Making sure that every student has what she or he needs for academic success can be a daunting task for teachers. They must assess, instruct, monitor, and evaluate student achievement. The sooner they begin, the sooner they can intervene on the behalf of their students. 

Educators often took as long as six weeks or more to get to know their students. Referring a student for learning or behavior disorders took longer. Students lost precious instructional time while waiting on the interventions that would help them the most.

Teachers rely on machine learning and artificial intelligence to provide equitable access to learning for all students, regardless of learning or behavior disorders. Modern edtech programs create equity by assisting with comprehension. They adjust reading levels, offer in-text definitions, and provide pronunciation when prompted.

Machine learning also uses algorithms to diagnose learning and behavior disorders.

Pinpointing learning disorders 

Difficulties in learning can be frustrating for teachers and their students. This is where intervention in education takes center stage.

Learning disorders often manifest themselves most conspicuously in the areas of reading, writing, and math. Some children struggle with writing letters or numbers and interpreting them. They may also have difficulties with language, communication, seeing, or hearing.

Schools use a variety of assistive technology in the form of speech-to-text software and talking calculators. However, these tools are useful only when student need has been properly diagnosed. 

That’s when machine learning comes in. 

Artificial intelligence can identify learning disorders with greater accuracy than assessments administered by humans. According to Dr. Duncan Astle (University of Cambridge), “Machine learning revealed how diagnostic labels can obfuscate other learning disabilities that may be present, emphasizing the need for better interventions that address cognitive needs on an individual level.” 

Machine learning identifies behavioral disorders

The need for accurate and early behavior diagnosis has never been greater.

If you thought that the most common childhood illness is chickenpox or the flu, guess again. According to the Centers for Disease Control and Protection, mental disorders are the biggest childhood illness. They are most common in children between the ages of 2 and 17. Nearly 25% of our youth are affected, and one out of three of these children have been identified as having behavior problems.

What if machine learning could accurately predict which children are most susceptible to behavior disorders? What if we could predict attention deficit hyperactivity, autism, bipolar, and more? 

Behavioral disorders respond well to early intervention. Early on, researchers recognized the immense potential of using machine learning to diagnose autism. They also recognized the need for redundancy in the algorithms to reduce identification bias. Years later, scientists are still refining the formulas in the hopes that they will be able to diagnose the condition as early as two years of age. 

By analyzing the movement of infants and toddlers, researchers at University of Southern California and Universidad Carlos III de Madrid are developing algorithms that will assist in detecting developmental delays. They have been working in conjunction with parents and medical schools like Harvard and Stanford.  The diagnoses have been accurate 80-94% of the time.

When using machine learning to identify learning and behavior disorders, teachers can intervene much earlier on the behalf of learners. Students quickly get the academic support they need so they can keep up with their peers.

Ultimately, machine learning makes equitable access for everyone a reality rather than a possibility.