PhD position ISEP, EDITE Doctoral School

Discovering the impact of “learning by doing” in STEM education with physical/digital programmable devices and humanoid robots for children with special needs


Nowadays, lots of social attention is put on the importance of learning programming and STEM (Science, Technology, Engineering and Mathematics), starting as soon as from kindergarten. Several approaches of teaching programming and STEM can be used; two of the most adopted approaches are based on designing and programming digital/virtual environments or on observing and manipulating physical programmable objects such as robots. However, few works explore the cognitive impacts resulting from different types of interactions during the learning experience and the understanding of new concepts

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 gains can vary depending on the context and course taught [5, 6].
In our previous works [7, 8], we investigated the differences in learning gains when programming beginners design small programs with a block-based language and execute these programs on either a tangible object or its digital simulation. The targeted programming concepts were those of variables, conditional structures, and loops. To this end, we conducted an experiment with 36 participants aged 14-17 with little or no prior knowledge of programming. More precisions on the methodology and findings of this experiment can be found in [8]. 
In this thesis, we aim to further investigate the impact of “learning by doing” (or active learning) [9, 10, 11] in computing and STEM education. This study will involve the use of physical devices, in particular humanoid robots such as QTRobot and ARI. The goal is to see whether the use of easily programmable robots in specific learning scenarios helps learners to understand STEM related notions. Furthermore, the analysis of interactions during learning experiences, thanks to machine learning techniques, will let us retrieve specific learning patterns. 
Those interactions will be also studied through the analysis of the social signals captured by sensors which will be used during our experiments such as microphones, cameras, EEG headsets (EmotivEpoc), and eye-trackers (Tobii Pro Nano) [14].
This analysis will also allow us to explore the metacognitive [15, 16] impacts of this kind of learning experiences and to investigate whether physical computing is more beneficial than digital computing, in respect to metacognitive aspects and computing and STEM education. 
These experiments could be applied in two different research contexts: either to explore the use of such tools and sensors for the development of cognitive and social skills for children with special needs or to distinguish, based on different age groups, the learning strategies that children might apply when learning programming and STEM notions with humanoid robots.
Keywords: Learning Analytics, Educational Data Mining, Computer Science Education, STEM Education 

The PhD thesis would be financed by the EDITE doctoral school.


• M.Sc. degree in computer science or in cognitive science or robotics with strong programming skills
• Experience in Python, MySQL, C/C++, Java
• Strong interest in scientific research
• Scientific curiosity, large autonomy and ability to work independently

For further information:

Interested candidates must contact; sending a detailed CV, their Master’s notes and one or more letters of recommendation as well as applying directly in the doctoral school website (to apply is necessary to create an account).

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. In Proceedings 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). 
[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)

Dernière mise à jour : 26 mai, 2020 - 15:27