Machine Learning for Education: Student Models and Conceptual Understanding

Related lectures (8)

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Explores the benefits of problem-solving before instruction in a linear algebra course for mechanical engineers at ETH Zürich.

Explores designing lessons with a focus on learning goals and constraints.

Explores safe learning in automatic control systems, covering challenges, risk-sensitive control, and predictive safety filters.

Discusses the impact of interaction, biases, and feedback on learning tools and educational settings.

Delves into the definition and capabilities of robots based on various sources.

Covers the basics of experimental design and analysis, focusing on statistical techniques like ANOVA, regression, mediation, and moderation.

Covers ethical considerations in NLP, focusing on bias, toxicity, and misinformation.

Discusses how robots manipulate objects using natural language instructions and integrates vision-language models for enhanced performance.