Data (or learning) profiles are comprehensive web-based electronic files of students’ assessment scores, grades, attendance, and demographic information. Profiles increase availability of data in real-time, making it possible for educators to process and act on the data results instantaneously. Once a student enrolls in school, all educators have immediate access to that learner profile, which details his/her academic and behavior habits.
The value of this information is priceless to an instructor. With this information, school staff can develop instructional plans right away to meet the academic needs of their students. They no longer have to wait for paper records or spend weeks re-evaluating each learner.
Aside from being a powerful data dissemination tool, it is also a learning analytic program.
Data profiles also captures and analyzes new statistical information on how students learn. Basically, this profile provides each student with a personalized curriculum. The personalized curriculum identifies the skills students lacks and new content knowledge he or she needs to become proficient at grade level.
Access to this type of data makes it easier for educators to improve their instructional decisions. This information helps instructors to focus their decisions on teaching, assessing knowledge, and skills that students need versus what the school perceives they need.
However, there is one drawback to data profiles
It is true that data profiles provide school staff with a “comprehensive portrait” of a child’s academic strengths and challenges. However, it also provides educators with a reductionist point of view of a student’s learning potential. With this approach to teaching and learning, students are no more than their statistical output (assessment scores).
As school systems move towards data profiles, it’s important that they find a balance between increasing student achievement and ensuring that students are not generalized by these profiles.
1st International Conference on Learning Analytics and Knowledge, Banff, Alberta, February 27–March 1, 2011, as cited in George Siemens and Phil Long, “Penetrating the Fog: Analytics in Learning and Education,” EDUCAUSE Review, vol. 46, no. 5 (September/October 2011).