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doi: 10.3934/fods.2021032
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Facilitating API lookup for novices learning data wrangling using thumbnail graphics

Lovisa Sundin 1,, , Nourhan Sakr 2, , Juho Leinonen 3, and  Quintin Cutts 1,

1. 

University of Glasgow, UK
2. 

American University in Cairo, Egypt
3. 

University of Helsinki, Finland

* Corresponding author: l.sundin.1@research.gla.ac.uk

Received  July 2021 Revised  September 2021 Early access December 2021

With the rising demand for data science skills, the ability to wrangle data programmatically becomes a crucial barrier. In this paper, we discuss the centrality of API (application programming interface) lookup to data wrangling, and how an ontology-structured command menu could facilitate it. We design thumbnail graphics as visual alternatives to explaining data wrangling operations and use a survey to validate their quality. We furthermore predict that thumbnail graphics make the menu more navigable, improving lookup efficiency and performance. Our predictions are tested using Slice N Dice, an online data wrangling tutorial platform that collects learner activity. It includes both non-programmatic and programmatic data wrangling exercises. Participants from a multi-institutional sample (n = 200) were randomly assigned the tutorial either with or without thumbnail graphics. Our results show that thumbnail graphics reduce the need for clarifications, thereby assisting API lookup for novices learning data wrangling. We further present some negative results regarding performance gain and follow up with a discussion on why the differences are subtle and how they can be improved. Last but not least, we complement our statistical results with a qualitative study where we receive positive feedback from our participants on the design and helpfulness of the thumbnail graphics.

Keywords: Data science education, data wrangling, data preparation, R, NumPy, pandas.
Mathematics Subject Classification: Primary: 68-01; Secondary: 62-04.
Citation: Lovisa Sundin, Nourhan Sakr, Juho Leinonen, Quintin Cutts. Facilitating API lookup for novices learning data wrangling using thumbnail graphics. Foundations of Data Science, doi: 10.3934/fods.2021032
References:
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S. Sosnovsky and T. Gavrilova, Development of educational ontology for c-programming, in XI-th International Conference, vol. 1, 2005, 127. Google Scholar

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L. Sundin and Q. Cutts, Introducing data wrangling using graphical subgoals-findings from an e-learning study, in Proceedings of the Eighth ACM Conference on Learning@ Scale, 2021, 267–270. doi: 10.1145/3430895.3460155.  Google Scholar

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X. Zhang and P. J. Guo, Ds. js: Turn any webpage into an example-centric live programming environment for learning data science, in Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology, 2017, 691–702. doi: 10.1145/3126594.3126663.  Google Scholar

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show all references

References:
[1]

V. Aleksić and M. Ivanović, Introductory programming subject in european higher education, Informatics in Education, 15 (2016), 163-182.  doi: 10.15388/infedu.2016.09.  Google Scholar

[2]

A. C. Bart, J. Tibau, E. Tilevich, C. A. Shaffer and D. Kafura, Blockpy: An open access data-science environment for introductory programmers, Computer, 50 (2017), 18-26.  doi: 10.1109/MC.2017.132.  Google Scholar

[3]

B. Baumer, A data science course for undergraduates: Thinking with data, The American Statistician, 69 (2015), 334-342.  doi: 10.1080/00031305.2015.1081105.  Google Scholar

[4]

Y. Ben-David Kolikant and Z. ma'ayan, Computer science students' use of the internet for academic purposes: Difficulties and learning processes, Computer Science Education, 28 (2018), 211-231.  doi: 10.1080/08993408.2018.1528045.  Google Scholar

[5]

J. Brandt, P. J. Guo, J. Lewenstein, M. Dontcheva and S. R. Klemmer, Two studies of opportunistic programming: Interleaving web foraging, learning, and writing code, in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2009, 1589–1598. doi: 10.1145/1518701.1518944.  Google Scholar

[6]

J. E. Broatch, S. Dietrich and D. Goelman, Introducing data science techniques by connecting database concepts and dplyr, Journal of Statistics Education, 27 (2019), 147-153.  doi: 10.1080/10691898.2019.1647768.  Google Scholar

[7]

M. Cembalo, A. De Santis and U. Ferraro Petrillo, Savi: A new system for advanced sql visualization, in Proceedings of the 2011 Conference on Information Technology Education, 2011, 165–170. doi: 10.1145/2047594.2047641.  Google Scholar

[8]

CrowdFlower, Data Science Report 2016, http://www2.cs.uh.edu/ ceick/UDM/CFDS16.pdf, 2016, [Online; accessed 10-May-2021]. Google Scholar

[9]

T. Diamantopoulos, G. Karagiannopoulos and A. L. Symeonidis, Codecatch: Extracting source code snippets from online sources, in Proceedings of the 6th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, 2018, 21–27. Google Scholar

[10]

B. Dorn, A. Stankiewicz and C. Roggi, Lost while searching: Difficulties in information seeking among end-user programmers, Proceedings of the American Society for Information Science and Technology, 50 (2013), 1-10.  doi: 10.1002/meet.14505001059.  Google Scholar

