Here's what should really be happening with our vast information systems that we keep building to manage student data:
- Analyze student performance in specific software applications, in written work, and on a variety of assessments to accurately assess student learning styles. This information, along with identified strengths and weaknesses and relevant supporting materials should be provided early and often to teachers so they can stand a chance of meaningfully differentiating instruction.
- Use adaptive learning systems for practice, homework, and ongoing assessment. These systems help students continue to build skills with which they struggle, advance quickly where they excel, and provide useful data to teachers who can again better differentiate instruction among students (no small task with 30 kids in a class, a third of whom may also have special needs).
- Continuously analyze incoming data from these tools and traditional assessments aligned with thoughtful standards to identify deficiencies or potential disabilities as early as possible (and then assess the success of whatever interventions a school puts into place).
- Aggregate the data above with demographic and socioeconomic data to identify students at risk or those who are not growing as expected; subtle shifts in academic progress can be easily detected with the right algorithms, but as class sizes increase, it's quite difficult for teachers to observe and act upon these changes, many of which can be signs of larger issues in a student's life.
- Provide big-picture data to schools and teachers, e.g., subsets of students who are failing to meet particular standards. This sort of information is vital to strategic planning, but, to date, has too often been based on limited snapshots of data (like yearly state exams) and has been used to punish teachers and districts instead of engineer strategic shifts in curriculum and instruction.
- Once all of these elements are in place, BI and analytics tools will be able to meaningfully and objectively identify the professional development needs of specific teachers. If kinesthetic learners in Mrs. Smith's class repeatedly achieve at lower levels than students in her class or other kinesthetic learners in the same grade, then it won't be hard to outline opportunities for her own professional growth and continuing education. Want to implement merit pay without a mutiny? Then let a broad, transparent, robust data set objectively identify the teachers who so clearly stand out from the pack that it brooks no argument.
There are obviously major obstacles in terms of equity, access to resources, and the breadth and depth of available software to make this happen. But until we can take an holistic approach to analyzing and acting on a large body of student data, we'll be spinning our wheels with high-stakes tests that do far more harm than good.
Teachers need to be able to focus on aspects of learning that a computer can't quantify or analyze. How well do students collaborate? How effectively do they communicate? Where are specific learning disabilties or areas of academic and social challenge interfere with their learning? And how can they adjust instruction to best address those challenges? How can gifted students remain motivated and challenged? How can average students find niches in which they shine? And how can struggling learners overcome countless obstacles? Computers will never replace great teachers, but they should free teachers to do their jobs more effectively and ensure that students are more than just data points.