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This is a list of papers and related URLs cited by "Survey of Natural Language Processing for Education: Taxonomy, Systematic Review, and Future Trends".

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Survey-of-Natural-Language-Processing-for-Education-Taxonomy-Systematic-Review-and-Future-Trends

This is a list of papers and dataset URLs cited by our paper "Survey of Natural Language Processing for Education: Taxonomy, Systematic Review, and Future Trends".

Contents

Papers

Introduction & Taxonomy

  1. Learn to explain: Multimodal reasoning via thought chains for science question answering, NeurIPS (2022)

    Lu, P., Mishra, S., Xia, T., Qiu, L., Chang, K.-W., Zhu, S.-C., Tafjord, O., Clark, P., Kalyan, A. [pdf]

  2. Theoremqa: A theorem-driven question answering dataset, arXiv preprint arXiv:2305.12524 (2023)

    Chen, W., Yin, M., Ku, M., Wan, E., Ma, X., Xu, J., Xia, T., Wang, X., Lu, P. [pdf]

  3. Diverse and informative dialogue generation with context-specific commonsense knowledge awareness, ACL (2020)

    Wu, S., Li, Y., Zhang, D., Zhou, Y., Wu, Z. [pdf]

  4. Learning sentence embeddings with auxiliary tasks for cross- domain sentiment classification, EMNLP (2016)

    Yu, J., Jiang, J. [pdf]

  5. Complex Knowledge Base Question Answering: A Survey, IEEE (2022)

    Lan Y, He G, Jiang J. [pdf]

  6. Eduqa: Educational domain question answering system using conceptual network mapping, CASSP (2019)

    Agarwal, A., Sachdeva, N., Yadav, R.K., Udandarao, V., Mittal, V., Gupta, A., Mathur, A. [link]

  7. Grammatical error correction: A survey of the state of the art, CL 49(3) (2023)

    Bryant, C., Yuan, Z., Muhammad, R., Qorib, Q., Cao, H., Ng, H., Briscoe, T. [pdf]

  8. The gap of semantic parsing: A survey on automatic math word problem solvers, TPAMI 42(9) (2019)

    Zhang, D., Wang, L., Zhang, L., Dai, B.T., Shen, H.T. [link]

  9. Natural language processing for enhancing teaching and learning, AAAI (2016)

    D. Litman. [link]

  10. Automated grading and feedback tools for programming education: A systematic review, TOCE (2023)

Messer, M., Brown, N.C.C., K ̈olling, M., Shi, M. [pdf]

  1. Natural language processing for enhancing teaching and learning, AAAI (2016)

    Litman, D. [link]

  2. Engineering education in the era of chatgpt: Promise and pitfalls of generative ai for education, EDUCON (2023)

    Qadir, J. [link]

  3. A Beginner’s Guide to Introduce Artificial Intelligence in Teaching and Learning, Springer, ??? (2023)

    Kurni, M., Mohammed, M.S., Srinivasa, K. [link]

  4. Bioact: Biomedical knowledge base construction using active learning, bioRxiv (2022)

    Wright, D., Gentile, A.L., Faux, N., Beck, K.L. [link]

  5. Information extraction from scientific paper using rhetorical classifier, CEEI (2011)

    Khodra, M.L., Widyantoro, D.H., Aziz, E., Bambang, R.T. [link]

  6. Context-aware document simplification, ACL Finding (2023)

    Cripwell, L., Legrand, J., Gardent, C. [pdf]

  7. Diffusion-lm improves controllable text generation, Advances in Neural Information Processing Systems (2022)

    Li, X., Thickstun, J., Gulrajani, I., Liang, P.S., Hashimoto, T.B. [pdf]

Question Answering

Education question answering systems: a survey, MECS (2021)

Soares, T.G., Azhari, A., Rokhman, N., Wonarko, E. [pdf]

Textbook Question Answering

  1. Are you smarter than a sixth grader? textbook question answering for multimodal machine comprehension, MECS (2021)

    Kembhavi, A., Seo, M., Schwenk, D., Choi, J., Farhadi, A., Hajishirzi, H. [pdf]

  2. A diagram is worth a dozen images, ECCV (2016)

    Kembhavi, A., Salvato, M., Kolve, E., Seo, M., Hajishirzi, H., Farhadi, A. [link]

  3. Dvqa: Understanding data visualizations via question answering, CVPR (2018)

    Kafle, K., Price, B., Cohen, S., Kanan, C. [pdf]

  4. Visuo-linguistic question answering (vlqa) challenge, arXiv preprint arXiv:2005.00330 (2020)

    Sampat, S.K., Yang, Y., Baral, C. [pdf]

  5. What disease does this patient have? a large-scale open domain question answering dataset from medical exams, Applied Sciences (2021)

    Jin, D., Pan, E., Oufattole, N., Weng, W.-H., Fang, H., Szolovits, P. [link]

  6. Medmcqa: A large-scale multi-subject multi-choice dataset for medical domain question answering, CHIL (2022)

    Pal, A., Umapathi, L.K., Sankarasubbu, M. [pdf]

  7. R-VQA: learning visual relation facts with semantic attention for visual question answering, SIGKDD (2018)

    Lu, P., Ji, L., Zhang, W., Duan, N., Zhou, M., Wang, J. [pdf]

  8. An image is worth 16x16 words: Transformers for image recognition at scale, arXiv preprint arXiv:2010.11929 (2020)

    Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M.,Minderer, M., Heigold, G., Gelly, S., et al. [pdf]

  9. Question-guided hybrid convolution for visual question answering, ECCV (201)

    Gao, P., Li, H., Li, S., Lu, P., Li, Y., Hoi, S.C., Wang, X. [pdf]

  10. Dynamic fusion with intra-and inter-modality attention flow for visual quetion answering, CVPR (2019)

    Gao, P., Jiang, Z., You, H., Lu, P., Hoi, S.C., Wang, X., Li, H. [pdf]

  11. Transform-retrieve-generate: Natural language-centric outside-knowledge visual question answering, CVPR (2022)

    Gao, F., Ping, Q., Thattai, G., Reganti, A., Wu, Y.N., Natarajan, P. [pdf]

