“Automated prediction of item difficulty in reading comprehension using long short-term memory,”
Abstract
Standardized tests are an important tool in education. During the test preparation process, the difficulty of each test item needs to be defined, which previously relied on expert validation or pretest for the most part, requiring a considerable amount of labor and cost. These problems can be overcome by using machines to predict the difficulty of the test items. In this study, long short-term memory (LSTM) will be used to predict the test item difficulty in reading comprehension. Experimental results show that the proposed method has a good prediction for agreement rate.
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With automatic testing systems and large quantities of historical test logs, deep learning has been increasingly used for feature extraction and/or difficulty estimation in QDP.
“With automatic testing systems and ready availability of large quantities of historical test logs, deep learning has been increasingly used for feature extraction (word embeddings, question representations, etc.) and/or difficulty estimation (Fang et al., 2019; Lin et al., 2019; Xue et al., 2020).”
From Proactive and reactive engagement of artificial intelligence methods for education: a review (2023) · cites this paper as Lin et al., 2019
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533 · see citing works
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1412.3555 · Cited by 11,008 · see citing works
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., et al. (2016) · arXiv · doi:10.48550/arxiv.1609.08144 · Cited by 5,319 · see citing works
The prediction of TOEFL reading item difficulty: implications for construct validity
Freedle, R. O., & Kostin, I. W. (1993) · Language Testing · doi:10.1177/026553229301000203 · Cited by 85 · see citing works
Sheehan, K. M., Kostin, I. W., Napolitano, D., & Flor, M. (2014) · The Elementary School Journal · doi:10.1086/678294 · Cited by 80 · see citing works
Hsu, F.-Y., Lee, H.-M., Chang, T.-H., & Sung, Y. (2018) · Information Processing & Management · doi:10.1016/j.ipm.2018.06.007 · Cited by 41 · see citing works
Self-citationTHE PREDICTION OF SAT READING COMPREHENSION ITEM DIFFICULTY FOR EXPOSITORY PROSE PASSAGES
Freedle, R. O., & Kostin, I. W. (1991) · ETS Research Report Series · doi:10.1002/j.2333-8504.1991.tb01396.x · Cited by 39 · see citing works
Online Readability and Text Complexity Analysis with TextEvaluator
Napolitano, D., Sheehan, K. M., & Mundkowsky, R. (2015) · doi:10.3115/v1/n15-3020 · Cited by 29 · see citing works
A Two-Stage Approach for Generating Unbiased Estimates of Text Complexity
Sheehan, K. M., Flor, M., & Napolitano, D. (2013) · Cited by 28 · see citing works
Textual complexity as a predictor of difficulty of listening items in language proficiency tests.
Loukina, A., Yoon, S., Sakano, J., Wei, Y., & Sheehan, K. H. (2016) · International Conference on Computational Linguistics · Cited by 24 · see citing works
Discourse Mode Identification in Essays
Song, W., Wang, D., Fu, R., Liu, L., Liu, T., & Hu, G. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-1011 · Cited by 23 · see citing works
USING A NEURAL NET TO PREDICT ITEM DIFFICULTY
Boldt, R. F., & Freedle, R. O. (1996) · ETS Research Report Series · doi:10.1002/j.2333-8504.1996.tb01709.x · Cited by 11 · see citing works
Item Difficulty Predictors of a Multiple-choice Reading Test
전유아, & 신택수 (2010) · English Teaching · doi:10.15858/engtea.65.4.201012.257 · Cited by 3 · see citing works
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AlKhuzaey, S., Grasso, F., Payne, T. R., & Tamma, V. A. M. (2023) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-023-00362-1 · Cited by 51 · see citing works
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- Authors
- Lin, L.-H., Chang, T.-H., & Hsu, F.-Y.
- Affiliations
- National Kaohsiung University of Science and Technology (TW)
- National Taiwan Normal University (TW)
- Venue
- 2019 International Conference on Asian Language Processing (IALP)
- Publisher
- IEEE
- Published
- 2019-11
- Pages
- 132-135
- OpenAlex
- W3012420325
- Cited by
- 13
- References
- 14
- Citations by year
- 2025: 3 · 2024: 2 · 2023: 6 · 2022: 1 · 2021: 1
- FWCI
- 0.40
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Conference on Asian Language Processing (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Information Systems › Educational Technology and Assessment
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- item difficulty prediction, reading comprehension, long short-term memory, standardized testing, item difficulty, agreement rate
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