Improving Performance in College Algebra Using Technology

Author Information
Author(s): 
Dr. Connie Johnson
Author(s): 
Tonya Troka
Author(s): 
Judith Komar
Institution(s) or Organization(s) Where EP Occurred: 
Colorado Technical University
Effective Practice Abstract/Summary
Abstract/Summary of Effective Practice: 

With the incorporation of its adaptive learning (AL) platform, intelliPath, Colorado Technical University (CTU) was able to postively impact results across several key metrics observed for its college algebra course. The results of CTU's approach to college algebra indicate many improvements including a reduction in student failure and withdraw rates. Additionally, CTU's general approach to adaptive learning includes the following characteristics as cornerstones: 1) personalized learning, 2) modifications of learner pathway, 3) informed in real-time, and 4) evidence of knowledge.

Description of the Effective Practice
Description of the Effective Practice: 

Challenge: College algebra is utilized as a gateway course for engineering and technology students at Colorado Technical University (CTU). In January 2012, CTU had high failure and withdrawal rates in college algebra.
A Solution: CTU's approach to revising college algebra using multiple interventions including Adaptive Learning (AL) technology within its online courses.

The results of this approach to college algebra indicate many improvements including a reduction in student failure and withdrawal rates.

The AL model at CTU has a Learning Analytic Engine that is synced with the course delivery platform and serves up content and assessments. The engine's job is to serve up the right content to the right learner at the right time. The engine uses all data to adapt content, assessments, directional setps, and messages. When a learner is working in the system, the engine checks his or her knowledge state every 3-5 minutes.

Learners progress through the course utilizing the adaptive learning technology in five phases: 1) students complete Determine Knowledge (pre-assessment); 2) the learning engine creates a personalized learning map with the results of the Determine Knowledge; 3) the learner works in leassons within the learning map, collapsing the lessons he or she knows and concentrates on the lessons he or she doesn't know, and learning is visible in real time; 4) the learner traverses the map and works on lessons in clusters, moving forward and backward on his or her individual path (the engine serves up the right content at the right time, and all is visible real-time to the learner) as needed; 5) the learner demonstrates knowledge by covering objectives, which allows the learner to apply knowledge to course objectives.

Instructors access a dashboard with five key pieces of information: student questions, concepts with which the entire class is struggling, concepts in which the entire class is excelling, individual students who are struggling, and individual students who are excelling.

In January of 2013, the average final score for college algebra was 67.4%. In November of 2013 the agerage final score increased to 74.1%. In July of 2014, 75.3% was the average final score.

Knowledge state growth per course objective was 17.77% for the time period January 2013 through July 2014. The lowest term knowldge state grown was 5.17% and the highest was 36.22%. More commonly, terms saw a knowledge state grown between 8-9% for all students.

The student retention rate for college algebra at CTU in January 2013 was 81% and was as high as 93% in January of 2014. The results include the percentage of students who completed the course and earned a letter grade (A--F).

Detailed information of this effective practice can be found in the supporting document attached to this submission (Colorado Tech - Improving College Algebra with Technology.pdf).

Supporting Information for this Effective Practice
Evidence of Effectiveness: 

In January of 2013, the average final score for college algebra was 67.4%. In November of 2013 the agerage final score increased to 74.1%. In July of 2014, 75.3% was the average final score.

Knowledge state growth per course objective was 17.77% for the time period January 2013 through July 2014. The lowest term knowldge state grown was 5.17% and the highest was 36.22%. More commonly, terms saw a knowledge state grown between 8-9% for all students.

The student retention rate for college algebra at CTU in January 2013 was 81% and was as high as 93% in January of 2014. The results include the percentage of students who completed the course and earned a letter grade (A--F).

How does this practice relate to pillars?: 

Learning Effectiveness
College algebra is, in general, a challenged course not just for Colorado Technical University, but across higher education. The CTU approach to college algebra of adding adaptive learning technology was able to impact key metrics, and has served as a model for other courses within the institution's curriculum that lead to student success.

Equipment necessary to implement Effective Practice: 

The adaptive learning platform is crucial to the implementation of this effective practice. The platflorm must consist of:
-Engineers and analysts capable of building, configuring, and calibrating the adaptive learning platform
-Process for efficiently incorporating technology and changes into the adaptive learning platform

Content engineering is another critical component. Content creators are capable of creating disaggregated content, learning maps, and assessments. A process for mapping topics to content and applying analytics for continuous improvement is incorporated, as well as a tool set for managing adaptive content.

References, supporting documents: 

References cited in supporting document attached to this submission (Colorado Tech - Improving College Algebra with Technology.pdf):

1. Blair, R., Kirkman, E. E., & Maxwell, J. W. (2013). Statistical abstract of undergraduate programs in the mathematical sciences in the Unites States: Fall 2010 CBMS survey. Retrieved from http://www.ams.org/profession/data/cbms-survey/cbms2010-Report.pdf
2. Mayes, R. (2004). Restructuring college algebra. International Journal of Technology in Mathematics Education, 11(2), 63–73.
3. Schunn, C. D., & Patchan, M. M. (2009). An evaluation of accelerated learning in the CMU open learning initiative course “logic & proofs” (Technical report). Pittsburgh, PA: Learning Research and Development Center. Retrieved from http://oli.cmu.edu/wpoli/wpcontent/uploads/2012/10/Schunn_2009_Evaluatio...
4. Knewton. (n.d.). Knewton technology helped more Arizona State University students succeed. Retrieved from http://www.knewton.com/assets-v2/downloads/asu-case-study.pdf
5. Small, D. (2002, May/June). An urgent call to improve traditional college algebra programs. MAA Focus. (Summary of the Conference to Improve College algebra held at the U.S. Military Academy, February 7–10, 2002.)
6. DeBra, P. (2006). Web-based educational hypermedia. In C. Romero, & S. Ventura (Eds.), Data mining and e-learning (pp. 3–17). Southampton, UK: WIT Press.
7. Rajan, R. (2013). Adaptive learning market acceleration program RFP Q & A webinar. Retrieved from: gatesfoundation.org.
8. Brusilovsky, P., & Millán, E. (2007). User models for adaptive hypermedia and adaptive educational systems. In P. Brusilovsky, A. Kobsa, & W. Nejdl (Eds.), The adaptive web (pp. 3–53). New York: Springer.
9. Newman, A. (2013). Learning to adapt: A case for accelerating adaptive learning in higher education. Retrieved from http://tytonpartners.com/library/accelerating-adaptive-learning-in-highe...

Contact(s) for this Effective Practice
Effective Practice Contact: 
Dr. Connie Johnson
Email this contact: 
cjohnson@coloradotech.edu