Built on

Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach (2019). https://doi.org/10.48550/ARXIV.1907.11692, https://arxiv.org/abs/1907.11692
Original
1907
Earlier work this paper cites.
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., Davison, J., Shleifer, S., von Platen, P., Ma, C., Jernite, Y., Plu, J., Xu, C., Scao, T.L., Gugger, S., Drame, M., Lhoest, Q., Rush, A.M.: Huggingface’s transformers: State-of-the-art natural language processing (2019). https://doi.org/10.48550/ARXIV.1910.03771, https://arxiv.org/abs/1910.03771
Original
1910
Earlier work this paper cites.
Page, E.B.: The imminence of grading essays by computer. The Phi Delta Kappan 47
1966
Earlier work this paper cites.
Koedinger, K.R., Anderson, J.R., Hadley, W.H., Mark, M.A.: Intelligent tutoring goes to school in the big city. International Journal of Artificial Intelligence in Education 8
1997
Earlier work this paper cites.
Brusilovsky, P., Peylo, C.: Adaptive and intelligent web-based educational systems. International Journal of Artificial Intelligence in Education 13
2003
Earlier work this paper cites.
Burstein, J.: The e-rater® scoring engine: Automated essay scoring with natural language processing. (2003)
2003
Earlier work this paper cites.
Ritter, S., Anderson, J.R., Koedinger, K.R., Corbett, A.: Cognitive tutor: Applied research in mathematics education. Psychonomic bulletin & review 14
2007
Earlier work this paper cites.
Koedinger, K.R., Baker, R.S., Cunningham, K., Skogsholm, A., Leber, B., Stamper, J.: A data repository for the edm community: The pslc datashop. Handbook of educational data mining 43
2010
Earlier work this paper cites.
Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)
Original
2014
Earlier work this paper cites.
Fancsali, S.E., Ritter, S.: Context personalization, preferences, and performance in an intelligent tutoring system for middle school mathematics. In: International conference on learning analytics and knowledge. pp. 73–77 (2014)
2014
Earlier work this paper cites.
Pane, J.F., Griffin, B.A., McCaffrey, D.F., Karam, R.: Effectiveness of cognitive tutor algebra i at scale. Educational Evaluation and Policy Analysis 36
2014
Earlier work this paper cites.

Similar

Lan, A.S., Vats, D., Waters, A.E., Baraniuk, R.G.: Mathematical language processing: Automatic grading and feedback for open response mathematical questions. In: Proceedings of the ACM conference on learning@scale. pp. 167–176 (2015)
2015
Cited alongside, same era.
Aleven, V., McLaughlin, E.A., Glenn, R.A., Koedinger, K.R.: Instruction based on adaptive learning technologies. Handbook of research on learning and instruction pp. 522–560 (2016)
2016
Cited alongside, same era.
Roschelle, J., Feng, M., Murphy, R.F., Mason, C.A.: Online mathematics homework increases student achievement. AERA open 2
2016
Cited alongside, same era.
Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)
Original
2017
Cited alongside, same era.
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding (2018), https://arxiv.org/abs/1810.04805

Then

Baral, S., Botelho, A.F., Erickson, J.A., Benachamardi, P., Heffernan, N.T.: Improving automated scoring of student open responses in mathematics. International Educational Data Mining Society (2021)
2021
Later among the works it cites.
Hendrycks, D., Burns, C., Kadavath, S., Arora, A., Basart, S., Tang, E., Song, D., Steinhardt, J.: Measuring mathematical problem solving with the math dataset. NeurIPS (2021)
2021
Later among the works it cites.
Murphy, K.P.: Machine learning: A probabilistic perspective. MIT Press (2021)
2021
Later among the works it cites.
Shen, J.T., Yamashita, M., Prihar, E., Heffernan, N.T., Wu, X., Lee, D.: Mathbert: A pre-trained language model for general NLP tasks in mathematics education. CoRR abs/2106.07340
Original
2021
Later among the works it cites.
Wang, Z., Lan, A.S., Baraniuk, R.G.: Mathematical formula representation via tree embeddings. In: iTextbooks@ AIED. pp. 121–133 (2021)

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2023

Algebra Error Classification with Large Language Models

McNichols, Hunter, Zhang, Mengxue, Lan, Andrew

Understand

Automated feedback as students answer open-ended math questions has significant potential in improving learning outcomes at large scale.

A key part of automated feedback systems is an error classification component, which identifies student errors and enables appropriate, predefined feedback to be deployed.
Most existing approaches to error classification use a rule-based method, which has limited capacity to generalize.
Existing data-driven methods avoid these limitations but specifically require mathematical expressions in student responses to be parsed into syntax trees.

