Academic Computing Infrastructure Program Evaluation
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Academic computing is one major component of Information Technology infrastructure affecting the availability and utilization of technologies at universities. The study here evaluated two different colleges at the University of Oregon in comparison to a minimal logic model proposed here, the Support for Academic Computing Model (SAC). Based on the differences in IT needs and implementation of existing instructional technology services, the evaluation investigated the utility of the logic model and information regarding the two settings. The two colleges are the College of Education (COE) and the School of Architecture and Allied Arts (AAA). My hypothesis is that empirical evaluation studies based on a comparison with a base logic model for infrastructure needs across contexts may help to provide information to better align resources. Results show that a strong use case of 100% of faculty interviewed at COE rely on Learning Management Systems (LMSs), Data Visualization and Video & Audio tools, making them a core part of the SAC model. Most faculty interviewed in AAA utilize LMSs at 89%, then Productivity/Content Creation/Research Tools at 83%, and as an extension Instructional Media Tools at 46%, which helps to validate the SAC model across this second context. Other information in the model evaluation allows more specific comparisons of gaps in areas such as access to resources, knowledge of and about resources, mission-driven need for resources, and some patterns. Common themes that emerged from the faculty interviews are the need to showcase technology usage among colleagues, that services are not always well advertised, that technology may not be accessible or that there may be issues regarding limited or unclear funding for both support and resources that limits their use. This indicates that this style of a model might be helpful in planning and evaluating academic computing support programs and services. Future work would be needed to investigate the degree to which intervening according to the findings of such a model might be efficacious to improve the perceived quality of services or the usage patterns and outcomes, as well as the degree to which such a model could be generalized and evolve over time.