Learning sciences

Summary

Learning sciences (LS) is an interdisciplinary field that works to further scientific, humanistic, and critical theoretical understanding of learning as well as to engage in the design and implementation of learning innovations, and the improvement of instructional methodologies. LS research traditionally focuses on cognitive-psychological, social-psychological, cultural-psychological and critical theoretical foundations of human learning, as well as practical design of learning environments. Major contributing fields include cognitive science, computer science, educational psychology, anthropology, and applied linguistics. Over the past decade, researchers have expanded their focus to include the design of curricula, informal learning environments, instructional methods, and policy innovations. Domain definition As an emerging discipline, LS is still in the process of defining itself. Accordingly, the identity of the field is multifaceted, and varies from institution to in

Official source

https://en.wikipedia.org/wiki/Learning_sciences

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related publications (3)

Related publications

Related people

Related units

Related concepts

Related courses (12)

Related courses

Related lectures (6)

Related lectures

Cellulan World: Interactive Platform to Learn Swarm Behaviors

Barbara Bruno, Pierre Dillenbourg, Hala Khodr, Aditi Kothiyal

Swarming behaviors are ubiquitous in nature, and an important topic to teach and learn. In the learning sciences domain of complex systems understanding, studies have shown that novices tend to assume that centralized control and leadership should exist whenever they see complex patterns. In this paper, we present an educational framework consisting of a swarm of virtual and/or physical agents (robots) as a game-based learning environment to introduce the underlying rules of swarm behaviors.

IFAAMAS2022

Learner-Computer Interaction

Pierre Dillenbourg, Kshitij Sharma

Learner-Computer Interaction (LCI) research addresses the design, development and use of interactive technologies to support and amplify human learning. LCI is based on the rationale that learning while interacting with technology is a complex, multi-layered phenomenon, thus, designing the conditions for engaging in meaningful learning is vital in the 21st century, yet, it remains a challenging process. LCI developments are expected to contribute towards a coherent new, high-impact way of understanding and building learner-centered interaction concepts to support the design of future learning environments. LCI provides an interdisciplinary playground for researchers and professionals across all areas of learning technologies, psychology, learning science and human computer interaction (NCI), with an ultimate objective of providing a forum at the intersection of these topical areas. LCI aims to develop a critical discussion, debate and co-development of ideas and approaches about the next generation of learning environments and their interaction design capacities, the form of these capacities and the way they can be identified, utilized and enhanced to help us improve the contemporary learning technologies and users' learning experience.

ASSOC COMPUTING MACHINERY2018

By modeling pedagogical scenarios as directed geometrical graphs and proposing an associated modeling language, this book describes how rich learning activities, often designed for small classes, can be scaled up for use with thousands of participants. With the vertices of these graphs representing learning activities and the edges capturing the pedagogical relationship between activities, individual, team, and class-wide activities are integrated into a consistent whole. The workflow mechanisms modeled in the graphs enable the construction of scenarios that are richer than those currently implemented in MOOCs. The cognitive states of learners in two consecutive activities feed a transition matrix, which encapsulates the probability of succeeding in the second activity, based on success in the former. This transition matrix is summarized by a numerical value, which is used as the weight of the edge. This pedagogical framework is connected to stochastic models, with the goal of making learning analytics more appealing for data scientists. However, the proposed modeling language is not only useful in learning technologies, it also allows researchers in learning sciences to formally describe the structure of any lesson, from an elementary school lesson with 20 students to an online course with 20,000 participants.

EPFL Press2015