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For Whom the Bell (Curve) Tolls? Classes That Yield Too Many “A”s!
Much is being said about the impact of Artificial Intelligence (AI) in the hands of students resulting in “too many “A”s being granted! We are seeing colleges and universities across the country cracking down on “grade inflation.”
It is my long-held belief that striving to have a near-equal number of “A”s and “F”s in a college class is a grossly misdirected goal. This always seemed to me to be better applied to a sorting guideline for assembly line manufacturing. It strikes me that the more “A”s earned in a well–designed class using quality grading rubrics, the better. If viable, relevant, up-to-date learning outcomes are well-assessed, then higher grades on average are commendable.
Yet, in recent weeks we have learned that a couple of our higher education institutions, the venerable Harvard and Yale universities, are considering policies designed to reduce the percentage of “A”s that may be earned in a given class. Writing in the New York Times, Mark Arsenault, reports “One Solution for Too Many A’s? Harvard Considers Giving A+ Grades.” That seems a bit disingenuous; merely changing the title of the grade by adding a plus sign in order to reduce the disreputable number of “A”s posted in a class.
The Times article goes on to quote the dean of undergraduate education. “A number of you tightened up your grading this fall, and your efforts have made a meaningful difference,” the dean, Amanda Claybaugh, wrote in an email to the faculty Monday afternoon. Grades of A fell to 53.4 percent of grades awarded in the fall semester, from 60.2 percent in the prior academic year, Dr. Claybaugh reported. “I know this change wasn’t easy,” she added, noting that some faculty members had said they were receiving less favorable course evaluations from students.
The concern at Yale seems to be similar. Jaeha Jang writes in the Yale Daily News
While grade inflation may seem like a self-imposed, correctable problem for professors, they told the News that the reality is more complicated. The pressure for student enrollment in their courses, as well as the significance of student evaluations in their review process, incentivize instructors to give generous grades, they said.
The issue is not a wholly new topic, nor one that is confined to a few Ivy League schools. Jane Nam shares essential data in the Best Colleges Web Site in the May, 2024 report on “Grade Inflation in College: Trends and Why It Happens.”
The average college GPA was 3.15 in 2020.Note Reference[1]
The median college GPA increased by 21.5% in the span of 30 years (1990-2020).Note Reference[1], Note Reference[2]
Public, four-year institutions saw the largest GPA jump of all school types, increasing their average GPA by 17% over a decade.Note Reference[1], Note Reference[2]
Economics students experienced the highest increase in GPA of all majors, with an 18% rise from 1990-2020.Note Reference[1], Note Reference[2]
Lighter grading standards during the pandemic, schools’ efforts to boost student retention rates, and pressure on faculty to improve student reviews may be drivers behind grade inflation.
There are multiple points of pressure that tend to inflate grading at both the institutional and individual faculty member levels. The cycle builds upon itself, year by year with incremental increases in higher grades.
Yet, I believe, we are operating under a system flawed at its very foundation. The very tool that may be able to rescue us from this building crisis of poor assessments, AI, is the one that is blamed for inflated evaluations of final papers and projects. The flaw is not inherent in AI, rather it is in the failure of faculty members to apply the technology in a way that cultivates learning among all students and accurately assesses mastery of the course content. It is more of a pedagogical breakdown in assessment than a technological artifact.
Our current system of classes deposits 20, 30 or more students in a class with varying depth and breadth of subject matter knowledge; diverse skill levels with analytical and synthesizing tools; and uneven critical and creative abilities. One faculty member teaching several classes in a semester cannot easily guide the diverse groups of students to success, since that would require a more personalized mode of instruction that is responsive to the differing needs of each student. The current model is loosely based on an assembly line of pouring information, knowledge, and skills into the brains of students as they move together at a rigid calendar-driven pace. The hope is that they all will achieve wisdom. Yet, without adapting to the varying needs of the individual students, a significant number are almost always left behind, receiving lower scores on assessments and “C”, “D” or worse at the end of the term.
Fortunately, we are now equipped by AI to effectively and efficiently implement Mastery Learning and supplant the age-old assembly line model with a framework designed to enable all students, over time, to achieve mastery of the desired learning outcomes. The current outmoded system carries the assumption that students can progress even in cases where their achievement is at a less than mastery level. Given the scaffolding nature of knowledge and skill building, we risk creating a flawed scaffold in many of our students who do not fully understand and cannot adequately apply all of the necessary principles, methods, skills and knowledge in a class or degree program. Some are graduated with a substandard grade point average lacking mastery of the topic and likely to see the scaffold of their learning fail them in their subsequent careers.
The Mastery Learning model instead ensures that students do not progress through the course without achieving mastery of each module.
Mastery learning (or, as it was initially called, "learning for mastery"; also known as "mastery-based learning") is an instructional strategy and educational philosophy, first formally proposed by Benjamin Bloom in 1968.[1] Mastery learning maintains that students must achieve a level of mastery (e.g., 90% on a knowledge test) in prerequisite knowledge before moving forward to learn subsequent information. If a student does not achieve mastery on the test, they are given additional support in learning and reviewing the information and then tested again. This cycle continues until the learner accomplishes mastery, and they may then move on to the next stage.
Using AI, we can continuously monitor each student’s progress through frequent formative assessments of the students. These assessments can be delivered via AI. If a student falls below the 90% (or whatever level is designated), AI can assess the “wrong” answers that were submitted in order to prescribe the best content and pedagogical model to employ in engaging the student. This is repeated as many times as necessary in order to achieve true mastery before moving to the next module with the infinitely patient AI program presenting materials in the best learning context for the individual learner. The supported scaffolding model ensures that students are not lost along the way. What varies is the time or calendar of completing all the modules and final assessment at the mastery level. The instructor monitors progress and intervenes with individual students as needed. Hence, in the Mastery Learning model, all students effectively earn an “A”, however, they do not uniformly complete each class in an 18-week semester.
Prior to AI, we lacked tools to efficiently assess and address the underlying shortcomings of individual students in each of the modules. Now, we can ensure that every student completing a class has mastered the material. However, some students may master the material in a month, others in six months. It seems to me that the time taken for mastery is well spent, rather than an incomplete or erroneous understanding of the course content. You may want to check out more resources on this topic:
Khan Academy on Mastery Learning https://youtu.be/GWa48XRnLh0?si=dkD8n_Qqs19nFNxF
Teach for Mastery Learning, Not Test Scores - Sal Khan https://www.youtube.com/watch?v=-MTRxRO5SRA
4 Design Principles for Introducing Mastery Learning, Eric Hutson, Medium.com https://medium.com/globalonlineacademy/4-design-principles-for-introducing-mastery-learning-e3887e04bc4c
How to Set up Mastery-Based Grading in Your Classroom, Kareem Farah, Cult of Pedagogy https://www.cultofpedagogy.com/mastery-based-grading/
At last, perhaps the Bell Curve will come to John Donne’s conclusion: “Ask Not for Whom the Bell Tolls… It tolls for thee.”
Online: Trending Now: https://www.insidehighered.com/opinion/blogs/online-trending-now
Link to Unanticipated presentation: https://sites.google.com/view/unanticipated/home/
Link to Ray's EduAI Advisor: https://chatgpt.com/g/g-pLDOh2PHk-ray-s-eduai-advisor
Link to Ray Schroeder Brief CV https://bit.ly/44ZHlu9