Using video analysis and learning analytics to understand programming trajectories in data science activities with Scratch

In this paper, we describe a new automated tool to analyze how students create their projects on Scratch 3.0, with the goal of understanding learning trajectories in a way that considers students’ programming processes and practices, moving beyond the analysis of computational thinking concepts as evidence of learning. Drawing on a combination of qualitative video analysis and temporal learning analytics, we also present preliminary data from a pilot study that illustrates some possibilities afforded by this type of analytical tool. We expect that our tool can help researchers to better understand learning in the context of data visualization activities with block-based programming languages by shedding light on processes that are usually invisible and, thus, better support students in their diverse learning pathways.

[1]  Ana Marilza Pernas,et al.  ScratchAnalytics: Um Framework para Coleta e Análise de Interações em Projetos Scratch , 2020 .

[2]  Yue Yin,et al.  Assessing computational thinking: A systematic review of empirical studies , 2020, Comput. Educ..

[3]  Yuning Xu,et al.  A Principled Approach to Designing a Computational Thinking Practices Assessment for Early Grades , 2020, SIGCSE.

[4]  Michael S. Horn,et al.  Modeling and Measuring High School Students’ Computational Thinking Practices in Science , 2020, Journal of Science Education and Technology.

[5]  Francisco J. García-Peñalvo,et al.  Clickstream for learning analytics to assess students' behavior with Scratch , 2019, Future Gener. Comput. Syst..

[6]  Bodong Chen,et al.  Critical issues in designing and implementing temporal analytics , 2018, J. Learn. Anal..

[7]  Bodong Chen,et al.  Time for Change: Why Learning Analytics Needs Temporal Analysis , 2017, J. Learn. Anal..

[8]  Shuchi Grover,et al.  A Framework for Using Hypothesis-Driven Approaches to Support Data-Driven Learning Analytics in Measuring Computational Thinking in Block-Based Programming Environments , 2017, ACM Trans. Comput. Educ..

[9]  Marcos Romn-Gonzlez,et al.  Which cognitive abilities underlie computational thinking? Criterion validity of the Computational Thinking Test , 2017 .

[10]  Deborah A. Fields,et al.  Studying situated learning in a constructionist programming camp: A multimethod microgenetic analysis of one girl's learning pathway , 2016, IDC.

[11]  Taylor Martin,et al.  Macro Data for Micro Learning: Developing the FUN! Tool for Automated Assessment of Learning , 2016, L@S.

[12]  Lisa Quirke,et al.  Combining Big Data and Thick Data Analyses for Understanding Youth Learning Trajectories in a Summer Coding Camp , 2016, SIGCSE.

[13]  Michael S. Horn,et al.  Defining Computational Thinking for Mathematics and Science Classrooms , 2016 .

[14]  Mark Guzdial,et al.  A future for computing education research , 2014, Commun. ACM.

[15]  D. Koller,et al.  Programming Pluralism: Using Learning Analytics to Detect Patterns in the Learning of Computer Programming , 2014 .

[16]  Ryan Shaun Joazeiro de Baker,et al.  Educational Data Mining and Learning Analytics: Applications to Constructionist Research , 2014, Technology, Knowledge and Learning.

[17]  Diana Franklin,et al.  Hairball: lint-inspired static analysis of scratch projects , 2013, SIGCSE '13.

[18]  Paulo Blikstein,et al.  Using learning analytics to assess students' behavior in open-ended programming tasks , 2011, LAK.

[19]  Eric Rosenbaum,et al.  Scratch: programming for all , 2009, Commun. ACM.

[20]  Gregorio Robles,et al.  Bad Smells in Scratch Projects: A Preliminary Analysis , 2019, TACKLE@EC-TEL.

[21]  Charles B. Hodges,et al.  Emerging Research, Practice, and Policy on Computational Thinking , 2017 .

[22]  Shuchi Grover,et al.  Assessing Algorithmic and Computational Thinking in K-12: Lessons from a Middle School Classroom , 2017, Emerging Research, Practice, and Policy on Computational Thinking.

[23]  Taylor Martin,et al.  Measuring Computational Thinking Development with the FUN! Tool , 2017, Emerging Research, Practice, and Policy on Computational Thinking.

[24]  David A. Gillam.  A Framework for K-12 Science Education: Practices, Crosscutting Concepts, and Core Ideas , 2015 .

[25]  Gregorio Robles,et al.  Analyze your Scratch projects with Dr . Scratch and assess your Computational Thinking skills , 2015 .

[26]  Barbara Boucher Owens,et al.  CSTA K--12 Computer Science Standards: Revised 2011 , 2011 .