Walk into almost any secondary classroom today and you’ll find a familiar scene: students facing a projected slide deck, copying line-by-line whatever appears on the screen. At first glance, this looks like learning. Students are busy. Pens are moving. Paper is filling up with information. And as teachers, we feel good—after all, students now “have the notes.”

But having notes and learning from notes are two very different things.

For many students, traditional note-taking—especially transcription-based note-taking—creates only the illusion of learning. It feels productive but offers little long-term impact on memory or understanding. And for years, cognitive science has been remarkably consistent about this: passive note-taking produces weak learning gains, while active retrieval produces durable learning gains.

In this post, I want to unpack why copying notes from slides is so ineffective, why it often leads to inflated confidence, and what we can replace it with if we’re serious about strengthening memory, deepening understanding, and supporting long-term retention.

I’ll reference research throughout, including ideas from my presentation 10 Ways We Get Retrieval Wrong, which you can explore on your own.

The Note-Taking Problem: Copying ≠ Thinking

The core issue with transcription-based note-taking is simple: it requires almost no cognitive effort.

Students don’t have to transform, organize, evaluate, or connect information—they simply transfer text from Point A (the slide) to Point B (their notebook). This is a fundamentally passive act, and passive acts build weak memories. It also teaches students that passively writing notes – or passively reading notes over and over will help increase performance.

Shallow Processing Leads to Fragile Learning

In my 10 Ways We Get Retrieval Wrong presentation, I name this problem directly: shallow processing results in “low cognitive effort” and “weak neural connections.”

This aligns with decades of cognitive psychology research. When the brain isn’t working hard, it doesn’t encode material robustly. Memories built through low effort fade quickly. They remain fragile, susceptible to forgetting. Next time you are visiting some place you’ve never been, resist the urge to enter the destination in your GPS and listen to turn-by-turn directions to navigate you there. By reviewing a map, intently watching for landmarks and street names, making the wrong turn and recovering from that mistake, you’ll immediately find you remember the directions much more than you would if you listened to turn-by-turn directions to navigate you there. The cognitive demand required to manually navigate strengthens new neurons and makes them more resistant.

The Fluency Illusion: Feeling Like We Know Something When We Don’t

Transcribing notes also creates what Robert Bjork calls the fluency illusion—the tendency to mistake familiarity for understanding. When students see the same information repeatedly (on slides, in their notebooks), the content feels familiar. That familiarity can produce a sense of mastery.

In reality, nothing durable has been stored.

Fluency ≠ understanding. Recognition ≠ recall.

This is why many students are confused when they read their notes over and over but still perform poorly on their assessment.

How Students Typically Study Notes—and Why It Doesn’t Work

Consider what many middle and high school students actually do when preparing for a test. I see this repeatedly, and I’ve watched it with my own 8th grader: students sit down, open their notes, and simply read them over and over again.

They believe that rereading equals studying.
They believe that exposure equals memory.
They believe that familiarity equals readiness.

But rereading is one of the least effective study strategies available. In a landmark study, Roediger & Karpicke (2006) found that students who reread learned far less than students who retrieved—even if the rereaders felt more confident. Students need to know that rereading isn’t the same as testing yourself. There is zero retrieval involved in rereading notes.

Rereading improves retrieval strength temporarily—students can recall information in the moment because it’s fresh—but it does little to build storage strength, which determines whether students will remember the information days or weeks later.

This also connects directly to Bjork’s distinction between retrieval strength (short-term accessibility) and storage strength (long-term durability). Notes that are only reread do not increase storage strength at all.

Rereading notes is like going to the gym, watching everyone else work out, and expecting to get stronger.

You’re near the action, but the work isn’t being done by you.

What Actually Builds Memory? Effort + Retrieval

Durable learning is the result of effortful thinking. When the brain has to struggle—just a bit—to retrieve or reconstruct information, the memory trace becomes stronger.

Decades of research support this:

• Effortful retrieval strengthens both retrieval strength and storage strength (Bjork, 1994).

• The act of pulling information out of memory is what consolidates it (Roediger & Butler, 2011).

• Generation effects show we remember more when we generate information ourselves rather than receive it (Slamecka & Graf, 1978).

Simply put:

The brain remembers what it works on, not what it copies.

