Guest post from Thomas Stribling, a writer for the IBM Software Group.
In his spare time, Thomas works on a doctoral dissertation about developing better learning environments in K-12 public schools.
Going back to school for my elementary school-aged children meant one thing… new school supplies!
Our annual pilgrimage to the office supply store was serious business. They had to pick out a “cool” notebook binder. And, oh no … using last year’s scissors, pens and tape would not do!
But for many students, going back to school means anxiety – from meeting new friends, fitting in and fear of failure. In fact, Human Illnesses and Behavioral Health lists a number of reasons for why students fail – including depression, family problems and learning disabilities.
This site points out that students at risk of school failure need to be identified as early as possible if they are to receive the help they need. Parents, teachers, school counselors and mental health professionals all play important roles in identifying and assisting students with risk factors associated with failure.
K-12 school administrators and teachers also have to pay close attention to which students are at risk of failing, because a portion of their schools’ funding can be based on their students’ academic performance. The harsh reality for today’s schools: more failing students equals less money for those schools.
What if school administrators could predict which students would underperform before an important benchmark assessment? Or identify at-risk students years before they fail or drop out? What if teachers could know what type of support best helps at-risk students succeed?
Using data a school or district typically collects, predictive analytics software can identify students who are statistically likely to fail or drop out – allowing teachers to make timely interventions to get them back on track. The software can also measure the effect of remedial actions taken by teachers, so that the system learns which interventions have the most positive effect in any given scenario. Read more about this in Dr. Nicole Alioto’s recent blog post.
For instance, the Hamilton County Department of Education (HCDE) oversees 78 schools in Tennessee. They collect test scores and data on student attendance, behavior and demographics – plus data on teacher qualifications and experience. Then, using predictive analytics software, the HCDE merges and analyzes the data to provide daily updates to teachers on their student and classroom profiles.
This gives teachers deep insight into the needs of individual students. They can adjust their teaching style to provide additional instruction and study time when necessary and to intervene with students who are likely to drop out.
Recently the HCDE achieved its best No Child Left Behind results in its history. It also reduced its annual dropout rate by 25 percent. Watch more in the video below.
Students often need more than a trendy notebook binder and a shiny pair of blunt-end scissors to get excited about going to school. And now with predictive analytics, K-12 schools can ensure those new supplies get good use… all year long.
For more information:
· Learn how Gwinnett County Public Schools leverages predictive analytics to identify at-risk students and improve student performance
· Watch a demo to see how to improve student performance with IBM predictive analytics