  12. Vqa: Visual question answering, CCV (2015)

    Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C.L., Parikh, D. [pdf]

  13. Ask your neurons: A neural-based approach to answering questions about images, CCV (2015)

    Malinowski, M., Rohrbach, M., Fritz, M. [pdf]

  14. Hierarchical question-image co-attention for visual question answering, NeurIPS (2016)

    Lu, J., Yang, J., Batra, D., Parikh, D. [pdf]

  15. Training recurrent answering units with joint loss minimization for vqa, arXiv preprint arXiv:1606.03647 (2016)

    Noh, H., Han, B. [pdf]

  16. Show, attend and tell: Neural image caption generation with visual attention, CML (2015)

    Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhudinov, R., Zemel, R., Bengio, Y. [pdf]

  17. Stacked attention networks for image question answering, CVPR (2016)

    Yang, Z., He, X., Gao, J., Deng, L., Smola, A. [pdf]

  18. Learning to compose neural networks for question answering, arXiv preprint arXiv:1601.01705 (2016)

    Andreas, J., Rohrbach, M., Darrell, T., Klein, D. [pdf]

  19. Multimodal compact bilinear pooling for visual question answering and visual grounding, arXiv preprint arXiv:1606.01847 (2016)

    Fukui, A., Park, D.H., Yang, D., Rohrbach, A., Darrell, T., Rohrbach, M. [pdf]

  20. Hadamard product for low-rank bilinear pooling, arXiv preprint arXiv:1610.04325 (2016)

    Kim, J.-H., On, K.-W., Lim, W., Kim, J., Ha, J.-W., Zhang, B.-T. [pdf]

  21. Few-Shot Question Answering by Pretraining Span Selection, (2019)

    Ram, O., Kirstain, Y., Berant, J., Globerson, A., Levy, O. [pdf]

  22. Domain-agnostic question-answering with adversarial training, (2019)

    Lee, S., Kim, D., Park, J. [pdf]

  23. From clozing to comprehending: Retrofitting pre-trained language model to pre-trained machine reader, arXiv preprint arXiv:2212.04755 (2022)

    Xu, W., Li, X., Zhang, W., Zhou, M., Bing, L., Lam, W., Si, L. [pdf]

  24. Reverse-engineering visualizations: Recovering visual encodings from chart images, CGF (2017)

    Poco, J., Heer, J. [pdf]

  25. T-SciQ: Teaching Multimodal Chain-of-Thought Reasoning via Large Language Model Signals for Science Question Answering, CGF (2017)

    Wang, L., Hu, Y., He, J., Xu, X., Liu, N., Liu, H., Shen, H.T. [pdf]

  26. Augmenting black-box llms with medical textbooks for clinical question answering, arXiv preprint arXiv:2309.02233 (2023)

    Wang, Y., Ma, X., Chen, W. [pdf]

  27. Visual Instruction Tuning, (2023)

    Wang, Y., Ma, X., Chen, W. [pdf]

  28. Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models, (2023)

    ] Lu, P., Peng, B., Cheng, H., Galley, M., Chang, K.-W., Wu, Y.N., Zhu, S.- C., Gao, J. [pdf]

  29. Theoremqa: A theorem-driven question answering dataset, arXiv preprint arXiv:2305.12524 (2023)

    Chen, W., Yin, M., Ku, M., Wan, E., Ma, X., Xu, J., Xia, T., Wang, X., Lu, P. [pdf]

Math Word Problem Solving

  1. How well do computers solve math word problems? large-scale dataset construction and evaluation, ACL (2016)

    Liu, H., Li, C., Wu, Q., Lee, Y.J. [pdf]

  2. Annotating derivations: A new evaluation strategy and dataset for algebra word problems, arXiv preprint arXiv:11609.07197 (2016)

    Upadhyay, S., Chang, M.-W. [pdf]

  3. Deep neural solver for math word problems, EMNLP (2017)

    Wang, Y., Liu, X., Shi, S. [pdf]

  4. MathQA: Towards interpretable math word problem solving with operation-based formalisms, (2019)

    Amini, A., Gabriel, S., Lin, S., Koncel-Kedziorski, R., Choi, Y., Hajishirzi, H. [pdf]

  5. A diverse corpus for evaluating and developing English math word problem solvers, (2020)

    Miao, S.-y., Liang, C.-C., Su, K.-Y. [pdf]

  6. Training Verifiers to Solve Math Word Problems, (2021)

    Cobbe, K., Kosaraju, V., Bavarian, M., Chen, M., Jun, H., Kaiser, L., Plappert, M., Tworek, J., Hilton, J., Nakano, R., Hesse, C., Schulman, J. [pdf]

  7. Iconqa: A new benchmark for abstract diagram understanding and visual language reasoning, (2021)

    Lu, P., Qiu, L., Chen, J., Xia, T., Zhao, Y., Zhang, W., Yu, Z., Liang, X., Zhu, S.-C. [pdf]

  8. Dynamic prompt learning via policy gradient for semi-structured mathematical reasoning, arXiv preprint arXiv:2209.14610 (2022)

    Lu, P., Qiu, L., Chang, K.-W., Wu, Y.N., Zhu, S.-C., Rajpurohit, T., Clark, P., Kalyan, A. [pdf]

  9. The gap of semantic parsing: A survey on automatic math word problem solvers, TPAMI (2020)

    Zhang, D., Wang, L., Zhang, L., Dai, B.T., Shen, H.T. [link]

  10. Translating a math word problem to a expression tree, (2018)

    Wang, L., Wang, Y., Cai, D., Zhang, D., Liu, X. [pdf]

  11. Semantically-aligned equation generation for solving and reasoning math word problems, arXiv preprint arXiv:1811.00720 (2019)

    Chiang, T.-R., Chen, Y.-N. [pdf]

  12. Tree-structured decoding for solving math word problems, EMNLP-IJCNLP (2019)

    Liu, Q., Guan, W., Li, S., Kawahara, D. [pdf]