Why Student-Created Notes Outperform Teacher-Provided Notes

Now, this does not mean that notes have no value. Notes can be powerful—but only when students are creating, transforming, or organizing ideas themselves. This has to be explicitly taught to students.

Student-Created Notes Require Cognitive Demand

When students must:

• summarize information in their own words,

• make decisions about what is important,

• connect new ideas to prior learning,

• sketch, map, or represent relationships,

they are engaging in deeper processing—what Craik & Lockhart called levels of processing theory. This kind of note-taking is cognitively demanding and therefore memory-strengthening.

Student-Created Notes Activate Retrieval Systems

Creating notes forces students to:

• pull from memory,

• articulate meaning,

• reconstruct concepts,

—all of which strengthen learning.

These are note-taking strategies, too—just not the traditional, passive kind.

Three High-Impact Alternatives to Traditional Note-Taking

Below are three research-based strategies that outperform transcription and align with what we know about retrieval, memory, and long-term retention.

✅ Brain Dumps (Free Recall)

What it is:
Students close their notes and write down everything they remember about a topic, concept, or lesson.

Why it works:

• It forces retrieval—the strongest known strategy for improving long-term learning.

• It surfaces misconceptions.

• It increases both retrieval strength and storage strength.

Research Base:
Roediger & Karpicke (2006) demonstrated that retrieval practice significantly outperforms rereading in long-term retention.

How teachers can use it:

• Begin class with a 2–3 minute brain dump on yesterday’s lesson.

• After a unit, ask students to brain-dump everything they know about the topic before reviewing.

• Pair students to compare and add missing ideas.

This single strategy outperforms hours of rereading.

✅ Student-Generated Questions

What it is:
Students create test questions, quiz questions, or “What might the teacher ask?” questions about the content.

Why it works:
Creating questions requires deep sensemaking. Students must understand the content well enough to identify what is important and how to ask about it.

How teachers can use it:

• Have students create three multiple-choice and two short-answer questions after a lesson.

• Build a class Kahoot, Wayground (Quizizz), or Gimkit from student-generated items.

• Use student questions in warm-ups or exit tickets.

Question-generation turns passive receivers into active thinkers.

✅ Concept Mapping or Schematic Notes

What it is:
Students transform notes into a diagram, map, or visual representation of how ideas connect.

Why it works:
Concept mapping forces students to organize and structure information—deep processing that strengthens memory pathways.

How teachers can use it:

• Replace “Copy this definition” with “Create a diagram showing how these three ideas connect.”

• Have students turn a page of notes into a visual map at the end of class.

• Use concept maps as a warm-up to reactivate prior knowledge.

Concept mapping transforms information into understanding.

Small Shifts Teachers Can Make Tomorrow

You don’t have to overhaul your classroom to make note-taking more meaningful. Here are a few small, high-leverage shifts:

• Don’t put full sentences or complete explanations on slides.
  This forces students to think, not copy.

• Use retrieval before teaching.
  Ask, “What do you already know about…?” before showing anything.

• Replace “Copy this down” with “Summarize this in your own words.”

• Stop assuming students know how to study.
  Teach them how to retrieve, not reread.

• Integrate short, low-stakes retrieval every day to strengthen memory gradually rather than cram before assessments.

These subtle changes dramatically increase cognitive demand, memory strength, and comprehension.

Closing: Moving From Teaching Notes to Teaching Learning

Note-taking, as it is traditionally done in many classrooms, offers little return on investment. It feels safe, predictable, and efficient—but it leaves students with fragile memories, inflated confidence, and minimal long-term retention.

The goal of schooling is not to produce pages of well-organized notebooks.
It’s to build thinkers with durable, flexible knowledge.

When we shift from passive transcription to active retrieval, we honor the way the brain actually learns. We help students strengthen the neural pathways required for recall. We equip them with study strategies that will serve them far beyond our classrooms.

And we give them something more valuable than beautiful notes—we give them the ability to remember, understand, and apply what they’ve learned.

Teachers often lament a lack of student motivation—but too often, we overlook how our own systems and classroom practices quietly crush it. The truth is, students want to succeed. But the structures we build either fuel or frustrate that desire. As Rick Stiggins reminds us, “You can destroy or enhance a student’s motivation to succeed more quickly and more permanently through the use of assessment than with any other tools you have at your disposal.”

