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Brahman, Faeze; Varghese, Nikhil; Bhat, Suma; Chaturvedi, Snigdha – International Educational Data Mining Society, 2020
Despite several advantages of online education, lack of effective student-instructor interaction, especially when students need timely help, poses significant pedagogical challenges. Motivated by this, we address the problems of automatically identifying posts that express confusion or urgency from Massive Open Online Course (MOOC) forums. To this…
Descriptors: Automation, Online Courses, Discussion Groups, Identification
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Lishan Zhang; Linyu Deng; Sixv Zhang; Ling Chen – IEEE Transactions on Learning Technologies, 2024
With the popularity of online one-to-one tutoring, there are emerging concerns about the quality and effectiveness of this kind of tutoring. Although there are some evaluation methods available, they are heavily relied on manual coding by experts, which is too costly. Therefore, using machine learning to predict instruction quality automatically…
Descriptors: Automation, Classification, Artificial Intelligence, Tutoring
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Valderama, Julius; Oligo, Jubert – International Journal of Evaluation and Research in Education, 2021
Retention is the ability to retain information in the mind, either in short-term or long-term memory. Memory in the long-term is more ideal. Thus, this has become a challenge for educators on how to transfer ideas in short-term memory to long-term memory. To concretize the effect of time on mathematics learning retention, a randomized pre-test…
Descriptors: Retention (Psychology), Mathematics Education, Student Motivation, Short Term Memory
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Kornwipa Poonpon; Paiboon Manorom; Wirapong Chansanam – Contemporary Educational Technology, 2023
Automated essay scoring (AES) has become a valuable tool in educational settings, providing efficient and objective evaluations of student essays. However, the majority of AES systems have primarily focused on native English speakers, leaving a critical gap in the evaluation of non-native speakers' writing skills. This research addresses this gap…
Descriptors: Automation, Essays, Scoring, English (Second Language)
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Kuhaneswaran Banujan; Samantha Kumara; Senthan Prasanth; Nirubikaa Ravikumar – International Journal of Education and Development using Information and Communication Technology, 2023
Examinations are one way of evaluating students. To ensure the production of valid exams, frameworks such as Bloom's taxonomy are utilised when preparing questions. Bloom's taxonomy is a well-known framework that categorises educational objectives into six hierarchical levels of cognitive complexity. However, manually categorising exam questions…
Descriptors: Artificial Intelligence, Test Construction, Classification, Foreign Countries
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Michael, Boniface; Michael, Rashmi – Journal of International Education in Business, 2019
Purpose: The purpose of this paper is to explore the association between memory (short- and long-term), a foundational cognition in learning and face-to-face, video-based and flipped instructional modalities. Design/methodology/approach: This study used a one-way analysis of variance and linear regression analyses to compare students' aggregated…
Descriptors: Short Term Memory, Long Term Memory, College Students, Business Administration Education
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Firoozi, Tahereh; Bulut, Okan; Epp, Carrie Demmans; Naeimabadi, Ali; Barbosa, Denilson – Journal of Applied Testing Technology, 2022
Automated Essay Scoring (AES) using neural networks has helped increase the accuracy and efficiency of scoring students' written tasks. Generally, the improved accuracy of neural network approaches has been attributed to the use of modern word embedding techniques. However, which word embedding techniques produce higher accuracy in AES systems…
Descriptors: Computer Assisted Testing, Scoring, Essays, Artificial Intelligence
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Akram, Bita; Min, Wookhee; Wiebe, Eric; Mott, Bradford; Boyer, Kristy Elizabeth; Lester, James – International Educational Data Mining Society, 2018
A key affordance of game-based learning environments is their potential to unobtrusively assess student learning without interfering with gameplay. In this paper, we introduce a temporal analytics framework for stealth assessment that analyzes students' problem-solving strategies. The strategy-based temporal analytic framework uses long short-term…
Descriptors: Educational Games, Problem Solving, Educational Environment, Short Term Memory
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Yuchen Wang; Juxiang Zhou; Zijie Li; Shu Zhang; Xiaoyu Han – IEEE Transactions on Learning Technologies, 2024
Graded reading is one of the important ways of English learning. How to automatically judge and grade the difficulty of the English reading corpus is of great significance for precision teaching and personalized learning. However, the current rule-based readability assessment methods have some limitations, such as low efficiency and poor accuracy.…
Descriptors: Computational Linguistics, Reading Materials, Readability, Semantics
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Mao, Ye; Shi, Yang; Marwan, Samiha; Price, Thomas W.; Barnes, Tiffany; Chi, Min – International Educational Data Mining Society, 2021
As students learn how to program, both their programming code and their understanding of it evolves over time. In this work, we present a general data-driven approach, named "Temporal-ASTNN" for modeling student learning progression in open-ended programming domains. Temporal-ASTNN combines a novel neural network model based on abstract…
Descriptors: Programming, Computer Science Education, Learning Processes, Learning Analytics
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Chen, Fu; Cui, Ying – Journal of Learning Analytics, 2020
Predictive analytics in higher education has become increasingly popular in recent years with the growing availability of educational big data. Particularly, a wealth of student activity data is available from learning management systems (LMSs) in most academic institutions. However, previous investigations into predictive analytics in higher…
Descriptors: Time on Task, Student Behavior, Integrated Learning Systems, Grade Prediction
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Zheng, Yafeng; Gao, Zhanghao; Shen, Jun; Zhai, Xuesong – IEEE Transactions on Learning Technologies, 2023
A text semantic classification is an essential approach to recognizing the verbal intention of online learners, empowering reliable understanding, and inquiry for the regulations of knowledge construction amongst students. However, online learning is increasingly switching from static watching patterns to the collaborative discussion. The current…
Descriptors: Semantics, Classification, Electronic Learning, Computer Mediated Communication
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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
Talandron-Felipe, May Marie P.; Rodrigo, Ma. Mercedes T. – Research and Practice in Technology Enhanced Learning, 2021
The incubation effect (IE) is a problem-solving phenomenon composed of three phases: pre-incubation where one fails to solve a problem; incubation, a momentary break where time is spent away from the unsolved problem; and post-incubation where the unsolved problem is revisited and solved. Literature on IE was limited to experiments involving…
Descriptors: Problem Solving, Physics, Educational Games, Learning Processes
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Xiaxia Cao; Yao Zhao; Xiang Li – Education and Information Technologies, 2024
Various studies have been conducted on applying intelligent recognition technology, especially speech recognition technology to improve English learning ability, mostly listening and speaking. However, few studies have touched on how image-to-text recognition technology can be used for writing. The present research was conducted to fill this gap…
Descriptors: Captions, Second Language Learning, Second Language Instruction, Teaching Methods
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