Cyberinfrastructure Capabilities and Limitations

Cyberinfrastructure Capabilities

Educational practice and research are poised for radical transformation afforded by the cyberinfrastructure for education. The cyberinfrastructure affords unlimited access to open educational resources (OER), or interactive online resources offered under free or liberal licensing agreements, that promise to support a transformative and more effective vision of education. Teachers can access these resources to create customized activities for their students, instead of the one-size-fits-all textbook approach. These tailored activities, in turn, can be easily contributed back to the cyberinfrastructure to support other teachers and learners. In this way, teachers become producers as well as consumers of resources. As noted, cyberinfrastructure-provided content is available in a variety of media and formats, such as interactive simulations, models, and educational games. For teachers, resources can also include curricula, assessments, textbook-supporting materials, as well as teacher-created materials.

Such cyberinfrastructure resources are also fueling radical changes in curriculum. For example in the United States, many schools are increasingly turning to freely available cyberinfrastructure OER to supplement and ultimately supplant their traditional print materials. Schools see this strategy as a way to respond to the rising costs of curricular materials, to take advantage of the growing number of students possessing digital devices, as well as to provide dynamic resources to meet the needs of a 21st-century workforce.

The cyberinfrastructure can also collect and aggregate vast warehouses of users’ behavioral data as they interact and communicate over the network. Researchers are increasingly mining these data sets, referred to as big data, in a process called educational data mining. Extracted patterns can then be used to fuel algorithms that make inferences about student learning, a field called learning analytics. Such algorithms can look for patterns and relationships in these data sets in order to help make the cyberinfrastructure systems more responsive and personalized to the needs of individual students. For example, such algorithms can be used to diagnose a student’s learning difficulties in order to recommend personalized learning activities.

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