Analytics for Learning | SRI International

Toggle Menu
analytics

Analytics for Learning

Through A4L, SRI Education is exploring the measurement of noncognitive factors, learning processes, and learning behaviors in digital learning environments. This NSF-funded project is organizing community- and capacity-building activities among researchers in the field.

The availability of data coming from digital learning environments is creating the possibility to measure learning like never before. The Analytics for Learning project (A4L) is a network of researchers exploring the measurement of noncognitive factors, learning processes, and learning behaviors in digital learning environments by supporting three core community- and capacity- building activities:

  1. Develop design patterns and worked examples of measures for use in digital learning environments
  2. Engage and support a community of researchers and students
  3. Disseminate resources (e.g., design patterns and worked examples) to this community

Borrowing from the Evidence-Centered Design (ECD) Assessment Framework, the project is developing design patterns, a design tool typically used by assessment development experts. In this project, design patterns are being employed as narrative tools that can be used to support the development and implementation of measures of noncognitive factors by researchers not familiar with the factors or how to measure them.

Another aspect of the project involves supporting the community of researchers and students in the field exploring similar measurement challenges in digital learning environments. The A4L team has served as consultants, organizing webinars and conference sessions to support established and upcoming researchers in using advanced analytical techniques on various data sources. Examples of data sources include digital curricula, simulations, and intelligent tutoring systems among others. Some of the diverse analytical techniques needed to effectively measure noncognitive require attention to learning theory and merging multiple types of data (e.g., discourse data and click-stream data). The A4L team brings this set of expertise to these discussions and gatherings.

Finally, all design patterns, technical papers and workshop slides presented at our events are shared with the wider community through our project website. These community building efforts are building capacity in a burgeoning field of learning analytics. The A4L team is always looking to invite more members to this community for collaborative work towards the purpose of improving what we learn about how children learn in digital learning environments.

This material is based upon work supported by the National Science Foundation through Grant SMA-1338487. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.