  13. A goal-driven tree-structured neural model for math word problems, jcai (2019)

    Xie, Z., Sun, S. [pdf]

  14. Graph-to-tree learning for solving math word problems, ACL (2020)

    Zhang, J., Wang, L., Lee, R.K.-W., Bin, Y., Wang, Y., Shao, J., Lim, E.-P. [pdf]

  15. Hms: A hierarchical solver with dependency-enhanced understanding for math word problem, AAAI (2021)

    Lin, X., Huang, Z., Zhao, H., Chen, E., Liu, Q., Wang, H., Wang, S. [link]

  16. Learning by fixing: Solving math word problems with weak supervision, AAAI (2021)

    Hong, Y., Li, Q., Ciao, D., Huang, S., Zhu, S.-C. [pdf]

  17. Teacher-student networks with multiple decoders for solving math word problem, JCAI (2020)

    Zhang, J., Lee, R.K.-W., Lim, E.-P., Qin, W., Wang, L., Shao, J., Sun, Q. [pdf]

  18. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, JCAI (2020)

    Zhang, J., Lee, R.K.-W., Lim, E.-P., Qin, W., Wang, L., Shao, J., Sun, Q. [pdf]

  19. Plan-and-solve prompting: Improving zero-shot chain-of-thought reasoning by large language models, arXiv preprint arXiv:2305.04091 (2023)

    Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., Zhou, D. [pdf]

  20. Automatic chain of thought prompting in large language models, arXiv preprint arXiv:2210.03493 (2022)

    Zhang, Z., Zhang, A., Li, M., Smola, A. [pdf]

  21. Least-to-most prompting enables complex reasoning in large language models, arXiv preprint arXiv:2205.10625 (2022)

    Zhou, D., Sch ̈arli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Bousquet, O., Le, Q., Chi, E. [pdf]

  22. Progressive-hint prompting improves reasoning in large language models, arXiv preprint arXiv:2304.09797 (2023)

    Zheng, C., Liu, Z., Xie, E., Li, Z., Li, Y. [pdf]

  23. Tora: A tool-integrated reasoning agent for mathematical problem solving, arXiv preprint arXiv:2309.17452 (2023)

    Gou, Z., Shao, Z., Gong, Y., Yang, Y., Huang, M., Duan, N., Chen, W., et al. [pdf]

  24. Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks, TMLR (2023)

    Chen, W., Ma, X., Wang, X., Cohen, W.W. [pdf]

Question Construction

Automatic question generation and answer assessment: a survey, RPTEL (2021)

Das, B., Majumder, M., Phadikar, S., Sekh, A.A. [link]

Question Generation

  1. A feasibility study of answer-agnostic question generation for education, arXiv preprint arXiv:2203.08685 (2022)

    Dugan, L., Miltsakaki, E., Upadhyay, S., Ginsberg, E., Gonzalez, H., Choi, D., Yuan, C., Callison-Burch, C. [pdf]

  2. Question answering and question generation as dual tasks, arXiv preprint arXiv:1706.02027 (2017)

    Tang, D., Duan, N., Qin, T., Yan, Z., Zhou, M. [pdf]

  3. Crowdsourcing multiple choice science questions, arXiv preprint arXiv:1707.06209 (2017)

    Welbl, J., Liu, N.F., Gardner, M. [pdf]

  4. Race: Large-scale reading comprehension dataset from examinations, arXiv preprint arXiv:1704.04683 (2017)

    Lai, G., Xie, Q., Liu, H., Yang, Y., Hovy, E. [pdf]

  5. Fantastic questions and where to find them: Fairytaleqa–an authentic dataset for narrative comprehension, arXiv preprint arXiv:2203.13947 (2022)

    Xu, Y., Wang, D., Yu, M., Ritchie, D., Yao, B., Wu, T., Zhang, Z., Li, T.J.- J., Bradford, N., Sun, B., et al. [pdf]

  6. Towards data-effective educational question generation with prompt-based learning, SAI (2023)

    Wu, Y., Nouri, J., Megyesi, B., Henriksson, A., Duneld, M., Li, X. [link]

  7. Eqg-race: Examination-type question generation, AAAI, vol. 35 (2021)

    Jia, X., Zhou, W., Sun, X., Wu, Y. [pdf]

  8. Investigating educational and noneducational answer selection for educational question generation, EEE Access (2022)

    Steuer, T., Filighera, A., Tregel, T. [pdf]

  9. Educational question generation of children storybooks via question type distribution learning and event-centric summarization, arXiv preprint arXiv:2203.14187 (2022)

    Zhao, Z., Hou, Y., Wang, D., Yu, M., Liu, C., Ma, X. [pdf]

  10. Learningq: a large-scale dataset for educational question generation, CWSM, vol. 12 (2018)

    Chen, G., Yang, J., Hauff, C., Houben, G.-J. [pdf]

  11. Khanq: A dataset for generating deep questions in education, CCL (2022)

    Gong, H., Pan, L., Hu, H. [pdf]

  12. Eduqg: A multi-format multiple-choice dataset for the educational domain, EEE Access (2023)

    Hadifar, A., Bitew, S.K., Deleu, J., Develder, C., Demeester, T. [pdf]

  13. Automated question generation methods for intelligent english learning systems and its evaluation, CCE, vol. 670 (2004)

    Kunichika, H., Katayama, T., Hirashima, T., Takeuchi, A. [link]

  14. Identifying where to focus in reading comprehension for neural question generation, EMNLP (2017)

    Du, X., Cardie, C. [pdf]

  15. Neural generation of diverse questions using answer focus, contextual and linguistic features, arXiv preprint arXiv:1809.02637 (2018)

    Harrison, V., Walker, M. [pdf]

  16. Neural question generation from text: A preliminary study, NLPCC (2018)

    Zhou, Q., Yang, N., Wei, F., Tan, C., Bao, H., Zhou, M. [pdf]

  17. Question generation using sequence-to-sequence model with semantic role labels, (2022)

    Naeji, A. [pdf]

