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Zhu, Xinhua; Wu, Han; Zhang, Lanfang – IEEE Transactions on Learning Technologies, 2022
Automatic short-answer grading (ASAG) is a key component of intelligent tutoring systems. Deep learning is an advanced method to deal with recognizing textual entailment tasks in an end-to-end manner. However, deep learning methods for ASAG still remain challenging mainly because of the following two major reasons: (1) high-precision scoring…
Descriptors: Intelligent Tutoring Systems, Grading, Automation, Models
Feng Hsu Wang – IEEE Transactions on Learning Technologies, 2024
Due to the development of deep learning technology, its application in education has received increasing attention from researchers. Intelligent agents based on deep learning technology can perform higher order intellectual tasks than ever. However, the high deployment cost of deep learning models has hindered their widespread application in…
Descriptors: Learning Processes, Models, Man Machine Systems, Cooperative Learning
Hyeongdon Moon; Richard Lee Davis; Seyed Parsa Neshaei; Pierre Dillenbourg – International Educational Data Mining Society, 2025
Knowledge tracing models have enabled a range of intelligent tutoring systems to provide feedback to students. However, existing methods for knowledge tracing in learning sciences are predominantly reliant on statistical data and instructor-defined knowledge components, making it challenging to integrate AI-generated educational content with…
Descriptors: Artificial Intelligence, Natural Language Processing, Automation, Information Management
Sha, Lele; Rakovic, Mladen; Lin, Jionghao; Guan, Quanlong; Whitelock-Wainwright, Alexander; Gasevic, Dragan; Chen, Guanliang – IEEE Transactions on Learning Technologies, 2023
In online courses, discussion forums play a key role in enhancing student interaction with peers and instructors. Due to large enrolment sizes, instructors often struggle to respond to students in a timely manner. To address this problem, both traditional machine learning (ML) (e.g., Random Forest) and deep learning (DL) approaches have been…
Descriptors: Computer Mediated Communication, Discussion Groups, Artificial Intelligence, Intelligent Tutoring Systems
Scandura, Joseph M. – Technology, Instruction, Cognition and Learning, 2018
This paper summarizes key stages in development of the Structural Learning Theory (SLT) and explains how and why it is now possible to model human tutors in a highly efficient manner. The paper focuses on evolution of the SLT, a deterministic theory of teaching and learning, on which AuthorIT authoring and TutorIT delivery systems have been built.…
Descriptors: Artificial Intelligence, Models, Tutors, Learning Theories
Deep Learning + Student Modeling + Clustering: A Recipe for Effective Automatic Short Answer Grading
Zhang, Yuan; Shah, Rajat; Chi, Min – International Educational Data Mining Society, 2016
In this work we tackled the task of Automatic Short Answer Grading (ASAG). While conventional ASAG research makes prediction mainly based on student answers referred as Answer-based, we leveraged the information about questions and student models into consideration. More specifically, we explore the Answer-based, Question, and Student models…
Descriptors: Automation, Grading, Artificial Intelligence, Test Format
Ogan, Amy; Walker, Erin; Baker, Ryan; Rodrigo, Ma. Mercedes T.; Soriano, Jose Carlo; Castro, Maynor Jimenez – International Journal of Artificial Intelligence in Education, 2015
In recent years, there has been increasing interest in automatically assessing help seeking, the process of referring to resources outside of oneself to accomplish a task or solve a problem. Research in the United States has shown that specific help-seeking behaviors led to better learning within intelligent tutoring systems. However, intelligent…
Descriptors: Help Seeking, Cultural Differences, Automation, Intelligent Tutoring Systems
Liu, Ran; Davenport, Jodi; Stamper, John – International Educational Data Mining Society, 2016
The increasing use of educational technologies in classrooms is producing vast amounts of process data that capture rich information about learning as it unfolds. The field of educational data mining has made great progress in using log data to build models that improve instruction and advance the science of learning. Thus far, however, the…
Descriptors: Educational Technology, Data Analysis, Automation, Data
Feng, Mingyu, Ed.; Käser, Tanja, Ed.; Talukdar, Partha, Ed. – International Educational Data Mining Society, 2023
The Indian Institute of Science is proud to host the fully in-person sixteenth iteration of the International Conference on Educational Data Mining (EDM) during July 11-14, 2023. EDM is the annual flagship conference of the International Educational Data Mining Society. The theme of this year's conference is "Educational data mining for…
Descriptors: Information Retrieval, Data Analysis, Computer Assisted Testing, Cheating
McNamara, Danielle S.; Crossley, Scott A.; Roscoe, Rod – Grantee Submission, 2013
The Writing Pal is an intelligent tutoring system that provides writing strategy training. A large part of its artificial intelligence resides in the natural language processing algorithms to assess essay quality and guide feedback to students. Because writing is often highly nuanced and subjective, the development of these algorithms must…
Descriptors: Intelligent Tutoring Systems, Natural Language Processing, Writing Instruction, Feedback (Response)
Rafferty, Anna N., Ed.; Whitehill, Jacob, Ed.; Romero, Cristobal, Ed.; Cavalli-Sforza, Violetta, Ed. – International Educational Data Mining Society, 2020
The 13th iteration of the International Conference on Educational Data Mining (EDM 2020) was originally arranged to take place in Ifrane, Morocco. Due to the SARS-CoV-2 (coronavirus) epidemic, EDM 2020, as well as most other academic conferences in 2020, had to be changed to a purely online format. To facilitate efficient transmission of…
Descriptors: Educational Improvement, Teaching Methods, Information Retrieval, Data Processing
Peer reviewedElen, Jan – Instructional Science, 1998
Discusses GTE (Generic Tutoring Environment) and courseware engineering and argues that GTE's theoretical knowledge base focuses on teaching as a good model for any kind of instruction and thus reduces its generic nature. Two examples of weak automation for instructional design are described that have broader knowledge bases. (Author/LRW)
Descriptors: Automation, Computer Software Development, Courseware, Instructional Design
Hu, Xiangen, Ed.; Barnes, Tiffany, Ed.; Hershkovitz, Arnon, Ed.; Paquette, Luc, Ed. – International Educational Data Mining Society, 2017
The 10th International Conference on Educational Data Mining (EDM 2017) is held under the auspices of the International Educational Data Mining Society at the Optics Velley Kingdom Plaza Hotel, Wuhan, Hubei Province, in China. This years conference features two invited talks by: Dr. Jie Tang, Associate Professor with the Department of Computer…
Descriptors: Data Analysis, Data Collection, Graphs, Data Use
Barnes, Tiffany, Ed.; Chi, Min, Ed.; Feng, Mingyu, Ed. – International Educational Data Mining Society, 2016
The 9th International Conference on Educational Data Mining (EDM 2016) is held under the auspices of the International Educational Data Mining Society at the Sheraton Raleigh Hotel, in downtown Raleigh, North Carolina, in the USA. The conference, held June 29-July 2, 2016, follows the eight previous editions (Madrid 2015, London 2014, Memphis…
Descriptors: Data Analysis, Evidence Based Practice, Inquiry, Science Instruction
Lynch, Collin F., Ed.; Merceron, Agathe, Ed.; Desmarais, Michel, Ed.; Nkambou, Roger, Ed. – International Educational Data Mining Society, 2019
The 12th iteration of the International Conference on Educational Data Mining (EDM 2019) is organized under the auspices of the International Educational Data Mining Society in Montreal, Canada. The theme of this year's conference is EDM in Open-Ended Domains. As EDM has matured it has increasingly been applied to open-ended and ill-defined tasks…
Descriptors: Data Collection, Data Analysis, Information Retrieval, Content Analysis
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