Clickstream for learning analytics to assess students' behavior with Scratch

Abstract The construction of knowledge through computational practice requires to teachers a substantial amount of time and effort to evaluate programming skills, to understand and to glimpse the evolution of the students and finally to state a quantitative judgment in learning assessment. The field of learning analytics has been a common practice in research since last years due to their great possibilities in terms of learning improvement. Both, Big and Small data techniques support the analysis cycle of learning analytics and risk of students’ failure prediction. Such possibilities can be a strong positive contribution to the field of computational practice such as programming. Our main objective was to help teachers in their assessments through to make those possibilities effective. Thus, we have developed a functional solution to categorize and understand students’ behavior in programming activities based in Scratch. Through collection and analysis of data generated by students’ clicks in Scratch, we proceed to execute both exploratory and predictive analytics to detect patterns in students’ behavior when developing solutions for assignments. We concluded that resultant taxonomy could help teachers to better support their students by giving real-time quality feedback and act before students deliver incorrectly or at least incomplete tasks.

[1]  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..

[2]  Sze Yee Lye,et al.  Review on teaching and learning of computational thinking through programming: What is next for K-12? , 2014, Comput. Hum. Behav..

[3]  Edith Schonberg,et al.  Visualization and Analysis of Clickstream Data of Online Stores for Understanding Web Merchandising , 2004, Data Mining and Knowledge Discovery.

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

[5]  Barry G. Becker,et al.  Case study: e-commerce clickstream visualization , 2001, IEEE Symposium on Information Visualization, 2001. INFOVIS 2001..

[6]  R. Dale Wilson,et al.  Using clickstream data to enhance business‐to‐business web site performance , 2010 .

[7]  Stefan Pero.  Modeling Programming Skills of Students in an Educational Recommender System , 2013, UMAP.

[8]  Marcelo Worsley,et al.  Programming Pluralism: Using Learning Analytics to Detect Patterns in the Learning of Computer Programming , 2014, Journal of the Learning Sciences.

[9]  Alexander Repenning,et al.  Real Time Assessment of Computational Thinking , 2014, 2014 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC).

[10]  Linda M. Seiter,et al.  Modeling the learning progressions of computational thinking of primary grade students , 2013, ICER.

[11]  George Siemens,et al.  Learning Analytics and Educational Data Mining , 2016 .

[12]  Amjad Jumaah Frhan.  Website Clickstream Data Visualization Using Improved Markov Chain Modelling In Apache Flume , 2017 .

[13]  Petri Ihantola,et al.  How novices tackle their first lines of code in an IDE: analysis of programming session traces , 2014, Koli Calling.

[14]  Steven Furnell,et al.  A practical evaluation of Web analytics , 2004, Internet Res..

[15]  Paulo Blikstein,et al.  Modeling how students learn to program , 2012, SIGCSE '12.

[16]  Manpreet Singh,et al.  Classification and Prediction Based Data Mining Algorithms to Predict Slow Learners in Education Sector , 2015 .

[17]  Baldoino Fonseca dos Santos Neto,et al.  Evaluating the effectiveness of educational data mining techniques for early prediction of students' academic failure in introductory programming courses , 2017, Comput. Hum. Behav..

[18]  P. Chatterjee,et al.  Modeling the Clickstream: Implications for Web-Based Advertising Efforts , 2003 .

[19]  R. Dale Wilson.  Using Web Traffic Analysis for Customer Acquisition and Retention Programs in Marketing , 2004 .

[20]  Kenneth R. Koedinger,et al.  Learning Curve Analysis for Programming: Which Concepts do Students Struggle With? , 2016, ICER.

[21]  George Siemens,et al.  Learning analytics and educational data mining: towards communication and collaboration , 2012, LAK.

[22]  Mehedi Hasan,et al.  Educational data mining: A mining model for developing students' programming skills , 2014, The 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014).

[23]  Ma. Mercedes T. Rodrigo,et al.  Predicting at-risk novice Java programmers through the analysis of online protocols , 2011, ICER.

[24]  Pablo Garaizar,et al.  Exploring the Progression of Early Programmers in a Set of Computational Thinking Challenges via Clickstream Analysis , 2020, IEEE Transactions on Emerging Topics in Computing.

[25]  Taylor Martin,et al.  Using Learning Analytics to Understand the Learning Pathways of Novice Programmers , 2013 .

[26]  Kannan Srinivasan,et al.  Modeling Online Browsing and Path Analysis Using Clickstream Data , 2004 .

[27]  Marc Alier Forment,et al.  Google analytics for time behavior measurement in Moodle , 2014, 2014 9th Iberian Conference on Information Systems and Technologies (CISTI).

[28]  Sylvain Sénécal,et al.  Consumers' decision-making process and their online shopping behavior: a clickstream analysis , 2005 .

[29]  Mark Sherman,et al.  Learning analytics for the assessment of interaction with App Inventor , 2015, 2015 IEEE Blocks and Beyond Workshop (Blocks and Beyond).

[30]  Benjamin Xiang-Yu Xie,et al.  Progression of computational thinking skills demonstrated by App Inventor users , 2016 .

[31]  María José Casany Guerrero,et al.  The student’s progress snapshot a hybrid text and visual learning analytics dashboard , 2018 .

[32]  Francisco José García-Peñalvo.  Formación en la sociedad del conocimiento, un programa de doctorado con una perspectiva interdisciplinar , 2014 .

[33]  Ryan S. Baker,et al.  Educational Data Mining and Learning Analytics , 2014 .

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

[35]  Charles E. McDowell,et al.  A First Step in Learning Analytics: Pre-processing Low-Level Alice Logging Data of Middle School Students , 2013, EDM 2013.

[36]  Ari Korhonen,et al.  Unlocking the Potential of Learning Analytics in Computing Education , 2017, ACM Trans. Comput. Educ..

[37]  Bruce L Sherin,et al.  Learning Analytics and Computational Techniques for Detecting and Evaluating Patterns in Learning: An Introduction to the Special Issue , 2013 .

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

[39]  Gregory Dyke.  Which aspects of novice programmers' usage of an IDE predict learning outcomes , 2011, SIGCSE '11.