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Direct linkChen, Huan; Wang, Ye; Li, You; Lee, Yugyung; Petri, Alexis; Cha, Teryn – Education and Information Technologies, 2023
Artificial intelligence (AI) has been widely adopted in higher education. However, the current research on AI in higher education is limited lacking both breadth and depth. The present study fills the research gap by exploring faculty members' perception on teaching AI and data science related courses facilitated by an open experiential AI…
Descriptors: College Faculty, Computer Science Education, Control Groups, Data Science
Olelewe, Chijioke Jonathan; Agomuo, Emmanuel E.; Obichukwu, Peter Uzochukwu – Education and Information Technologies, 2019
Achieving learner engagement in the teaching and learning process is paramount towards ensuring knowledge retention in QBASIC programming. This study focuses on effects of b-learning and face-to-face (F2F) on college students' engagement and retention in QBASIC programming. The study adopted quasi-experimental design with non-equivalent group…
Descriptors: College Students, Learner Engagement, Retention (Psychology), Programming
Petrovic, J.; Pale, P.; Jeren, B. – Education and Information Technologies, 2017
This study aimed to investigate the effects of using online formative assessments on students' learning achievements. Using a quasi-experimental study design with one control group (no formative assessments available), and two experimental groups receiving feedback in available online formative assessments (knowledge of the correct response--KCR,…
Descriptors: Computer Assisted Testing, Formative Evaluation, Feedback (Response), Difficulty Level
