Assessment Issues

Category: When Analytics Works

Analytics used for assessment can score student work with accuracy and precision. Students recognize this. But students have mixed feelings about such systems, preferring "comments from teachers or peers rather than computers." (Roscoe, et.al., 2017) It is arguable that students may prefer human assessment because they may feel more likely to be seen as an individual with individual flair, rather than erroneously deviating from the expectations of the analytics engine. As one college official says, ""Everyone makes snap judgments on students, on applicants, when first meeting them. But what worries me about AI is AI can't tell the heart of a person and the drive a person has."

A significant ethical issue arises when assessments are made based on predictive data, rather than actual practice. For example, "The growing use of AI in the criminal justice system risks interfering with rights to be free from interferences with personal liberty. One example is in recidivism risk-scoring software used across the U.S. criminal justice system to inform detainment decisions at nearly every stage, from assigning bail to criminal sentencing" (Access Now, 2018:19).

In the case of predictive analytics in learning technology, systems can identify factors statistically correlated with worse performance. This allows institutions to minimize their own risk, at the expense of students. "Institutions can then treat 'high risk' individuals differently, with the aim of ensuring they do not end up counting as negative statistics for completion" (Scholes, 2016). Schools may respond with enrolment limitations or assignment of additional work, but this may in some cases harm, rather than help, individual students.

Examples and Articles

Top challenges to testing automation in 2021
"Your organization should be aware of the technical challenges to automated testing as well as other issues you may face." Direct Link

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