  18. Multiqg-ti: Towards question generation from multi-modal sources, arXiv preprint arXiv:2307.04643 (2023)

    Wang, Z., Baraniuk, R. [pdf]

  19. Multi-task learning with language modeling for question generation, arXiv preprint arXiv:1908.11813 (2019)

    Zhou, W., Zhang, M., Wu, Y. [pdf]

  20. Natural question generation with reinforcement learning based graph-to-sequence model, arXiv preprint arXiv:1910.08832 (2019)

    Chen, Y., Wu, L., Zaki, M.J. [pdf]

  21. A recurrent bert-based model for question generation, MRQA (2019)

    Chan, Y.-H., Fan, Y.-C. [pdf]

  22. Towards human-like educational question generation with large language models, AIED (2022)

    Wang, Z., Valdez, J., Basu Mallick, D., Baraniuk, R.G. [link]

  23. Machine comprehension by text-to-text neural question generation, arXiv preprint arXiv:1705.02012 (2017)

    Yuan, X., Wang, T., Gulcehre, C., Sordoni, A., Bachman, P., Subramanian, S., Zhang, S., Trischler, A. [pdf]

  24. Answer-focused and position-aware neural question generation, EMNLP (2018)

    Sun, X., Liu, J., Lyu, Y., He, W., Ma, Y., Wang, S. [pdf]

  25. Improving question generation with sentence-level semantic matching and answer position inferring, AAAI, vol. 34 (2020)

    Ma, X., Zhu, Q., Zhou, Y., Li, X. [pdf]

  26. Difficulty controllable generation of reading comprehension questions, arXiv preprint arXiv:1807.03586 (2018)

    Gao, Y., Bing, L., Chen, W., Lyu, M.R., King, I. [pdf]

  27. Guiding the growth: Difficulty-controllable question generation through step-by-step rewriting, arXiv preprint arXiv:2105.11698 (2021)

    Cheng, Y., Li, S., Liu, B., Zhao, R., Li, S., Lin, C., Zheng, Y. [pdf]

  28. Difficulty-controllable neural question generation for reading comprehension using item response theory, BEA (2023)

    Uto, M., Tomikawa, Y., Suzuki, A. [pdf]

  29. Applications of Item Response Theory to Practical Testing Problems, Routledge, ??? (2012)

    Lord, F.M. [link]

  30. I do not understand what i cannot define: Automatic question generation with pedagogically-driven content selection, arXiv preprint arXiv:2110.04123 (2021)

    Steuer, T., Filighera, A., Meuser, T., Rensing, C. [pdf]

  31. Diverse content selection for educational question generation, EACL (2023)

    Hadifar, A., Bitew, S.K., Deleu, J., Hoste, V., Develder, C., Demeester, T. [pdf]

  32. Question generation for adaptive education, arXiv preprint arXiv:2106.04262 (2021)

    Srivastava, M., Goodman, N. [pdf]

  33. Difficulty-controlled question generation in adaptive education for few-shot learning, ADMA (2023)

    Wang, Y., Li, L. [link]

Distractor Generation

  1. Crowdsourcing multiple choice science questions, arXiv preprint arXiv:1707.06209 (2017)

    Welbl, J., Liu, N.F., Gardner, M. [pdf]

  2. Automatic distractor generation for multiple choice questions in standard tests, arXiv preprint arXiv:2011.13100 (2020)

    Qiu, Z., Wu, X., Fan, W. [pdf]

  3. Distractor generation for multiple choice questions using learning to rank, BEA (2018)

    Liang, C., Yang, X., Dave, N., Wham, D., Pursel, B., Giles, C.L. [pdf]

  4. Learning to reuse distractors to support multiple choice question generation in education, EEE-TLT (2022)

    Bitew, S.K., Hadifar, A., Sterckx, L., Deleu, J., Develder, C., Demeester, T. [pdf]

  5. Computer-aided generation of multiple-choice tests, HLT-NAAC (2003)

    Mitkov, R., et al. [pdf]

  6. Automatic question generation for vocabulary assessment, HLT/EMNLP (2005)

    Brown, J., Frishkoff, G., Eskenazi, M. [pdf]

  7. Multi-source soft labeling and hard negative sampling for retrieval distractor ranking, TLT (2023)

    Wang, J., Rong, W., Bai, J., Sun, Z., Ouyang, Y., Xiong, Z. [link]

  8. Generating distractors for reading comprehension questions from real examinations, AAAI, vol. 33 (2019)

    Gao, Y., Bing, L., Li, P., King, I., Lyu, M.R. [pdf]

  9. Distractor generation for multiple-choice questions with predictive prompting and large language models, arXiv preprint arXiv:2307.16338 (2023)

    Bitew, S.K., Deleu, J., Develder, C., Demeester, T. [pdf]

  10. Ranking multiple choice question distractors using semantically informed neural networks, CIKM (2020)

Sinha, M., Dasgupta, T., Mandav, J. [link]

Automated Assessment

Analyzing the quality of submissions in online programming courses, arXiv preprint arXiv:2301.11158 (2023)

Tigina, M., Birillo, A., Golubev, Y., Keuning, H., Vyahhi, N., Bryksin, T. [pdf]

Automated Essay Scoring

  1. A new dataset and method for automatically grading ESOL texts, ACL (2011)

    Yannakoudakis, H., Briscoe, T., Medlock, B. [pdf]

  2. Toefl11: A corpus of non-native english, ETS Research Report Series 2013(2) (2013)

    Blanchard, D., Tetreault, J., Higgins, D., Cahill, A., Chodorow, M. [link]

  3. Annotating argument components and relations in persuasive essays, COLING (2014)

    Stab, C., Gurevych, I. [pdf]

  4. A prompt-independent and interpretable automated essay scoring method for chinese second language writing, CCL (2021)

    Wang, Y., Hu, R. [pdf]

  5. Task-independent features for automated essay grading, BEA (2015)

    Zesch, T., Wojatzki, M., Scholten-Akoun, D. [pdf]

  6. Flexible domain adaptation for automated essay scoring using correlated linear regression, EMNLP (2015)

    Phandi, P., Chai, K.M.A., Ng, H.T. [pdf]

