Predictive analytics has, like so many things in education these days, become a new hot strategy for improving student learning. When used accurately, data gathered from predictive analytics can help to identify areas of need in student populations and/or methods that are successfully reaching students, as well as predict and monitor student performance. To truly engage with and target learner needs, however, any research methods must be employed efficiently and effectively. Below are three areas to consider before schools decide to use predictive analytics.

Clear Objectives

Like any assessment, an attempt to collect data should always begin with clear objectives. For example, one question to consider might be: is there a specific problem the school is attempting to solve? If so, which type of data needs to be collected to best understand and resolve this problem? Other objectives for data collection might relate to funding, which is often tied to learner analytics and teacher success or to test scores, most especially if a student population seems to be struggling in a particular subject area. Gathering data in these areas may aid a school in receiving much-needed funding or in restructuring how a subject is being disseminated to students. 

Adequate Time and Resources

During and after data collection, plenty of time and resources must be provided to each part of the process. A common criticism of educational research is that data often fails to reflect the true spectrum of learner and educator needs, primarily because research is many times completed hastily or as part of a one-off project. In other cases, data collection is requested by a school or district, but not enough funding or researchers are dedicated to the task. Thus, those collecting data can only spend so much time accumulating and analyzing results. To gain a clear picture of what the data is telling researchers, patience and time are essential.

Responsible Data Handling

Learner data is sensitive; it includes information about individuals that is personal to them and their learning needs. These seem like obvious statements, but unfortunately, the handling of sensitive data does not always reflect the need for delicacy. Before data is collected, schools need to determine critical factors. Where will data be stored once it is collected? Who will have access to it, and with whom will it be shared? What security measures will be taken to ensure that a breach does not occur? Without answers to these questions, data collection should not begin.

Change for the Better

Ultimately, the use of predictive analytics can be invaluable to schools and their districts, and more specifically, to the learners they serve. To identify the best methods for initiating forward progress, though, schools should enter into research thoughtfully and efficiently. Change for the sake of change has a high rate of failure and serves no one, but change based on clearly identified objectives, thorough analysis of results, and an emphasis on learner privacy can ensure that future generations of learners are met with an education that is tailored to their needs.