Delves into social cognition in education, emphasizing collaborative learning benefits, pitfalls, and future trends.
Covers the effect of X on Y through experiments and hypothesis testing.
Explores ANOVA, moderation, and statistical analysis methods in digital education, emphasizing their significance in educational research.
Delves into learner modelling, emphasizing the significance of understanding students' behaviors to enhance learning activities.
Covers the design principles for problem-solving activities and guidelines for instruction activities.
Delves into measuring learning effects in digital education and analytics, covering research questions, variables, experimental design, and bias solutions.
Covers ANOVA, mediation, and moderation analyses in R for CS-411 Digital Education.
Delves into orchestration graphs, transition probabilities, and learning analytics for predicting student states.
Emphasizes the importance of detailing design processes and consolidating learning through engaging activities and critical feature analysis.
Covers the basics of experimental design and analysis, focusing on statistical techniques like ANOVA, regression, mediation, and moderation.