  7. Off-topic essay detection using short prompt texts, HLT-NAACL (2010)

    Louis, A., Higgins, D. [pdf]

  8. Automated essay scoring by maximizing human-machine agreement, EMNLP (2013)

    Chen, H., He, B. [pdf]

  9. Neural automated essay scoring considering logical structure, AIED (2023)

    Yamaura, M., Fukuda, I., Uto, M. [link]

  10. Enhancing automated essay scoring performance via fine-tuning pre-trained language models with combination of regression and ranking, EMNLP (2020)

    Yang, R., Cao, J., Wen, Z., Wu, Y., He, X. [pdf]

  11. Neural automated essay scoring incorporating handcrafted features, CCL (2020)

    Uto, M., Xie, Y., Ueno, M. [pdf]

  12. On the effectiveness of curriculum learning in educational text scoring, AAAI (2023)

    Zeng, Z., Gasevic, D., Chen, G. [link]

  13. Prompt agnostic essay scorer: A domain generalization approach to cross-prompt automated essay scoring, arXiv preprint arXiv:2008.01441 (2008)

    Ridley, R., He, L., Dai, X., Huang, S., Chen, J. [pdf]

  14. Improving domain generalization for prompt-aware essay scoring via disentangled representation learning, ACL (2023)

    Jiang, Z., Gao, T., Yin, Y., Liu, M., Yu, H., Cheng, Z., Gu, Q. [pdf]

  15. Learning from graph propagation via ordinal distillation for one-shot automated essay scoring, WWW (2021)

    Jiang, Z., Liu, M., Yin, Y., Yu, H., Cheng, Z., Gu, Q. [link]

  16. Aggregating multiple heuristic signals as supervision for unsupervised automated essay scoring, ACL (2023)

    Wang, C., Jiang, Z., Yin, Y., Cheng, Z., Ge, S., Gu, Q. [pdf]

  17. Feature enhanced capsule networks for robust automatic essay scoring, Machine Learning and Knowledge Discovery in Databases. (2021)

    Sharma, A., Kabra, A., Kapoor, R. [pdf]

  18. A prompt-independent and interpretable automated essay scoring method for Chinese second language writing, CCL (2021)

    Yupei, W., Renfen, H. [pdf]

  19. Modeling thesis clarity in student essays, ACL (2013)

    Persing, I., Ng, V. [pdf]

  20. Modeling prompt adherence in student essays, ACL (2014)

    Persing, I., Ng, V. [pdf]

Automated Code Scoring

  1. Analyzing the quality of submissions in online programming courses, arXiv preprint arXiv:2301.11158 (2023)

    Tigina, M., Birillo, A., Golubev, Y., Keuning, H., Vyahhi, N., Bryksin, T. [pdf]

  2. Hyperstyle: A tool for assessing the code quality of solutions to programming assignments, SIGCSE (2022)

    Birillo, A., Vlasov, I., Burylov, A., Selishchev, V., Goncharov, A., Tikhomirova, E., Vyahhi, N., Bryksin, T. [pdf]

  3. Detecting code quality issues in pre-written templates of programming tasks in online courses, arXiv preprint arXiv:2304.12376 (2023)

    Birillo, A., Artser, E., Golubev, Y., Tigina, M., Keuning, H., Vyahhi, N., Bryksin, T. [pdf]

  4. Scale-driven automatic hint generation for coding style, TS (2016)

    Roy Choudhury, R., Yin, H., Fox, A. [pdf]

  5. Automated critique of early programming antipat- terns, SIGCSE, pp. 738–744 (2019)

    Ureel II, L.C., Wallace, C. [pdf]

  6. A tutoring system to learn code refactoring, SIGCSE (2021)

    Keuning, H., Heeren, B., Jeuring, J. [pdf]

  7. Proposed assessment framework based on bloom taxonomy cognitive competency: Introduction to programming, CSCA (2018)

    Lajis, A., Nasir, H.M., Aziz, N.A. [pdf]

  8. Overcode: Visualizing variation in student solutions to programming problems at scale, TOCHI 22(2) (2015)

    Glassman, E.L., Scott, J., Singh, R., Guo, P.J., Miller, R.C. [pdf]

  9. Re-use of programming patterns or problem solving? representation of scratch programs by tgraphs to support static code analysis, WiPSCE (2020)

    Talbot, M., Geldreich, K., Sommer, J., Hubwieser, P. [pdf]

  10. Codemaster–automatic assessment and grading of app inventor and snap! programs, NFEDU 17(1) (2018)

​ Von Wangenheim, C.G., Hauck, J.C., Demetrio, M.F., Pelle, R., Cruz Alves, N., Barbosa, H., Azevedo, L.F. [pdf]

  1. Petcha: a programming exercises teaching assistant, TiCSE (2012)

    Queir ́os, R.A.P., Leal, J.P. [pdf]

  2. Automatic grading of student code with similarity measurement, ECML PKDD (2023)

    Wang, D., Zhang, E., Lu, X. [pdf]

  3. Suggesting accurate method and class names, FSE (2015)

    Allamanis, M., Barr, E.T., Bird, C., Sutton, C. [pdf]

  4. A novel neural source code representation based on abstract syntax tree, CSE (2019)

    Zhang, J., Wang, X., Zhang, H., Sun, H., Wang, K., Liu, X. [link]

  5. code2vec: Learning distributed representations of code, PACMPL (2019)

    Alon, U., Zilberstein, M., Levy, O., Yahav, E. [pdf]

  6. Global relational models of source code, CLR (2019)

    Hellendoorn, V.J., Sutton, C., Singh, R., Maniatis, P., Bieber, D. [pdf]

  7. Codebert: A pre-trained model for programming and natural languages, arXiv preprint arXiv:2002.08155 (2020)

    Feng, Z., Guo, D., Tang, D., Duan, N., Feng, X., Gong, M., Shou, L., Qin, B., Liu, T., Jiang, D., et al. [pdf]

