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Is it feasible to teach query programming in three different languages in a single session?: A study on a pattern-oriented tutorial and cheat sheets

Published: 05 September 2019 Publication History

Abstract

Undergraduates and postgraduates in science subjects are increasingly expected to conduct their data analyses using R, SQL and Python. This requires of instructors to develop resources that get students up and running quickly. This study presents and evaluates a learning design that (1) uses a pattern-oriented tutorial to teach language-independent key operations for implementing data analytic queries, and (2) uses cheat sheets to show how these operations map onto language-specific syntax. The evaluation study (N=21) concludes that using this approach, two thirds of the data science novices sampled could implement simple to moderately complex queries in all the aforementioned languages within two hours. A permutation test moreover produced a significant main effect of language, with SQL ranking the highest in accuracy. The results form part of a general discussion on the merits and language-dependent feasibility of pattern-oriented aids for accelerated data science instruction.

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  • (2021)Visual recipes for slicing and dicing data: teaching data wrangling using subgoal graphicsProceedings of the 21st Koli Calling International Conference on Computing Education Research10.1145/3488042.3488063(1-10)Online publication date: 17-Nov-2021
  • (2020)Crowdsourcing Content Creation for SQL PracticeProceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education10.1145/3341525.3387385(349-355)Online publication date: 15-Jun-2020

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cover image ACM Other conferences
UKICER '19: Proceedings of the 2019 Conference on United Kingdom & Ireland Computing Education Research
September 2019
81 pages
ISBN:9781450372572
DOI:10.1145/3351287
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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  • Univ of Kent at Canterbury: University of Kent at Canterbury

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Association for Computing Machinery

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Published: 05 September 2019

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Author Tags

  1. CS education
  2. Python
  3. R
  4. SQL
  5. data science
  6. programming instruction
  7. programming patterns
  8. programming schemas
  9. query visualisation

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  • Refereed limited

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  • EPSRC

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UKICER
UKICER: UK & Ireland Computing Education Research Conference
September 5 - 6, 2019
Canterbury, United Kingdom

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View all
  • (2021)Visual recipes for slicing and dicing data: teaching data wrangling using subgoal graphicsProceedings of the 21st Koli Calling International Conference on Computing Education Research10.1145/3488042.3488063(1-10)Online publication date: 17-Nov-2021
  • (2020)Crowdsourcing Content Creation for SQL PracticeProceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education10.1145/3341525.3387385(349-355)Online publication date: 15-Jun-2020

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