The Learning Sciences is a growing interdisciplinary research field focused on understanding real-world learning and improving learning environments. In our joint doctoral program, we explore various research themes to advance our understanding of learning and teaching.
One area of study is technology-enhanced teaching and learning, which aims to use technologies to improve the teaching and learning processes. Topics include the integration of augmented and virtual reality in education, the introduction of computational thinking skills in school curricula via novel programming tools or robots, and the development of AI tools to support teaching and learning.. Technology-enhanced teaching and learning also includes classroom orchestration, which is the process of managing individual, team, and class-wide activities, in real-time, to enhance student engagement and learning, while managing all of the constraints of a daily classroom (time, discipline, curriculum, etc).
Another research focus is embodied learning, aiming to make abstract concepts in sciences and mathematics more accessible through embodied interactions and gamification. This research focus leverages recent technological advancements such as extended reality and haptic feedback devices to enable new forms of embodied interaction and learning. With embodied interaction technologies, the challenging, abstract ideas of science and mathematics can become more grounded, accessible, and intuitive.
Another area of focus investigates the effectiveness of instructional designs in STEM fields, such as productive failure and project-based learning. Productive failure involves introducing problem-solving activities before instruction to prepare students for deeper comprehension and efficient learning. Project-based learning, on the other hand, promotes the development of transversal skills, such as problem-solving, communication, and teamwork. This area of focus investigates the wide range of factors that impact learning, including the nature of the project itself, teacher support, and students’ behaviors during the project.
Another topic is the use of data and machine learning to understand and intervene in teaching and learning processes. With learning management systems and other digital learning tools, new sources of high-resolution interaction data regarding students’ activities is able to be captured. Machine learning methods applied to this data can provide insights into student behavior and performance that are otherwise unavailable or not evident to teachers. These algorithms are used to predict students’ failure, understand students’ behaviors, and describe their performance and learning processes. Additionally, these methods make it easier for students to learn at their own pace, because the data that is logged as students work with digital learning tools can be used to adapt and personalize the content to the learner.
The final theme concerns the advancement of informal and lifelong learning and focuses on designing learning spaces that promote ubiquitous and informal learning for all ages. Research in this area explores the interaction between learners and physical spaces across various settings, emphasizing the need for mixed-use spaces.