PhD position ISEP Paris
Discovering the impact of “learning by doing” in programming and STEM education exploiting Machine Learning techniques
Nowadays lots of social attention is set on how important it is to learn programming and STEM (Science, Technology, Engineering and Mathematics), even since youth. Different approaches of teaching STEM and coding are used: two of the most adopted are based on physical manipulation thanks to robots or programmable objects or on exploiting digital/virtual environments. At the same time, few works explore which kind of cognitive- psychological impacts these kind of learning interactions can cause and how students feel during the learning experience.
Research in Computer Science Education (CSE) has long tried to introduce robots in programming courses. Oftentimes, the objective is to foster students' interest and creativity through “the design of tangible and interactive object using programmable hardware” , also known as physical computing. In this regard, results indicate that students experience an increase in motivation [2, 3] and that underrepresented populations in Computer Science (CS) courses feel empowered . However, learning outcomes can vary depending on the context and course taught [5, 6].
In our previous works [7, 8], we have investigated a learning programming environment we developed in a block-based language exploiting either a tangible object or its digital simulation: we conducted an experiment with 36 participants aged 14-17 with little or no prior knowledge of programming in a half a day learning experiment. We wanted to analyse if there were any differences in learning gain between the two groups and which kind of metacognitive processes are triggered in this type of learning experience.
In this project, we aim to investigate two separate case studies. The first one aims to further identify the benefits of using a tangible object and/or its simulation for students learning the elementary concepts of programming (i.e., variables, conditional structures, and loops). The second one involves the use of a tangible object (ie. a robot) for STEM education. In particular, the goal is to see whether the use of an easily programmable robot in specific learning scenarios helps learners to understand STEM related notions.
In this way we want to further explore the impact of “learning by doing” (or active learning) [9, 10, 11] in computing and STEM education exploiting Machine Learning techniques.
At first, we want to identify how people can learn by using Machine Learning techniques on data produced by learners during their activities (Learning Analytics) [12, 13]: in this way we could search for patterns or strategies used by learners to solve some exercises and compare them with social signals captured by different sensors that can be used during our experiments (i.e. microphones, camera and eye-trackers ). This analysis will also allow us to further
study the metacognitive [15, 16] impacts of this kind of learning experiences and to deeply investigate whether physical computing is more beneficial than digital computing in respect to metacognitive aspects and computing and STEM education.
Secondly we want to extend this study to three participant age groups: primary (6-10 years old), middle (11-15 years old), and high school (16-19 years old) and follow them over a longer learning experience (at least 4-5 weeks). This will let us compare the benefits of using a robot and/or a simulation and to customize STEM learning experiences over a longer period depending on the learners’ age.
The PhD thesis will take place at ISEP (Paris) in the context of a partnership with University Sacro Cuore Milan (Italy). This thesis would be financed by the EDITE doctoral school.
• M.Sc. degree in computer science or in cognitive science with strong programming skills
• Experience in Python, MySQL, C/C++, Java and/or relevant machine learning library
• Strong interest in scientific research
• Scientific curiosity, large autonomy and ability to work independently
For further information:
Deadline for application: 9th May 2019
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 Fessard, G., Renna, I., & Wang, P. Are There Differences in Learning Gains When Programming a Tangible Object or a Simulation? In Proceedings of the 24th ACM Conference on Innovation and Technology in Computer Science Education (2019, accepted).
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Dernière mise à jour : 9 mai, 2019 - 12:21