Here are ten common teacher practices that unintentionally destroy student motivation—and what we can do instead.

1. Not Allowing Retakes or Redos

Rick Wormeli calls this “the classic punishment for learning at a different rate.” When we deny students the opportunity to try again, we send a message that the timing of their learning matters more than the learning itself. Mastery learning requires flexibility. As Wormeli writes, “Students can learn without grades, but they can’t learn without feedback.” Retakes are not about leniency—they’re about aligning our grading with how real learning happens.

2. Only Allowing Retakes Below a Certain Score

Few things scream “grades over growth” louder than policies that say students with a 69 can retake but those with a 70 cannot. This arbitrary cutoff perpetuates point-chasing instead of learning. Douglas Reeves points out, “If the grade doesn’t accurately communicate what students know and can do, it has no value.” When we treat assessment as communication rather than currency, the obsession with thresholds disappears.

3. Withholding Student Access to Their Assessments

When students never see what they missed or how they performed, the opportunity for reflection dies. It’s like practicing basketball without ever seeing if the ball went through the hoop. Assessment without feedback is just judgment. We must normalize students examining their work, identifying patterns, and setting next steps. Carol Ann Tomlinson notes, “Assessment is a photograph; learning is a movie.” Let students see the footage.

4. Teaching in Ambiguity

Ambiguity is the enemy of motivation. When students don’t know what success looks like—or even what they’re supposed to be learning—they disengage. We should strive for clarity around learning goals and progress. Every student should be able to answer two questions at any point in your class:

1. What am I supposed to be learning?

2. How close am I to mastering it?

When students can visualize the target, they aim higher. If students can’t picture success, they’ll stop striving for it. Exemplars, rubrics, and well-aligned learning objectives help students see mastery as attainable. As John Hattie’s research on feedback confirms, clear success criteria are among the strongest influences on student achievement. When we demystify excellence, motivation follows.

5. Grading for Completion

Grading for completion might seem like a harmless way to encourage responsibility, but it sends all the wrong signals to our learners. When students earn full credit simply for turning something in, we reinforce the currency of compliance—not the value of accuracy or understanding.

This practice unintentionally teaches students that effort, not evidence, is what counts. The result? A false sense of mastery that often crumbles when they face a summative assessment on the same concepts.

As Douglas Reeves reminds us, “Grades should reflect evidence of learning, not the mechanics of task management.” Completion grades do the opposite—they communicate that getting it done is more important than getting it right.

When students realize that correctness, depth, and understanding don’t actually influence their grades, their motivation to learn for learning’s sake evaporates. Instead of grading for completion, we can give feedback for effort and assign grades for evidence—a subtle but powerful shift that keeps motivation rooted in growth, not compliance.

I’m a math teacher, so I can empathize with teachers who resort to grading for completion because they don’t know how to give actionable feedback on student work. There are so many digital tools that can allow students to practice while also evaluating proficiency and offering feedback. We need to lean into those opportunities so the burden of feedback, particularly on low-level verbs like solve, calculate, and simplify, don’t rest exclusively on our shoulders.

6. Grading Everything

When every assignment “counts,” learning becomes exhausting. Grading every practice task communicates that performance is more important than growth. It sends a signal to students to just get the points no matter what – cheat, copy, plagiarize, or use AI. Instead, protect practice. Give feedback without grades. As Wormeli reminds us, “The purpose of practice is to improve performance, not to be judged.” Students need room to make mistakes safely before it matters.

7. Averaging Grades Over Time

Averaging punishes growth. Imagine if your doctor averaged your blood pressure readings from the last six months instead of looking at your most recent ones. Learning is developmental. When students demonstrate mastery, earlier failures should no longer define them. Reeves emphasizes, “The most accurate grade reflects the most recent and consistent evidence of learning.”

8. Treating Compliance as Learning

Completing worksheets, showing up on time, completing bell ringers,  or turning in homework doesn’t necessarily mean learning occurred. Yet many grading systems reward compliance over competence. When students see that effort, obedience, or neatness matter more than understanding, motivation evaporates. Grades should reflect evidence of learning—not work habits or personality traits. Today’s grades are often a messy soup of academic achievement mixed with attendance, punctuality, smiled at the teacher, brought in tissue boxes, and turned in my bellringers every Friday. No one knows exactly what the grade means. Disaggregating behavior and academic achievement actually make both more important and allows us to communicate growth separately.

