Graphical scaffolding for the learning of data wrangling APIs

Sundin, Lovisa (2022) Graphical scaffolding for the learning of data wrangling APIs. PhD thesis, University of Glasgow.

Abstract

In order for students across the sciences to avail themselves of modern data streams, they must first know how to wrangle data: how to reshape ill-organised, tabular data into another format, and how to do this programmatically, in languages such as Python and R. Despite the cross-departmental demand and the ubiquity of data wrangling in analytical workflows, the research on how to optimise the instruction of it has been minimal. Although data wrangling as a programming domain presents distinctive challenges - characterised by on-the-fly syntax lookup and code example integration - it also presents opportunities. One such opportunity is how tabular data structures are easily visualised. To leverage the inherent visualisability of data wrangling, this dissertation evaluates three types of graphics that could be employed as scaffolding for novices: subgoal graphics, thumbnail graphics, and parameter graphics. Using a specially built e-learning platform, this dissertation documents a multi-institutional, randomised, and controlled experiment that investigates the pedagogical effects of these. Our results indicate that the graphics are well-received, that subgoal graphics boost the completion rate, and that thumbnail graphics improve navigability within a command menu. We also obtained several non-significant results, and indications that parameter graphics are counter-productive. We will discuss these findings in the context of general scaffolding dilemmas, and how they fit into a wider research programme on data wrangling instruction.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
• Colleges/Schools: College of Science and Engineering > School of Computing Science
• Supervisor's Name: Cutts, Professor Quintin
• Date of Award: 2022
• Depositing User: Theses Team
• Unique ID: glathesis:2022-83122
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 13 Sep 2022 14:52
• Last Modified: 13 Sep 2022 14:54
• Thesis DOI: 10.5525/gla.thesis.83122
• URI: https://theses.gla.ac.uk/id/eprint/83122

Related URLs

• Article DOI
• Article DOI
• Article DOI
• Article DOI