The Microsoft Services Student Analytics Solution provides capabilities that will empower your schools to improve the academic experience by helping you identify performance gaps among your students.Kontakt zu Partner aufnehmen
The modern academic organization faces numerous challenges. It is tasked with improving student academic experience and outcome with limited time and resources. Students come to the classroom with various educational backgrounds, cultural influences, academic strengths and weaknesses, and often undiscovered learning differences. Educational institutions must address all of these factors to prepare every student for a successful future. Critical to this effort is the ability to predict and remediate those students who are at risk of failure.
When a student drops out of school, that choice negatively impacts both his life and his community as a whole. For example, every year over 1.2 million students drop out of high schools in the United States alone. That’s one student every 26 seconds, or 7,000 students a day. High school dropouts are also more likely to commit crimes, estimated at about 75% of all crimes in the US.
There are countless reasons why an at-risk student may choose to drop out. Some of the most common reasons include missed too many school days, was getting poor grades, had to support a family, started to work, etc, according to the National Dropout Prevention Center at Clemson University and include push, pull, and falling out factors. Given the limited time and resources you have, how can you determine which factors might be influencing a given student?
With our extensive experience in education and strategic methodology, Microsoft Services can help you plan and implement student analytics capabilities that will empower your schools to improve the academic experience.
Student analytics can help you identify performance gaps among your students. You can use analytics to view grades, assessment scores, and attendance records, and to identify underperforming students.
The system uses machine learning to reveal:
• Potential risk profiles in the current batch likely to struggle in various academic areas
• Potential dropout profiles
• Potential profiles unlikely to meet academic excellence KPIs
Once we’ve used student analytics to isolate incidents, we can analyze the results and take proactive steps to improve student outcomes.
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