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Yuan-Chen Liu; Tzu-Hua Huang; Chien-Chia Huang – Interactive Learning Environments, 2024
In this study, an interactive programming learning environment was built with two types of error prompt functions: 1) the key prompt and 2) step-by-step prompt. A quasi-experimental study was conducted for five weeks, in which 75 sixth grade students from disadvantaged learning environments in Taipei, Taiwan, were divided into three groups: 1) the…
Descriptors: Programming, Computer Science Education, Cues, Grade 6
Grace Leah Akinyi; Robert Oboko; Lawrence Muchemi – Electronic Journal of e-Learning, 2024
The future of university learning in Sub-Saharan Africa has become increasingly digitally transformed by both e-Learning, and learning analytics, post-COVID-19 pandemic. Learning analytics intervention is critical for effective support of socially-shared regulated learning skills, which are crucial for twenty-first-century e-Learners.…
Descriptors: Electronic Learning, Student Attitudes, Learning Analytics, Feedback (Response)

Arun-Balajiee Lekshmi-Narayanan; Priti Oli; Jeevan Chapagain; Mohammad Hassany; Rabin Banjade; Vasile Rus – Grantee Submission, 2024
Worked examples, which present an explained code for solving typical programming problems are among the most popular types of learning content in programming classes. Most approaches and tools for presenting these examples to students are based on line-by-line explanations of the example code. However, instructors rarely have time to provide…
Descriptors: Coding, Computer Science Education, Computational Linguistics, Artificial Intelligence
Xiaoni Zhang – Journal of Information Systems Education, 2025
This teaching tip explores the integration of AI tools into database education. The author describes how instructors can use AI tools to prepare teaching materials and how students can use AI to facilitate database development. The teaching tips provided encompass both course-level objectives and assignment-specific strategies. The inclusion of AI…
Descriptors: Databases, Technology Integration, Critical Thinking, Thinking Skills
Hui Zhang; Yi Zhang; Tao Xu; Yun Zhou – Educational Technology Research and Development, 2024
Virtual Reality (VR) is increasingly recognized as a promising tool to enhance learning, yet research on the use of VR instructional approaches for online learning remains limited. The present study aims to address this research gap by examining the effects of VR instructional approaches and textual cues on learning. We conducted an educational VR…
Descriptors: Teaching Methods, Cognitive Ability, Computer Simulation, Cues
Ghadeer Sawalha; Imran Taj; Abdulhadi Shoufan – Cogent Education, 2024
Large language models present new opportunities for teaching and learning. The response accuracy of these models, however, is believed to depend on the prompt quality which can be a challenge for students. In this study, we aimed to explore how undergraduate students use ChatGPT for problem-solving, what prompting strategies they develop, the link…
Descriptors: Cues, Artificial Intelligence, Natural Language Processing, Technology Uses in Education
Martha Partridge; Yen-En Kuo; Nattanan Hamapongnitinan; Liming Chen; Haoyuan Huang – International Journal for Students as Partners, 2024
Students as Partners (SaP) approaches have gained more and more traction in higher education in recent years (Dai & Matthews, 2022). Rooted in values such as reciprocity and shared responsibility, SaP can offer opportunities for internationalizing the curriculum and departing from traditional teacher-student hierarchies (Green & Baxter,…
Descriptors: Artificial Intelligence, Digital Literacy, Higher Education, Teacher Student Relationship