Welcome to Realizeit labs

This site is where we'll share some of the inner workings of the Realizeit Research and Development team: the projects we're working on, meetings we're attending, collaborations we're involved in, papers we're publishing. Make sure to follow us to stay up to date on what is happening.

If you are interested in using the Realizeit platform, please visit www.realizeitlearning.com.

We are always interested in hearing new ideas and discussing potential research collaborations. If you are interested in working with us, or one of our partner institutions on a research project then let us know.

Adaptive Learning: A Stabilizing Influence
Oct 22, 2018  • Colm Howlin

Recently we had a paper Adaptive Learning: A Stabilizing Influence Across Disciplines and Universities published in the Online Learning Journal based on some of the collaborative research that we have...

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Principal Component Analysis - A Primer
Oct 22, 2018  • Colm Howlin  Resource Post

This is a resource post that gives a very brief overview of Principal Components Analysis (PCA). We create these resource posts to provide a basic introduction to some techniques and...

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Boosting Self-Regulated Learning
Oct 3, 2018  • Eoin Gubbins

As an adaptive learning system, Realizeit provides tremendous flexibility for students, allowing them to learn in their own way, at their own pace, and at times that are most convenient...

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  • The 28th ICDE World Conference on Online Learning - Dublin, Ireland

    Key Questions When Designing Adaptive Courses

    Colm Howlin (Realizeit), George Mitchell (Realizeit)

    Date: TBD - Time: TBD - Room: TBD

    Link

  • WCET's 31st Annual Meeting 2019 - Denver, CO

    A Collaborative Approach to Adaptive Predictive Analytics

    Connie Johnson (Colorado Technical University), Patsy Moskal (University of Central Florida), Chuck Dziuban (University of Central Florida), Colm Howlin (Realizeit)

    Nov 5, 2019 - 2:15 PM - Colo G-H

    Link
  • OLC Accelerate 2019 - Orlando, FL

    Adaptive Learning Predictive Analytics: Two Domains

    Chuck Dziuban (University of Central Florida), Patsy Moskal (University of Central Florida), Connie Johnson (Colorado Technical University), Colm Howlin (Realizeit)

    Nov 20, 2019 - 3:45 PM - Northern Hemisphere C

    Link
  • 1.
    Colm Howlin and Charles Dziuban (2019)
    Detecting Outlier Behaviors in Student Progress Trajectories Using a Repeated Fuzzy Clustering Approach
    Proceedings of the 12th International Conference on Educational Data Mining, 2019
    Go to paper View slides
    Abstract

    Clustering of educational data allows similar students to be grouped, in either crisp or fuzzy sets, based on their similarities. Standard approaches are well suited to identifying common student behaviors; however, by design, they put much less emphasis on less common behaviors or outliers. The approach presented in this paper employs fuzzing clustering in the identification of these outlier behaviors. The algorithm is an iterative one, where clustering is applied, outliers identified, the data restricted to the outliers, and the process repeated. This approach produces a clustering that is crisp between each iteration and fuzzy within. It arose as a consequence of trying to cluster student progress trajectories in an adaptive learning platform. Included are results from applying the repeated fuzzy clustering algorithm to data from multiple courses and semesters at the University of Central Florida, (N=5,044).
    2.
    C Howlin, E. Lindsay, M.E.D. van den Bogaard and J. Morgan (2018)
    Pathways of students’ progress through an on-demand online curriculum
    Proceedings of 126th ASEE Annual Conference, 2019
    Go to paper
    Abstract

    Charles Sturt University makes its underpinning technical curriculum available to its students using an on-demand online system they call their Topic Tree. The tree is a directed acyclic graph where nodes represent topics to be learned, and edges represent the prerequisite relationships that exist between the topics. Branches on the topic tree represent concentrations in an area of knowledge, sub-branches (water quality, fluid mechanics, etc.) represent distinct subsets of knowledge - specialty. Delivery of the technical content is in three-hour modules, and students are free to choose the order in which they engage with these topics.

    Previous work has identified that students engage with the on-demand curriculum much as they engage with on-demand entertainment platforms such as Netflix, completing long sequences of topics with short periods between them – the traditional “binge” model of consumption.

    This paper presents a more fine-grained analysis of students’ pathways through the topic tree, focusing on the distance between successive topics completed by the students. Students’ progress is characterized by a three-dimensional framework – time, distance, and purpose.

    In general, pathways through the tree fall into one of four patterns: Forward movement along a branch of the tree, Movement backward along a branch of the tree, Repeating the same topic, Switching to a different branch of the tree (backward distance to the junction of the branches combined with a forward distance along the new branch)

    Different students engage with the topic tree using different combinations of these pathways, distance absolute distance traveled through the topics, and different time gaps between activities on the topics. This paper will identify the different combinations that can be found in the student log data.
    3.
    Charles Dziuban, Colm Howlin, Patsy Moskal, Connie Johnson, Mitchell Eid and Brandon Kmetz (2019)
    Adaptive Learning: Context and Complexity
    E-Mentor 5(77), 7-39. doi:10.15219/em77.1384
    Go to paper
    Abstract

    This article describes a research partnership between the University of Central Florida and Colorado Technical University, with their common adaptive learning platform provider, Realizeit. The study examines component scores at the two institutions in mathematics and nursing based on a number of Realizeit system metrics. Although the principal components across disciplines and universities remained constant, student scores on those dimensions varied considerably. This indicates that adaptive learning is influenced by context and complexity. The context aspect helps frame student learning regarding knowledge, engagement, communication, and growth as they experience variability from faculty approaches to instruction. Complexity indicates a nonlinear learning pattern for the adaptive process in which the emergent property shows that interactions among the individual elements result in a more realistic model for explaining how students function in contemporary higher education. The authors raise a number of implementation issues for adaptive learning.