Setting Trends for Educational Technologies within the National Science Foundation

Setting Trends for Educational Technologies within the National Science Foundation

Bruce C. Howard (Wheeling Jesuit University, USA) and Laura J. Curtis (Wheeling Jesuit University, USA)
DOI: 10.4018/978-1-60566-936-6.ch027
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Our research team evaluated 18 months of National Science Foundation (NSF) program announcements and awarded programs to discern the amount and type of emphasis placed upon educational technologies. NSF issued 65 solicitations for proposals with 53.8 percent calling for educational technology components. A sampling of 366 of the 1,180 funded projects, showed that 34.7 percent included educational technology. Twenty-five percent of the projects were in biology and cognitive science, with another 40% in general science, computer science, technical education, engineering, and math. Many types of educational technologies were funded, with an emphasis on cognitive tutors/intelligent agents, distance learning, and online communities.
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Data Source

There are 11 program areas and one cross-cutting area within NSF generally arranged by science discipline. Education is included as a program area called Education and Human Resources (EHR). All EHR programs are in support of research conducted in the other 10 program areas. NSF program announcements/solicitations (hereafter, “solicitations”) for EHR programs were accessed from an online database ( There were 506 solicitations in the database, 65 of which fell into the 18-month period of interest.

We conducted three stages of analysis, using two types of data: the text from solicitations for each of the 65 programs and abstracts from awarded proposals. In stage one we wanted to develop an operational definition of educational technology to be used in the subsequent stages. We derived the definition from the data to control for the varying perspectives of the investigators. We randomly selected 100 abstracts from awarded proposals from among the 65 programs. Four investigators read the abstracts and wrote down the terms indicating an educational technology was included. The group reached consensus around the terms and then used them to create the operational definition. The results section explains this process in more depth.

In stage two we used the definition to examine the text of the 65 solicitations to determine the degree to which they included educational technologies. For the subset of solicitations that did include educational technologies, we developed a dataset of abstracts from the awarded proposals. In stage three we analyzed the text of the abstracts to identify specifics of the proposed educational technologies. Figure 1 provides an illustration of the process.

Figure 1.

The three stages of the research process


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