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Teo Susnjak – International Journal of Artificial Intelligence in Education, 2024
A significant body of recent research in the field of Learning Analytics has focused on leveraging machine learning approaches for predicting at-risk students in order to initiate timely interventions and thereby elevate retention and completion rates. The overarching feature of the majority of these research studies has been on the science of…
Descriptors: Prediction, Learning Analytics, Artificial Intelligence, At Risk Students
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Samah AlKhuzaey; Floriana Grasso; Terry R. Payne; Valentina Tamma – International Journal of Artificial Intelligence in Education, 2024
Designing and constructing pedagogical tests that contain items (i.e. questions) which measure various types of skills for different levels of students equitably is a challenging task. Teachers and item writers alike need to ensure that the quality of assessment materials is consistent, if student evaluations are to be objective and effective.…
Descriptors: Test Items, Test Construction, Difficulty Level, Prediction
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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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Robert-Mihai Botarleanu; Micah Watanabe; Mihai Dascalu; Scott A. Crossley; Danielle S. McNamara – International Journal of Artificial Intelligence in Education, 2024
Age of Acquisition (AoA) scores approximate the age at which a language speaker fully understands a word's semantic meaning and represent a quantitative measure of the relative difficulty of words in a language. AoA word lists exist across various languages, with English having the most complete lists that capture the largest percentage of the…
Descriptors: Multilingualism, English (Second Language), Second Language Learning, Second Language Instruction
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Geden, Michael; Emerson, Andrew; Carpenter, Dan; Rowe, Jonathan; Azevedo, Roger; Lester, James – International Journal of Artificial Intelligence in Education, 2021
Game-based learning environments are designed to provide effective and engaging learning experiences for students. Predictive student models use trace data extracted from students' in-game learning behaviors to unobtrusively generate early assessments of student knowledge and skills, equipping game-based learning environments with the capacity to…
Descriptors: Game Based Learning, Middle School Students, Microbiology, Secondary School Science
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Rahimi, Zahra; Litman, Diane; Correnti, Richard; Wang, Elaine; Matsumura, Lindsay Clare – International Journal of Artificial Intelligence in Education, 2017
This paper presents an investigation of score prediction based on natural language processing for two targeted constructs within analytic text-based writing: 1) students' effective use of evidence and, 2) their organization of ideas and evidence in support of their claim. With the long-term goal of producing feedback for students and teachers, we…
Descriptors: Scoring, Automation, Scoring Rubrics, Natural Language Processing
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Suleman, Raja M.; Mizoguchi, Riichiro; Ikeda, Mitsuru – International Journal of Artificial Intelligence in Education, 2016
Negotiation mechanism using conversational agents (chatbots) has been used in Open Learner Models (OLM) to enhance learner model accuracy and provide opportunities for learner reflection. Using chatbots that allow for natural language discussions has shown positive learning gains in students. Traditional OLMs assume a learner to be able to manage…
Descriptors: Metacognition, Intelligent Tutoring Systems, Natural Language Processing, Models
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Valdés Aguirre, Benjamín; Ramírez Uresti, Jorge A.; du Boulay, Benedict – International Journal of Artificial Intelligence in Education, 2016
Sharing user information between systems is an area of interest for every field involving personalization. Recommender Systems are more advanced in this aspect than Intelligent Tutoring Systems (ITSs) and Intelligent Learning Environments (ILEs). A reason for this is that the user models of Intelligent Tutoring Systems and Intelligent Learning…
Descriptors: Intelligent Tutoring Systems, Models, Open Source Technology, Computers