Manu Kapur: Catalogue data in Autumn Semester 2021 |  |
| Name | Prof. Dr. Manu Kapur |
| Field | Learning Sciences and Higher Education |
| Address | Professur für Lernwissenschaften ETH Zürich, RZ J 3 Clausiusstrasse 59 8092 Zürich SWITZERLAND |
| Telephone | +41 44 632 25 89 |
| manukapur@ethz.ch | |
| URL | http://www.manukapur.com |
| Department | Humanities, Social and Political Sciences |
| Relationship | Full Professor |
| Number | Title | ECTS | Hours | Lecturers | ||||||||||||||||||||||||||||||||
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| 851-0252-04L | Behavioral Studies Colloquium  | 0 credits | 2K | E. Stern, U. Brandes, D. Helbing, C. Hölscher, M. Kapur, C. Stadtfeld | ||||||||||||||||||||||||||||||||
| Abstract | This colloquium offers an opportunity to discuss recent and ongoing research and scientific ideas in the behavioral sciences, both at the micro- and macro-levels of cognitive, behavioral and social science. The colloquium features invited presentations from internal and external researchers as well as presentations of doctoral students close to submitting their dissertation research plan. | |||||||||||||||||||||||||||||||||||
| Learning objective | Participants are informed about recent and ongoing research in different branches of the behavioral sciences. Presenting doctoral students obtain feedback on their dissertation research plan. | |||||||||||||||||||||||||||||||||||
| Content | This colloquium offers an opportunity to discuss recent and ongoing research and scientific ideas in the behavioral sciences, both at the micro- and macro-levels of cognitive, behavioral and social science. It covers a broad range of areas, including theoretical as well as empirical research in social psychology, research on higher education, sociology, modeling and simulation in sociology, decision theory and behavioral game theory, economics, research on learning and instruction, cognitive psychology and cognitive science. The colloquium features invited presentations from internal and external researchers as well as presentations of doctoral students close to submitting their dissertation research plan. | |||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Doctoral students in D-GESS can obtain 2 credit points for presenting their dissertation research plan. | |||||||||||||||||||||||||||||||||||
| 851-0252-12L | The Science of Learning From Failure  Number of participants limited to 60. | 2 credits | 2S | M. Kapur, E. Ziegler | ||||||||||||||||||||||||||||||||
| Abstract | We can learn from failure. But, what does “failure” mean? And, what, how, and why do we learn from failure? This course covers research from the cognitive, educational, and learning sciences that addresses the role of failure in human learning. Students will critically examine how failure affects thinking, knowledge, creativity, problem-solving, and motivation. | |||||||||||||||||||||||||||||||||||
| Learning objective | Students will: - Critically read and analyze articles on research that addresses failure in learning - Participate in in-class problem-solving activities around research in failure - Discuss and reflect upon topics in both online and face-to-face formats - Engage in activities through the online platform - Complete a final paper on a subtopic related to failure in learning By the end of the course, students should be able to: - Demonstrate a critical understanding of the role that failure plays in learning - Discuss how and why failure can benefit learning - Discuss how and why failure does not facilitate learning - Apply understanding to a related sub-topic | |||||||||||||||||||||||||||||||||||
| Content | We learn from our mistakes, or rather, we hope that we do. Another way to say this is that we can learn from failure. But, what does “failure” mean? What, how, and why do we learn from failure? This course covers research from the cognitive, educational, and learning sciences that addresses the role of failure in human learning. Students will critically examine how failure affects development of knowledge, creativity, problem-solving, and general thinking and learning. More specifically, they will have the opportunity to question and evaluate the potential relationships between the facets around failure within individual, interactional, cultural, societal, and global contexts through seminal readings and problem-solving activities. Students from any discipline are welcome to this course to learn more about how failure can be harnessed to improve our knowledge, capabilities, innovations, teamwork, and contribute to the larger global world. | |||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | This seminar is an interactive course, thus attendance and classroom participation are required. Processing of online tasks is a requirement for obtaining credit points. The course is held as 2 separate courses with each a maximum of 30 students: one course in German and one course in English. | |||||||||||||||||||||||||||||||||||
| 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. | |||||||||||||||||||||||||||||||||||
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| 851-0256-00L | Future Learning Initiative Colloquium | 0.5 credits | 1K | M. Kapur | ||||||||||||||||||||||||||||||||
| Abstract | This colloquium offers an opportunity for students to present and discuss their ongoing projects broadly related to the science of learning. The colloquium also welcomes students from other disciplines who are interested in understanding the nature of formal and informal learning as a complex phenomenon across multiple, interacting levels: neural, cognitive, embodied, social, and cultural. | |||||||||||||||||||||||||||||||||||
| Learning objective | Students will have opportunities to develop their own ideas in the field of learning sciences and to communicate their ideas in oral presentations and in written papers. To achieve credit for the course, students are expected to either present their own research or provide scholarly feedback on the presented research. | |||||||||||||||||||||||||||||||||||
| Content | This colloquium offers an opportunity for students to discuss their ongoing research and scientific ideas in the learning sciences. This includes research aimed at understanding the nature of formal and informal learning as a complex phenomenon across multiple, interacting levels: neural, cognitive, embodied, social, and cultural. The colloquium also offers an opportunity for students from other disciplines to discuss their ideas in so far as they have some relation to the Future Learning Initiative at ETH or to the science of learning more broadly. Existing Future Learning Initiative projects include productive failure and preparation for future learning, neural basis of learning, mixed reality environments, physical spaces and learning, interdisciplinarity in life sciences education, embodied learning and gaming, abstract mathematical cognition, learning of ethics, project-based learning, and assessment validity. | |||||||||||||||||||||||||||||||||||