  8. Learning and evaluating contextual embedding of source code, CML (2020)

    Kanade, A., Maniatis, P., Balakrishnan, G., Shi, K. [pdf]

Error Correction

Language Error Correction

  1. A simple recipe for multilingual grammatical error correction, (2021)

    Rothe, S., Mallinson, J., Malmi, E., Krause, S., Severyn, A. [pdf]

  2. The conll-2014 shared task on grammatical error correction, CoNLL (2014)

    Ng, H.T., Wu, S.M., Briscoe, T., Hadiwinoto, C., Susanto, R.H., Bryant, C. [pdf]

  3. The bea-2019 shared task on grammatical error correction, BEA (2019)

    Bryant, C., Felice, M., Andersen, Ø.E., Briscoe, T. [pdf]

  4. Introduction to sighan 2015 bake-off for chinese spelling check, SIGHAN (2015)

    Tseng, Y.-H., Lee, L.-H., Chang, L.-P., Chen, H.-H. [pdf]

  5. Overview of ctc 2021: Chinese text correction for native speakers, arXiv preprint arXiv:2208.05681 (2022)

    Zhao, H., Wang, B., Wu, D., Che, W., Chen, Z., Wang, S. [pdf]

  6. Fcgec: Fine-grained corpus for chinese grammatical error correction, arXiv preprint arXiv:2210.12364 (2022)

    Xu, L., Wu, J., Peng, J., Fu, J., Cai, M. [pdf]

  7. Flacgec: A chinese grammatical error correction dataset with fine-grained linguistic annotation, CIKM (2023)

    Du, H., Zhao, Y., Tian, Q., Wang, J., Wang, L., Lan, Y., Lu, X. [pdf]

  8. Czech grammar error correction with a large and diverse corpus, TACL (2022)

    N ́aplava, J., Straka, M., Strakov ́a, J., Rosen, A. [pdf]

  9. Grammar error correction in morphologically rich languages: The case of russian, TACL (2019)

    Rozovskaya, A., Roth, D. [pdf]

  10. The wiked error corpus: A corpus of corrective wikipedia edits and its application to grammatical error correction, LNCS (2014)

    Grundkiewicz, R., Junczys-Dowmunt, M. [pdf]

  11. Developing nlp tools with a new corpus of learner spanish, LRE (2020)

    Davidson, S., Yamada, A., Mira, P., Carando, A., S ́anchez-Guti ́errez, C., Sagae, K. [pdf]

  12. Ua-gec: Grammatical error correction and fluency corpus for the ukrainian language, arxiv (2021)

    Syvokon, O., Nahorna, O. [pdf]

  13. Neural grammatical error correction for romanian, CTAI (2020)

    Cotet, T.-M., Ruseti, S., Dascalu, M. [link]

  14. Automated postediting of documents, arXiv: Computation and Language (1994)

    Knight, K., Chander, I. [pdf]

  15. The illinois-columbia system in the conll-2014 shared task, CoNLL (2014)

    Rozovskaya, A., Chang, K.-W., Sammons, M., Roth, D., Habash, N. [pdf]

  16. Sequence to sequence learning with neural networks, arXiv: Computation and Language,arXiv: Computation and Language (2014)

    Sutskever, I., Vinyals, O., Le, Q. [pdf]

  17. Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension, arXiv preprint arXiv:1910.13461 (2019)

    Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., Zettlemoyer, L. [pdf]

  18. Exploring the limits of transfer learning with a unified text-to-text transformer, JMLR 21(1) (2020)

    Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P.J. [pdf]

  19. Interpretability for language learners using example-based grammatical error correction, arXiv preprint arXiv:2203.07085 (2022)

    Kaneko, M., Takase, S., Niwa, A., Okazaki, N. [pdf]

  20. Syngec: Syntax-enhanced grammatical error correction with a tailored gec-oriented parser, arXiv preprint arXiv:2210.12484 (2022)

    Zhang, Y., Zhang, B., Li, Z., Bao, Z., Li, C., Zhang, M. [pdf]

  21. Csyngec: Incorporating constituent-based syntax for gram- matical error correction with a tailored gec-oriented parser, arXiv preprint arXiv:2211.08158 (2022)

    Zhang, Y., Li, Z. [pdf]

  22. Seq2Edits: Sequence transduction using span-level edit operations, EMNLP (2020)

    Stahlberg, F., Kumar, S. [pdf]

  23. Parallel iterative edit models for local sequence transduction, (2019)

    Awasthi, A., Sarawagi, S., Goyal, R., Ghosh, S., Piratla, V. [pdf]

  24. EditNTS: An neu- ral programmer-interpreter model for sentence simplification through explicit editing, (2019)

    Dong, Y., Li, Z., Rezagholizadeh, M., Cheung, J.C.K. [pdf]

  25. Encode, tag, realize: High-precision text editing, arXiv preprint arXiv:1909.01187 (2019)

    Malmi, E., Krause, S., Rothe, S., Mirylenka, D., Severyn, A. [pdf]

  26. Gector – grammatical error correction: Tag, not rewrite, BEA(2020)

    Omelianchuk, K., Atrasevych, V., Chernodub, A., Skurzhanskyi, O. [pdf]

  27. TemplateGEC: Improving grammatical error correction with detection template, Rogers, A., Boyd-Graber, J., Okazaki, N. (eds.) Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (2023)

    Li, Y., Liu, X., Wang, S., Gong, P., Wong, D.F., Gao, Y., Huang, H., Zhang, M. [pdf] [code]

  28. Is chatgpt a highly fluent grammatical error correction system? a comprehensive evaluation, arXiv preprint arXiv:2304.01746 (2023)

    Fang, T., Yang, S., Lan, K., Wong, D.F., Hu, J., Chao, L.S., Zhang, Y. [pdf]

  29. Building a large annotated corpus of learner english: The nus corpus of learner english, BEA (2013)

    Dahlmeier, D., Ng, H., Wu, S. [pdf]

  30. The principles for building the “international corpus of learner chinese”, Applied Linguistics (2011)

    Baoli, Z.

