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A Conceptual Framework to Aid Attribute Selection in Machine Learning Student Performance Prediction Models
2021
International Journal of Interactive Mobile Technologies
One of the important key applications of learning analytics is offering an opportunity to the institutions to track the student's academic activities and provide them with real-time adaptive consultations if the student academic performance diverts towards the inadequate outcome. Still, numerous barriers exist while developing and implementing such kind of learning analytics applications. Machine learning algorithms emerge as useful tools to endorse learning analytics by building models capable
doi:10.3991/ijim.v15i15.20019
fatcat:hmvp6i7d5bhzznss2icvd7us5e