Built on

Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach (2019). https://doi.org/10.48550/ARXIV.1907.11692, https://arxiv.org/abs/1907.11692
Original
1907
Earlier work this paper cites.
Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., Davison, J., Shleifer, S., von Platen, P., Ma, C., Jernite, Y., Plu, J., Xu, C., Scao, T.L., Gugger, S., Drame, M., Lhoest, Q., Rush, A.M.: Huggingface’s transformers: State-of-the-art natural language processing (2019). https://doi.org/10.48550/ARXIV.1910.03771, https://arxiv.org/abs/1910.03771
Original
1910
Earlier work this paper cites.
Page, E.B.: The imminence of grading essays by computer. The Phi Delta Kappan 47
1966
Earlier work this paper cites.
Koedinger, K.R., Anderson, J.R., Hadley, W.H., Mark, M.A.: Intelligent tutoring goes to school in the big city. International Journal of Artificial Intelligence in Education 8
1997
Earlier work this paper cites.
Brusilovsky, P., Peylo, C.: Adaptive and intelligent web-based educational systems. International Journal of Artificial Intelligence in Education 13
2003
Earlier work this paper cites.
Burstein, J.: The e-rater® scoring engine: Automated essay scoring with natural language processing. (2003)
2003
Earlier work this paper cites.
Ritter, S., Anderson, J.R., Koedinger, K.R., Corbett, A.: Cognitive tutor: Applied research in mathematics education. Psychonomic bulletin & review 14
2007
Earlier work this paper cites.
Koedinger, K.R., Baker, R.S., Cunningham, K., Skogsholm, A., Leber, B., Stamper, J.: A data repository for the edm community: The pslc datashop. Handbook of educational data mining 43
2010
Earlier work this paper cites.
Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)
Original
2014
Earlier work this paper cites.
Fancsali, S.E., Ritter, S.: Context personalization, preferences, and performance in an intelligent tutoring system for middle school mathematics. In: International conference on learning analytics and knowledge. pp. 73–77 (2014)
2014
Earlier work this paper cites.
Pane, J.F., Griffin, B.A., McCaffrey, D.F., Karam, R.: Effectiveness of cognitive tutor algebra i at scale. Educational Evaluation and Policy Analysis 36
2014
Earlier work this paper cites.

Similar

Lan, A.S., Vats, D., Waters, A.E., Baraniuk, R.G.: Mathematical language processing: Automatic grading and feedback for open response mathematical questions. In: Proceedings of the ACM conference on learning@scale. pp. 167–176 (2015)
2015
Cited alongside, same era.
Aleven, V., McLaughlin, E.A., Glenn, R.A., Koedinger, K.R.: Instruction based on adaptive learning technologies. Handbook of research on learning and instruction pp. 522–560 (2016)
2016
Cited alongside, same era.
Roschelle, J., Feng, M., Murphy, R.F., Mason, C.A.: Online mathematics homework increases student achievement. AERA open 2
2016
Cited alongside, same era.
Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)
Original
2017
Cited alongside, same era.
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding (2018), https://arxiv.org/abs/1810.04805

Then

Baral, S., Botelho, A.F., Erickson, J.A., Benachamardi, P., Heffernan, N.T.: Improving automated scoring of student open responses in mathematics. International Educational Data Mining Society (2021)
2021
Later among the works it cites.
Hendrycks, D., Burns, C., Kadavath, S., Arora, A., Basart, S., Tang, E., Song, D., Steinhardt, J.: Measuring mathematical problem solving with the math dataset. NeurIPS (2021)
2021
Later among the works it cites.
Murphy, K.P.: Machine learning: A probabilistic perspective. MIT Press (2021)
2021
Later among the works it cites.
Shen, J.T., Yamashita, M., Prihar, E., Heffernan, N.T., Wu, X., Lee, D.: Mathbert: A pre-trained language model for general NLP tasks in mathematics education. CoRR abs/2106.07340
Original
2021
Later among the works it cites.
Wang, Z., Lan, A.S., Baraniuk, R.G.: Mathematical formula representation via tree embeddings. In: iTextbooks@ AIED. pp. 121–133 (2021)

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

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Original

2018

Cited alongside, same era.

Heeren, B., Jeuring, J., Sosnovsky, S.A., Drijvers, P., Boon, P., Tacoma, S., Koops, J., Weinberger, A., Grugeon-Allys, B., Chenevotot-Quentin, F., van Wijk, J., van Walree, F.: Fine-grained cognitive assessment based on free-form input for math story problems. In: Pammer-Schindler, V., Pérez-Sanagustín, M., Drachsler, H., Elferink, R., Scheffel, M. (eds.) Lifelong Technology-Enhanced Learning - 13th European Conference on Technology Enhanced Learning, EC-TEL 2018, Leeds, UK, September 3-5, 2018, Proceedings. Lecture Notes in Computer Science, vol. 11082, pp. 262–276. Springer (2018). https://doi.org/10.1007/978-3-319-98572-5_20, https://doi.org/10.1007/978-3-319-98572-5_20

2018

Cited alongside, same era.

Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., Grave, E., Ott, M., Zettlemoyer, L., Stoyanov, V.: Unsupervised cross-lingual representation learning at scale. arXiv preprint arXiv:1911.02116 (2019)

Original

2019

Cited alongside, same era.

Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al.: Language models are unsupervised multitask learners. OpenAI blog 1

2019

Cited alongside, same era.

Wang, Z., Lan, A., Waters, A., Grimaldi, P., Baraniuk, R.: A meta-learning augmented bidirectional transformer model for automatic short answer grading. In: Proc. 12th Int. Conf. Educ. Data Mining (EDM). pp. 1–4 (2019)

2019

Cited alongside, same era.

Erickson, J.A., Botelho, A.F., McAteer, S., Varatharaj, A., Heffernan, N.T.: The automated grading of student open responses in mathematics. In: International Conference on Learning Analytics & Knowledge. p. 615–624 (2020)

2020

Cited alongside, same era.

Uto, M., Uchida, Y.: Automated short-answer grading using deep neural networks and item response theory. In: International Conference on Artificial Intelligence in Education. pp. 334–339 (2020)

2020

Cited alongside, same era.

2021