Code Error Correction

  1. Defects4j: a database of existing faults to enable controlled testing studies for java programs, SSTA (2014)

    Just, R., Jalali, D., Ernst, M.D. [pdf]

  2. The manybugs and introclass benchmarks for automated repair of c programs, TSE (2015)

    Le Goues, C., Holtschulte, N., Smith, E.K., Brun, Y., Devanbu, P., Forrest, S., Weimer, W. [link]

  3. Quixbugs: a multi-lingual program repair benchmark set based on the quixey challenge, SPLASH Companion (2017)

    Lin, D., Koppel, J., Chen, A., Solar-Lezama, A. [pdf]

  4. Practical program repair in the era of large pre- trained language models, arXiv preprint arXiv:2210.14179 (2022)

    Xia, C., Wei, Y., Zhang, L. [pdf]

  5. An empirical study on learning bug-fixing patches in the wild via neural machine translation, TOSEM (2018)

    Tufano, M., Watson, C., Bavota, G., Penta, M., White, M., Poshyvanyk, D. [pdf]

  6. Automating code review activities by large-scale pre-training, ESEC/FSE (2022)

    Li, Z., Lu, S., Guo, D., Duan, N., Jannu, S., Jenks, G., Majumder, D., Green, J., Svyatkovskiy, A., Fu, S., Sundaresan, N. [pdf]

  7. Exploring the Potential of ChatGPT in Automated Code Refinement: An Empirical Study, (2023)

    Guo, Q., Cao, J., Xie, X., Liu, S., Li, X., Chen, B., Peng, X. [pdf]

  8. Fine-grained and accurate source code differencing, ASE (2014)

    Falleri, J.-R., Morandat, F., Blanc, X., Martinez, M., Monperrus, M. [pdf]

  9. Learning and evaluating contextual embedding of source code, arxiv (2019)

    Kanade, A., Maniatis, P., Balakrishnan, G., Shi, K. [pdf]

  10. Codebert: A pre-trained model for programming and natural languages, EMNLP (2020)

    Feng, Z., Guo, D., Tang, D., Duan, N., Feng, X., Gong, M., Shou, L., Qin, B., Liu, T., Jiang, D., Zhou, M. [pdf]

  11. Graphcodebert: Pre-training code representations with data flow, arXiv preprint arXiv:2009.08366 (2020)

    Guo, D., Ren, S., Lu, S., Feng, Z., Tang, D., Liu, S., Zhou, L., Duan, N., Svyatkovskiy, A., Shengyu, F., Tufano, M., Deng, S., Clement, C., Drain, D., Sundaresan, N., Yin, J., Jiang, D., Zhou, M. [pdf]

  12. Unixcoder: Unified cross-modal pre-training for code representation, arXiv preprint arXiv:2203.03850

    Guo, D., Lu, S., Duan, N., Wang, Y., Zhou, M., Yin, J.[pdf]

  13. Unified pre-training for program understanding and generation, NAACL (2021)

    Ahmad, W., Chakraborty, S., Ray, B., Chang, K.-W. [pdf]

  14. CodeT5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation, Moens, M.-F., Huang, X., Specia, L., Yih, S.W.-t. (eds.) EMNLP (2021)

    Wang, Y., Wang, W., Joty, S., Hoi, S.C.H. [pdf]

  15. Codepad: Sequence-based code generation with pushdown automaton, (2022)

    Dong, Y., Jiang, X., Liu, Y., Li, G., Jin, Z.[pdf]

Demo & Future Trends & Conclusion

  1. Primeqa: The prime repository for state-of-the-art multilingualquestion answering research and development, arXiv preprint arXiv:2301.09715 (2023)

    Sil, A., Sen, J., Iyer, B., Franz, M., Fadnis, K., Bornea, M., Rosenthal, S., McCarley, S., Zhang, R., Kumar, V., et al. [pdf]

  2. Mwptoolkit: an open-source framework for deep learning-based math word problem solvers, AAAI, vol. 36 (2022)

    Lan, Y., Wang, L., Zhang, Q., Lan, Y., Dai, B.T., Wang, Y., Zhang, D., Lim, E.- P. [link]

  3. A practical toolkit for multilingual question and answer generation, arXiv preprint arXiv:2305.17416 (2023)

    Ushio, A., Alva-Manchego, F., Camacho-Collados, J. [pdf]

  4. Questimator: Generating knowledge assessments for arbitrary topics, JCAI (2016)

    Guo, Q., Kulkarni, C., Kittur, A., Bigham, J.P., Brunskill, E. [pdf]

  5. Lingglewrite: a coaching system for essay writing, ACL (2020)

    Tsai, C.-T., Chen, J.-J., Yang, C.-Y., Chang, J.S. [pdf]

  6. Allecs: A lightweight language error correction system, EACL (2023)

    Qorib, M.R., Moon, G., Ng, H.T. [pdf]

  7. Codegeex: A pre-trained model for code generation with multilingual evaluations on humaneval-x, arXiv preprint arXiv:2303.17568 (2023)

    Zheng, Q., Xia, X., Zou, X., Dong, Y., Wang, S., Xue, Y., Wang, Z., Shen, L., Wang, A., Li, Y., et al. [pdf]

  8. A Beginner’s Guide to Introduce Artificial Intelligence in Teaching and Learning, Springer, ??? (2023)

    Kurni, M., Mohammed, M.S., Srinivasa, K. [link]

  9. Prompting large language models with chain-of-thought for few-shot knowledge base question generation, EMNLP (2023)

    Liang, Y., Wang, J., Zhu, H., Wang, L., Qian, W., Lan, Y. [pdf]

  10. ChatGPT or Grammarly? Evaluating ChatGPT on Grammatical Error Correction Benchmark, BEA (2023)

    Wu, H., Wang, W., Wan, Y., Jiao, W., Lyu, M. [pdf]

  11. Augmenting black-box llms with medical textbooks for clinical question answering, arXiv preprint arXiv:2309.02233 (2023)

    Wang, Y., Ma, X., Chen, W. [pdf]

  12. Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks, TMLR (2023)

