Clickstream for learning analytics to assess students’ behavior with Scratch

Daniel Amo Filvà*, Marc Alier Forment, Francisco José García-Peñalvo, David Fonseca Escudero, María José Casañ

*Corresponding author for this work

Research output: Indexed journal article Articlepeer-review

61 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)673-686
Number of pages14
JournalFuture Generation Computer Systems
Volume93
DOIs
Publication statusPublished - Apr 2019

Keywords

  • Big data
  • Clickstream
  • Learning analytics
  • Programming
  • Scratch

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