Take5 #129 It’s not (just) what you do – it’s the way that you do it

This #Take5 is brought to you from Robin Sulkosky and explores labour-based grading. Robin has been teaching language- and writing-related subjects for over a decade, and currently teaches in the First-Year Writing Program at Howard University in Washington, D.C. Robin’s pedagogical interests have focused sharply on the power of instructional design to improve learning across diverse instructional contexts. This post features two alternative grading systems to start conversation in the broader teaching community about where traditional systems might fail students. 

Given that Learning Development is not about ‘fixing’ the student – but could well be about ways of ‘fixing the system’ we thought you might be very interested in an alternative approach to assessment and grading. Perhaps this is something you already do – and you have a piece to offer JLDHE on just this topic? Or perhaps it is something that you might now lobby for within your own contexts? 

INTRODUCTION: IT’S ALL ABOUT ENGAGEMENT

I am a US-based higher education instructor who has been experimenting with alternative grading in my writing classes for the last several years. Despite numerous downstream adjustments to my scoring tools, I became dissatisfied and began to question the efficacy of the traditional grading scheme itself in my instructional context, which is as follows:

• Institution: US higher education, a historically Black college/ university (HBCU)
• Learners: freshmen ages 18 – 21, including international students
• Courses: general education, core graduation requirement, freshmen writing
• Outcomes: built from principles-based objectives, written by others (not by me)

This blog post is a record of how I began to move away from using points and weighted averages to derive letter grades, instead moving towards time-on-task and engagement. I judged my instructional context well suited for such a move. The philosophical assumptions that support this move can be summarized from Inoue (2022, pp. 201–204), many of which apply to my instructional context directly:

1. Student learning cannot be forced beyond knowable limits
2. Literacy practices are best learned through grading ecologies of “compassion, encouragement, and risk taking”
3. Individual student-writer success is connected to the success of their classmates, “all is one”
4. Students teach themselves while teachers can only facilitate
5. Good assessment assumes students want to learn and is not punitive when they do only the minimum

LABOR-BASED AND ENGAGEMENT-BASED GRADING

My alternative system is adapted from Asao Inoue’s labor-based grading scheme, itself inspired by Paulo Freire’s Pedagogy of the Oppressed (Inoue, 2022, 20–24). Inoue doubts that traditional grades have much meaning in the instructional context that I have described above, positing that grades by their very existence negatively impact the learning process by creating distracting external incentives that disrupt learning and discourage risk-taking in composition (144). In short, Inoue’s idea is to track and evaluate student time-on-task (i.e., student labor) as the basis for letter grades. Inoue provides an illustration of this grading ecology, linking it to his assumptions about learning in his writing classroom:

Figure 1 – Labor-based Grading Ecology

Note. Grades applied to student labor and student labor applied to the writing process (ecological parts). Quality of writing and writing products are incidental. From Labor-Based Grading Contracts (2nd ed., p. 201), by A. Inoue, 2022, WAC Clearinghouse.

Over the years, my adaptation of Inoue’s labor-based system has tilted somewhat away from Inoue’s vision. To contrast, my version of labor-based grading may perhaps better be referred to as engagement-based grading. Time-on-task (i.e., labor) is still a critical component, but my version de-emphasizes Inoue’s student-teacher negotiation of and creation of labor contracts; rather, mine favors less student input. Despite this key difference, the system is still designed instead to increase learner attention, motivation, and satisfaction, an inspiration I take from the  ARCS motivation theory:

Keller’s ARCS Model of motivation can be perceived as a problem solving approach to learning that instructional designers can use to develop even more engaging eLearning activities. 

Figure 2 – Engagement-Based Grading Ecology

Note. “Engagement-based grading” is a misnomer as labor is still its basis. In this adaptation, labor is divided into time and attention/ motivation, with the latter getting special attention through other elements of the course design.

