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Orkun Kocak; Sahin Idil – Journal of Education in Science, Environment and Health, 2025
This study is developing a Deep Learning model automating the coding of drawings students provide about climate change phenomena in our world, as a learning contribution through formative assessment. We started first with ResNet50 architecture, but ultimately, we settled on MobileNetV2 reduced architecture for the sake of being able to integrate…
Descriptors: Climate, Artificial Intelligence, Accuracy, Environmental Education
Rebeckah K. Fussell; Megan Flynn; Anil Damle; Michael F. J. Fox; N. G. Holmes – Physical Review Physics Education Research, 2025
Recent advancements in large language models (LLMs) hold significant promise for improving physics education research that uses machine learning. In this study, we compare the application of various models for conducting a large-scale analysis of written text grounded in a physics education research classification problem: identifying skills in…
Descriptors: Physics, Computational Linguistics, Classification, Laboratory Experiments
Jionghao Lin; Wei Tan; Lan Du; Wray Buntine; David Lang; Dragan Gasevic; Guanliang Chen – IEEE Transactions on Learning Technologies, 2024
Automating the classification of instructional strategies from a large-scale online tutorial dialogue corpus is indispensable to the design of dialogue-based intelligent tutoring systems. Despite many existing studies employing supervised machine learning (ML) models to automate the classification process, they concluded that building a…
Descriptors: Classification, Dialogs (Language), Teaching Methods, Computer Assisted Instruction
Zhang, Mengxue; Baral, Sami; Heffernan, Neil; Lan, Andrew – International Educational Data Mining Society, 2022
Automatic short answer grading is an important research direction in the exploration of how to use artificial intelligence (AI)-based tools to improve education. Current state-of-the-art approaches use neural language models to create vectorized representations of students responses, followed by classifiers to predict the score. However, these…
Descriptors: Grading, Mathematics Instruction, Artificial Intelligence, Form Classes (Languages)
Qing Wang; Xizhen Cai – Journal of Statistics and Data Science Education, 2024
Support vector classifiers are one of the most popular linear classification techniques for binary classification. Different from some commonly seen model fitting criteria in statistics, such as the ordinary least squares criterion and the maximum likelihood method, its algorithm depends on an optimization problem under constraints, which is…
Descriptors: Active Learning, Class Activities, Classification, Artificial Intelligence
E. Gothai; S. Saravanan; C. Thirumalai Selvan; Ravi Kumar – Education and Information Technologies, 2024
In recent years, online education has been given more and more attention with the widespread use of the internet. The teaching procedure divides space and makes time for online learning; though teachers cannot control the learners accurately, the state of education calculates learners' learning situation. This paper explains that the discourse…
Descriptors: Artificial Intelligence, Discourse Analysis, Classification, Comparative Analysis
Li, Yuheng; Rakovic, Mladen; Poh, Boon Xin; Gaševic, Dragan; Chen, Guanliang – International Educational Data Mining Society, 2022
Learning objectives, especially those well defined by applying Bloom's taxonomy for Cognitive Objectives, have been widely recognized as important in various teaching and learning practices. However, many educators have difficulties developing learning objectives appropriate to the levels in Bloom's taxonomy, as they need to consider the…
Descriptors: Educational Objectives, Taxonomy, Universities, Cognitive Ability
Holmes, Wayne; Iniesto, Francisco; Anastopoulou, Stamatina; Boticario, Jesus G. – International Review of Research in Open and Distributed Learning, 2023
Increasingly, Artificial Intelligence (AI) is having an impact on distance-based higher education, where it is revealing multiple ethical issues. However, to date, there has been limited research addressing the perspectives of key stakeholders about these developments. The study presented in this paper sought to address this gap by investigating…
Descriptors: Artificial Intelligence, Distance Education, Higher Education, Teaching Methods
Caruso, Marcelo – European Educational Research Journal, 2023
Age-classes are a salient feature of modern schooling. Yet how did age-grouping come to prevail in entire school systems? And how was this form of grouping related to educational and pedagogic discussions at the time of its emergence? The article addresses these issues by looking at the historical context within which age classes came to a…
Descriptors: Educational History, Elementary School Students, School Administration, Classification
Thaweesak Chanpradit; Phakkaramai Samran; Siriprapa Saengpinit; Pailin Subkasin – Journal of English Teaching, 2024
AI-generated paraphrasing tools, especially QuillBot and Paraphrasing Tool, play a crucial role in preventing plagiarism in academic writing. However, their effectiveness and proficiency have been questioned, particularly regarding the adequacy of their strategies. This qualitative study analyzed the paraphrasing strategies and proficiency levels…
Descriptors: English (Second Language), Second Language Learning, Second Language Instruction, Phrase Structure
Ryo Yoshii – Paedagogica Historica: International Journal of the History of Education, 2023
The identification, diagnosis, and categorisation of students who qualified for special education have created long-standing controversy. This article explores Maximilian P. E. Groszmann's measurement practices, which were intended to facilitate instruction in the early twentieth-century United States. In 1900, Groszmann established a private…
Descriptors: Classification, Identification, Educational History, Students with Disabilities
Djatmika; Hikmawati, Ahfi; Sumarlam – Indonesian Journal of English Language Teaching and Applied Linguistics, 2021
This article discusses the relationship between the mental intelligence of children with autism and their capability in understanding the complexity of sentence structure represented in utterances performed by their teachers. In addition, this study also explains the complexity of the sentence structure produced by the autistic children in…
Descriptors: Intelligence, Sentence Structure, Autism, Pervasive Developmental Disorders
Mohamed, Mohamed Zulhilmi bin; Hidayat, Riyan; Suhaizi, Nurain Nabilah binti; Sabri, Norhafiza binti Mat; Mahmud, Muhamad Khairul Hakim bin; Baharuddin, Siti Nurshafikah binti – International Electronic Journal of Mathematics Education, 2022
The advancement of technology like artificial intelligence (AI) provides a chance to help teachers and students solve and improve teaching and learning performances. The goal of this review is to add to the conversation by offering a complete overview of AI in mathematics teaching and learning for students at all levels of education. A systematic…
Descriptors: Artificial Intelligence, Mathematics Instruction, Meta Analysis, Databases
Higuera-Martínez, Oscar Iván; Corazza, Giovanni Emanuele; Fernández-Samacá, Liliana – European Journal of Engineering Education, 2022
This article presents how Problem- and Project-Based Learning (PBL) in engineering education can exploit the theoretical framework of the Space-Time (ST)-Continuum, according to which educational contexts can be classified in terms of the tightness vs. looseness of the relevant conceptual space S and available time T. By crossing these two…
Descriptors: Problem Based Learning, Engineering Education, Teaching Methods, Intervention
Shi, Yang; Schmucker, Robin; Chi, Min; Barnes, Tiffany; Price, Thomas – International Educational Data Mining Society, 2023
Knowledge components (KCs) have many applications. In computing education, knowing the demonstration of specific KCs has been challenging. This paper introduces an entirely data-driven approach for: (1) discovering KCs; and (2) demonstrating KCs, using students' actual code submissions. Our system is based on two expected properties of KCs: (1)…
Descriptors: Computer Science Education, Data Analysis, Programming, Coding

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