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Wevquery: Testing Hypotheses about Web Interaction Patterns
Wevquery is a web event query tool designed to help bridge the gap between designers and data analysts, allowing users without data analysis experience to perform flexible and visual data queries. The tool was tested in a proof of concept involving real user data, which revealed insights on user interactions, such as unnecessary hovering actions and scrolling behaviors, leading to actionable recommendations for web design improvements. Future work will focus on enhancing support for non-experts and implementing sequence pattern mining.
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Wevquery: Testing Hypotheses about Web Interaction Patterns
- 1. WevQuery: Testing Hypothesesabout Web Interaction Patterns Aitor Apaolaza Markel Vigo EICS 2017 June 28, 2017 - Lisbon, Portugal
- 2. WevQuery EICS 2017 Motivation ● Wedon’t know what users do ● We can get the data, but we don’t have the expertise to handle it ● Data is big, confusing and difficult to handle 2
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- 4. WevQuery EICS 2017 Lab settings ●Strengths ○ Provide a deep understanding ○ Allows for controlling variables ○ Good for internal validity ● Weaknesses ○ Resource demands: time for data analysis ○ Guinea Pig effect and response bias ○ Task driven ○ Risks to external validity ○ Recruitment problem 4
- 5. WevQuery EICS 2017 Remote settings ●Strengths ○ Good for external validity (if naturalistic) ○ Ecologically valid ○ Users are easily recruited ○ Easy to implement ● Weaknesses ○ Resource demands: have the right infrastructure ○ Cannot control the confounding variables 5
- 6. WevQuery EICS 2017 Remote logfile analysis ● Common technique for HCI and IR ● Large data: millions of datapoints from thousands of users ● Current approaches have one or more drawbacks: ○ Not informative enough ○ Not straightforward for non-specialists ○ Not flexible ○ Not scalable ○ Lack of support for decision-making ● WevQuery addresses the above issues 6
- 7. WevQuery EICS 2017 What’s WevQuery ●Web Event Query ● Bridge the gap between designers and data analysts ● Guided, flexible and visual query system ● Users without data analysis experience ● Highly scalable 7
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- 20. WevQuery EICS 2017 Proof ofconcept ● Defined two hypothetical scenarios where designers argue about how their website is being used ● Hypotheses tested on real data ○ 5.7m datapoints consisting of events ○ 2,445 users on 3,287 web pages ○ 2 months 20
- 21. WevQuery EICS 2017 Proof ofconcept ● Defined two hypothetical scenarios where designers argue about how their website is being used ● Hypotheses tested on real data ○ 5.7m datapoints consisting of events ○ 2,445 users on 3,287 web pages ○ 2 months 21
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- 24. WevQuery EICS 2017 Scenario 1 24 Userswill spend too much time hovering over every element Designer A Designer B I think is a nice way to remove clutter from the Web page
- 25. WevQuery EICS 2017 Scenario 1 25 Userswill spend too much time hovering over every element Designer A Designer B Hypothesis There will be unnecessarily long hovering actionI think is a nice way to remove clutter from the Web page
- 26. WevQuery EICS 2017 I thinkis a nice way to remove clutter from the Web page Scenario 1 26 Hypothesis There will be unnecessarily long hovering action WevQuery Users will spend too much time hovering over every element Designer A Designer B
- 27. WevQuery EICS 2017 Scenario 1 ●Results ○ 29,770 episodes containing mouseover and mouseout ○ Half of them (14,850 cases) contains >3 seconds hover actions ● Refinement ○ Increased threshold to 10 seconds (down to 8,277 cases) ○ Homepage is the source of most 27
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- 29. WevQuery EICS 2017 Scenario 1 ●Results ○ 29,770 episodes containing mouseover and mouseout ○ Half of them (14,850 cases) contains >3 seconds hover actions ● Refinement ○ Increased threshold to 10 seconds (down to 8,277 cases) ○ Homepage is the source of most 29
