{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T14:54:27Z","timestamp":1773500067635,"version":"3.50.1"},"reference-count":70,"publisher":"Elsevier BV","issue":"1","license":[{"start":{"date-parts":[[2024,4,22]],"date-time":"2024-04-22T00:00:00Z","timestamp":1713744000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,4,22]],"date-time":"2024-04-22T00:00:00Z","timestamp":1713744000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"SERI"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Artif Intell Educ"],"published-print":{"date-parts":[[2025,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Writing high-quality procedural texts is a challenging task for many learners. While example-based learning has shown promise as a feedback approach, a limitation arises when all learners receive the same content without considering their individual input or prior knowledge. Consequently, some learners struggle to grasp or relate to the feedback, finding it redundant and unhelpful. To address this issue, we present , an adaptive learning system designed to enhance procedural writing through personalized example-based learning. The core of our system is a multi-step example retrieval pipeline that selects a higher quality and contextually relevant example for each learner based on their unique input. We instantiate our system in the domain of cooking recipes. Specifically, we leverage a fine-tuned Large Language Model to predict the quality score of the learner\u2019s cooking recipe. Using this score, we retrieve recipes with higher quality from a vast database of over\n                    <jats:bold>180,000<\/jats:bold>\n                    recipes. Next, we apply  to select the semantically most similar recipe in real-time. Finally, we use domain knowledge and regular expressions to enrich the selected example recipe with personalized instructional explanations. We evaluate  in a 2\n                    <jats:italic>x<\/jats:italic>\n                    2 controlled study (personalized vs. non-personalized examples, reflective prompts vs. none) with 200 participants. Our results show that providing tailored examples contributes to better writing performance and user experience.\n                  <\/jats:p>","DOI":"10.1007\/s40593-024-00405-1","type":"journal-article","created":{"date-parts":[[2024,4,22]],"date-time":"2024-04-22T15:01:31Z","timestamp":1713798091000},"page":"330-366","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Enhancing Procedural Writing Through Personalized Example Retrieval: A Case Study on Cooking Recipes"],"prefix":"10.1016","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1242-3134","authenticated-orcid":false,"given":"Paola","family":"Mejia-Domenzain","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0631-0636","authenticated-orcid":false,"given":"Jibril","family":"Frej","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4794-395X","authenticated-orcid":false,"given":"Seyed Parsa","family":"Neshaei","sequence":"additional","affiliation":[]},{"given":"Luca","family":"Mouchel","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1343-0627","authenticated-orcid":false,"given":"Tanya","family":"Nazaretsky","sequence":"additional","affiliation":[]},{"given":"Thiemo","family":"Wambsganss","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8968-9649","authenticated-orcid":false,"given":"Antoine","family":"Bosselut","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0672-0415","authenticated-orcid":false,"given":"Tanja","family":"K\u00e4ser","sequence":"additional","affiliation":[]}],"member":"78","published-online":{"date-parts":[[2024,4,22]]},"reference":[{"key":"405_CR1","doi-asserted-by":"publisher","unstructured":"Adoniou, M. (2013). Drawing to support writing development in english language learners. Language and Education, 27(3), 261\u2013277. Retrieved from https:\/\/doi.org\/10.1080\/09500782.2012.704047","DOI":"10.1080\/09500782.2012.704047"},{"key":"405_CR2","doi-asserted-by":"publisher","unstructured":"Afrin, T., Kashefi, O., Olshefski, C., Litman, D., Hwa, R., & Godley, A. (2021). Effective interfaces for student-driven revision sessions for argumentative writing. