Identifying Critical Features for Formative Essay Feedback with Artificial Neural Networks and Backward Elimination

Mohsin Abbas; Peter van Rosmalen; Marco Kalz

doi:10.1007/978-3-030-29736-7_30

Identifying Critical Features for Formative Essay Feedback with Artificial Neural Networks and Backward Elimination

Mohsin Abbas^*, Peter van Rosmalen^*, Marco Kalz^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

Abstract

For predicting and improving the quality of essays, text analytic metrics (surface, syntactic, morphological and semantic features) can be used to provide formative feedback to the students. In this study, the intent was to find a small number of features that exhibit a fair proxy of the scores given by the human raters. Using an existing corpus and a text analysis tool for the Dutch language, a large number of features were extracted. Artificial neural networks, Levenberg Marquardt algorithm and backward elimination were used to reduce the number of extracted features automatically. Irrelevant features were eliminated based on the inter-rater agreement between predicted and human scores calculated using Cohen’s Kappa ( κ ). By using our algorithm, the number of features in this study was reduced from 457 to 23. The selected features were grouped into six different categories. Of these categories, we believe that the features present in the groups “Word Difficulty” and “Lexical Diversity” are most useful for providing automated formative feedback to the students. The approach presented in this research paper is the first step towards our ultimate goal of providing meaningful formative feedback to the students for enhancing their writing skills and capabilities.

Original language	English
Title of host publication	Transforming Learning with Meaningful Technologies, EC-TEL 2019
Editors	Maren Scheffel, Julien Broisin, Viktoria Pammer-Schindler, Andri Loannou, Jan Schneider
Publisher	Springer International Publishing
Pages	396-408
Number of pages	13
Volume	11722
Edition	1
ISBN (Electronic)	978-3-030-29736-7
ISBN (Print)	978-3-030-29735-0
DOIs	https://doi.org/10.1007/978-3-030-29736-7_30
Publication status	Published - 9 Sept 2019

Publication series

Series	Lecture Notes in Computer Science
Volume	11722

Keywords

Formative feedback
Natural Language Processing
Neural Networks
Backward Elimination
Dimensionality reduction
Feature selection
ALGORITHM

Access to Document

10.1007/978-3-030-29736-7_30Licence: CC BY

Cite this

Abbas, M., van Rosmalen, P., & Kalz, M. (2019). Identifying Critical Features for Formative Essay Feedback with Artificial Neural Networks and Backward Elimination. In M. Scheffel, J. Broisin, V. Pammer-Schindler, A. Loannou, & J. Schneider (Eds.), Transforming Learning with Meaningful Technologies, EC-TEL 2019 (1 ed., Vol. 11722, pp. 396-408). Springer International Publishing. https://doi.org/10.1007/978-3-030-29736-7_30

Abbas, Mohsin ; van Rosmalen, Peter ; Kalz, Marco. / Identifying Critical Features for Formative Essay Feedback with Artificial Neural Networks and Backward Elimination. Transforming Learning with Meaningful Technologies, EC-TEL 2019. editor / Maren Scheffel ; Julien Broisin ; Viktoria Pammer-Schindler ; Andri Loannou ; Jan Schneider. Vol. 11722 1. ed. Springer International Publishing, 2019. pp. 396-408 (Lecture Notes in Computer Science, Vol. 11722).

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title = "Identifying Critical Features for Formative Essay Feedback with Artificial Neural Networks and Backward Elimination",

abstract = "For predicting and improving the quality of essays, text analytic metrics (surface, syntactic, morphological and semantic features) can be used to provide formative feedback to the students. In this study, the intent was to find a small number of features that exhibit a fair proxy of the scores given by the human raters. Using an existing corpus and a text analysis tool for the Dutch language, a large number of features were extracted. Artificial neural networks, Levenberg Marquardt algorithm and backward elimination were used to reduce the number of extracted features automatically. Irrelevant features were eliminated based on the inter-rater agreement between predicted and human scores calculated using Cohen{\textquoteright}s Kappa ( κ ). By using our algorithm, the number of features in this study was reduced from 457 to 23. The selected features were grouped into six different categories. Of these categories, we believe that the features present in the groups “Word Difficulty” and “Lexical Diversity” are most useful for providing automated formative feedback to the students. The approach presented in this research paper is the first step towards our ultimate goal of providing meaningful formative feedback to the students for enhancing their writing skills and capabilities.",

keywords = "Formative feedback, Natural Language Processing, Neural Networks, Backward Elimination, Dimensionality reduction, Feature selection, ALGORITHM",

author = "Mohsin Abbas and {van Rosmalen}, Peter and Marco Kalz",

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year = "2019",

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Abbas, M, van Rosmalen, P & Kalz, M 2019, Identifying Critical Features for Formative Essay Feedback with Artificial Neural Networks and Backward Elimination. in M Scheffel, J Broisin, V Pammer-Schindler, A Loannou & J Schneider (eds), Transforming Learning with Meaningful Technologies, EC-TEL 2019. 1 edn, vol. 11722, Springer International Publishing, Lecture Notes in Computer Science, vol. 11722, pp. 396-408. https://doi.org/10.1007/978-3-030-29736-7_30

