Replacing MCAs: Algorithms to Predict, Adjust 6-12 Scholars' Flukes, Wobbles, and Trends in Learning (Twin Cities specific)

Many educators and parents rightly argue that MCA assessments fail to capture the full brilliance and potential of each student. If these scores are unreliable measures of learning, then school and district leaders have a responsibility to adopt more precise, classroom-based systems for identifying student success and struggle. It is no longer acceptable to rely on outdated, one-size-fits-all tests. The time is now to leverage technology, specifically, adaptive algorithms, to monitor real-time progress, diagnose learning patterns, and ensure every student receives the support they need to succeed. Anything less is educational malpractice.

By Don Allen, Ed.S., M.A. Ed., MAT  - Journal of A Black Teacher (2025)

In the data-rich but insight-poor school systems of today, leaders of school districts need to adopt technology to monitor and assist student learning from grades 6 to 12. The stakes are too significant to depend on conventional grading or antiquated assessments such as the MCAs. Instead, we require real-time, behavior-aware tools that shed light on how and why students fail or succeed, not merely if they pass.

1. Learning Analytics Personalized

With education algorithms, like the Fluke-Trend-Wobble Analyzer (FTWA) outlined in your report, district leaders can translate raw student data into meaningful, student-friendly labels:

Trendency: Shows consistent increase or decrease.

Fluke: Identifies outlier performance that is not consistent with the pattern.

Wobble: Demonstrates inconsistent performance that necessitates coaching.

These insights go far beyond one-size-fits-all generic assessments. They deliver diagnostic power, offering teachers and leaders a nuanced picture of the learning path of every student.

2. Data-Driven Teaching and Coaching

When educators can see and read learning data, they transition from grading to guiding. A teacher who notices a "wobble" pattern knows to look into student engagement or stress. A "fluke" reminds the team to question if something out of the ordinary happened. A "negative trend" triggers scaffolding or intervention before failure takes hold.

This is the heart of good teaching: making timely adjustments based on objective evidence, not guesswork. Teachers are diagnosticians, not just dispensers of content.

3. Empowered Building Leadership

Leader-builders, deans, principals, and instructional coaches require tools that reveal to them more than GPA or standardized test scores. FTWA-driven dashboards allow them to:

• Monitor class- or grade-level trends in real time

• Target professional development to teachers on the basis of what their students really need

• Identify early warning signs of academic difficulty before they manifest in test scores

• This is proactive leadership, not damage control.

4. System-Level Accountability with Humanity

For district leaders, algorithmic tools make equity visible. They are able to:

• Determine which student groups wobble the most

• Uncover whether a school's interventions are assisting or obstructing

• Provide teachers with just-in-time coaching suggestions tailored to individual learners

Instead of boiling students down to numbers, these technologies contextualize performance by integrating grades, teacher feedback, socio-emotional data, and time-on-task patterns.

5. Equity and Innovation Go Hand-in-Hand

Black, Brown, and historically underserved students tend to be harmed most by delayed interventions and aggregated data. By transitioning to systems that identify flukes, trends, and wobbles, district leaders can intervene before failure becomes the norm and can support innovative practices that reach students where they are. 

Final Thought 

Technology, when used intelligently, allows education leaders to move from "rear-view" reporting to "real-time" responsiveness. By leveraging learning algorithms and visual dashboards, district leadership bridges the gap between assessment and action. Simply saying, "This student failed," is no longer adequate. We must ask—and answer—Why, and what are we doing about it? This is not just innovation. It's instructional justice.

Let’s Walk Through It…

To define flukes, Trends, and wobbles in student learning through an educational algorithm, we need a system that tracks learning behavior over time, identifies anomalies, patterns, and inconsistencies, and then classifies them accordingly. Here's a proposed algorithm structure using machine learning and instructional analysis principles:

 Conceptual Definitions (for the algorithm)

•  Fluke: A one-time unexpected success or failure not consistent with prior or subsequent performance (e.g., an A on a test when a student usually gets Cs).

•  Trend (short for Trendency): A consistent and observable pattern over time that shows a direction in learning (e.g., a student improving steadily in writing mechanics).

•  Wobble: An inconsistency in learning — not random like a fluke, but oscillating performance that lacks stability (e.g., alternating between high and low quiz scores despite regular study habits).

Algorithm: Fluke-Trend-Wobble Analyzer (FTWA)

 Step 1: Input Data Collection

 Collect multimodal data:

•    Scores (assignments, tests, projects)

•    Time on task and engagement (LMS logs, clickstreams)

•    Feedback from teachers (rubrics, narrative comments)

•    Self-assessments and reflections (student voice)

•    Socioemotional/contextual data (attendance, wellbeing surveys)

 Step 2: Normalize and Prepare Data

• Scale all numeric data to a standard metric (e.g., 0–100).

