http://orcid.org/0000-0003-3596-5585
Citations (33)
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Beng Huat See, Stephen Gorard, Binwei Lu, Lan Dong & Nadia Siddiqui. (2022) Is technology always helpful?: A critical review of the impact on learning outcomes of education technology in supporting formative assessment in schools. Research Papers in Education 37:6, pages 1064-1096.
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Qing-Ke Fu, Di Zou, Haoran Xie & Gary Cheng. (2022) A review of AWE feedback: types, learning outcomes, and implications. Computer Assisted Language Learning 0:0, pages 1-43.
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Xiaoming Zhai, Yue Yin, James W. Pellegrino, Kevin C. Haudek & Lehong Shi. (2020) Applying machine learning in science assessment: a systematic review. Studies in Science Education 56:1, pages 111-151.
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Articles from other publishers (30)
Paul P. Martin, David Kranz, Peter Wulff & Nicole Graulich. (2023) Exploring new depths: Applying machine learning for the analysis of student argumentation in chemistry. Journal of Research in Science Teaching.
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Yi Song, Mengxiao Zhu & Jesse R. Sparks. (2023) Exploring the Role of Process Data Analysis in Understanding Student Performance and Interactive Behavior in a Game-Based Argument Task. Journal of Educational Computing Research 61:5, pages 1096-1120.
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Jaeyong Lee, Gyeong-Geon Lee & Hun-Gi Hong. (2023) Automated Assessment of Student Hand Drawings in Free-Response Items on the Particulate Nature of Matter. Journal of Science Education and Technology 32:4, pages 549-566.
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Paul P. Martin & Nicole Graulich. (2023) When a machine detects student reasoning: a review of machine learning-based formative assessment of mechanistic reasoning. Chemistry Education Research and Practice 24:2, pages 407-427.
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Xiaoming Zhai, Kevin C. Haudek & Wenchao Ma. (2022) Assessing Argumentation Using Machine Learning and Cognitive Diagnostic Modeling. Research in Science Education 53:2, pages 405-424.
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Mustafa Umut Demirezen, Ozgur Yilmaz & Elif Ince. (2023) New models developed for detection of misconceptions in physics with artificial intelligence. Neural Computing and Applications.
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Moriah Ariely, Tanya Nazaretsky & Giora Alexandron. (2022) Machine Learning and Hebrew NLP for Automated Assessment of Open-Ended Questions in Biology. International Journal of Artificial Intelligence in Education 33:1, pages 1-34.
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Michael Sailer, Elisabeth Bauer, Riikka Hofmann, Jan Kiesewetter, Julia Glas, Iryna Gurevych & Frank Fischer. (2023) Adaptive feedback from artificial neural networks facilitates pre-service teachers’ diagnostic reasoning in simulation-based learning. Learning and Instruction 83, pages 101620.
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Huy A. Nguyen, Hayden Stec, Xinying Hou, Sarah Di & Bruce M. McLaren. 2023. Responsive and Sustainable Educational Futures. Responsive and Sustainable Educational Futures
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Margarita Olivera‐Aguilar, Hee‐Sun Lee, Amy Pallant, Vinetha Belur, Matthew Mulholland & Ou Lydia Liu. (2022) Comparing the Effect of Contextualized Versus Generic Automated Feedback on Students' Scientific Argumentation. ETS Research Report Series 2022:1, pages 1-14.
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Manuel Ninaus & Michael Sailer. (2022) Zwischen Mensch und Maschine: Künstliche Intelligenz zur Förderung von Lernprozessen. Lernen und Lernstörungen 11:4.
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Manuel Ninaus & Michael Sailer. (2022) Closing the loop – The human role in artificial intelligence for education. Frontiers in Psychology 13.
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Xinjie Deng. (2022) A Bibliometric Analysis of Automated Writing Evaluation in Education Using VOSviewer and CitNetExplorer from 2008 to 2022. International Journal of Technology-Enhanced Education 1:1, pages 1-22.
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Andrea Mapplebeck & Lynda Dunlop. (2019) Oral Interactions in Secondary Science Classrooms: a Grounded Approach to Identifying Oral Feedback Types and Practices. Research in Science Education 51:S2, pages 957-982.
