Review on evaluation techniques for better student learning outcomes using machine learning

The paper represents review on student learning outcomes on the basis of various evaluation parameters which plays an important role in an education system. Student learning outcomes along with other attributes are taken into consideration like learner factor, learner engagement, learning strategies use, teacher experience, motivational beliefs and technology in learning etc. With the help of examination and evaluation we can measure student learning outcome. Classification Algorithms like Decision Tree, Naïve Bayes and Support Vector Machine can help us to classify student’s performance. This classifier helps in tracking student performance. With the use of machine learning techniques we are trying to identify whether learning outcome is achieved or not. Students learning evaluation should be done on regular basis so that true learning outcomes can be measure. Once learning outcome is evaluated on regular basis, its aggregation should be done to sum up the learning outcome of course.