[11]

I. Drosos, T. Barik, P. J. Guo, R. DeLine and S. Gulwani, Wrex: A unified programming-by-example interaction for synthesizing readable code for data scientists, in Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 2020, 1–12. doi: 10.1145/3313831.3376442.  Google Scholar

[12]

T. Erickson, M. Wilkerson, W. Finzer and F. Reichsman, Data moves, Technology Innovations in Statistics Education, 12 (2019). doi: 10.5070/T5121038001.  Google Scholar

[13]

H. Fangohr, A comparison of c, matlab, and python as teaching languages in engineering, in International Conference on Computational Science, Springer, 2004, 1210–1217. doi: 10.1007/978-3-540-25944-2_157.  Google Scholar

[14]

K. A. T. Folland, viSQLizer: An Interactive Visualizer for Learning SQL, Master's thesis, Norwegian University of Science and Technology, 2016. Google Scholar

[15]

G. Gao, F. Voichick, M. Ichinco and C. Kelleher, Exploring programmers' api learning processes: Collecting web resources as external memory, in 2020 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), IEEE, 2020, 1–10. doi: 10.1109/VL/HCC50065.2020.9127274.  Google Scholar

[16]

M. Ichinco, W. Y. Hnin and C. L. Kelleher, Suggesting api usage to novice programmers with the example guru, in Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 2017, 1105–1117. doi: 10.1145/3025453.3025827.  Google Scholar

[17]

M. Ichinco and C. Kelleher, The need for improved support for interacting with block examples, in 2017 IEEE Blocks and Beyond Workshop (B & B), IEEE, 2017, 69–70. doi: 10.1109/BLOCKS.2017.8120415.  Google Scholar

[18]

S. Kandel, J. Heer, C. Plaisant, J. Kennedy, F. Van Ham, N. H. Riche, C. Weaver, B. Lee, D. Brodbeck and P. Buono, Research directions in data wrangling: Visualizations and transformations for usable and credible data, Information Visualization, 10 (2011), 271-288.  doi: 10.1177/1473871611415994.  Google Scholar

[19]

S. Kandel, A. Paepcke, J. Hellerstein and J. Heer, Wrangler: Interactive visual specification of data transformation scripts, in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2011, 3363–3372. doi: 10.1145/1978942.1979444.  Google Scholar

[20]

C. Kelleher and M. Ichinco, Towards a model of API learning, in 2019 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), IEEE, 2019, 163–168. doi: 10.1109/VLHCC.2019.8818850.  Google Scholar

[21]

R. Kimball, Data wrangling, Information Management, 18 (2008), 8.   Google Scholar

[22]

A. J. Ko and Y. Riche, The role of conceptual knowledge in api usability, in 2011 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), IEEE, 2011, 173–176. doi: 10.1109/VLHCC.2011.6070395.  Google Scholar

[23]

S. Krishnamurthi and K. Fisler, Data-centricity: A challenge and opportunity for computing education, Communications of the ACM, 63 (2020), 24-26.  doi: 10.1145/3408056.  Google Scholar

[24]

S. Kross and P. J. Guo, Practitioners teaching data science in industry and academia: Expectations, workflows, and challenges, in Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 2019, 1–14. doi: 10.1145/3290605.3300493.  Google Scholar

[25]

W. McKinney, Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, "O'Reilly Media, Inc.", 2012. Google Scholar

[26]

J. C. Nesbit and O. O. Adesope, Learning with concept and knowledge maps: A meta-analysis, Review of Educational Research, 76 (2006), 413-448.  doi: 10.3102/00346543076003413.  Google Scholar

[27]

H. Niu, I. Keivanloo and Y. Zou, Learning to rank code examples for code search engines, Empirical Software Engineering, 22 (2017), 259-291.  doi: 10.1007/s10664-015-9421-5.  Google Scholar

[28]

A. M. Olney and S. D. Fleming, A cognitive load perspective on the design of blocks languages for data science, in 2019 IEEE Blocks and Beyond Workshop (B & B), IEEE, 2019, 95–97. doi: 10.1109/BB48857.2019.8941224.  Google Scholar

[29] A. Paivio, Mental Representations: A Dual Coding Approach, Oxford University Press, 1990.  doi: 10.1093/acprof:oso/9780195066661.001.0001.  Google Scholar
[30]

N. Paton, Automating data preparation: Can we? should we? must we?, in 21st International Workshop on Design, Optimization, Languages and Analytical Processing of Big Data, 2019, 1–5. Google Scholar

[31]

D. Qiu, B. Li and H. Leung, Understanding the api usage in java, Information and Software Technology, 73 (2016), 81-100.  doi: 10.1016/j.infsof.2016.01.011.  Google Scholar

[32]

RStudio, RStudio Cheat Sheets, https://github.com/rstudio/cheatsheets, 2021, [Online; accessed 03-June-2021]. Google Scholar

[33]

D. Schuff, Data science for all: A university-wide course in data literacy, in Analytics and Data Science, Springer, 2018, 281–297. doi: 10.1007/978-3-319-58097-5_20.  Google Scholar

