*3.3. Data as a Dehumanizing Force in Education*

Although big data inherently requires human input to exist, researchers have long criticized the fact that humans often use big data in dehumanizing ways, resulting in students, teachers, administrators, and other stakeholders feeling powerless and less autonomous in their education experiences. Nazarenko and Khronusova explained that at the post-secondary level, where class sizes may be larger, the lack of personal education and discussion between students and lecturers may be marginalized and replaced by an emphasis on big data to inform teaching strategies and practices, many of which may be automated and Internet-based [15]. Here, Nazarenko and Khronusova argued that students may unintentionally experience depression and a feeling of social isolation if their process and educational experience is too reliant on big data and too separated from human interaction with their teachers [15].

Dishon also argued that educational environments should be personalized to the point that data-driven decision-making does not infringe upon one's sense of a naturalistic learning environment [20]. However, as teachers and administrators continue to use data to make informed decisions, stakeholders may begin feeling as if they are numbers and not people, placing a wedge between a student and their teacher and eroding trust within this important relationship [20]. Similarly, Johnson reasoned that as educational organizations and individual teachers gather data to make decisions, students may feel that their privacy is violated to the point where they do not feel as if they are individual learners. Johnson continued by saying that big data can contribute to "relationships [that] can easily be seen as contributing to a collectivization of subject, where all are treated identically based on the assumption that they are all 'typical' students" [21] (p. 5), resulting in students feeling unnecessarily homogenized and unimportant.

Perhaps most importantly, big data and its ability to accomplish educational goals has been known to historically marginalize communities of color and those belonging to underrepresented groups. In this way, big data can be seen as a tool of educational inequity and not the other way around. In their discussion of big data and Australian education systems, Buchanan and McPherson described this phenomenon as the "datafication of the learner" [16] (p. 30). This datafication can weaken student-teacher and school-community relationships, thus marginalizing many stakeholders [16]. In a discussion of the critical use of big data toward racial equity, Gillborn et al. explained:

Quantitative data is often used to shut down, silence, and belittle equity work. Whenever governments, employers, or educators are challenged on their poor performance in relation to an under-represented group, they will typically reach for statistics in an effort to show that they are really much better than you might think. [22] (pp. 174–175)

Here, the authors reason that many school systems' underserved students of color or other groups and the use of big data can be a mechanism of masking educational inequities instead of identifying equity gaps and stemming them [22]. Moreover, Gillborn et al. suggested that the way in which communities of color and other marginalized groups are not engaged with data collection and analysis further marginalizes these communities, placing the students in a position of being surveilled without being served [22].
