News Categories: Forschung

When it comes to soft skills like project planning, communicating and risk assessing, there is often a gap between the employer’s expectation and what engineering graduates bring to the table. Engineering schools could do more to prepare their graduates for the job market by incorporating these skills into their curricula. Various kinds of team-based projects are used to address this challenge, but a new study clarifies which approaches work best.

“Although team-based projects are very common in engineering programs, we do not really know to what extent students learn professional skills from this type of experience,” says Cyril Picard, a PhD student at EPFL’s Laboratory for Applied Mechanical Design. For his thesis, he looked at what approaches could promote the development of these skills among engineering students. The research was carried out jointly with Prof. Jürg Schiffmann, the head of Picard’s lab, and Cécile Hardebolle and Roland Tormey from EPFL’s Teaching Support Center (CAPE). Their paper, titled “Which professional skills do students learn in engineering team-based projects?,” appears in the European Journal of Engineering Education.

Learning soft skills doesn’t happen automatically

Professional skills tend to be addressed implicitly in traditional curricula. When courses include projects, students are often expected to pick up these skills alongside the technical concepts – but often the technical concepts are the only ones addressed explicitly.

“It’s widely believed that by working in teams, students will automatically acquire soft skills like project management,” says Picard. “Our study aimed to find out whether that is actually true.” The research team used a questionnaire developed at EPFL to assess students’ professional skills in two Bachelor classes and one Master class in mechanical engineering taught by Prof. Schiffmann. The results indicate that while team-based projects do help students learn technical skills and prepare for exams, they do not necessarily equip them with professional skills.

Targeted instruction needed

The Master class used for this study explicitly addressed planning and risk management through both theoretical discussions and individual feedback, which was not the case for the two Bachelor classes. As a consequence, the researchers could compare the two groups of students and gain valuable insights. “The students who worked on team-based projects but were not given specific instructions on project management and risk assessment did not learn much about those things,” says Picard. “On the other hand, students who received classroom instruction and project-specific feedback on those skills, and were then assessed on them, ended up acquiring them.”

“There is no secret”, adds Hardebolle, who works as a teaching advisor at CAPE, “if we want students to develop professional skills, we need to support them in this task.”

Small changes with a big impact

Prof. Schiffmann was not surprised by the study’s findings. “Teaching always involves building students’ awareness of the specific issues we want to address,” he says. The study shows that “real gains can be achieved by making a relatively small effort – such as including theory and project-specific feedback, particularly in the area of planning and risk management. Our findings are notable in that they can help us better understand how projects can be effectively incorporated into traditional classes.”

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Prior to coming to EPFL, Tanja Käser was a senior data scientist at the Swiss Data Science Center of ETH Zurich. As she explains, her interdisciplinary research combines computer and education science. She uses artificial intelligence (AI), machine learning, algorithms, and data mining to model and predict human learning and behavior. This facilitates, for example, the customization of learning tools.

IC: Can you tell us briefly about your research background? What inspired you pursue a combination of education and computer science?

TK: I completed my master’s and PhD degrees in computer science at ETH Zurich. My research uses machine learning to understand and improve human learning. I am particularly interested in creating accurate models of human behavior and learning.

I chose this research direction because I am very fascinated by how humans learn, and also because of my desire to have an impact on society with my research; i.e., through providing high-quality education to everyone.

IC: What is your mission for the Digital Vocation, Education, and Training Laboratory?

TK: My vision is to use technology to support the vocational education of students, to help them become better learners. Increasing digitization means that knowledge circles are becoming shorter, which in turn necessitates the adaptation of knowledge and skills.

Digital environments, such as interactive simulations, have the potential to teach students new content, while at the same time allowing them to practice important skills for learning. The use of AI to personalize these learning environments will also enable students to learn content more efficiently, and to develop more effective learning strategies.

IC: In your opinion, what are some of the most interesting problems to be pursued in the field of digital education today? 

TK: Gaining a full understanding of student learning, so that we can accurately model it and hence facilitate more effective and efficient teaching. Education is both about students learning material effectively, as well as about preparing them for continuing to learn on their own. Until now, a lot of digital and online education has been focused more on the first part. However, computers also make it easier to observe how students learn by gathering data on their interactions and learning processes, and to therefore gain new insights.

I am also interested in the explainability of model decisions. Today, we have powerful AI models, but they are often a black box. It can be hard to explain to teachers and students how models make decisions. I think this is important for education, so that that people using these models understand how decisions are made.

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