Oshiorenua Imokhai
Academic and research departments
About
My research project
Using Immersive environments to improve data visualisation for businessesData analytics has become very key for strategic decision making over the last decade for businesses and government alike. It uncovers facts, trends, and insights by understanding patterns around data that reveal which actions would best align with overarching organizational goals. Despite the hype around new computational tools for analysing Big Data and doing Predictive Analytics, there is evidence that individual decision makers struggle to understand 2D graphs, tables and their associated statistical concepts and meanings. Statistics, the bedrock of Data Analytics, is often quoted as one of the hardest subjects to learn.
New forms of interactive 3D or 4D data visualisation are becoming possible with the use of Augmented Reality (AR) headsets and controllers such as the Microsoft HoloLens. This might be referred to as Immersive Analytics (IA) because it allows the user to be immersed in dynamic data spaces. This could potentially increase the efficiency and comprehension of data visualisation. This project aims to compare immersive data analytics with traditional techniques. Specifically, the investigators will evaluate immersive analytics compared to standard data visualizations for data interpretation, statistics learning, and speed of understanding across various DataViz types to identify areas for significant improvement. These will be tested on both students and decision makers in real world settings.
Supervisors
Data analytics has become very key for strategic decision making over the last decade for businesses and government alike. It uncovers facts, trends, and insights by understanding patterns around data that reveal which actions would best align with overarching organizational goals. Despite the hype around new computational tools for analysing Big Data and doing Predictive Analytics, there is evidence that individual decision makers struggle to understand 2D graphs, tables and their associated statistical concepts and meanings. Statistics, the bedrock of Data Analytics, is often quoted as one of the hardest subjects to learn.
New forms of interactive 3D or 4D data visualisation are becoming possible with the use of Augmented Reality (AR) headsets and controllers such as the Microsoft HoloLens. This might be referred to as Immersive Analytics (IA) because it allows the user to be immersed in dynamic data spaces. This could potentially increase the efficiency and comprehension of data visualisation. This project aims to compare immersive data analytics with traditional techniques. Specifically, the investigators will evaluate immersive analytics compared to standard data visualizations for data interpretation, statistics learning, and speed of understanding across various DataViz types to identify areas for significant improvement. These will be tested on both students and decision makers in real world settings.
University roles and responsibilities
- Teaching Assistant for level 6 & 7
My qualifications
Teaching
Applied Analytics (Level 6)
- Support students in applying advanced data analysis techniques using Tableau, Tableau Prep, and Excel.
- Guide learners in developing interactive dashboards, performing data cleaning, and drawing inferences from data for decision making.
- Assist in designing and grading coursework that emphasizes real-world analytics applications.
Foundations of Analytics (Level 5)
- Assisting students in quantitative research methods and statistical analysis using SPSS.
- Demonstrate practical use of descriptive and inferential statistics to interpret business data at a beginner level.
- Provide tutorials and feedback to help students build analytical reasoning and interpretation skills.
Project Management (Level 6)
- Facilitate learning in project planning, scheduling, and monitoring using Microsoft Project.
- Support students in developing Gantt charts, critical path analysis, and resource allocation.
Publications
Graphical perception studies have largely focused on how accurate participants can be at extracting quantitative information from glyphs in various visualization types, including infographics. However, most studies assume that there is only one correct way to encode information, for example to compare two circles the encoding mechanism must be relative area. We suspect that this assumption is over-simplistic, especially for glyphs that have an obvious two- dimensional structure, and that some participants use relative height instead. This is a crucial, but it seems often overlooked, aspect of graphical perception studies, and indeed of data visualization in general. If participants are not using the same encoding mechanism that the designer has used, this represents a clear systematic bias that can induce large errors in viewers’ estimates. We therefore assessed which encoding mechanism was being used for several glyphs, including bars, squares, circles, triangles and human icons. We found that almost half of participants’ estimates were likely based on the encoding mechanism of relative height, not area, for all two-dimensional glyphs. A strong relationship was found between the relative size of the two glyphs being compared and the encoding mechanism used. Participants were much more likely to choose relative height if the smaller glyph was over 60% of the size of the larger glyph. In contrast for the one-dimensional glyph (bar), almost all participants chose relative height as the encoding mechanism. This study therefore shows the importance of considering what encoding mechanisms participants could use, and thus tailoring infographics and visualizations to avoid such ambiguity.