JDPLS Potential Areas of Research

Here are some possible areas of research within the joint doctoral program in learning sciences, describing past and ongoing projects in learning sciences at EPFL and ETH Zurich. For more ongoing projects, please see the webpages of the professors affiliated with the program.

Technology-Enhanced Teaching and learning

This theme focuses on utilizing the affordances of novel technologies to improve the teaching and learning processes.

Classroom orchestration 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). The availability of novel learning technologies such as AR/VR/MR, robotics and interactive simulations provides teachers with opportunities to enhance the teaching-learning process by orchestrating complex technology-enhanced learning scenarios in the classroom. However, the opportunity comes at the cost of an additional mental load to the teacher in terms of deciding how and when to use the technologies, and in what social context (individual or collaborative). Typically, this load increases as the classroom size increases. Teachers therefore need to be supported in orchestrating rich learning scenarios in large classrooms. In this project we aim to identify teacher orchestration challenges while using learning technologies in large classrooms and designing supports for teachers such as orchestration tools and dashboards.

A common structure of the vocational education and training system in Switzerland is that students learn in schools for one or two days per week with the rest of the week dedicated to apprenticeship at a workplace. Although this system is effective for developing professional competences, one of the challenges is the diversity of practical experiences obtained by the apprentice. The practical experience gained from a workplace is often limited to the specific situations the apprentices are exposed to and may not cover the whole spectrum of the practical experience related to the profession. In this project our goal is to expand the experience of the learners in vocational education. Considering digital technologies such as augmented and virtual reality as a means to address this challenge, the research goal is to design and evaluate activities in the expanded digital space that expand the practical experience of the apprentices.

Due to the digitization of society, computational thinking skills have become an integral part of the school curriculum. Activities such as block programming with Scratch and robotics using robots such as Thymio have been introduced in the curriculum from the primary grades as a means to develop computational thinking skills. The challenge is to design digital learning activities using programming tools or robots which target computational thinking skills rather than students’ ability to program or build robots. The goal of this project is twofold. Firstly to design, implement and evaluate appropriate digital learning activities that can support the acquisition of computational thinking skills among school children. Secondly, it aims to train teachers to be able to effectively and efficiently conduct these activities in their classrooms.

In this interdisciplinary project we will integrate knowledge from the learning sciences and neurosciences to investigate (i) if the intuitive understanding of physics, mediated by motor actions, can be used to gain a better understanding of the formal principles of mechanics and (ii) which neural mechanisms underpin this process. Research in cognitive neuroscience indicates that humans interact with the world by building internal simulations of the world through everyday experience. We also know that there are numerous physics misconceptions in resistant to direct instruction. What remains unexplored is the motor-skill connection between these neuro-embodied mechanisms and misconceptions. By designing learning environments that link cognition and motor skills, we propose to explore a new route towards acquiring in-depth understanding of physics and thus address one of the stickier problems in human learning.

Mathematics is a difficult subject, in part due to the abstract nature of its concepts. While lectures work for certain students, research demonstrates that learning by lecture is ineffective or inadequate for most learners. Recent research on this topic has demonstrated that instruction can be more effective if students are engaged in collaborative problem-solving activities prior to instruction. These “Productive Failure” activities typically lead to suboptimal solutions, yet this “failure” prepares students for deeper understanding and more efficient learning during subsequent instruction. By laying the groundwork for a broader university implementation of productive failure, this project brings together learning scientists and mathematics teachers to design Productive Failure-based activities in the teaching of linear algebra to first-year Engineering students.

University students are expected to master highly abstract ideas presented as symbolic formulae. To scientists and mathematicians, these symbols stand for powerful ideas. For many students, however, the inability to “see” these ideas leads to learning difficulties and a loss of interest. This could be avoided if students could recall their everyday, embodied know-how to explore the ideas taught in the classroom. This project aims to tackle this educational and technological challenge, and advance the use of embodied cognition and gamification to create innovative learning technologies. The proliferation of technologies that enable embodied interaction means that now is the time to make this vision a reality. Imagine students touching virtual molecules and actually feeling the differential haptics of their actions. With embodied-interaction technologies, we believe that the challenging, abstract ideas of sciences and mathematics can become more accessible, intuitive, and even more enjoyable.

Ethical challenges are inherent to scientific endeavours, as scientists are often faced with moral dilemmas during their research. As global leaders in scientific and technological research, ETH Zurich and EPFL are at the forefront of cutting-edge innovation, a position that requires ethical decision-making. We propose a systematic design, implementation, and evaluation of a comprehensive program of research on the learning of ethics at ETH Zurich and EPFL. Our graduates will not only read about ethical issues but will be immersed in a culture of social responsibility. To maximize its effectiveness, this research project will be integrated with core and basic teaching programs at ETH Zurich and EPFL.

