Examples of Learning Outcomes Assessment at Purdue University (West Lafayette)

Learning outcomes assessment at Purdue University (West Lafayette) happens at various levels and in multiple ways. Following are some examples.

Biology

Laurie Iten, Associate Professor, for her first-year majors' biology course created with the help of Information Technology at Purdue (ITaP) an early warning system that identifies at-risk students early enough to give them an opportunity to adjust their behavior (effort), and to improve their chances of success in her course. To identify students at risk of not earning an A or B in her class, at the end of each week of the semester they ran an algorithm predicting student success. This algorithm, based on data from the course management system, and student information systems, identifies students at risk of receiving a C, D, or F in her course. Beginning the second week of the semester, at-risk students received interventions encouraging and inviting them to seek and utilize resources that can help them with their performance. This early warning system increased student’s help-seeking behavior and fewer students received Ds and Fs in her course.

Information Technology at Purdue (ITaP)

Technology plays an important role in the facilitation of student learning. In an effort to understand how technology is specifically improving factors related to learning effectiveness, Information Technology at Purdue (ITAP) gathers feedback about academic technologies from thousands of students, staff, and faculty annually. The data gathered is used to create “learning profiles” for each of the technologies. These profiles are based on 11 core metrics—4 global metrics (ease of use, importance, satisfaction, and facilitation of learning) and 7 metrics based on Chickering and Gamson’s 7 Principles for Good Practice in Undergraduate Education (faculty-student communication, student-student collaboration, active learning, prompt feedback, time on task, high expectations, and respect for diverse learning styles).

The data contained in the “learning profiles” can be used to drive IT decision-making in more sophisticated ways. The examination of any given technology’s learning profile provides insights into the unique and common functional impact of a technology as it relates to other technologies. These insights aid with the process of reducing unnecessary burdens on support infrastructure and fiscal resources through identifying technologies with the same basic impact and through defining gaps by identifying areas where no solution is having the appropriate impact.

Overall, this framework provides data about the value of individual and collective information technologies for the learning environment. With this information in hand, better institutional decisions can be made regarding the use of technology for the support of teaching and learning.