9. Treating Feedback as a Grade Instead of a Conversation

Completing worksheets, showing up on time, or turning in bell ringers doesn’t necessarily mean learning occurred. Yet many grading systems reward compliance over competence. When students see that effort, obedience, or neatness matter more than understanding, motivation evaporates.

Too often, today’s grades are a messy soup of academic achievement mixed with attendance, punctuality, behavior, participation, and who smiled at the teacher that week. No one—students, parents, or teachers—really knows what the grade means.

As Douglas Reeves argues, “Grades should be pure measures of academic performance, not a blend of conduct and achievement.” When we disaggregate behavior from academics, we actually make both more meaningful—and we communicate progress with far more clarity.

10. Ignoring Student Voice and Ownership

When students have no voice in how they learn or demonstrate understanding, motivation becomes external. Choice fuels ownership. As assessment expert Susan Brookhart notes, “Students are more motivated when they see themselves as active partners in assessment, not passive subjects of it.” Whether it’s co-creating rubrics, creating assessment questions, choosing how to show mastery, or reflecting on feedback, voice transforms compliance into commitment. If you can’t 1:1 conference with each of your students after your assessments, then you’re doing it wrong. Implement testing windows instead of testing days. Give shorter, more frequent assessments, rather than long assessments that happen 1 or 2 times a quarter. If you’re doing it right, students should be able to assess their own progress and pinpoint what they need to practice before attempting the retake. This requires a brief conversation.

Final Thought

Student motivation doesn’t vanish on its own—it’s eroded by systems that prioritize grades over growth, speed over mastery, and compliance over curiosity. Motivation thrives when students believe their effort matters, their progress counts, and their learning is seen. The reasons for a lack of motivation are likely hiding in plain sight.

When we talk about “data” in K–12 education, most of what we mean can be traced back to a simple event:
a student answers a question, and the system marks it right or wrong.

Legacy tools — like Curriculum Associates, Renaissance, and Imagine Learning — have built incredible infrastructures around that event. Their platforms host massive, standards-aligned item banks and can pinpoint whether a student can identify main ideas, infer meaning, or solve equations. The psychometrics are sound, the dashboards are clean, and the accountability demands are met.

But even when those multiple-choice items reach higher levels of Bloom’s taxonomy, the data they produce is still binary. It’s precision data within a narrow band — helpful for system-level reporting, but not a full picture of how students actually think, reason, and grow.

The Limits of Right and Wrong

There’s no denying the usefulness of those data points. Teachers need them. Districts need them. Policymakers depend on them.

Yet, they tell only one part of the story.
They tell us whether a student reached the answer, not how they got there.
They capture outcomes, not processes.

Imagine watching only the scoreboard of a basketball game without ever seeing the players move. You’d know the score — but you’d miss the teamwork, the decisions, the creativity, and the perseverance that define the game. That’s where we are with learning data.

The Emergence of AI-First Tools

Generative AI opens a new dimension.

Instead of asking students to select an answer, AI tools can ask them to explain one. Instead of marking a response right or wrong, AI can analyze reasoning, probe for justification, and generate follow-up questions that stretch understanding.

Platforms like SchoolAI, MagicSchool, Curipod, and Khanmigo are early examples of this shift. They’re not just generating worksheets or quizzes — they’re capturing thinking. They can identify patterns like:

• How students revise their ideas after feedback

• Whether they transfer learning to a new context

• How they express curiosity, empathy, or problem-solving

This is qualitative cognitive data — evidence of thought, not just performance.

A Broader Picture of the Learner

When AI tools capture reasoning patterns, they begin to paint a portrait rather than a scorecard. They can reveal tendencies: Is this learner analytical? Creative? Risk-averse? Collaborative? That’s data of a different kind — messy, contextual, and profoundly human.

For teachers, this offers a chance to teach responsively, not reactively. For students, it transforms assessment into conversation. And for systems, it’s an opportunity to align metrics with the skills that truly matter in a world driven by adaptability and insight.

