Vol. 15 No. 2 (2011)
With recent advancements in computing power and data mining techniques, an age of large scale, continuous assessment, at-risk student intervention, outcome prediction, and learning environment redesign has dawned. This panel will explore how emerging data driven decision making systems will impact students, faculty, institutions, and the global learning landscape. Innovative next generation technology will enable a level of detail in course design and curricular improvement coupled with increased personalization of the learning experience. Kaplan University shares practices in development: The Course Level Assessment (CLA) program specifies learning outcomes supported by rubrics at the course level. Data collected on student performance on learning outcomes identifies problems with the teaching, the learning, and/or the course design feeding a continuous improvement cycle. A learning recognition, assessment, and portability platform enables the learner to customize her degree plan and maximize her placement towards the degree, avoiding redundant learning. All three examples illuminate the deeper point that through technology enables a level of granularity about the teaching-learning process that leads to consistent, reliable, and valid assessment of learning wherever and whenever it occurs. And it is this consistent, reliable, and valid assessment of learning outcomes at the course and program levels that will define academic quality in the 21st century as we drive towards mass post-secondary education. As computing power continues to increase, the use of data driven decision making in higher education is becoming more prevalent. This issue explores how new technologies are being deployed and the impact that they have on retention, progression, learning outcomes and the construction of ever more sophisticated learning environments. Topics include data mining strategies, applications for eLearning, institutional barriers related to large scale analytics and next generation applications. The issue focuses on both implementation and administrative issues related to large scale data driven decision making in higher education.