[34]

B. Shneiderman, Teaching programming: A spiral approach to syntax and semantics, Computers & Education, 1 (1977), 193-197.  doi: 10.1016/0360-1315(77)90008-2.  Google Scholar

[35]

H. A. Simon, The structure of ill structured problems, Artificial Intelligence, 4 (1973), 181-201.  doi: 10.1016/0004-3702(73)90011-8.  Google Scholar

[36]

S. Sosnovsky and T. Gavrilova, Development of educational ontology for c-programming, in XI-th International Conference, vol. 1, 2005, 127. Google Scholar

[37]

L. Sundin and Q. Cutts, Introducing data wrangling using graphical subgoals-findings from an e-learning study, in Proceedings of the Eighth ACM Conference on Learning@ Scale, 2021, 267–270. doi: 10.1145/3430895.3460155.  Google Scholar

[38]

P. Teetor, R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics, "O'Reilly Media, Inc.", 2011. Google Scholar

[39]

K. Thayer, S. E. Chasins and A. J. Ko, A theory of robust api knowledge, ACM Transactions on Computing Education (TOCE), 21 (2021), 1-32.  doi: 10.1145/3444945.  Google Scholar

[40]

Tidyblocks.tech, TidyBlocks, https://github.com/tidyblocks/tidyblocks, 2021, [Online; accessed 21-Feb-2021]. Google Scholar

[41]

D. Weinberger, Everything is Miscellaneous: The Power of the New Digital Disorder, Macmillan, 2007. Google Scholar

[42]

D. Weintrop and U. Wilensky, To block or not to block, that is the question: Students' perceptions of blocks-based programming, in Proceedings of the 14th International Conference on Interaction Design and Children, 2015, 199–208. doi: 10.1145/2771839.2771860.  Google Scholar

[43]

H. Wickham and G. Grolemund, R for Data Science: Import, Tidy, Transform, Visualize, and Model Data, "O'Reilly Media, Inc.", 2016. Google Scholar

[44]

X. Zhang and P. J. Guo, Ds. js: Turn any webpage into an example-centric live programming environment for learning data science, in Proceedings of the 30th Annual ACM Symposium on User Interface Software and Technology, 2017, 691–702. doi: 10.1145/3126594.3126663.  Google Scholar

[45]

Y. Zhu, L. M. Hernandez, P. Mueller, Y. Dong and M. R. Forman, Data acquisition and preprocessing in studies on humans: What is not taught in statistics classes?, The American Statistician, 67 (2013), 235-241.  doi: 10.1080/00031305.2013.842498.  Google Scholar

20,15]">Figure 1.  Kelleher & Ichinko's Collection and Organization of Information for Learning (COIL) model [20,15]
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Figure 2.  The platform is split into three parts. Part 1 introduces the user to an ontology of data wrangling operations. Part 2 introduces programming. Part 3 contains 18 programmatic data wrangling exercises
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Figure 3.  The sidebar menu and a Part 1 operation card under the two conditions
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Figure 4.  A snippet from the sidebar menu. Calculate is one of five top-level categories, while the next level is split by data structure (e.g. dataframes). In TG, each leaf node has a thumbnail graphic
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Figure 5.  Three examples of graphical thumbnail, using color in different ways to convey operation semantics
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Figure 6.  Part 1 contains a series of exercises in which the user selects operations from the menu and drags it to the corresponding subgoal
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Figure 7.  Part 3 involves programming exercises. The user is guided by a list of subgoals, each of which has associated hints. The sidebar menu serves as a menu for looking up API documentation (shown above). In reality, the menu, subgoals and documentation are all tabs within the same sidebar panel
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Figure 8.  Participants were asked to rate their experience with Excel, Python, and R on a five-point Likert-scale (1 = Not at all to 5 = Advanced). The distribution among people who started and completed each part is illustrated and does not provide any visual evidence for differences that would reflect that more experiences participants are more likely to persevere
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Figure 9.  The distributions in the number of tooltip events per person, grouped by condition, in Part 1 (left) and Part 3 (right). Dashed lines indicate medians. In both cases, the TG group uses the tooltip significantly less often
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Figure 10.  Total number of menu clicks per participant in Part 3, grouped by condition. Dashed lines indicate medians
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Figure 11.  Total reading times of operation cards per person, grouped by condition. Dashed lines indicate medians. The TG group is quicker on average, but the difference is non-significant
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Figure 12.  Total time on task per person for Part 1 (left) and 3 (right), grouped by condition. Dashed lines indicate medians. The median differences are in both parts negligible
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Figure 13.  Number of incorrect attempts per person for Part 1 (left) and 3 (right), grouped by condition. Dashed lines indicate medians. In both parts, the TG group makes fewer incorrect attempts, but the difference is not significant
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Figure 14.  Responses to the evaluation survey item asking participants how helpful they found the thumbnail graphics and tooltips (1 = Not at all, 5 = Very much) in Part 1 (N = 187) and Part 3 (N = 115). This survey was given both after Part 1 (left) and Part 3 (right)
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