Does This Reflect the World We Live In?

Completely. The modern workforce no longer rewards people who can simply recall facts — we have machines for that. It rewards those who can interpret, synthesize, and apply information in unpredictable contexts.

In other words, our world no longer grades you on what you know but on how you think with what you know.

Education should mirror that reality. Legacy systems measure mastery; AI systems can measure metacognition. Legacy data shows what’s visible; AI data can illuminate what’s invisible — curiosity, persistence, and reasoning.

The Cautionary Balance

Of course, this evolution comes with responsibility.
AI is only as good as the data and design behind it. If misused, it can introduce bias or overreach into spaces where human judgment should prevail. The goal is not to replace teacher insight but to enhance it — to give teachers better mirrors for seeing student thought.

The future of learning analytics shouldn’t be about collecting more data — it should be about collecting better data: data that honors the complexity of human thinking.

From Points to Patterns

If the last generation of educational tools gave us points of data, the next will give us patterns of thought. And that’s where the promise of AI in education truly begins: not in speeding up what we already do, but in helping us see learners — and learning itself — more completely.

Closing Reflection

AI in education isn’t just about efficiency; it’s about illumination. It can help us see what’s been hidden beneath the surface of a right answer for far too long — the human process of thinking, trying, revising, and imagining.

That’s the story worth telling, and the kind of data worth collecting.

For the last two years I’ve had the privilege of piloting dozens of SchoolAI Spaces with teachers across West Virginia. Each experience has convinced me that when AI is carefully choreographed, it frees up cognitive room for deeper analysis, reflection, and creativity — the very muscles we worry AI might atrophy.

Below is the blueprint we use to keep the human (the learner and the teacher) at the center.

1. Anchor AI to a Thinking Taxonomy

The SchoolAI Space Taxonomy for Teachers outlines four distinct thinking goals the bot can support — Access, Practice, Create, Simulate, and Transform — and pairs each with example Spaces such as Guided Research, Writing Coach, or Debate Opponent. When I co‑design a new Space, we start by naming the column we want to strengthen (What kind of thinking is missing right now?) and design AI prompts that scaffold that goal instead of shortcutting it.

Classroom snapshot: In a Grade 7 social‑studies unit on WWI, our Historical Scenario Space placed students in the War Cabinet. The bot surfaced primary‑source telegrams and asked, “What variables matter most for your next move?” Students had to evaluate, justify, and re‑evaluate as the scenario evolved. AI supplied the context, but students supplied the strategy.

2. Dial the “AI Power Spectrum” — Less Help Early, More Coaching Late

Page 2 of the taxonomy introduces an AI Power Spectrum — from Independent Inquiry (0 % AI) through Collaborative Author (100 % AI). Think of it like a dimmer switch:

1. Independent Inquiry – The bot only handles housekeeping (rubrics, resources).
2. Reflective Probing – Socratic nudges (“Why…?”, “How might…?”).
3. Scaffold & Hint – Sentence frames or organizers when students stall.
4. Co‑Ideator & Expert Coach – Brainstorm with targeted feedback, never stealing ownership.
5. Collaborative Author – Iterative revision cycles with tracked changes.

Early in a unit I keep the dial low, forcing productive struggle. As mastery grows (or a deadline looms), we slide the dial right, letting AI speed up cyclical feedback so we can spend class time on discussion, peer critique, and transfer.

3. Capture Creativity‑as‑Evidence

Traditional benchmarks rarely reward imagination, yet creativity is evidence of higher‑order thinking. When we build a Space around creative checkpoints, the AI pauses learners at purposeful moments—after a rough storyboard, a 15‑second melody, or three lines of code—and invites them to drop that artifact into the chat. In seconds the bot:

• Tags skills demonstrated (e.g., narrative pacing, melodic contour, or loop logic).
• Surfaces one gentle nudge (“What mood shift could make this scene more suspenseful?”).
• Logs the artifact on a teacher dashboard so growth is visible long before the final product.

Students still own the imaginative heavy‑lifting; AI simply turns each draft into actionable evidence we can respond to today, not weeks later. Here’s a sample Space that supports students as they create a comic strip. You can also check out this Space that guides students through the creation of their 30-second Film Festival project.

