Faculty and IT Integration

Leveraging a faculty learning community has helped me do things I didn’t know how to do with technology, including podcasting my lectures.

The phenomenon of social networking software like Facebook has been widely publicized. At UNC-Chapel Hill, over 90 percent of first year students are Facebook users. But is there utility for faculty and staff? Yes! This presentation will describe the uses of Facebook that might benefit many of us.

The ELI Student/Faculty Questionnaire is designed to help you explore student and faculty experiences and expectations with technology in teaching and learning. Specifically, the questionnaire examines student and faculty

• Familiarity with learning technologies,
• Expectations for the use of technology in teaching and learning, and
• Views on how students prefer to learn.

This questionnaire provides insight into possible gaps that may exist at your institution, such as

• The difference between the learning technologies that are expected by today's students and those that your faculty feel comfortable using, and
• Students' self-described learning preferences contrasted with faculty perceptions of those preferences.

This paper describes a four-step needs assessment process that institutions can use to determine the gaps between current and desired states of technology infrastructure, facilitating rapid decision making. The framework described focuses on aligning learning outcomes and technology tools and can help institutions compare the current state of technology infrastructure with a desired state to best meet identified learning outcomes.

CMC\'s FITness program allowed faculty to rapidly integrate teaching with technology and help struggling students catch up. Grants from the Mellon Foundation and the Fletcher Jones Foundation funded specialized programs to help students meet faculty expectations of their technical knowledge, skill, and application.

This ECAR Key Findings explores the practices and perspectives of IT organizations that support the academic research enterprise. To collect, analyze, and distribute information across an expanding range academic disciplines and geographic locations, research efforts rely heavily on IT infrastructure, people, and a broad range of IT services. Ever-larger data sets are being collected and shared, simulations and visualization are becoming routine tools, and the co-evolution of science and computing increasingly requires scientists to have solid grounding in information management.

This ECAR Roadmap explores the practices and perspectives of IT organizations that support the academic research enterprise. To collect, analyze, and distribute information across an expanding range academic disciplines and geographic locations, research efforts rely heavily on IT infrastructure, people, and a broad range of IT services. Ever-larger data sets are being collected and shared, simulations and visualization are becoming routine tools, and the co-evolution of science and computing increasingly requires scientists to have solid grounding in information management.

This ECAR study explores the practices and perspectives of IT organizations that support the academic research enterprise. To collect, analyze, and distribute information across an expanding range academic disciplines and geographic locations, research efforts rely heavily on IT infrastructure, people, and a broad range of IT services. Ever-larger data sets are being collected and shared, simulations and visualization are becoming routine tools, and the co-evolution of science and computing increasingly requires scientists to have solid grounding in information management. This study reports the results of a variety of research initiatives: a literature review, quantitative and qualitative data from 328 higher education institutions (315 U.S. and 13 Canadian institutions), and five in-depth cases studies. In addition, ECAR published What Do Researchers Need? Higher Education IT from the Researcher's Perspective, to supplement this study.

Asking whether technology improves learning may seem straightforward, but the answer is not simple. The challenge begins with defining assessment and is compounded by the complexities of people, technology, and educational organizations. This paper clarifies technology assessment by exploring the definitions, methods, and realistic expectations it can address.