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Aysel Saricaoglu & Zeynep Bilki. (2021) Voluntary use of automated writing evaluation by content course students. ReCALL 33:3, pages 265-277.
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Xiaoming Zhai, Lehong Shi & Ross H. Nehm. (2020) A Meta-Analysis of Machine Learning-Based Science Assessments: Factors Impacting Machine-Human Score Agreements. Journal of Science Education and Technology 30:3, pages 361-379.
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Deniz Saribas & Ertan Çetinkaya. (2020) Pre-Service Teachers’ Analysis of Claims About COVID-19 in an Online Course. Science & Education 30:2, pages 235-266.
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Sarah Maestrales, Xiaoming Zhai, Israel Touitou, Quinton Baker, Barbara Schneider & Joseph Krajcik. (2021) Using Machine Learning to Score Multi-Dimensional Assessments of Chemistry and Physics. Journal of Science Education and Technology 30:2, pages 239-254.
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Hee-Sun Lee, Gey-Hong Gweon, Trudi Lord, Noah Paessel, Amy Pallant & Sarah Pryputniewicz. (2021) Machine Learning-Enabled Automated Feedback: Supporting Students’ Revision of Scientific Arguments Based on Data Drawn from Simulation. Journal of Science Education and Technology 30:2, pages 168-192.
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Xiaoming Zhai, Joseph Krajcik & James W. Pellegrino. (2021) On the Validity of Machine Learning-based Next Generation Science Assessments: A Validity Inferential Network. Journal of Science Education and Technology 30:2, pages 298-312.
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Cong Wang, Xiufeng Liu, Lei Wang, Ying Sun & Hongyan Zhang. (2020) Automated Scoring of Chinese Grades 7–9 Students’ Competence in Interpreting and Arguing from Evidence. Journal of Science Education and Technology 30:2, pages 269-282.
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Karen R. Young, Henry E. Schaffer, Jasmine B. James & Maria T. Gallardo-Williams. (2020) Tired of Failing Students? Improving Student Learning Using Detailed and Automated Individualized Feedback in a Large Introductory Science Course. Innovative Higher Education 46:2, pages 133-151.
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Abdul Halim, Elmi Mahzum, Muhammad Yacob, Irwandi Irwandi & Lilia Halim. (2021) The Impact of Narrative Feedback, E-Learning Modules and Realistic Video and the Reduction of Misconception. Education Sciences 11:4, pages 158.
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Galina Deeva, Daria Bogdanova, Estefanía Serral, Monique Snoeck & Jochen De Weerdt. (2021) A review of automated feedback systems for learners: Classification framework, challenges and opportunities. Computers & Education 162, pages 104094.
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Dirk Krüger & Moritz Krell. (2020) Maschinelles Lernen mit Aussagen zur ModellkompetenzMachine Learning with Responses Related to Modeling Competence. Zeitschrift für Didaktik der Naturwissenschaften 26:1, pages 157-172.
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Fayyaz Ahmad Faize & Mubeen Akhtar. (2020) Addressing environmental knowledge and environmental attitude in undergraduate students through scientific argumentation. Journal of Cleaner Production 252, pages 119928.
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Mengxiao Zhu, Ou Lydia Liu & Hee-Sun Lee. (2020) The effect of automated feedback on revision behavior and learning gains in formative assessment of scientific argument writing. Computers & Education 143, pages 103668.
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Bo Pei, Wanli Xing & Hee‐Sun Lee. (2019) Using automatic image processing to analyze visual artifacts created by students in scientific argumentation. British Journal of Educational Technology 50:6, pages 3391-3404.
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Hee‐Sun Lee, Amy Pallant, Sarah Pryputniewicz, Trudi Lord, Matthew Mulholland & Ou Lydia Liu. (2019) Automated text scoring and real‐time adjustable feedback: Supporting revision of scientific arguments involving uncertainty. Science Education 103:3, pages 590-622.
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Ton de Jong. (2019) Moving towards engaged learning in STEM domains; there is no simple answer, but clearly a road ahead. Journal of Computer Assisted Learning 35:2, pages 153-167.
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