    Chen, W., Ma, X., Wang, X., Cohen, W.W. [pdf]

  13. Is chatgpt a highly fluent grammatical error correction system? a comprehensive evaluation, arXiv preprint arXiv:2304.01746 (2023)

    Fang, T., Yang, S., Lan, K., Wong, D.F., Hu, J., Chao, L.S., Zhang, Y. [pdf]

  14. Difficulty controllable generation of reading comprehension questions, arXiv preprint arXiv:1807.03586 (2018)

    Gao, Y., Bing, L., Chen, W., Lyu, M.R., King, I. [pdf]

  15. Guiding the growth: Difficulty-controllable question generation through step-by-step rewriting, arXiv preprint arXiv:2105.11698 (2021)

    Cheng, Y., Li, S., Liu, B., Zhao, R., Li, S., Lin, C., Zheng, Y. [pdf]

  16. Difficulty-controllable neural question generation for reading comprehension using item response theory, BEA (2023)

    Uto, M., Tomikawa, Y., Suzuki, A. [pdf]

  17. Learning to compose neural networks for question answering, arXiv preprint arXiv:1601.01705 (2016)

    Andreas, J., Rohrbach, M., Darrell, T., Klein, D. [pdf]

  18. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models, JCAI (2020)

    Zhang, J., Lee, R.K.-W., Lim, E.-P., Qin, W., Wang, L., Shao, J., Sun, Q. [pdf]

  19. Flacgec: A chinese grammatical error correction dataset with fine-grained linguistic annotation, CIKM (2023)

    Du, H., Zhao, Y., Tian, Q., Wang, J., Wang, L., Lan, Y., Lu, X. [pdf]

  20. Question generation for adaptive education, arXiv preprint arXiv:2106.04262 (2021)

    Srivastava, M., Goodman, N. [pdf]

  21. Difficulty-controlled question generation in adaptive education for few-shot learning, ADMA (2023)

    Wang, Y., Li, L. [link]

  22. Retuyt-inco at bea 2023 shared task: Tuning open-source llms for generating teacher responses, BEA (2023)

    Baladn, A., Sastre, I., Chiruzzo, L., Ros, A. [pdf]

  23. Educhat: A large-scale language model-based chatbot system for intelligent education, arXiv preprint arXiv:2308.02773 (2023)

    Dan, Y., Lei, Z., Gu, Y., Li, Y., Yin, J., Lin, J., Ye, L., Tie, Z., Zhou, Y., Wang, Y., et al. [pdf]

Dataset

The URL of the datasets we mentioned are listed here.

QA

Textbook QA

Dataset URL
TQA http://textbookqa.org
Geometry3K https://lupantech.github.io/inter-gps/
AI2D https://github.com/allenai/dqa-net
ScienceQA https://scienceqa.github.io/
MedQA https://github.com/jind11/MedQA
MedMCQA https://medmcqa.github.io
TheoremQA https://github.com/wenhuchen/TheoremQA

MWP

Dataset URL
Dolphin-18K http://research.microsoft.com/en-us/projects/dolphin/
DRAW-1K https://www.microsoft.com/en-us/research/publication/annotating-derivations-a-new-evaluation-strategy-and-dataset-for-algebra-word-problems/
Math23K https://ai.tencent.com/ailab/nlp/dialogue/#datasets
MathQA https://math-qa.github.io/math-QA/
ASDiv https://github.com/chaochun/nlu-asdiv-dataset
GSM8K https://github.com/openai/grade-school-math
IconQA https://iconqa.github.io/
TABMWP https://promptpg.github.io/

QC

Dataset URL
SciQ https://allenai.org/data/sciq
RACE https://www.cs.cmu.edu/~glai1/data/race/
FairytaleQA https://github.com/uci-soe/FairytaleQAData
LearningQ https://dataverse.mpi-sws.org/dataverse/icwsm18
KHANQ https://github.com/Huanli-Gong/KhanQ
EduQG https://github.com/hadifar/question-generation
MCQL https://github.com/harrylclc/LTR-DG
Televic https://github.com/semerekiros/dist-retrieval

AA

AES

Dataset URL
CLC-FCE http://www.ilexir.com/
ASAP https://www.kaggle.com/c/asap-aes
TOEFL 11 https://catalog.ldc.upenn.edu/LDC2014T06
ICLE
HSK http://yuyanziyuan.blcu.edu.cn/en/info/1043/1501.htm

EC

LEC

Dataset URL
LANG-8 https://sites.google.com/site/naistlang8corpora/home
CLANG-8 https://github.com/google-research-datasets/clang8
CoNLL-2014
BEA-2019 https://www.cl.cam.ac.uk/research/nl/bea2019st/
SIGHAN
CTC https://destwang.github.io/CTC2021-explorer/
FCGEC https://github.com/xlxwalex/FCGEC
FlaCGEC https://github.com/hyDududu/FlaCGEC
GECCC https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-4639
RULEC-GEC https://github.com/arozovskaya/RULEC-GEC
Falko-MERLIN https://github.com/adrianeboyd/boyd-wnut2018
COWS-L2H https://github.com/ucdaviscl/cowsl2h
UA-GEC https://github.com/grammarly/ua-gec
RONACC https://github.com/TeodorMihai/RoGEC

CEC

Dataset URL
Defects4J https://github.com/rjust/defects4j
ManyBugs https://repairbenchmarks.cs.umass.edu/
IntroClass https://repairbenchmarks.cs.umass.edu/
QuixBugs https://github.com/jkoppel/QuixBugs
Bugs2Fix
CodeReview https://github.com/microsoft/CodeBERT/tree/master/CodeReviewer
CodeReview-New

Citation

@article{lan2024survey,
  title={Survey of Natural Language Processing for Education: Taxonomy, Systematic Review, and Future Trends},
  author={Lan, Yunshi and Li, Xinyuan and Du, Hanyue and Lu, Xuesong and Gao, Ming and Qian, Weining and Zhou, Aoying},
  journal={arXiv preprint arXiv:2401.07518},
  year={2024}
}

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