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1\u201313). ACM. Retrieved from https:\/\/doi.org\/10.1145\/3411764.3445683","DOI":"10.1145\/3411764.3445683"},{"key":"405_CR3","doi-asserted-by":"crossref","unstructured":"Agarwal, R., & Karahanna, E. (2000). Time flies when you\u2019re having fun: cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24(4), 665\u2013694. Retrieved 2022-09-13 from http:\/\/www.jstor.org\/stable\/3250951","DOI":"10.2307\/3250951"},{"key":"405_CR4","doi-asserted-by":"publisher","unstructured":"Ahmed, U.Z., Srivastava, N., Sindhgatta, R., & Karkare, A. (2020). Characterizing the pedagogical benefits of adaptive feedback for compilation errors by novice programmers. In: Proceedings of the ACM\/IEEE 42nd International Conference on Software Engineering: Software Engineering Education and Training (pp. 139\u2013150). ACM. Retrieved from https:\/\/doi.org\/10.1145\/3377814.3381703","DOI":"10.1145\/3377814.3381703"},{"key":"405_CR5","doi-asserted-by":"publisher","unstructured":"Alamri, H., Lowell, V., Watson, W., & Watson, S.L. (2020). Using personalized learning as an instructional approach to motivate learners in online higher education: Learner self-determination and intrinsic motivation. Journal of Research on Technology in Education, 52(3), 322\u2013352. Retrieved from https:\/\/doi.org\/10.1080\/15391523.2020.1728449","DOI":"10.1080\/15391523.2020.1728449"},{"issue":"2","key":"405_CR6","doi-asserted-by":"publisher","first-page":"128","DOI":"10.33394\/jollt.v7i2.1960","volume":"7","author":"V Alviana","year":"2019","unstructured":"Alviana, V. (2019). The effect of recipe demonstration technique on students\u2019 writing competence in procedural text. Journal of Languages and Language Teaching, 7(2), 128\u2013131.","journal-title":"Journal of Languages and Language Teaching"},{"issue":"2","key":"405_CR7","doi-asserted-by":"publisher","first-page":"364","DOI":"10.24071\/llt.v24i2.3371","volume":"24","author":"S Ambarwati","year":"2021","unstructured":"Ambarwati, S., & Listyani, L. (2021). Procedural essay writing: Students\u2019 problems and strategies. LLT Journal: A Journal on Language and Language Teaching, 24(2), 364\u2013379.","journal-title":"LLT Journal: A Journal on Language and Language Teaching"},{"key":"405_CR8","doi-asserted-by":"publisher","unstructured":"Bassen, J., Balaji, B., Schaarschmidt, M., Thille, C., Painter, J., Zimmaro, D. & Mitchell, J.C. (2020). Reinforcement learning for the adaptive scheduling of educational activities. In: CHI \u201920: CHI Conference on Human Factors in Computing Systems (pp. 1\u201312). ACM. Retrieved from https:\/\/doi.org\/10.1145\/3313831.3376518","DOI":"10.1145\/3313831.3376518"},{"key":"405_CR9","doi-asserted-by":"crossref","unstructured":"Bie\u0144, M., Gilski, M., Maciejewska, M., Taisner, W., Wisniewski, D., & Lawrynowicz, A. (2020). RecipeNLG: A cooking recipes dataset for semi-structured text generation. In: Proceedings of the 13th International Conference on Natural Language Generation (pp. 22\u201328). ACL. Retrieved from https:\/\/aclanthology.org\/2020.inlg-1.4","DOI":"10.18653\/v1\/2020.inlg-1.4"},{"key":"405_CR10","doi-asserted-by":"publisher","unstructured":"Bimba, A.T., Idris, N., Al-Hunaiyyan, A., Mahmud, R.B., & Shuib, N.L.B.M. (2017). Adaptive feedback in computer-based learning environments: a review. Adaptive Behavior, 25(5), 217\u2013234. Retrieved from https:\/\/doi.org\/10.1177\/1059712317727590","DOI":"10.1177\/1059712317727590"},{"key":"405_CR11","unstructured":"Brown, T.B., & et\u00a0al. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems 33. Retrieved from https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/1457c0d6bfcb4967418bfb8ac142f64a-Abstract.html"},{"key":"405_CR12","doi-asserted-by":"publisher","unstructured":"Chi, M.T., Bassok, M., Lewis, M.W., Reimann, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science, 13(2), 145\u2013182. Retrieved from https:\/\/doi.org\/10.1016\/0364-0213(89)90002-5","DOI":"10.1016\/0364-0213(89)90002-5"},{"key":"405_CR13","doi-asserted-by":"crossref","unstructured":"Compeau, D.R., & Higgins, C.A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189\u2013211. Retrieved from http:\/\/www.jstor.org\/stable\/249688","DOI":"10.2307\/249688"},{"key":"405_CR14","unstructured":"Cooper, A., Reimann, R., & Cronin, D. (2007). About face 3: the essentials of interaction design (3rd edition). Wiley Pub."},{"key":"405_CR15","doi-asserted-by":"crossref","unstructured":"Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319\u2013340. Retrieved 2022-09-13 from http:\/\/www.jstor.org\/stable\/249008","DOI":"10.2307\/249008"},{"key":"405_CR16","doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M., Lee, K., & Toutanova, K. (2019). BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 4171\u20134186). Association for Computational Linguistics. Retrieved from https:\/\/doi.org\/10.18653\/v1\/n19-1423","DOI":"10.18653\/v1\/n19-1423"},{"key":"405_CR17","doi-asserted-by":"publisher","unstructured":"Doroudi, S., Kamar, E., Brunskill, E., & Horvitz, E. (2016). Toward a learning science for complex crowdsourcing tasks. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (pp. 2623\u20132634). ACM. Retrieved from https:\/\/doi.org\/10.1145\/2858036.2858268","DOI":"10.1145\/2858036.2858268"},{"key":"405_CR18","doi-asserted-by":"publisher","unstructured":"Fan, X., Luo, W., Menekse, M., Litman, D., & Wang, J. (2017). Scaling reflection prompts in large classrooms via mobile interfaces and natural language processing. Proceedings of the 22nd International Conference on Intelligent User Interfaces (pp. 363\u2013374). ACM. Retrieved from https:\/\/doi.org\/10.1145\/3025171.3025204","DOI":"10.1145\/3025171.3025204"},{"key":"405_CR19","unstructured":"Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. Retrieved from arXiv:2203.05794"},{"key":"405_CR20","doi-asserted-by":"publisher","unstructured":"Gururangan, S., Marasovic, A., Swayamdipta, S., Lo, K., Beltagy, I., Downey, D., & Smith, N.A. (2020). Don\u2019t stop pretraining: Adapt language models to domains and tasks. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 8342\u20138360). ACL. Retrieved from https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.740","DOI":"10.18653\/v1\/2020.acl-main.740"},{"key":"405_CR21","doi-asserted-by":"publisher","unstructured":"Hilbert, T.S., Renkl, A., Kessler, S., & Reiss, K. (2008). Learning to prove in geometry: Learning from heuristic examples and how it can be supported. Learning and Instruction, 18(1), 54\u201365. Retrieved from https:\/\/doi.org\/10.1016\/j.learninstruc.2006.10.008","DOI":"10.1016\/j.learninstruc.2006.10.008"},{"key":"405_CR22","doi-asserted-by":"publisher","unstructured":"Hosseini, R., & Brusilovsky, P. (2017). A study of concept-based similarity approaches for recommending program examples. New Review of Hypermedia and Multimedia, 23(3), 161\u2013188. Retrieved from https:\/\/doi.org\/10.1080\/13614568.2017.1356878","DOI":"10.1080\/13614568.2017.1356878"},{"key":"405_CR23","doi-asserted-by":"publisher","unstructured":"Hu, G., Ahmed, M., & L\u2019Abb\u00e9, M.R. (2022). Natural language processing and machine learning approaches for food categorization and nutrition quality prediction compared to traditional methods. The American Journal of Clinical Nutrition, 553\u2013563. Retrieved from https:\/\/doi.org\/10.1016\/j.ajcnut.2022.11.022","DOI":"10.1016\/j.ajcnut.2022.11.022"},{"key":"405_CR24","doi-asserted-by":"publisher","unstructured":"Jin, R., & Si, L. (2004). A study of methods for normalizing user ratings in collaborative filtering. Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 568\u2013569). ACM. Retrieved from https:\/\/doi.org\/10.1145\/1008992.1009124","DOI":"10.1145\/1008992.1009124"},{"key":"405_CR25","unstructured":"Kingma, D.P., & Ba, J. (2015). Adam: A method for stochastic optimization. 3rd International Conference on Learning Representations. Retrieved from arXiv:1412.6980"},{"key":"405_CR26","volume-title":"Evaluating training programs: The four levels","author":"DL Kirkpatrick","year":"1994","unstructured":"Kirkpatrick, D. L. (1994). Evaluating training programs: The four levels. San Francisco: Berrett-Koehler Publishers."},{"key":"405_CR27","doi-asserted-by":"crossref","unstructured":"Landis, J.R., & Koch, G.G. (1977). An application of hierarchical kappa-type statistics in the assessment of majority agreement among multiple observers. Biometrics, 33(2), 363\u2013374. Retrieved from http:\/\/www.jstor.org\/stable\/2529786","DOI":"10.2307\/2529786"},{"key":"405_CR28","unstructured":"Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D. & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. Retrieved from arXiv:1907.11692"},{"key":"405_CR29","doi-asserted-by":"publisher","unstructured":"Majumder, B.P., Li, S., Ni, J., & McAuley, J. (2019). Generating personalized recipes from historical user preferences. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (pp. 5976\u20135982). ACL. Retrieved from https:\/\/doi.org\/10.18653\/v1\/D19-1613","DOI":"10.18653\/v1\/D19-1613"},{"key":"405_CR30","unstructured":"Max, L., Alex, S., & Dmytro, L. (2022). Grammarly. Retrieved from https:\/\/app.grammarly.com\/"},{"key":"405_CR31","doi-asserted-by":"publisher","unstructured":"Mayfield, E., & Black, A.W. (2020). Should you fine-tune bert for automated essay scoring? In: Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications (pp. 151\u2013162). Association for Computational Linguistics. Retrieved from https:\/\/doi.org\/10.18653\/v1\/2020.bea-1.15","DOI":"10.18653\/v1\/2020.bea-1.15"},{"key":"405_CR32","doi-asserted-by":"publisher","unstructured":"Mejia-Domenzain, P., Marras, M., Giang, C., Cattaneo, A., & K\u00e4ser, T. (2022). Evolutionary clustering of apprentices\u2019 self- regulated learning behavior in learning journals. IEEE Transactions on Learning Technologies, 1\u201314. Retrieved from https:\/\/doi.org\/10.1109\/TLT.2022.3195881","DOI":"10.1109\/TLT.2022.3195881"},{"key":"405_CR33","unstructured":"Mouchel, L., Wambsganss, T., Mejia-Domenzain, P., & K\u00e4ser, T. (2023). Understanding revision behavior in adaptive writing support systems for education. International Conference on Educational Data Mining, 445\u2013452. Retrieved from arXiv:2306.10304"},{"key":"405_CR34","unstructured":"Nah, F.F. (2003). A study on tolerable waiting time: How long are web users willing to wait? 9th Americas Conference on Information Systems (p. 285). AIS. Retrieved from http:\/\/aisel.aisnet.org\/amcis2003\/285"},{"key":"405_CR35","unstructured":"Neelakantan, A., Xu, T., Puri, R., Radford, A., Han, J.M., Tworek, J. & et\u00a0al. (2022). Text and code embeddings by contrastive pre-training. Retrieved from arXiv:2201.10005"},{"key":"405_CR36","doi-asserted-by":"publisher","unstructured":"N\u00fcckles, M., H\u00fcbner, S., & Renkl, A. (2009). Enhancing self-regulated learning by writing learning protocols. Learning and Instruction, 19(3), 259\u2013271. Retrieved from https:\/\/doi.org\/10.1016\/j.learninstruc.2008.05.002","DOI":"10.1016\/j.learninstruc.2008.05.002"},{"key":"405_CR37","unstructured":"Ostmann, B.G.O., & Baker, J.L. (2001). The recipe writer\u2019s handbook, revised and expanded. Harvest."