Discovering the impact of “learning by doing” in programming and STEM education exploiting Machine Learning techniques

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Context:

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.

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PhD subject:

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” [1], 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 [4]. 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 [14]). 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.

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Skills/Requirements:
• 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

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For further information:

Interested candidates must contact ilaria.renna@isep.fr, patrick.wang@isep.fr florence.rossant@isep.fr sending a detailed CV, their Master’s notes and one or more letters of recommendation

Deadline for application: 9th May 2019

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Bibliography :
[1] M. Przybylla and R. Romeike. Key Competences with Physical Computing. KEYCIT 2014: Key Competencies in Informatics and ICT , 7:351, 2015 
[2] S. Sentance, J. Waite, S. Hodges, E. MacLeod, and L. Yeomans. Creating Cool Stuff: Pupils’ Experience of the BBC micro:bit. InProceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education, pages 531–536. ACM, 2017 
[3] S. Hodges, J. Scott, S. Sentance, C. Miller, N. Villar, S. Schwiderski-Grosche, K. Hammil, and S. Johnston. .NET Gadgeteer: A New Platform for K-12 Computer Science Education. In Proceeding of the 44th ACM Technical Symposium on Computer Science Education, pages 391–396. ACM, 2013 
[4] S. Sentance and S. Schwiderski-Grosche. Challenge and Creativity: Using .NET Gadgeteer in Schools. In Proceedings of the 7th Workshop in Primary and Secondary Computing Education, pages 90–100. ACM, 2012. 
[5] B. Fagin and L. Merkle. Measuring the Effectiveness of Robots in Teaching Computer Science. In ACM SIGCSE Bulletin, volume 35, pages 307–311. ACM, 2003. 
[6] D. C. Cliburn. Experiences with the LEGO Mindstorms throughout the Undergraduate Computer Science Curriculum. In Proceedings. Frontiers in Education. 36th Annual Conference, pages 1–6, 2006 
[7] Fessard, G., Renna, I., & Wang, P. Comparing the Effects of Using a Tangible Object or a Simulation in Learning Elementary CS Concepts: A Case Study with Block-Based Programming. In Proceedings of the 50th ACM Technical Symposium on Computer Science 
Education (pp. 1274-1274). ACM, 2019. 
[8] 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). 
[9] Mani, M., Alkabour, N., & Alao, D. (2014, October). Evaluating effectiveness of active learning in computer science using metacognition. In 2014 IEEE Frontiers in Education Conference (FIE) Proceedings (pp. 1-8). IEEE. 
[10] Hoachlander, G., & Yanofsky, D. (2011). Making STEM real. Educational Leadership, 68(6), 60-65. 
[11] Boy, G. A. (2013, August). From STEM to STEAM: toward a human-centred education, creativity & learning thinking. In Proceedings of the 31st European conference on cognitive ergonomics (p. 3). ACM. 
[12] Baker R.S., Inventado P.S. (2014) Educational Data Mining and Learning Analytics. In: Larusson J., White B. (eds) Learning Analytics. Springer, New York, NY 
[13] Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thüs, H. (2013). A reference model for learning analytics. International Journal of Technology Enhanced Learning, 4(5-6), 318-331. 
[14] van Gog, T., & Jarodzka, H. (2013). Eye tracking as a tool to study and enhance cognitive and metacognitive processes in computer-based learning environments. In International handbook of metacognition and learning technologies (pp. 143-156). Springer, New York, NY. 
[15] Flavell, J.H.: Metacognition and cognitive monitoring: a new area of cognitive- developmental inquiry. American Psychologist, 34 (10), 906–911 (1979) 
[16] Livingston, J.A.: Metacognition: An overview. (2003)