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Yumeng Zhu; Caifeng Zhu; Tao Wu; Shulei Wang; Yiyun Zhou; Jingyuan Chen; Fei Wu; Yan Li – Education and Information Technologies, 2025
With the prevalence of Large Language Model-based chatbots, middle school students are increasingly likely to engage with these tools to complete their assignments, raising concerns about its potential to harm students' learning motivation and learning outcomes. However, we know little about its real impact. Through quasi-experiment research with…
Descriptors: Artificial Intelligence, Assignments, Middle School Students, Influence of Technology
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Eunhye Shin – Journal of Computer Assisted Learning, 2025
Background: Analysing classroom dialogue is a widely used approach for understanding students' learning, often requiring team-based collaborative research. This presents a challenge for single researchers due to the labour-intensive nature of the process. Emerging advancements in large language models (LLMs) such as ChatGPT, enhance qualitative…
Descriptors: Artificial Intelligence, Technology Uses in Education, Science Education, Coding
Greene Nolan, Hillary; Vang, Mai Chou – Digital Promise, 2023
Providing feedback to students in a sustainable way represents a perennial challenge for secondary teachers of writing. Employing artificial intelligence (AI) tools to give students personalized and immediate feedback holds great promise. Project Topeka offered middle school teachers pre-curated teaching materials, foundational texts and videos,…
Descriptors: Middle School Students, Grade 7, Grade 8, Predictor Variables
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Traci C. Eshelman – Turkish Online Journal of Educational Technology - TOJET, 2024
The purpose of this multiple intrinsic case study was to describe how Northeastern United States middle school teachers and students engaged with a new automated writing evaluation tool used to score and provide feedback on extended essay assignments to improve teaching and learning writing. Richard Elmore's (1993) instructional core framework is…
Descriptors: High School Teachers, Learner Engagement, Automation, Writing Evaluation
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L. Hannah; E. E. Jang; M. Shah; V. Gupta – Language Assessment Quarterly, 2023
Machines have a long-demonstrated ability to find statistical relationships between qualities of texts and surface-level linguistic indicators of writing. More recently, unlocked by artificial intelligence, the potential of using machines to identify content-related writing trait criteria has been uncovered. This development is significant,…
Descriptors: Validity, Automation, Scoring, Writing Assignments
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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
Barnes, Tiffany, Ed.; Desmarais, Michel, Ed.; Romero, Cristobal, Ed.; Ventura, Sebastian, Ed. – International Working Group on Educational Data Mining, 2009
The Second International Conference on Educational Data Mining (EDM2009) was held at the University of Cordoba, Spain, on July 1-3, 2009. EDM brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large data sets to answer educational research questions. The increase in instrumented…
Descriptors: Data Analysis, Educational Research, Conferences (Gatherings), Foreign Countries