- 30. WevQuery EICS 2017 Scenario 1 ●Results ○ 29,770 episodes containing mouseover and mouseout ○ Half of them (14,850 cases) contains >3 seconds hover actions ● Refinement ○ Increased threshold to 10 seconds (down to 8,277 cases) ○ Homepage is the source of most ● Findings ○ Identified an <a> element that when hovered discloses up to 45 links ○ They decide to change the hover to a toggle ○ They will use A/B testing to determine the effectiveness of the change 30
- 31. WevQuery EICS 2017 ● Results ○29,770 episodes containing mouseover and mouseout ○ Half of them (14,850 cases) contains >3 seconds hover actions ● Refinement ○ Increased threshold to 10 seconds (down to 8,277 cases) ○ Homepage is the source of most ● Findings ○ Identified an <a> element that when hovered discloses up to 45 links ○ They decide to change the hover to a toggle ○ They will use A/B testing to determine the effectiveness of the change Scenario 1 31
- 32. WevQuery EICS 2017 Scenario 1 32 ●Results ○ 29,770 episodes containing mouseover and mouseout ○ Half of them (14,850 cases) contains >3 seconds hover actions ● Refinement ○ Increased threshold to 10 seconds (down to 8,277 cases) ○ Homepage is the source of most ● Findings ○ Identified an <a> element that when hovered discloses up to 45 links ○ They decide to change the hover to a toggle ○ They will use A/B testing to determine the effectiveness of the change
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- 35. WevQuery EICS 2017 Users rarelyuse the information at the bottom Fitting the entire Web page in the screen is important 35 Designer A Designer B Scenario 2
- 36. WevQuery EICS 2017 36 Scenario 2 DesignerA Designer B Hypothesis Users scroll down immediately after loading the page Users rarely use the information at the bottom Fitting the entire Web page in the screen is important
- 37. WevQuery EICS 2017 37 Hypothesis Users scrolldown immediately after loading the page WevQuery Scenario 2 Designer A Designer B Users rarely use the information at the bottom Fitting the entire Web page in the screen is important
- 38. WevQuery EICS 2017 Scenario 2 ●Results ○ 33,444 episodes containing a page load and either a mousewheel or a scroll ○ 18,729 contains >10 seconds load to scroll actions ● Refinement ○ Decreased threshold to 3, 1, and 0.5 seconds (down to 16,129, 11,885, and 8,903 cases) 38
- 39. WevQuery EICS 2017 Scenario 2 ●Results ○ 33,444 episodes containing a page load and either a mousewheel or a scroll ○ 18,729 contains >10 seconds load to scroll actions ● Refinement ○ Decreased threshold to 3, 1, and 0.5 seconds (down to 16,129, 11,885, and 8,903 cases) ● Findings ○ Highly frequent ○ Further refinement is decided 39
- 40. WevQuery EICS 2017 40 Scenario 2 ●Further refinement
- 41. WevQuery EICS 2017 41 Scenario 2(refined) ● Further refinement
- 42. WevQuery EICS 2017 Scenario 2(refined) ● Results ○ 371 occurrences ○ No clicks on elements at the bottom of the page ● Findings ○ Information at the bottom is not sought ○ Making pages fit the screen is beneficial 42
- 43. WevQuery EICS 2017 Proof ofconcept ● Designers decide to take a compromise solution ● On the performance ○ Run on a laptop ○ Mouseover and mouseout accounts for 2.8m events (out of 5.7m) ○ Execution time depends on event frequency (~50 seconds to ~2 minutes) 43
- 44. WevQuery EICS 2017 Conclusions ● Post-hoc ●Flexible ● Reusability of queries ● Scalable ● Hypotheses easy to refine and test iteratively ● Connects designers with data 44
- 45. WevQuery EICS 2017 45 Work inprogress
- 46. WevQuery EICS 2017 Future work ●Sequence pattern mining ● Further support for non-experts ● User evaluation 46
- 47. https://github.com/aapaolaza/wevquery aitor.apaolaza@manchester.ac.uk Aitor Apaolaza andMarkel Vigo EICS 2017 June 28, 2017 - Lisbon, Portugal Questions? The MOVING project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 693092.