Adaptive Analytics: Improving the Odds

Concurrent Session 2
Streamed Session

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Brief Abstract

We present cooperative research about how adaptive analytics combined with two variable domains improves the odds of student success in college algebra. We will show that demographic predictors such as grade point average can be used as mediators for metrics that respond well to instruction.  

Presenters

Patsy Moskal is the Director of the Digital Learning Impact Evaluation in the Research Initiative for Teaching Effectiveness at the University of Central Florida (UCF) where she evaluates the impact of technology-enhanced learning and serves as the liaison for faculty scholarship of teaching and learning. In 2011 Dr. Moskal was named an OLC Fellow in recognition of her groundbreaking work in the assessment of the impact and efficacy of online and blended learning. She has written and co-authored numerous works on blended and online learning and is a frequent presenter on these topics. Patsy's co-authored book--Conducting Research in Online and Blended Learning: New Pedagogical Frontiers--with Dziuban, Picciano, and Graham, was published in August 2015. She currently serves on the OLC Board of Directors.
Charles Dziuban is Director of the Research Initiative for Teaching Effectiveness at the University of Central Florida (UCF) where has been a faculty member since 1970 teaching research design and statistics and is the founding director of the university’s Faculty Center for Teaching and Learning. He received his Ph.D. from the University of Wisconsin. Since 1996, he has directed the impact evaluation of UCF’s distributed learning initiative examining student and faculty outcomes as well as gauging the impact of online, blended and lecture capture courses on the university. Chuck has published in numerous journals including Multivariate Behavioral Research, The Psychological Bulletin, Educational and Psychological Measurement, the American Education Research Journal, the Phi Delta Kappan, the Internet in Higher Education, the Journal of Asynchronous Learning Networks, and the Sloan-C View. His methods for determining psychometric adequacy have been featured in both the SPSS and the SAS packages. He has received funding from several government and industrial agencies including the Ford Foundation, Centers for Disease Control, National Science Foundation and the Alfred P. Sloan Foundation. In 2000, Chuck was named UCF’s first ever Pegasus Professor for extraordinary research, teaching, and service and in 2005 received the honor of Professor Emeritus. In 2005, he received the Sloan Consortium award for Most Outstanding Achievement in Online Learning by an Individual. In 2007 he was appointed to the National Information and Communication Technology (ICT) Literacy Policy Council. In 2010, Chuck was named an inaugural Sloan-C Fellow. In 2012 the University of Central Florida initiated the Chuck D. Dziuban Award for Excellence in Online Teaching for UCF faculty members in honor of Chuck’s impact on the field of online teaching and learning. In 2017 Chuck received UCF’s inaugural Collective Excellence award for his work strengthening the university’s impact with the Tangelo Park Program and assumed the position of University Representative to the Rosen Foundation Tangelo Park and Parramore programs.
Dr. Connie Johnson is Colorado Technical University's (CTU) chief academic officer and provost, working with both online and ground degree programs. She has oversight of academic affairs, including faculty, curriculum, classroom experience, and accreditation. During her time at CTU, Connie has initiated adaptive learning technology implementation, effective leadership of academics, women's leadership, leading academics through change, and effective technology implementation in the online classroom including the promotion of academics, faculty and student engagement through social media. Connie has served in higher education for over 20 years with extensive experience in online and ground teaching, administration, and leadership. Additionally, Connie has extensive experience in regional accreditation, curriculum implementation, and faculty training and development. She is a trained peer evaluator for the Higher Learning Commission (HLC), has completed and served as a facilitator in the ACE Chief Academic Officer Institute, and is a member of the CTU Board of Trustees. Her educational background includes a Doctorate of Education, organizational leadership emphasis (2010), and a Master of Business Administration in management (1991) from Nova Southeastern University; and a Bachelor of Science with honors in criminal justice from Florida State University. Dr. Connie Johnson is Colorado Technical University's (CTU) chief academic officer and provost, working with both online and ground degree programs. She has oversight of academic affairs, including faculty, curriculum, classroom experience, and accreditation. During her time at CTU, Connie has initiated adaptive learning technology implementation, effective leadership of academics, women's leadership, leading academics through change, and effective technology implementation in the online classroom including the promotion of academics, faculty and student engagement through social media. Connie has served in higher education for over 30 years with experience in online and campus teaching, administration, and leadership. Additionally, Connie has extensive experience in regional accreditation, curriculum implementation, and faculty training and development. She is a trained peer evaluator and team chair for the Higher Learning Commission (HLC), has completed and served as a facilitator in the ACE Chief Academic Officer Institute, and is a member of ACAO (Association of Chief Academic Officers) Board of Directors. Additionally, Connie serves as a member of the Every Learner Every Where (ELE) Network. Connie’s area of research and scholarship is in the areas of adaptive learning and faculty implementation of digital tools. Her educational background includes a Doctorate of Education, organizational leadership emphasis (2010), and a Master of Business Administration in management (1991) from Nova Southeastern University; and a Bachelor of Science with honors in criminal justice from Florida State University.

