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Leonora Kaldaras; Kevin Haudek; Joseph Krajcik – International Journal of STEM Education, 2024
We discuss transforming STEM education using three aspects: learning progressions (LPs), constructed response performance assessments, and artificial intelligence (AI). Using LPs to inform instruction, curriculum, and assessment design helps foster students' ability to apply content and practices to explain phenomena, which reflects deeper science…
Descriptors: Artificial Intelligence, Computer Assisted Instruction, STEM Education, Learning Trajectories
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Andreas de Barros; Alejandro J. Ganimian – Journal of Research on Educational Effectiveness, 2024
This is one of the first studies to evaluate the impact of computer-based individualized instruction in a developing country. We randomly assigned 1,528 students in grades 6-8 in 15 "model" public schools in Rajasthan, India who were using a computer-adaptive learning software to: a control group, in which they were only able to access…
Descriptors: Foreign Countries, Individualized Instruction, Computer Assisted Instruction, Grade 6
Richey, J. Elizabeth; McEldoon, Katherine; Tan, Elaine – Pearson, 2023
Pearson's Learning Foundations describe the optimal conditions for learning and reflect the learner experience Pearson hopes their products will create. Pearson does this by incorporating the Learning Design Principles. Each of the Learning Design Principles goes into detail about a key principle, supporting product design and marketing by…
Descriptors: Theory Practice Relationship, Research and Development, Individualized Instruction, Intelligent Tutoring Systems
Shalom Labkovski – ProQuest LLC, 2024
In an era of heightened accountability and expanding educational technology choices, mathematics educators must critically assess the effectiveness of the tools available to them. While deciding on technological tools, it is essential that we not only promote rigor in mathematics but also ensure closing learning gaps and improve students' beliefs…
Descriptors: Mathematical Aptitude, Mastery Learning, Individual Development, Course Evaluation
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Paola Mejia-Domenzain; Jibril Frej; Seyed Parsa Neshaei; Luca Mouchel; Tanya Nazaretsky; Thiemo Wambsganss; Antoine Bosselut; Tanja Käser – International Journal of Artificial Intelligence in Education, 2025
Writing high-quality procedural texts is a challenging task for many learners. While example-based learning has shown promise as a feedback approach, a limitation arises when all learners receive the same content without considering their individual input or prior knowledge. Consequently, some learners struggle to grasp or relate to the feedback,…
Descriptors: Writing Instruction, Academic Language, Content Area Writing, Cooking Instruction
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Kaur Kiran; Rohaida Mohd Saat; Lieven Demeester; Magdeleine Duan Ning Lew; Wei Leng Neo; Nopphol Pausawasdi; Thasaneeya Ratanaroutai Nopparatjamjomras – Contemporary Educational Technology, 2025
Online teaching during the COVID-19 pandemic compelled many instructors to seek efficient and effective ways to stay connected with their students and improve the learning experience by using a wide range of available technologies. This multiple-case study, in three South-East Asian universities, investigated whether the use of technology in…
Descriptors: Technology Uses in Education, Individualized Instruction, Computer Assisted Instruction, Web Based Instruction
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Bin Meng; Fan Yang – International Journal of Web-Based Learning and Teaching Technologies, 2025
This paper proposes a computer-aided teaching model using knowledge graph construction and learning path recommendation. It first creates a multimodal knowledge graph to illustrate complex relationships among knowledge. Learning elements and sequences are then used to form time sequences stored as directed graphs, supporting flexible path…
Descriptors: Students, Teachers, Computer Assisted Instruction, Knowledge Representation
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Huan Kang; Hong Chen – Education and Information Technologies, 2025
This study investigates the effects of online instructors' use of initiation and maintenance rapport-building strategies (RBS) on Chinese EFL learners' CALL motivation and cognitive load management. Mixed methods research was used to concurrently triangulate different strands of data on the effects of RBS on 86 randomly sampled EFL learners. The…
Descriptors: English (Second Language), Second Language Learning, Teacher Student Relationship, Cognitive Processes
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Wissal EL Fougour; Mohamed Erradi – International Journal of Technology in Education, 2025
Feedback is an integral aspect of developing self-regulated learning in that it enables the student an opportunity for reflection, making changes, and learning. The computer-based feedback system supports this systematic review in exploring how improvement in academic performance, metacognitive reasoning, and emotional resilience has taken place…
Descriptors: Computer Assisted Instruction, Feedback (Response), Individualized Instruction, Academic Achievement
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Da Teng; Xiangyang Wang; Yanwei Xia; Yue Zhang; Lulu Tang; Qi Chen; Ruobing Zhang; Sujin Xie; Weiyong Yu – Education and Information Technologies, 2025
The swift advancement of artificial intelligence, especially large language models (LLMs), has generated novel prospects for improving educational methodologies. Nonetheless, the successful incorporation of these technologies into pedagogical methods, such as flipped classrooms, continues to pose a challenge. This study investigates the…
Descriptors: Artificial Intelligence, Intelligent Tutoring Systems, Flipped Classroom, Technology Uses in Education
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Courtney Lewis – Knowledge Quest, 2022
The pandemic prompted Courtney Lewis and her fellow school librarians to dig deep and acknowledge that their information literacy instruction followed a just-in-time philosophy. Essentially, limited just-in-time information literacy instruction is like teaching a kid how to play soccer for seven days out of each year and expecting them to be a…
Descriptors: Information Literacy, Library Instruction, Individualized Instruction, Pacing
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Vykopal, Jan; Seda, Pavel; Svabensky, Valdemar; Celeda, Pavel – IEEE Transactions on Learning Technologies, 2023
Hands-on computing education requires a realistic learning environment that enables students to gain and deepen their skills. Available learning environments, including virtual and physical laboratories, provide students with real-world computer systems but rarely adapt the learning environment to individual students of various proficiency and…
Descriptors: Students, Educational Technology, Computer Assisted Instruction, Media Adaptation
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Lee, John S. Y. – ReCALL, 2022
Extracurricular reading is important for learning foreign languages. Text recommendation systems typically classify users and documents into levels, and then match users with documents at the same level. Although this approach can be effective, it has two significant shortcomings. First, the levels assume a standard order of language acquisition…
Descriptors: Second Language Learning, Reading Materials, Computer Assisted Instruction, Second Language Instruction
Ethan Prihar; Adam Sales; Neil Heffernan – Grantee Submission, 2023
This work proposes Dynamic Linear Epsilon-Greedy, a novel contextual multi-armed bandit algorithm that can adaptively assign personalized content to users while enabling unbiased statistical analysis. Traditional A/B testing and reinforcement learning approaches have trade-offs between empirical investigation and maximal impact on users. Our…
Descriptors: Trust (Psychology), Learning Management Systems, Learning Processes, Algorithms
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Said A. Salloum; Khaled Mohammad Alomari; Aseel M. Alfaisal; Rose A. Aljanada; Azza Basiouni – Smart Learning Environments, 2025
The integration of artificial intelligence in educational environments has the potential to revolutionize teaching and learning by enabling real-time analysis of students' emotions, which are crucial determinants of engagement, motivation, and learning outcomes. However, accurately detecting and responding to these emotions remains a significant…
Descriptors: Artificial Intelligence, Emotional Response, Psychological Patterns, Individualized Instruction
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