The goal of the MINT Learning Center at ETH Zurich is the sustainable optimization of school-based learning opportunities in the STEM subjects. To improve general science education, researchers at the MINT Learning Center—in collaboration with gymnasium teachers—are developing teaching units for central topics in physics, mathematics, and chemistry. The collaboration combines cutting-edge research with wisdom of practice. During the course of this collaboration, teachers benefit from being introduced to empirically-validated teaching practices and materials, while researchers benefit from seeing their work tested in the field

Leveraging big data in education

This theme focuses on leveraging data and machine learning to understand and intervene in the processes of teaching and learning.

Providing academic support to students so that they do not fail in school is a critical challenge faced by teachers. It is difficult for teachers to keep track of and integrate the various activities done by students and predict whether a student will pass or fail. With learning management systems and other digital learning tools, data regarding students’ activities is captured. Machine learning methods applied to this data can then provide insights into student behavior and performance that is otherwise unavailable or not evident to teachers. These algorithms can reliably predict students’ failure in school based on their past and current behaviours and performance. Such a prediction can be leveraged by a teacher to provide extra support to a student who needs it. The goal of this project is to use available student data to build prediction models of student performance based on various behavioural indicators.

Computational models of the processes of teaching and learning can support the process of design and evaluation of novel learning strategies and technologies. For instance, a simulation of students, based on probabilistic models built from assumptions about students learning processes and trained on student data, has been used to understand the behavior of students in MOOCs, to analyze the inductive reasoning strategies of children, and to predict the rate of progress of students for activities in classrooms.

Similarly, we can consider the challenge where a teacher has to deliver the same sequence of examples to a diverse group of students with different prior knowledge and learning rates. One way to solve such a challenge is by using machine teaching, an inverse problem of machine learning, where the target is known. The goal of this project is to build  robust computational models of the teaching and learning processes which can guide the design and orchestration of complex learning scenarios in technology-enhanced classrooms.

Students are diverse and their learning depends on their backgrounds, prior knowledge and individual learning pace. It is therefore very difficult for teachers to address this diversity effectively in a class. Digital learning tools offer the opportunity for students to learn at their own pace. The data that is logged as students work with digital learning tools, such as learning management systems, MOOCs, simulations, robotics systems, AR/VR/MR systems and other learning software, can be used in order to assess the current state of their learning, and then adapt and personalize the content to the learner. This requires applying machine learning algorithms to the collected data in order to assess learners’ competencies and then presenting them with the content and feedback. The goal of this project is to build adaptation and personalization modules to improve the effectiveness of digital learning tools for different types of learners.

The appeal of collaborative learning is intuitive, as humans are indisputably social by nature. Moreover, evidence indicates that well-designed, collaborative learning environments increase both student learning and motivation. Business trends suggest that the workplace of the future will be increasingly interdisciplinary and collaborative as well. Numerous questions in this area remain open. Should we emphasize “knowledge sharing,” where group members develop similar competencies; or design for “knowledge interdependence,” where knowledge is distributed among group members? The goal of this work is to investigate the effectiveness and applicability of collaborative learning methods through extensive analyses of process data (self-explanations, chats, and log files), and to develop actionable guidelines for successful collaboration and collaborative learning.

Advancing Informal and Lifelong Learning

This theme focuses on designing learning spaces that support ubiquitous and informal learning for all ages.

A number of international projects are currently underway to support the development of physical spaces in which learning occurs. The working assumption of these projects is that student performance and satisfaction is improved by access to learning spaces where learning and discussions mix alongside work and play, and access to natural light and green spaces is optimized. To date, however, there has been no systematic assessment on how the needs and preferences of students can be best matched with the design and furnishing of learning spaces across the campus. Whether in Switzerland, the USA, or Singapore,it is evident that learning is not confined to universities, it is also happening in Starbucks and McDonalds, to name a few.. This suggests that students appropriate spaces for various purposes, including learning, and that there is a need for mixed-use spaces. This interdisciplinary project will develop a research framework to evaluate and validate the interaction of learners and physical spaces across various settings.

Science centers—like Technorama, the Swiss Science Center—provide hands-on experiences of hundreds of technological marvels and natural phenomena. Unlike traditional museums, everything in a science center can be touched, played, and experimented with: everything is hands-on. Recent advancements in embodied theory, motion-detecting technology, and educational design have led to the emergence of a new category of “hands-in” educational activities. In “hands-in” activities, movement becomes an integral part of knowledge development through the activity. For example, walking out of a beat can support the understanding of ratio, rate of change, and even gradients in differential calculus. Balancing can impact one’s sense of social (in)justice. Children’s understanding of elementary astrophysics can be supported through activities where they physically “act out” the trajectory of a meteor traveling through the Solar System. In a novel collaboration with Technorama, we are working on enhancing existing science exhibits and creating novel ones that put into practice the principles of “hands in” exploration.