},{"key":"405_CR38","unstructured":"Paassen, B., Hammer, B., Price, T.W., Barnes, T., Gross, S., & Pinkwart, N. (2018). The Continuous Hint Factory - Providing Hints in Vast and Sparsely Populated Edit Distance Spaces. Journal of Educational Data Mining, 10(1), 1\u201335. Retrieved from arXiv:1708.06564"},{"key":"405_CR39","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.jesp.2017.01.006","volume":"70","author":"E Peer","year":"2017","unstructured":"Peer, E., Brandimarte, L., Samat, S., & Acquisti, A. (2017). Beyond the turk: Alternative platforms for crowdsourcing behavioral research. Journal of Experimental Social Psychology, 70, 153\u2013163.","journal-title":"Journal of Experimental Social Psychology"},{"key":"405_CR40","doi-asserted-by":"publisher","unstructured":"Pel\u00e1nek, R. (2020). Measuring similarity of educational items: An overview. IEEE Transactions on Learning Technologies, 13(2), 354\u2013366. Retrieved from https:\/\/doi.org\/10.1109\/TLT.2019.2896086","DOI":"10.1109\/TLT.2019.2896086"},{"key":"405_CR41","doi-asserted-by":"publisher","unstructured":"Premlatha, K.R., & Geetha, T.V. (2015). Learning content design and learner adaptation for adaptive e-learning environment: a survey. Artificial Intelligence Review, 44(4), 443\u2013465. Retrieved from https:\/\/doi.org\/10.1007\/s10462-015-9432-z","DOI":"10.1007\/s10462-015-9432-z"},{"key":"405_CR42","doi-asserted-by":"publisher","unstructured":"Reimers, N., & Gurevych, I. (2019). Sentence-bert: Sentence embeddings using siamese bert-networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (pp. 3980\u20133990). ACL. Retrieved from https:\/\/doi.org\/10.18653\/v1\/D19-1410","DOI":"10.18653\/v1\/D19-1410"},{"key":"405_CR43","doi-asserted-by":"publisher","unstructured":"Renkl, A. (2002). Worked-out examples: instructional explanations support learning by self-explanations. Learning and Instruction, 12(5), 529\u2013556. Retrieved from https:\/\/doi.org\/10.1016\/S0959-4752(01)00030-5","DOI":"10.1016\/S0959-4752(01)00030-5"},{"key":"405_CR44","doi-asserted-by":"publisher","unstructured":"Renkl, A., Hilbert, T., & Schworm, S. (2009). Example-based learning in heuristic domains: A cognitive load theory account. Educational Psychology Review, 21(1), 67\u201378. Retrieved from https:\/\/doi.org\/10.1007\/s10648-008-9093-4","DOI":"10.1007\/s10648-008-9093-4"},{"key":"405_CR45","doi-asserted-by":"crossref","unstructured":"Ringenberg, M.A., & VanLehn, K. (2006). Scaffolding problem solving with annotated, worked-out examples to promote deep learning. Intelligent Tutoring Systems (pp. 625\u2013634). Springer Berlin Heidelberg.","DOI":"10.1007\/11774303_62"},{"key":"405_CR46","doi-asserted-by":"publisher","unstructured":"Robertson, S.E., & Walker, S. (1994). Some simple effective approximations to the 2-poisson model for probabilistic weighted retrieval. Proceedings of the 17th Annual International Conference on Research and Development in Information Retrieval. (pp. 232\u2013241). ACM. Retrieved from https:\/\/doi.org\/10.1007\/978-1-4471-2099-5_24","DOI":"10.1007\/978-1-4471-2099-5_24"},{"key":"405_CR47","doi-asserted-by":"publisher","unstructured":"Roelle, J., Kr\u00fcger, S., Jansen, C., & Berthold, K. (2012). The use of solved example problems for fostering strategies of self-regulated learning in journal writing. Education Research International. (2012). 751625. Retrieved from https:\/\/doi.org\/10.1155\/2012\/751625","DOI":"10.1155\/2012\/751625"},{"key":"405_CR48","doi-asserted-by":"publisher","unstructured":"Rogers, T., & Feller, A. (2016). Discouraged by peer excellence: Exposure to exemplary peer performance causes quitting. Psychological Science, 27(3), 365\u2013374. Retrieved from https:\/\/doi.org\/10.1177\/0956797615623770","DOI":"10.1177\/0956797615623770"},{"key":"405_CR49","unstructured":"Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019). DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. Retrieved from arXiv:1910.01108"},{"key":"405_CR50","doi-asserted-by":"publisher","unstructured":"Sato, K., & Matsushima, K. (2006). Effects of audience awareness on procedural text writing. Psychological Reports, 99(1), 51\u201373. Retrieved from