Identifying Critical Features for Formative Essay Feedback with Artificial Neural Networks and Backward Elimination. / Abbas, Mohsin; van Rosmalen, Peter; Kalz, Marco.
Transforming Learning with Meaningful Technologies, EC-TEL 2019. ed. / Maren Scheffel; Julien Broisin; Viktoria Pammer-Schindler; Andri Loannou; Jan Schneider. Vol. 11722 1. ed. Springer International Publishing, 2019. p. 396-408 (Lecture Notes in Computer Science, Vol. 11722).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review

TY - GEN

T1 - Identifying Critical Features for Formative Essay Feedback with Artificial Neural Networks and Backward Elimination

AU - Abbas, Mohsin

AU - van Rosmalen, Peter

AU - Kalz, Marco

PY - 2019/9/9

Y1 - 2019/9/9

N2 - For predicting and improving the quality of essays, text analytic metrics (surface, syntactic, morphological and semantic features) can be used to provide formative feedback to the students. In this study, the intent was to find a small number of features that exhibit a fair proxy of the scores given by the human raters. Using an existing corpus and a text analysis tool for the Dutch language, a large number of features were extracted. Artificial neural networks, Levenberg Marquardt algorithm and backward elimination were used to reduce the number of extracted features automatically. Irrelevant features were eliminated based on the inter-rater agreement between predicted and human scores calculated using Cohen’s Kappa ( κ ). By using our algorithm, the number of features in this study was reduced from 457 to 23. The selected features were grouped into six different categories. Of these categories, we believe that the features present in the groups “Word Difficulty” and “Lexical Diversity” are most useful for providing automated formative feedback to the students. The approach presented in this research paper is the first step towards our ultimate goal of providing meaningful formative feedback to the students for enhancing their writing skills and capabilities.

AB - For predicting and improving the quality of essays, text analytic metrics (surface, syntactic, morphological and semantic features) can be used to provide formative feedback to the students. In this study, the intent was to find a small number of features that exhibit a fair proxy of the scores given by the human raters. Using an existing corpus and a text analysis tool for the Dutch language, a large number of features were extracted. Artificial neural networks, Levenberg Marquardt algorithm and backward elimination were used to reduce the number of extracted features automatically. Irrelevant features were eliminated based on the inter-rater agreement between predicted and human scores calculated using Cohen’s Kappa ( κ ). By using our algorithm, the number of features in this study was reduced from 457 to 23. The selected features were grouped into six different categories. Of these categories, we believe that the features present in the groups “Word Difficulty” and “Lexical Diversity” are most useful for providing automated formative feedback to the students. The approach presented in this research paper is the first step towards our ultimate goal of providing meaningful formative feedback to the students for enhancing their writing skills and capabilities.

KW - Formative feedback

KW - Natural Language Processing

KW - Neural Networks

KW - Backward Elimination

KW - Dimensionality reduction

KW - Feature selection

KW - ALGORITHM

U2 - 10.1007/978-3-030-29736-7_30

DO - 10.1007/978-3-030-29736-7_30

M3 - Conference article in proceeding

SN - 978-3-030-29735-0

VL - 11722

T3 - Lecture Notes in Computer Science

SP - 396

EP - 408

BT - Transforming Learning with Meaningful Technologies, EC-TEL 2019

A2 - Scheffel, Maren

A2 - Broisin, Julien

A2 - Pammer-Schindler, Viktoria

A2 - Loannou, Andri

A2 - Schneider, Jan

PB - Springer International Publishing

ER -

Abbas M, van Rosmalen P, Kalz M. Identifying Critical Features for Formative Essay Feedback with Artificial Neural Networks and Backward Elimination. In Scheffel M, Broisin J, Pammer-Schindler V, Loannou A, Schneider J, editors, Transforming Learning with Meaningful Technologies, EC-TEL 2019. 1 ed. Vol. 11722. Springer International Publishing. 2019. p. 396-408. (Lecture Notes in Computer Science, Vol. 11722). doi: 10.1007/978-3-030-29736-7_30

Identifying Critical Features for Formative Essay Feedback with Artificial Neural Networks and Backward Elimination

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