Supporting Student Success with Intuitive, Approachable Data Visualization
New discoveries surrounding the mechanisms and function of human visual acuity have come with increasing rapidity, each clarifying the astounding pattern recognition, image retention, and processing speed of the human visual cognition system. However, the relationship between institutions of higher learning and data visualization can be classified as embryotic at best. This chapter will examine such a new paradigm in data visualization for higher education, examining obstacles and clearly defining benefits that have been illustrated through real world case studies utilizing appropriate technologies. For data visualization to offer the most tangible value as student success assets in an educational setting, visualizations need to be made as functionally and cognitively accessible SUPPORTING THE SUCCESS OF ADULT AND ONLINE STUDENTS 314 315 formal training and takes full advantage of the intrinsic human ability to learn, communicate, and decode information visually. Such methods have often been called transparent due to their clarity and immediate accessibility (Perer Schneiderman, 2008) The relationship between institutions of higher learning and data visualization, whether at the organizational or at the classroom level, can be classified as embryotic at best. Traditionally, faculty members and administrators review performance data as rows of raw numbers or in charts and graphs, and usually only after a semester has concluded. The story is even more frustrating for the students, whose interaction with the data is limited to a spreadsheet showing earned grades on assignments, weighted gradebooks per course, or progression through standardized education programs via GPA. However, should more thorough, intuitive, transparent data visualization methods be applied, the results have the potential to be transformative, and early research projects conducted with students in academic environments would support this thesis. For example, by coupling the bimodal distribution of student scores with early warning signs of failure, a grant based research project conducted at the Michigan Virtual University (Dickson, 2005) successfully performed visual data analyses to gauge student satisfaction with online courseware and lower the barrier to data driven decision making for faculty in the early intervention of student failure. In areas of future research identified from the study, the researchers encouraged the development of adapted models for visual data representation to fit the specific needs of faculty and students as the previous study had illustrated the applicability of dynamics of student success through visual meta analysis (Dickson, 2005) A similar undertaking by Canadian researchers successfully developed a Student Success System (S3) by using flexible predictive modeling, machine intelligence, and data visualization as an end to end solution for identifying at risk students and designing interventions to mitigate that risk (Essa Ayad, 2012) Additionally, researchers from North Carolina General Assembly, in partnership with JMP Software, a data visualization based interactive tool for desktop statistical discovery, conducted an experiment to expose students to data in a unique and exciting way by allowing them to engage with information and describe, visualize, and critique data sets from various verticals of industry. With limited focus on materials generally covered in traditional mathematics curriculum, students were invited to explore to students and faculty as possible, allowing both to actively share in data prudent for performance improvement and success.