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Albornoz-De Luise, Romina Soledad; Arevalillo-Herraez, Miguel; Arnau, David – IEEE Transactions on Learning Technologies, 2023
In this article, we analyze the potential of conversational frameworks to support the adaptation of existing tutoring systems to a natural language form of interaction. We have based our research on a pilot study, in which the open-source machine learning framework Rasa has been used to build a conversational agent that interacts with an existing…
Descriptors: Intelligent Tutoring Systems, Natural Language Processing, Artificial Intelligence, Models
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Behzad Mirzababaei; Viktoria Pammer-Schindler – IEEE Transactions on Learning Technologies, 2024
In this article, we investigate a systematic workflow that supports the learning engineering process of formulating the starting question for a conversational module based on existing learning materials, specifying the input that transformer-based language models need to function as classifiers, and specifying the adaptive dialogue structure,…
Descriptors: Learning Processes, Electronic Learning, Artificial Intelligence, Natural Language Processing
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Hao Zhou; Wenge Rong; Jianfei Zhang; Qing Sun; Yuanxin Ouyang; Zhang Xiong – IEEE Transactions on Learning Technologies, 2025
Knowledge tracing (KT) aims to predict students' future performances based on their former exercises and additional information in educational settings. KT has received significant attention since it facilitates personalized experiences in educational situations. Simultaneously, the autoregressive (AR) modeling on the sequence of former exercises…
Descriptors: Learning Experience, Academic Achievement, Data, Artificial Intelligence
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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
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Andres Neyem; Luis A. Gonzalez; Marcelo Mendoza; Juan Pablo Sandoval Alcocer; Leonardo Centellas; Carlos Paredes – IEEE Transactions on Learning Technologies, 2024
Software assistants have significantly impacted software development for both practitioners and students, particularly in capstone projects. The effectiveness of these tools varies based on their knowledge sources; assistants with localized domain-specific knowledge may have limitations, while tools, such as ChatGPT, using broad datasets, might…
Descriptors: Computer Software, Artificial Intelligence, Intelligent Tutoring Systems, Capstone Experiences
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Emiko Tsutsumi; Yiming Guo; Ryo Kinoshita; Maomi Ueno – IEEE Transactions on Learning Technologies, 2024
Knowledge tracing (KT), the task of tracking the knowledge state of a student over time, has been assessed actively by artificial intelligence researchers. Recent reports have described that Deep-IRT, which combines item response theory (IRT) with a deep learning method, provides superior performance. It can express the abilities of each student…
Descriptors: Item Response Theory, Academic Ability, Intelligent Tutoring Systems, Artificial Intelligence
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Rao, Dhawaleswar; Saha, Sujan Kumar – IEEE Transactions on Learning Technologies, 2020
Automatic multiple choice question (MCQ) generation from a text is a popular research area. MCQs are widely accepted for large-scale assessment in various domains and applications. However, manual generation of MCQs is expensive and time-consuming. Therefore, researchers have been attracted toward automatic MCQ generation since the late 90's.…
Descriptors: Multiple Choice Tests, Test Construction, Automation, Computer Software
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Sanchez-Ferreres, Josep; Delicado, Luis; Andaloussi, Amine Abbab; Burattin, Andrea; Calderon-Ruiz, Guillermo; Weber, Barbara; Carmona, Josep; Padro, Lluis – IEEE Transactions on Learning Technologies, 2020
The creation of a process model is primarily a formalization task that faces the challenge of constructing a syntactically correct entity, which accurately reflects the semantics of reality, and is understandable to the model reader. This article proposes a framework called "Model Judge," focused toward the two main actors in the process…
Descriptors: Models, Automation, Validity, Natural Language Processing