Andrea Zingoni, Juri Taborri, Giuseppe Calabrò
A machine learning-based classification model to support university students with dyslexia with personalized tools and strategies
Scientific reports
rapid development societal information electronic educational resources become indispensable component modern education response increasingly formidable challenges faced secondary school teachers study endeavors analyze explore application artificial intelligence ai methods enhance cognitive literacy initially discourse delves application aigenerated electronic images training instruction middle school educators subjecting thorough analysis emphasis placed elucidating pivotal role played ai electronic images elevating proficiency middle school teachers subsequently integrated intelligent device serves foundation establishing model applies intelligent classification algorithms based structure observed learning outcome solo model designed assess cognitive literacy teaching efficacy middle school educators performance juxtaposed classification algorithms support vector machine svm decision trees findings reveal following 600 iterations model svm algorithm achieves 77 accuracy rate recognizing teacher literacy whereas solo algorithm attains 80 concurrently spatial complexities svmbased solobased intelligent literacy improvement models determined 45 22 respectively notably discerned escalating iterations solo algorithm exhibits higher accuracy reduced spatial complexity evaluating teachers pedagogical literacy consequently utilization ai methodologies proves highly efficacious advancing electronic imaging technology enhancing efficacy image recognition educational instruction