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Surname Grades in Alphabetical Order
In a recent study conducted by researchers at the University of Michigan, it was discovered that students with surnames that come later in alphabetical order tend to receive lower grades compared to their counterparts whose surnames begin with letters earlier in the alphabet. This phenomenon was attributed to sequential grading biases and the default order of students’ submissions in Canvas, the widely used online learning management system, which ranks assignments based on the alphabetical order of students’ surnames.
The research team, led by Jun Li, an associate professor of technology and operations at U-M’s Ross School of Business, analyzed over 30 million grading records from U-M to uncover this disparity in grades. Surprisingly, they found that students with alphabetically disadvantaged surnames not only received lower grades but also faced more negative and less polite comments from graders. This discrepancy in grading quality can have a significant impact on students’ course grade-point averages and subsequently affect their career opportunities.
Research Findings and Implications
The researchers collected historical data from Canvas, supplemented by university registrar data, to study the grading patterns across different courses and institutions. Despite the data being specific to U-M, the researchers believe that their findings can be generalized to other educational settings. They noted a clear pattern of declining grading quality as graders evaluated more assignments, with students with surnames starting with A, B, C, D, or E receiving higher grades compared to those with later-in-the-alphabet surnames.
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The study revealed a 0.6-point gap in grades between students with surnames at the beginning and end of the alphabet, indicating a significant disparity in grading outcomes. This seemingly small difference can have real-world implications on students’ academic achievements and future prospects. The researchers emphasized that this unintentional bias in grading has a tangible social impact and needs to be addressed to ensure fairness in educational assessment.
Factors Contributing to the Disparity
One of the key factors identified by the research team was the potential impact of grader fatigue on the grading process. As grading assignments in alphabetical order can be a long and tedious task, graders may experience fatigue, leading to a decline in attention and cognitive abilities. This fatigue-induced bias could result in students with later-in-the-alphabet surnames receiving lower grades due to the order in which their assignments are assessed.
The researchers highlighted the importance of recognizing and mitigating this bias by implementing strategies such as grading assignments in a random order or distributing the workload among multiple graders. They also suggested that academic institutions could consider hiring more graders for larger classes to ensure a fair and accurate assessment process. By raising awareness about the impact of sequential bias on grading outcomes, educators can work towards creating a more equitable learning environment for all students.
Recommendations for Addressing the Bias
To address the inherent bias in grading based on alphabetical order, the researchers proposed several solutions that could help mitigate the disparity in grades. One simple fix suggested was to make random order the default setting for grading assignments in online learning management systems like Canvas. By eliminating the default alphabetical ranking, educators can reduce the impact of sequential bias on grading outcomes.
Additionally, the researchers recommended that academic institutions train graders to be more aware of potential biases in the grading process and provide guidelines for fair assessment practices. By promoting awareness and implementing best practices in grading, educators can ensure that all students receive equitable treatment and fair evaluation of their academic work. Through these efforts, the researchers hope to contribute to a more inclusive and unbiased educational system that values each student’s performance based on merit rather than their position in the alphabet.
Links to additional Resources:
1. https://www.sciencedirect.com/science/article/abs/pii/S016028961730023X 2. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5704638/ 3. https://www.pnas.org/content/114/44/11627.Related Wikipedia Articles
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Maya Richardson is a software engineer with a fascination for artificial intelligence (AI) and machine learning (ML). She has developed several AI applications and enjoys exploring the ethical implications and future possibilities of these technologies. Always on the lookout for articles about cutting-edge developments and breakthroughs in AI and ML, Maya seeks to keep herself updated and to gain an in-depth understanding of these fields.