4. Treat AI as a Thought Partner, Not an Answer Key

The best prompt I ever wrote for a Space simply said:

“After welcoming the learner, resist every urge to answer‑dump. Your job is to keep them talking, questioning, and revising until they declare readiness.”

When AI responds as a coach, we hear more student voice, not less. Our “Recall Radar” retrieval Space proves this daily: the bot flashes subtle cues, then asks learners to rate their certainty before revealing anything. That metacognitive pause is where durable learning happens. See my series of Spaces that support writers as they create their argumentative essay. You might also like this Space that takes students on a time-traveling adventure back to the Roaring 20s. Give that a preview and you’ll see how AI acts as a thought-partner, probing students to think critically about their decisions.

5. Leave the Heavy Lifting to Humans

AI can:

• Parse 30 exit tickets in seconds and flag patterns.
• Generate four divergent examples on demand.
• Track revision history and spotlight growth areas.

But only teachers can decide when struggle is productive, which prompt nudges curiosity instead of compliance, and how to celebrate the messy middle of learning.

Call to Action

If you’re ready to shift from “Is AI cheating?” to “Is AI challenging my students enough?”, download the full Space Taxonomy (above) and try designing a single activity where AI’s role is question‑asker, not answer‑teller.

I’ve seen a lot of traction in education communities recently around the idea that the impact of Ai has been relatively small. The panic that rushed across the community in November ’22 seems to have subsided. And though there are some great time-saving ways to leverage Ai, education is still largely unchanged. I would agree.

This is the best way I can communicate why tools like SchoolAi represent such a big leap forward. I know the graphic is a bit general but it’s true. There is a feedback limitation on current tech tools like iReady, IXL, even Khan Academy (excluding KA’s Khanmigo), and that limitation is that while correct/incorrect information is helpful, it tells only a small fraction of the story of that learner. I still don’t know much about that learner’s perceptions, misconceptions, or their thought process. A savvy teacher will even recognize brilliance in a student’s wrong answer, and most of the adaptive platforms dominating education don’t position wrong answers in a way that allows a teacher to capitalize on them. SchoolAi, tuned chatbots, represent a paradigm shift in the actionable feedback provided to teachers.

And don’t be naive, as soon as iReady, IXL, or other MTSS tools can afford to leverage Ai, they will. In fact, if they don’t, they’ll go out of business. There won’t be a need for tools that have a large database of problems and questions. Ai will adjust complexity on-the-fly, per student. The detailed report sent back to the teacher will make current reports look silly.

Assessments in the future will likely be personalized to every learner, measuring knowledge, understanding, application, analysis, synthesis, and evaluation in a way that meets that learner where they are. Perhaps teachers are not distributing the same test to every student on test day. As we’ve seen thus far, progression will be different in each content area. Now that GPT-4.5 has vision, Ai will give actionable feedback on math work. Snorkl.ai, for example, is giving actionable feedback on student’s handwritten work and the student’s verbal explanation.

It’s important to remember that teachers will still have a critical role in the classroom moving forward. The movement towards learner-centered instructional models is certainly increasing.

I have noticed others in the education community signaling 2024 as the end of the “free era” of Ai tools for teachers. I believe they’re right, but I’m going to soak up the golden era of Ai for Education as long as possible. I’ve been quite fascinated with the direction of these tools and the role they may play moving forward. I recently discovered SchoolAi and I’ve had some amazing experiences with it so far. I was reflecting on one such experience and reminded myself that my compass needs to remain pointed at sound pedagogy and increasing the value of the teacher. I think it’s easy to lose our way in this landscape. The shiny new tool can be alluring, but I fear the time we’re saving can come at a cost. Don’t use a tool for the sake of using a tool. If the tool enhances the value of the teacher and contributes to sound instructional moves, then we should consider its place in our classrooms.