SECRET SAUCE: ALTERNATIVE GRADING & INSTRUCTIONAL DESIGN 

Whatever the grading scheme, letter grades or grade-point scales must still be derived and reported. In trying to operationalize engagement and time-on-task to represent letter grades, instructional design best-practices have helped me tremendously. For example, I use backwards design to front objectives in the course-building process and build tests/ major assignments before any lessons. These large, complex assignments set a baseline for knowledge or skills that demonstrate what successful outcomes look like. Using task analysis techniques, I more clearly break down the techniques and knowledge that comprise the assessment. 

Example: In a writing course, learners should be able to understand the role of drafting and rhetorical principles in their writing process. A single assessment for these two goals is a multi-draft essay that requires rhetorical components to be adjusted across two or more drafts. But as every learning developer knows, this assessment also requires at least a dozen attenuated subtasks to be successful, such as analyzing the question, interpreting the task, planning activities like brainstorming, using strategic revising approaches, and so on.

Robert Mager (1997) has a wonderfully simple and effective set of task analysis techniques for the job of addressing complexity in assessments like this example (33–41). Such subdivision and hierarchy of prerequisite tasks is essential for creating smaller assignments whose sum achieves the objectives, but the real secret sauce for my grading scheme is that subdivision makes time-on-task more discrete and measurable. Then, to promote the engagement part of the equation, each small assignment is graded pass-fail and is not accepted late. To achieve a good grade, students must meet clearly defined minimum criteria in completing these fairly consistent short-work bursts.

By example we can again take the sample assignment introduced previously: a multi-draft essay that requires rhetorical components to be adjusted across two or more drafts.In such an assignment, the final draft may serve as the assessment in the traditionally graded course (“Your final research revision will count for 50% of your final grade.”). But with the engagement-focused adaptation of labor-based grading, the larger portions of the assignment are defined, divided, and subdivided; since each of these milestone assignments are necessary to produce a complex and effective writing product, they are equally weighted and all pass/fail. The essay revising assignment above might look as follows when subdivided:

1. Create prompts for a persuasive topic
2. Structure your answer to the prompts in an essay outline 
3. Draft a persuasive essay
4. Analyze a specific audience
5. Revise your persuasive essay for a specific audience
6. Understand the role of reflection in understand the writing process [Read a textbook excerpt]
7. Reflect on your revision

Each step is important and matters just as much as the rest. If a subtask is short, it counts less toward the weekly labor. But that weekly labor in total can still be measured out with a careful plan, and conceptual boundaries of each milestone are salient for students.

Overall,

• complexity is managed
• time is measurable
• subtask relationships to larger work are clarified
• a pace is established
• student attention is guided and sustained.

But my instructional context is one of many possible contexts. To what extent labor can be measured and assessments subdivided will certainly look different in your context.

STUDENT RESPONSES TO LABOR-BASED GRADING

By and large, student response is positive. There are of course a full spectrum of feelings on something so new and alien to them. From some past course evaluations, students provide feedback through

• positive thoughts: “I loved how specific the modules and daily assignments were.” and “The grading and assignment system was really helpful, and allowed me to get things done and learn, without the usual stress or pressures I feel in school.”
• bemused, critical questions: “In this grading system, what’s the incentive to do well?”
• and disappointment: “I would recommend the instructor to change his grading system from the labor based to the quality based because I never felt like I was challenged to improve my writing, and revisions under quality based grading would have challenged my writing more and led me to become a better writer.”

The final comment in particular has caught my immediate attention, so as I evaluate and iterate a new draft of my grading ecology, I am now considering simple differentiation in the form of a “challenge mode”: doing an assignment with challenging, but optional, set of minimum criteria, and providing some gamified reward for completion. The ecology’s emphases are always evaluated and changed iteratively semester to semester, in the spirit of best practice in instructional design.

ALTERNATIVE GRADING AND YOU

What does traditional grading look like in your instructional context? Does it capture what it is intended to capture? 

Further, and arguably more important, might it (along with your instructional objectives and strategies) obstruct student success in some ways?

References

• Inoue, A. (2022). Labor-based grading contracts: Building equity and inclusion in the compassionate writing classroom (2^nd ed.). WAC Clearinghouse.
• Mager, R. (1997).  Preparing instructional objectives (3^rd ed.). The Center for Effective Performance.

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