Additional Authors

Dr. Colm Howlin is the Principal Researcher at Realizeit and leads the research and analytics team. He has been with the company since it was founded 8 years ago. He is responsible for the development of the Adaptive Learning Engine within Realizeit and the Learning and Academic Analytics derived from learner data. Colm has a background in Applied Mathematics earning his B.Sc. and Ph.D. in Applied Mathematics from the University of Limerick and was a Research Fellow at Loughborough University in the UK. Colm has over 10 years’ experience working on research, educational data, analytics and statistical analysis, including spending time as a Consultant Statistician before joining Realizeit.

Extended Abstract

Adaptive platforms facilitate the design of courses and programs that personalize the student learning experience by customizing content while continually assessing learning outcomes. Requisite learning time for students varies, impacted by variables such as students who face challenges with mathematics readiness or students expressing ambivalence for the subject matter. However, the structure of an adaptive course equips students with unique, real-time, adjusted learning paths underpinned by continuous assessment that can accelerate their learning or extend their learning space beyond traditional boundaries such as semesters, depending on their achievement levels.

This presentation will describe a longitudinal, cross-institutional, adaptive learning evaluation between the research unit of the platform provider (Realizeit), the Research Initiative for Teaching Effectiveness at the University of Central Florida, and Colorado Technical University. Our 5-year continuing collaboration has taken place with the understanding that the three organizations must stay the course to achieve valid and meaningful information. 

After a contextualization of adaptive learning at our respective campuses and how we have accomplished collaborative research, we will discuss the methods and results from our ongoing analyses of the impact of educational analytics arguing that individual student prediction is not a tractable approach. We contend that integrating student information system data with real-time class data provided by Realizeit effectively improves the odds of success for like student cohorts.  We will present data about: 
1. Assessing the granularity of adaptive courses, 
2. Prototype analysis of student learning path behaviors, 
3. Student success based on the institutional contexts, 
4. The possibility of real-time adaptive predictive analytics, 
5. Simulating student behavior based on learning analytics, and 
6. Implications of adaptive learning for helping underserved student populations. 

Preliminary analyses indicates that adaptive learning has the potential to help alter students’ learning pathways to maximize their chances of success. Used effectively, this innovation can positively impact teaching and learning in higher education. 

The work in this presentation relates the concept of intersectionality that emerged in the late 20th century addressing educational and financial inequity citing the impossibility of decoupling concepts such and poverty and racism and that their interaction is more impactful that either concept considered separately. This line of thinking is rooted in several other disciplines--for instance, in Douglas Engelbart’s theory of integrated domains, interaction effect in statistical models, the emergent property of complex systems, entanglement in quantum physics, and in learning the intersection of cognitive, behaviors and affective characteristics of students. Understanding these principles and the basis of adaptive learning analytics can help us understand that one-to-one student prediction of risk or success must be based on the intersection of many components and that clearly the most effective approach is one that levels the playing field by increasing the odds of success. Nothing in higher education appears to operate independently because higher education is a complex system. Consider Taleb’s perspective: “The main idea behind complex systems is that the ensemble behaves in ways not predicted by its components. The interactions matter more than the nature of the units. Studying individual ants will almost never give us a clear indication of how the ant colony operates. For that, one needs to understand an ant colony as an ant colony, no less, no more, not a collection of ants. This is called an “emergent” property of the whole, but the parts and whole are different because what matters are the interactions between such parts. “