Lecture

Lesson Design: Backwards Design

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In course

CS-411: Digital education

This course addresses the relationship between specific technological features and the learners' cognitive processes. It also covers the methods and results of empirical studies: do student actually learn due to technologies? In fall 2025, P. Dillenbourg w ...

Description

This lecture covers the process of designing lessons with a focus on setting learning goals, assessment methods, and learning experiences. It emphasizes the importance of considering constraints such as timing, logistics, student backgrounds, and situational factors. The session also delves into creating measurable learning objectives using action verbs and task analysis to identify necessary skills and concepts.

Instructors (2)

Pierre Dillenbourg

A former teacher in elementary school, Pierre Dillenbourg graduated in educational science (University of Mons, Belgium). He started his research on learning technologies in 1984. In 1986, he has been on of the first in the world to apply machine learning to develop a self-improving teaching system. He obtained a PhD in computer science from the University of Lancaster (UK), in the domain of artificial intelligence applications for education. He has been assistant professor at the University of Geneva. He joined EPFL in 2002. He has been the director of Center for Research and Support on Learning and its Technologies, then academic director of Center for Digital Education, which implements the MOOC strategy of EPFL (over 2 million registrations). He is full professor in learning technologies in the School of Computer & Communication Sciences, where he is the head of the CHILI Lab: "Computer-Human Interaction for Learning & Instruction ». He is the director of the leading house DUAL-T, which develops technologies for dual vocational education systems (carpenters, florists,...). With EPFL colleagues, he launched in 2017 the Swiss EdTech Collider, an incubator with 80 start-ups in learning technologies. He (co-)-founded 4 start-ups, does consulting missions in the corporate world and joined the board of several companies or institutions. In 2018, he co-founded LEARN, the EPFL Center of Learning Sciences that brings together the local initiatives in educational innovation. He is a fellow of the International Society for Learning Sciences. He currently is the Associate Vice-President for Education at EPFL.

Patrick Jermann

After studies in Geneva (TECFA) and Pittsburgh (LRDC) I joined EPFL in 2003 to coordinate eLearning projects and conduct research in the field of Computer Supported Collaborative Learning (CSCL). Starting 2013 I am responsible for MOOCs production at the Center for Digital Education (CEDE).Former Associate Editor for the IEEE Transactions on Learning Technologies and former Member of the Editorial Board for the International Journal of Computer Supported Collaborative Learning (iJCSCL). Specialties: Interaction analysis, research methods, statistical methods, prototyping, software development, pedagogical design.

Official source

Related concepts (23)

Conceptual metaphor

In cognitive linguistics, conceptual metaphor, or cognitive metaphor, refers to the understanding of one idea, or conceptual domain, in terms of another. An example of this is the understanding of quantity in terms of directionality (e.g. "the price of peace is rising") or the understanding of time in terms of money (e.g. "I spent time at work today"). A conceptual domain can be any mental organization of human experience.

Conceptual blending

In cognitive linguistics, conceptual blending, also called conceptual integration or view application, is a theory of cognition developed by Gilles Fauconnier and Mark Turner. According to this theory, elements and vital relations from diverse scenarios are "blended" in a subconscious process, which is assumed to be ubiquitous to everyday thought and language. Much like memetics, it is an attempt to create a unitary account of the cultural transmission of ideas.

Natural-language understanding

Natural-language understanding (NLU) or natural-language interpretation (NLI) is a subtopic of natural-language processing in artificial intelligence that deals with machine reading comprehension. Natural-language understanding is considered an AI-hard problem. There is considerable commercial interest in the field because of its application to automated reasoning, machine translation, question answering, news-gathering, text categorization, voice-activation, archiving, and large-scale content analysis.

Fraction

A fraction (from fractus, "broken") represents a part of a whole or, more generally, any number of equal parts. When spoken in everyday English, a fraction describes how many parts of a certain size there are, for example, one-half, eight-fifths, three-quarters. A common, vulgar, or simple fraction (examples: and ) consists of an integer numerator, displayed above a line (or before a slash like ), and a non-zero integer denominator, displayed below (or after) that line.

Unit fraction

A unit fraction is a positive fraction with one as its numerator, 1/n. It is the multiplicative inverse (reciprocal) of the denominator of the fraction, which must be a positive natural number. Examples are 1/1, 1/2, 1/3, 1/4, 1/5, etc. When an object is divided into equal parts, each part is a unit fraction of the whole. Multiplying two unit fractions produces another unit fraction, but other arithmetic operations do not preserve unit fractions.

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