https:\/\/doi.org\/10.2466\/pr0.99.1.51-73","DOI":"10.2466\/pr0.99.1.51-73"},{"key":"405_CR51","doi-asserted-by":"publisher","unstructured":"Schwonke, R., Renkl, A., Krieg, C., Wittwer, J., Aleven, V., & Salden, R. (2009). The worked-example effect: Not an artefact of lousy control conditions. Computers in Human Behavior, 25(2), 258\u2013266. Retrieved from https:\/\/doi.org\/10.1016\/j.chb.2008.12.011","DOI":"10.1016\/j.chb.2008.12.011"},{"key":"405_CR52","doi-asserted-by":"publisher","unstructured":"Schworm, S., & Renkl, A. (2006). Computer-supported example-based learning: When instructional explanations reduce self-explanations. Computers & Education, 46(4), 426\u2013445. Retrieved from https:\/\/doi.org\/10.1016\/j.compedu.2004.08.011","DOI":"10.1016\/j.compedu.2004.08.011"},{"key":"405_CR53","doi-asserted-by":"publisher","unstructured":"Schworm, S., & Renkl, A. (2007). Learning argumentation skills through the use of prompts for self-explaining examples. Journal of Educational Psychology, 99(2), 285\u2013296. Retrieved from https:\/\/doi.org\/10.1037\/0022-0663.99.2.285","DOI":"10.1037\/0022-0663.99.2.285"},{"key":"405_CR54","doi-asserted-by":"publisher","unstructured":"Sellam, T., Das, D., & Parikh, A.P. (2020). BLEURT: learning robust metrics for text generation. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 7881\u20137892). ACL. Retrieved from https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.704","DOI":"10.18653\/v1\/2020.acl-main.704"},{"key":"405_CR55","doi-asserted-by":"publisher","unstructured":"Slade, C., & Downer, T. (2020). Students\u2019 conceptual understanding and attitudes towards technology and user experience before and after use of an eportfolio. Journal of Computing in Higher Education, 32(3), 529\u2013552. Retrieved from https:\/\/doi.org\/10.1007\/s12528-019-09245-8","DOI":"10.1007\/s12528-019-09245-8"},{"key":"405_CR56","doi-asserted-by":"publisher","unstructured":"Sun, C., Qiu, X., & Xu, Y. (2019). How to fine-tune BERT for text classification? Chinese Computational Linguistics - 18th China National Conference (Vol. 11856, pp. 194\u2013206). Springer. Retrieved from https:\/\/doi.org\/10.1007\/978-3-030-32381-3_16","DOI":"10.1007\/978-3-030-32381-3_16"},{"key":"405_CR57","doi-asserted-by":"publisher","unstructured":"Sweller, J. (1994). Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction, 4(4), 295\u2013312. Retrieved from https:\/\/doi.org\/10.1016\/0959-4752(94)90003-5","DOI":"10.1016\/0959-4752(94)90003-5"},{"key":"405_CR58","doi-asserted-by":"publisher","unstructured":"Traga\u00a0Philippakos, Z.A. (2019). Effects of strategy instruction with an emphasis on oral language and dramatization on the quality of first graders\u2019 procedural writing. Reading & Writing Quarterly, 35(5), 409\u2013426. Retrieved from https:\/\/doi.org\/10.1080\/10573569.2018.1547233","DOI":"10.1080\/10573569.2018.1547233"},{"key":"405_CR59","doi-asserted-by":"publisher","unstructured":"van Gog, T., & Rummel, N. (2010). Example-based learning: Integrating cognitive and social-cognitive research perspectives. Educational Psychology Review, 22(2), 155\u2013174. Retrieved from https:\/\/doi.org\/10.1007\/s10648-010-9134-7","DOI":"10.1007\/s10648-010-9134-7"},{"key":"405_CR60","doi-asserted-by":"publisher","unstructured":"van Gog, T., Paas, F., & van Merri\u00ebnboer, J.J. (2008). Effects of studying sequences of process-oriented and product-oriented worked examples on troubleshooting transfer efficiency. Learning and Instruction, 18(3), 211\u2013222. Retrieved from https:\/\/doi.org\/10.1016\/j.learninstruc.2007.03.003","DOI":"10.1016\/j.learninstruc.2007.03.003"},{"key":"405_CR61","doi-asserted-by":"publisher","unstructured":"Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273\u2013315. Retrieved from https:\/\/doi.org\/10.1111\/j.1540-5915.2008.00192.x","DOI":"10.1111\/j.1540-5915.2008.00192.x"},{"key":"405_CR62","doi-asserted-by":"publisher","unstructured":"Wang, W., Arya, D.M., Novielli, N., Cheng, J., & Guo, J.L.C. (2020). Argulens: