| Number | Title | ECTS | Hours | Lecturers |
|---|
| 263-5005-00L | Artificial Intelligence in Education   Number of participants limited to 75. | 5 credits | 2V + 1U + 1A | M. Sachan,
T. Sinha |
| Abstract | Artificial Intelligence (AI) methods have shown to have a profound impact in educational technologies, where the great variety of tasks and data types enable us to get benefit of AI techniques in many different ways. We will review relevant methods and applications of AI in various educational technologies, and work on problem sets and projects to solve problems in education with the help of AI. |
| Learning objective | The course will be centered around exploring methodological and system-focused perspectives on designing AI systems for education and analyzing educational data using AI methods. Students will be expected to a) engage in presentations and active in-class discussion, b) work on problem-sets exemplifying the use of educational data mining techniques, and c) undertake a final course project with feedback from instructors. |
| Content | The course will start with a general introduction to AI, where we will cover supervised and unsupervised learning techniques (e.g.,classification and regression models, feature selection and preprocessing of data, clustering, dimensionality reduction and text mining techniques) with a focus on application of these techniques in educational data mining. After the introduction of the basic methodologies, we will continue with the most relevant applications of AI in educational technologies (e.g., intelligent tutoring and student personalization, scaffolding open-ended discovery learning, socially-aware AI and learning at scale with AI systems). In the final part of the course, we will cover challenges associated with using AI in student facing settings. |
| Lecture notes | Lecture slides will be made available at the course Web site. |
| Literature | No textbook is required, but there will be regularly assigned readings from research literature, linked to the course website. |
| Prerequisites / Notice | There are no prerequisites for this class. However, it will help if the student has taken an undergraduate or graduate level class in statistics, data science or machine learning. This class is appropriate for advanced undergraduates and master students in Computer Science as well as PhD students in other departments. |
| 851-0255-00L | Introduction to Methods in Learning Sciences II Course registration targeted at students interested in learning sciences research and higher education. Language of performance assessment will be English. | 2 credits | 2S | M. Kapur,
T. Sinha |
| Abstract | The course aims at equipping students with a suite of advanced quantitative and qualitative tools to support their existing research and develop new lines of inquiry in the Learning Sciences. By providing opportunities to analyze empirical educational data, the course will allow students to develop an appreciation for the breadth of methods that can be employed to improve the process of learning |
| Learning objective | The course will be centered around exploring methodological perspectives by focusing on conceptual aspects of datasets and experiments in the Learning Sciences. Face-to-face meetings will be held every fortnight, although students will be expected to work individually on weekly tasks (e.g., discussing relevant literature, performing data analysis, finding patterns in data and linking them to educational theory) |
| Content | The course has the following components: a) advanced statistical methods (e.g., mediation and moderation), b) advanced qualitative methods (e.g., interaction analysis), c) computational methods (e.g., prediction and structured discovery with educational data) |
| Prerequisites / Notice | Participation in the introductory version of this course (851-0252-14L Introduction to Methods in Learning Sciences) should be helpful, but not necessary. The class will be designed to allow students with strong STEM backgrounds to catch up and fully participate. |
| Competencies | | Subject-specific Competencies | Techniques and Technologies | assessed | | Method-specific Competencies | Analytical Competencies | assessed | | Decision-making | assessed | | Problem-solving | assessed | | Social Competencies | Communication | assessed | | Cooperation and Teamwork | assessed | | Leadership and Responsibility | assessed | | Personal Competencies | Adaptability and Flexibility | assessed | | Creative Thinking | assessed | | Critical Thinking | assessed |
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