Higher education data mining and analytics, like learning analytics, may improve learning experiences and outcomes. However, such practices are rife with student privacy concerns and other ethics issues. It is crucial that student privacy expectations and preferences are considered in the design of educational data analytics. This study forefronts the student perspective by researching three unique futurized scenarios rooted in real-life systems and practices. Findings highlight student acceptance of data mining and analytics with particular limitations, namely transparency about analytics and consent mechanisms. Without such limitations, institutions risk losing their students' trust.