SchoolAi Teacher Dashboard

After a recent experience with SchoolAi, I came to the conclusion that Ai chatbots can provide an incredible opportunity to differentiate, promote deeper thinking, and evaluate learning at levels that previously required an immense amount of time. Perhaps my favorite part of SchoolAi are the live updating insights it provides the teacher. While the chatbot is simultaneously conversing with each student, it is also generating thoughtful insights, shedding light on student strengths and weaknesses, and conclusions it is making based on all the conversations. I was most impressed with the way the chatbot relentlessly probed student thinking with questions that encouraged the student to express their thinking in rich ways. In one experience, students were reviewing chapters 1-4 from The Hunger Games. The chatbot consistently asked students questions that required students to empathize with characters, connect events in the story to specific themes, and uncover additional themes of the story through plot details. These weren’t questions that could be asked in a multiple choice assessment.

I’ve since explored many other chatbots and applications in the classroom. I’ve determined that, when well designed, these experiences can be transformative. I’ll include some guidelines that I’ve come to value in my own experience creating these chatbots.

Is there a more divisive and combative conversation to have with a staff than the zero? Just ask the question, What should a student receive when they don’t submit their work? Let’s clarify, we’re talking about the zero on a traditional 100-point scale. For the purpose of this post, we’ll assume the traditional 10-point intervals in the 100-point scale. I am aware this varies wildly if you check schools, districts, or even states across the US.

I’ll begin by saying that many schools and districts have attempted to have this conversation, but some have back-pedaled when the explosion of deep emotions erupted across the school or district. The enemy here is not the zero. The enemy is the 100-point scale, and I’ll do my best to explain.

A zero on a 100-point scale is mathematically inequitable. The entire scale is too heavily weighted on the side of failure. When giving a 0, we actually give a student a score that is worse than failure.

K is for “kill grade”

If you look at the images above, you can see equal intervals between the other letter grades, but there’s this huge gap when we get to F. We could argue over what an A means, what a B means, or what a C means, but let’s hold that for another post. Whatever your descriptors are, F has to mean failure. In the traditional 100-point scale, it would appear there are degrees of failure. Take these humble descriptors as an example:

The zero has an undue deflationary effect on a student’s overall average, the same way the scale would have an undue inflationary effect if we flipped it.

I’ve never seen a teacher give a student a 140 on a test. I imagine they would look at me sideways if I asked them why. Of course, a 140 would be an inaccurate score that would inflate the average of the grade. The zero on a 100-point scale is just as inaccurate and just as deflating to that average score. Let’s do an experiment.

As you see above, the student received a zero and after 11 additional 85s in the gradebook, the student still had not raised the grade back to a B. This is an example of the hole a zero places students in, and it represents the deflationary effect a zero has on the average. The student who received a zero has little motivation moving forward because their grade has been falsified by the impact of the zero.

Let’s compare the effect of the zero versus establishing a floor of 50. Some schools or districts choose to use a 50 to represent missing work because the 50 maintains the equal intervals 100-90, 89-80, 79-70, 69-60, 59-50.

I appreciate the work of so many educators who influenced my thinking on this topic several years ago. Alexis Tamony created a wonderful YouTube video where she displays and discusses this very topic and I appreciate her influence on this post. Despite the evidence presented, I’m not naive. This is still a hard philosophical pill to swallow. I recommend schools and districts seriously consider moving to a scale where a zero makes sense. We use a 4-point scale to calculate GPA, for example. An A is worth a 4, B is a 3, C is a 2, D is a 1, and F is a 0. In this scale, educators could use zeroes that make sense. There are plenty of conversion charts out there if you feel the need to convert these to percentages. The use of percentages are primarily used to rank and sort students. Are there additional advantages to using a smaller scale? Yes! Inter-rater reliability increases dramatically when using a smaller scale. Think about it, can a human really discern learning to 101 different levels (0-100)? No. Can you really communicate the difference between a 78 and an 82?

There also seems to be this fear among some educators that if we establish a floor of 50, some students might do nothing until the end of the grading period where they turn in 2-3 assignments and suddenly they have a passing grade. Here’s an example:

The student had ten scores during the grading period and seven of those were a 50 for missing or deficient work. You see the student submitted three assignments and scored an 85 on those three, which has raised the average to a 60.5, barely a passing score (D) on the traditional 10-point intervals. This leads us to my final consideration. Measuring and communicating learning is very much a human act requiring professional judgment. Educators dismiss this act far too often by allowing computers and phony math to place the final declaration of learning on a student’s grade. As professionals, we should be using professional judgment anytime we place a grade. What would you do in this situation?