• Late buses, the early breakfast program, and their impact on learning success: If buses arrive late in the mornings, students might not be able to participate in the breakfast meal program–and students are not ready to learn if they don’t eat in the mornings. Typically, the breakfast meal program and the bus program are tracked by two different groups and in two different databases. But with predictive analytics, administrators can see how they work together and whether those two factors have an impact on learning.
• Identifying problems: One school district noticed that a single group of students was continually late to school every Wednesday morning. By applying IBM’s advanced analytics, administrators discovered that the parents of this group of students all worked the late shift on Tuesday night, then the early shift on Wednesday morning. The kids were staying up late on Tuesdays, then falling back asleep on Wednesday mornings after their parents woke them up and left for work, says Rob Dolan, a worldwide industry executive in business analytics for IBM. After identifying the problem, administrators were able to contact the parents and explain the situation, which was then rectified.
• Course planning: An instructor’s plan can be shaped, day to day, by data points. Imagine that an instructor has a lesson plan for Tuesday, teaches the class, then inputs feedback from Tuesday’s exercise into the system. That teacher might find that four of the five concepts taught on Tuesday resonated well with the students, but the fifth concept was a problem. That could suggest that the lesson plan for Wednesday focus on that fifth concept. “It becomes part of a process, changing activities in a classroom to provide for a better experience,” Gold says.
• College recruitment and retention: As many colleges and universities struggle with retention problems–students who finish their freshman or sophomore year and then drop out–some have used advanced analytics to discover what types of students they should be recruiting to find the attributes that make a student most likely to complete his or her college career. Institutions of higher learning also can look at what types of programs are most likely to help keep a student engaged. They can identify, for example, that “this type of student who lives in this dorm and with this major will most likely stay [in school] if I offer them this club,” explains David P. Whirlow, a senior managing consultant in business analytics and optimization for IBM.
• Optimizing the scheduling of classrooms: Syracuse University has used IBM’s advanced analytics to help schedule its facilities for classes, taking into account how students are moving between classrooms and the best way to schedule within the university’s budget constraints.
• Optimizing alumni donations: Another higher-education application of advanced analytics would be using the method to discover the best way to interact with the alumni community to boost donations.
Reaching the nirvana of ‘personalized instruction’
In recent years, the pie-in-the-sky ideal of learning has been the notion of individualized lesson plans. Through advanced analytics, the education industry is getting closer to that ideal.
Periodic assessments of a student’s knowledge, combined with other data such as the challenges specific to that student, the student’s demographic information, and information about his or her instructor (what type of certification the teacher has, how often he or she is absent, subject matter background, etc.), can help school leaders understand not only how a student is performing but how a student will perform under certain circumstances. This creates the opportunity to formulate specific lesson plans for that student.
“Picture Johnny with a Kindle, and what Johnny does on Wednesday is different from what Mary does on Wednesday,” Gold explains. “Their actions are shaping their own different lesson plans as we create a personalized experience.”
Jennifer Nastu is a freelance writer living in Colorado, who writes frequently about education and technology.
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