Edu Intel generates qualitative school intelligence data through role-based surveys, offering deeper insights. Ed Intel measure students’ affective characteristics, for example, their aspirations, engagement and resilience, filling important information gaps in wider analyses undertaken by ProLearning.

ProLearning includes Edu Intel, an automated online system to capture data from students. Our specialist measures focus on the affective characteristics of students that have a direct impact on learning, for example, aspirations, engagement and resilience.

Edu Intel undertakes this process automatically, and follows up to ensure that it captures the data for every student.

In addition, Edu Intel includes specialist measures of the components of school learning environments that impact on student learning, including many that have been highlighted in Professor John Hattie's recent research.

The data captured from students allows ProLearning not only to provide analyses that show the current levels of a range of key indicators of learning and school performance at many levels within schools, importantly it also provides a causal analysis for working out what is limiting learning and school performance.

 

What type of data can be captured?

ProLearning allows schools to capture data from individual students, enabling the personal characteristics of students and influences over student learning to be measured. Influences include engagement, aspirations, self-confidence, and resilience.

ProLearning has the capacity to store other information that is pertinent to the data analysis, for example, the quality of teaching, or quality of the school learning environment, etc.

A specialist Web-application like no other

Education Intel is a specialist Web-application for data capture tasks, built specifically for schools. It contains features not found in any other system. These include:

  • A library of items developed specifically for schools.
  • Benchmark data, based on responses to items that have been used multiple times, across years or across schools.
  • Automated follow-up of students who have not replied.
  • Capture of both quantitative and qualitative information from students.
  • Utilisation of recent advances in artificial intelligence to automate the process.

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