceptual Understanding, Machine Learning, Text Analysis",

author = "Samuel Cunningham-Nelson and Andrea Goncher and Wageeh Boles",

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booktitle = "Proceedings of 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018",

address = "United States",

note = "2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018, Engineering next-generation learning ; Conference date: 04-12-2018 Through 07-12-2018",

url = "https://www.tale2018.org/, https://1775b349-31b3-42ae-85cf-8678bf2f3e88.filesusr.com/ugd/fc5d2b_662d818506f94c73958122d27caf8104.pdf",

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Cunningham-Nelson, S, Goncher, A & Boles, W 2019, Categorising student responses for feedback based on multiple choice and text responses. in MJW Lee, S Nikolic, GKW Wong, J Shen, M Ros, LCU Lei & N Venkatarayalu (eds), Proceedings of 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018., 8615325, Proceedings of 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018, IEEE, Institute of Electrical and Electronics Engineers, United States, pp. 1179-1182, 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018, Wollongong, Australia, 04/12/18. https://doi.org/10.1109/TALE.2018.8615325

Categorising student responses for feedback based on multiple choice and text responses. / Cunningham-Nelson, Samuel; Goncher, Andrea; Boles, Wageeh.

Proceedings of 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018. ed. / Mark J.W. Lee; Sasha Nikolic; Gary K.W. Wong; Jun Shen; Montserrat Ros; Leon C. U. Lei; Neelakantam Venkatarayalu. United States : IEEE, Institute of Electrical and Electronics Engineers, 2019. p. 1179-1182 8615325 (Proceedings of 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018).

Research output: Book chapter/Published conference paper › Conference paper › peer-review

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T1 - Categorising student responses for feedback based on multiple choice and text responses

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AU - Goncher, Andrea

AU - Boles, Wageeh

PY - 2019/1/16

Y1 - 2019/1/16

N2 - Being able to provide fast and useful feedback to students is an essential part of the learning process. Multiple-choice questionnaires are tool often used, due to the ease of marking, and therefore rapid feedback to students. Since these however lack detail and confidence when checking understanding, an added textual component can help to verify students' understanding. In this paper we discuss a methodology for evaluating and categorizing students multiple-choice and text justification. This is applied to three examples of students comments and shows the usefulness of the added textual field.

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BT - Proceedings of 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018

A2 - Lee, Mark J.W.

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PB - IEEE, Institute of Electrical and Electronics Engineers

CY - United States

T2 - 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018

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ER -

Cunningham-Nelson S, Goncher A, Boles W. Categorising student responses for feedback based on multiple choice and text responses. In Lee MJW, Nikolic S, Wong GKW, Shen J, Ros M, Lei LCU, Venkatarayalu N, editors, Proceedings of 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018. United States: IEEE, Institute of Electrical and Electronics Engineers. 2019. p. 1179-1182. 8615325. (Proceedings of 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2018). https://doi.org/10.1109/TALE.2018.8615325

Categorising student responses for feedback based on multiple choice and text responses

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