Anatomy of community opinions on usability issues using argumentation models. Conference on Human Factors in Computing Systems (pp. 1\u201314). ACM. Retrieved from https:\/\/doi.org\/10.1145\/3313831.3376218","DOI":"10.1145\/3313831.3376218"},{"key":"405_CR63","doi-asserted-by":"crossref","unstructured":"Wambsganss, T., Niklaus, C., Cetto, M., S\u00f6llner, M., Handschuh, S., Leimeister, J.M. (2020). AL: an adaptive learning support system for argumentation skills. In Proceedings of the 2020 CHI conference on human factors in computing systems (pp. 1\u201314)","DOI":"10.1145\/3313831.3376732"},{"key":"405_CR64","doi-asserted-by":"crossref","unstructured":"Wieringa, D.R., & Farkas, D.K. (1991). Procedure writing across domains: nuclear power plant procedures and computer documentation. Proceedings of the 9th Annual International Conference on Systems Documentation (pp. 49\u201358).","DOI":"10.1145\/122778.122787"},{"key":"405_CR65","unstructured":"Wilson, J., Olinghouse, N.G., & Andrada, G.N. (2014). Does automated feedback improve writing quality? Learning Disabilities: A Contemporary Journal, 12(1), 93\u2013118. Retrieved from https:\/\/eric.ed.gov\/?id=EJ1039856"},{"key":"405_CR66","doi-asserted-by":"crossref","unstructured":"Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., & Brew, J. (2019). Huggingface\u2019s transformers: State-of-the-art natural language processing. Retrieved from arXiv:1910.03771","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"405_CR67","doi-asserted-by":"publisher","unstructured":"Wong, R.M., Lawson, M.J., & Keeves, J. (2002). The effects of self-explanation training on students\u2019 problem solving in high-school mathematics. Learning and Instruction, 12(2), 233\u2013262. Retrieved from https:\/\/doi.org\/10.1016\/S0959-4752(01)00027-5","DOI":"10.1016\/S0959-4752(01)00027-5"},{"key":"405_CR68","doi-asserted-by":"publisher","unstructured":"Zhu, M., Liu, O.L., & Lee, H.-S. (2020). The effect of automated feedback on revision behavior and learning gains in formative assessment of scientific argument writing. Computers & Education, 143, 103668. Retrieved from https:\/\/doi.org\/10.1016\/j.compedu.2019.103668","DOI":"10.1016\/j.compedu.2019.103668"},{"key":"405_CR69","doi-asserted-by":"publisher","unstructured":"Zhu, M., Zhang, M., & Deane, P. (2019). Analysis of Keystroke Sequences in Writing Logs. ETS Research Report Series, 2019(1), 1\u201316. Retrieved from https:\/\/doi.org\/10.1002\/ets2.12247","DOI":"10.1002\/ets2.12247"},{"key":"405_CR70","doi-asserted-by":"publisher","unstructured":"Zlabinger, M., Sabou, M., Hofst\u00e4tter, S., Sertkan, M., & Hanbury, A. (2020). DEXA: supporting non-expert annotators with dynamic examples from experts. Proceedings of the 43rd International conference on research and development in Information Retrieval (pp. 2109\u20132112). ACM. Retrieved from https:\/\/doi.org\/10.1145\/3397271.3401334","DOI":"10.1145\/3397271.3401334"}],"container-title":["International Journal of Artificial Intelligence in Education"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40593-024-00405-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40593-024-00405-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40593-024-00405-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T18:12:45Z","timestamp":1772647965000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40593-024-00405-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,22]]},"references-count":70,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["405"],"URL":"https:\/\/doi.org\/10.1007\/s40593-024-00405-1","relation":{},"ISSN":["1560-4292","1560-4306"],"issn-type":[{"value":"1560-4292","type":"print"},{"value":"1560-4306","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,22]]},"assertion":[{"value":"10 April 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 April 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}