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Showing 1 to 15 of 17 results Save | Export
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Phillips, Tanner M.; Saleh, Asmalina; Ozogul, Gamze – International Journal of Artificial Intelligence in Education, 2023
Encouraging teachers to reflect on their instructional practices and course design has been shown to be an effective means of improving instruction and student learning. However, the process of encouraging reflection is difficult; reflection requires quality data, thoughtful analysis, and contextualized interpretation. Because of this, research on…
Descriptors: Reflection, Artificial Intelligence, Natural Language Processing, Data Collection
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Amal Alrayes; Tara Fryad Henari; Dalal Abdulkarim Ahmed – Electronic Journal of e-Learning, 2024
This paper provides a thorough examination of the role of Artificial Intelligence (AI), particularly ChatGPT and other AI language models, in the realm of education. Drawing insights from existing literature and a novel study on educator perspectives, the paper delves into the potential advantages, ethical dilemmas, and factors shaping educators'…
Descriptors: Artificial Intelligence, Technology Uses in Education, Natural Language Processing, Intention
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Hwang, Haerim; Kim, Hyunwoo – Applied Linguistics, 2023
One of the important components in second language (L2) development is to produce clause-level units of form-meaning pairings or argument structure constructions. Based on the usage-based constructionist approach that language development entails an ability to use more diverse, more complex, and less frequent constructions, this study tested…
Descriptors: English (Second Language), Second Language Learning, Second Language Instruction, Predictor Variables
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Burstein, Jill; McCaffrey, Daniel; Beigman Klebanov, Beata; Ling, Guangming; Holtzman, Steven – Grantee Submission, 2019
Writing is a challenge and a potential obstacle for students in U.S. 4-year postsecondary institutions lacking prerequisite writing skills. This study aims to address the research question: Is there a relationship between specific features (analytics) in coursework writing and broader success predictors? Knowledge about this relationship could…
Descriptors: Undergraduate Students, Writing (Composition), Writing Evaluation, Learning Analytics
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Sinclair, Arabella J.; Schneider, Bertrand – International Educational Data Mining Society, 2021
Collaborative dialogue is rich in conscious and subconscious coordination behaviours between participants. This work explores collaborative learner dialogue through theories of alignment, analysing inter-partner movement and language use with respect to our hypotheses: that they interrelate, and that they form predictors of collaboration quality…
Descriptors: Dialogs (Language), Cooperative Learning, Correlation, Predictor Variables
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Lang, David; Wang, Alex; Dalal, Nathan; Paepcke, Andreas; Stevens, Mitchell L. – AERA Open, 2022
Committing to a major is a fateful step in an undergraduate education, yet the relationship between courses taken early in an academic career and ultimate major issuance remains little studied at scale. Using transcript data capturing the academic careers of 26,892 undergraduates enrolled at a private university between 2000 and 2020, we describe…
Descriptors: Undergraduate Students, Majors (Students), College Planning, Natural Language Processing
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Lee, Jeonghyun; Soleimani, Farahnaz; Irish, India; Hosmer, John, IV; Soylu, Meryem Yilmaz; Finkelberg, Roy; Chatterjee, Saurabh – Online Learning, 2022
In this study, we work towards a strategy to measure and enhance the quality of interactions in discussion forums at scale. We present a machine learning (ML) model which identifies the phase of cognitive presence exhibited by a student's post and suggest future applications of such a model to help online students develop higher-order thinking. We…
Descriptors: Online Courses, Models, Thinking Skills, Computer Mediated Communication
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Akpinar, Nil-Jana; Ramdas, Aaditya; Acar, Umut – International Educational Data Mining Society, 2020
Educational software data promises unique insights into students' study behaviors and drivers of success. While much work has been dedicated to performance prediction in massive open online courses, it is unclear if the same methods can be applied to blended courses and a deeper understanding of student strategies is often missing. We use pattern…
Descriptors: Learning Strategies, Blended Learning, Learning Analytics, Student Behavior
MacArthur, Charles A.; Jennings, Amanda; Philippakos, Zoi A. – Grantee Submission, 2018
The study developed a model of linguistic constructs to predict writing quality for college basic writers and analyzed how those constructs changed following instruction. Analysis used a corpus of argumentative essays from a quasi-experimental, instructional study with 252 students (MacArthur, Philippakos, & Ianetta, 2015) that found large…
Descriptors: College Students, Writing Skills, Writing Evaluation, Writing Achievement
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Jorge-Botana, Guillermo; Luzón, José M.; Gómez-Veiga, Isabel; Martín-Cordero, Jesús I. – Journal of Educational Computing Research, 2015
A latent semantic analysis-based automated summary assessment is described; this automated system is applied to a real learning from text task in a Distance Education context. We comment on the use of automated content, plagiarism, text coherence measures, and word weights average and their impact on predicting human judges summary scoring. A…
Descriptors: Foreign Countries, Distance Education, Regression (Statistics), Plagiarism
Lipschultz, Michael; Litman, Diane; Katz, Sandra; Albacete, Patricia; Jordan, Pamela – Grantee Submission, 2014
Post-problem reflective tutorial dialogues between human tutors and students are examined to predict when the tutor changed the level of abstraction from the student's preceding turn (i.e., used more general terms or more specific terms); such changes correlate with learning. Prior work examined lexical changes in abstraction. In this work, we…
Descriptors: Intelligent Tutoring Systems, Natural Language Processing, Semantics, Abstract Reasoning
Ezen-Can, Aysu; Boyer, Kristy Elizabeth – International Educational Data Mining Society, 2015
The tremendous effectiveness of intelligent tutoring systems is due in large part to their interactivity. However, when learners are free to choose the extent to which they interact with a tutoring system, not all learners do so actively. This paper examines a study with a natural language tutorial dialogue system for computer science, in which…
Descriptors: Intelligent Tutoring Systems, Natural Language Processing, Computer Science Education, Problem Solving
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Boyer, Kristy Elizabeth, Ed.; Yudelson, Michael, Ed. – International Educational Data Mining Society, 2018
The 11th International Conference on Educational Data Mining (EDM 2018) is held under the auspices of the International Educational Data Mining Society at the Templeton Landing in Buffalo, New York. This year's EDM conference was highly competitive, with 145 long and short paper submissions. Of these, 23 were accepted as full papers and 37…
Descriptors: Data Collection, Data Analysis, Computer Science Education, Program Proposals
Zafra, Amelia; Ventura, Sebastian – International Working Group on Educational Data Mining, 2009
The ability to predict a student's performance could be useful in a great number of different ways associated with university-level learning. In this paper, a grammar guided genetic programming algorithm, G3P-MI, has been applied to predict if the student will fail or pass a certain course and identifies activities to promote learning in a…
Descriptors: Foreign Countries, Programming, Academic Achievement, Grades (Scholastic)
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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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