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Personal Geographies: A Pilot Study of an Adaptive Learning Environment

#Twitter: 
#olc51407
Presenter(s)
Chuck Dziuban (University of Central Florida, USA)
Patsy Moskal (University of Central Florida, USA)
Session Information
October 14, 2015 - 3:45pm
Track: 
Technology and Emerging Learning Environments
Major Emphasis of Presentation: 
Research Study
Institutional Level: 
Multiple Levels
Audience Level: 
All
Session Type: 
Information Session
Location: 
Southern Hemisphere IV
Session Duration: 
45 Minutes
Session: 
Concurrent Session 4
Virtual Session
Abstract

This session will cover lessons learned from an evaluation of adaptive learning applied to online psychology, pathophysiology and college algebra courses.

Extended Abstract

The purpose of this session is to summarize research data for a pilot test of adaptive learning at the University of Central Florida. The motivation for this study originates from a growing acceptance that the current structure of higher education may not be optimizing learning opportunities for many students. This realization has created interest in the concept of personalized learning where students have the opportunity to gain knowledge at their own pace irrespective of the time required. Of course this places transformational pressure on the current educational system even in the online and blended learning environments. However, the "if time is constant, then learning is variable" contention is not new. For instance, in 1962 John Carroll wrote a paper entitled "A Model of School Learning" where he defined the adaptive concept with the following formula:
Degree of learning = f(time actually spent / time needed)
The denominator (time needed) of the equation represents three variables aptitude, ability to understand instruction, and quality of that instruction. Obviously these three variables can interact in a very complex way. The numerator defines itself with two variables time allowed for learning (the opportunity we give students to learn) and perseverance (this has evolved into the current notion of engagement). Interestingly, Carroll's little formula forms the basic theory of adaptive learning. Simple as it is, it has giving rise to innovations such as adaptive testing in certification examinations such as the Nursing Board, NCLEX, and a growing number of learning platforms some content-agnostic and some content-dependent.

Questions
The study was undertaken after an extensive vetting of adaptive learning programs by faculty members and staff at UCF that resulted in the selection of RealizeIT platform developed by CCKF. The two primary reasons for this decision were that the platform was content-agnostic and that it featured a sophisticated machine learning capability supported by Bayesian decision algorithms, uncertainty resolution, fuzzy classification, and item response theory that indicated a true "adaptive" platform. Five questions were addressed in the evaluation that was conducted in three courses general psychology, pathophysiology, and college algebra:
1. How do students react to adaptive learning?
2. What are the underlying constructs by which student evaluate adaptive learning?
3. How do faculty members evaluate adaptive learning?
4. What are the learning outcomes compared to other course modalities?
5. Can adaptive learning measures generated by the program predict independent university outcomes?

Methods
The study was conducted by the Research Initiative for Teaching Effectiveness (RITE) at UCF that designed several data collection protocols and analysis procedures:
1. A student survey cooperatively formulated by RITE, adaptive learning faculty, instructional designers, and CCKF. The results were resolved into their principal components and analyzed across student cohorts.
2. Grade distributions comparing adaptive learning with other course modalities (contingency analysis).
3. Results of the university end-of-course student evaluation protocol (contingency analysis).
4. Predictive modeling of course modules in general psychology with the psychology department general education examination requirement (add one modeling).
5. Predictive modeling of the internally generated RealizeIT direct and indirect measures with the psychology department general education examination requirement (add one modeling).
6. Determination of the optimal cut point on the psychology examination using the Harris index as a comparative measure (Harris index).
7. Faculty reflections on the efficacy of adaptive learning as pedagogical practice (contingency analysis).

Results
The results of the pilot study suggests that adaptive learning:
1. Produces equivalent grade distributions compared to other course modalities.
2. Exhibits equivalent performance on the psychology GEP exam with F2F and Web sections.
3. Produces test scores that exhibit smaller range and more symmetry than other modalities.
4. Proved effective for predicting external university measures.
5. Is evaluated by students' consideration of learning climate effectiveness and engagement efficacy constructs.
6. Results in positive student responses.
7. Produces end-of-course evaluations that are equivalent across all disciplines and modalities.
8. Is positively perceived by participating faculty, in spite of the additional workload.

Conclusion
The first round evaluation of adaptive learning indicates that although the learning curve for faculty and the CCKF providers was quite high, there are a number of challenges and advantages to this approach. The challenges come from fitting adaptive learning into traditional course structures since students can complete modules at their own pace. There is a distinct unbundling effect of adaptive learning. This raises an important question of just how widely this approach can be scaled across a large university and "fit" into existing structures such as semester, final exam schedule, etc. On the other hand, there appears to be much promise. First, adaptive learning has potential for diminishing course withdrawal and failure because it is competency-driven. In addition, it provides the opportunity for students to control their own learning environment. Thirdly, it causes a reconceptualization of course structure and sequence. Finally, in this presentation we will discuss the contention that adaptive learning can lead to adaptive predictive analytics where the system is dynamic instead of static.
Discussion/Interpretation
Although the concept of adaptive learning has a long history, the technology requirements for implementation have been prohibitive. Modern platforms, however, have eliminated this problem so that if an institution wants to become truly adaptive that is well within their reach. What we know from this first pilot study is that we have general learning equivalency. The findings suggest that adaptive learning is much more suited to disciplines with an inherent hierarchical structure in the curriculum rather than in areas of study that are fundamentally knowledge or comprehension-based. The primary question seems to be just how far are you willing to go with adaptive learning once you evaluate the opportunity costs against the added value. This session will cover lessons learned and discuss the opportunities and challenges with this instructional approach.