Concepedia

Using Learning Analytics to Understand the Learning Pathways of Novice Programmers

Matthew Berland, Taylor Martin, Tom Benton, Carmen Petrick Smith, Don Davis

Journal of the Learning Sciences · 2013 · 213 citations · 57 references

TL;DR

Tinkering is widely regarded as essential for novice programmers, and learning analytics provides a means to uncover relationships in their learning process. The study investigates how students move from exploration through tinkering to refinement, defining this progression as the EXTIRE pathway. Learning analytics techniques were applied to trace students’ progression across exploration, tinkering, and refinement stages, thereby constructing the EXTIRE model. The results empirically support previously theorized learning processes, demonstrate the importance of tinkering for novices, and offer a data‑driven framework for describing these learning pathways.

Abstract

Many have suggested that tinkering plays a critical role in novices learning to program, and recent work in learning analytics (Baker & Yacef, 2009 Baker, R. S. and Yacef, K. 2009. The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 1: 3–17. [Google Scholar] Blikstein, 2011) allows us to describe new relationships in the process. Using learning analytics, we explore how students progress from exploration, through tinkering, to refinement, a pathway that we term EXTIRE. The work contributes to learning sciences by: showing empirical support for previously theorized processes; identifying a role of tinkering in novices' learning; and presenting a data-driven approach to creating process descriptions. Furthermore, our findings illuminate how tinkering can be a valuable approach for novices.