• Translate qualitative feedback into tagged categories using NLP (e.g., “shows growth” → progress Trendency).

• Create a rolling average window (e.g., last three assignments) and compare against lifetime performance in that domain.

Step 3: Define Thresholds and Behavior

A. Fluke Detection (Anomaly Identification)

This involves determining if a score or result significantly deviates from the norm—essentially, identifying a fluke.

Here's how it works:

1. Look at recent scores (like the last few test scores).
   
   

2. Find the average of those scores (add them up and divide by how many there are).
   
   

3. Calculate the standard deviation—this tells us how much the scores usually spread out from the average.
   
   

4. Compare the current score to the average:

Note: If the new score is more than two standard deviations higher or lower than usual, it gets flagged as a fluke (a weird or unexpected result).

Then ask:

Was there something different this time?

• Was the topic unusual?
  
  

• Was it a new type of test?
  
  

• Were you sick or distracted?
  
  

If yes, that might explain the weird score. If no, we still note it as an anomaly.

Example:

Let’s say your last 5 test scores were: 85, 88, 90, 87, and 89.

• The average is about 87.8.
  
  

• The standard deviation might be around 2.
  
  

Now, your latest test score is 75. That’s way lower than usual. If it’s more than two standard deviations below the average, we call it a fluke, and we try to figure out why.  Maybe it was a different type of test, or perhaps something happened that day. In short: We're using math to identify scores that significantly deviate from your usual, and then checking if there's a reason for it.

 B. Trend Identification (Trend Analysis)

 Use linear regression or moving average:

•   …if regression_slope > +X over Y time units:

      label = 'positive Trendency'

•   elif regression_slope < X:

      label = 'negative Trendency'

  ```

 Thresholds (X and Y) are set by domain and grade level expectations.

Trend Analysis explained…

This part is about noticing patterns in your scores over time, like figuring out if you're getting better, worse, or staying the same.

Here’s how it works:

We examine a line that aligns with the direction of your scores. That line is made using something called linear regression (a mathematical way of drawing a "best fit" line through your data points), or a moving average (which smooths out ups and downs to show the big picture).

Then we look at the slope of the line:

• If the line is going up, that means you're improving.
  We call this a “positive Trendency.”
  
  

• If the line is going down, that means your scores are dropping.
  We call this a “negative Trendency.”

What about X and Y?

• X = How steep the line has to be to count as a real trend. (How significant the improvement or drop is.)
  
  

• Y = How much time we’re looking at. (Like the last five tests or over 2 months.)
  
  

These numbers depend on:

• What subject or topic are you working on (that’s the domain)?
  
  

• What grade level are you in (expectations are different in 7th vs. 12th grade).

Example:

Let’s say your last six quiz scores were:

70 → 72 → 75 → 78 → 80 → 85

If we draw a line through these points, it’s going up—that’s a positive trend because you're steadily improving. If you had:

85 → 82 → 78 → 75 → 70 → 68

That line goes down, a negative trend, which could mean something's wrong and we need to check in.

Bottom line: We use math to track your progress and label it as getting better, worse, or stable, based on what’s expected for your class and grade.

 C. Wobble Detection (Inconsistency Pattern)

 Identify variance around the trend line:

 if variance around trendline > threshold and no overall trend:

      label = 'wobble'

 Apply a rolling window to detect back-and-forth patterns (e.g., 80 → 65 → 85 → 60 → 82).

Step 4: Label and Visualize

Now that we've looked at your scores, it's time to tag them (like putting a label on what we’re seeing) and show them in a way that’s easy to understand, like on a dashboard.

For each student and each learning target (like a skill or topic you're supposed to master):

We ask:
What kind of pattern is this?

Then we tag it as one of these:

🔵 Trend (We call it a “Tendency” or “Trendency”)

If your scores are going up or down steadily over time, it’s a trend.

• 🟢 Green Dot = A clear pattern — you’re getting better (growth) or slipping (decline)

🔴 Fluke

If you suddenly get a score that’s way different than usual (like a 40 when you normally get 85s), we call that a fluke.

• 🔴 Red Flag = Something strange happened. We might want to look into it — maybe you were sick or rushed.

🟡 Wobble

If your scores go up and down a lot, like 85 → 60 → 90 → 70, that’s a wobble.

• 🟡 Yellow Dot = You’re inconsistent. You probably know the material, but need help staying steady — a coach or teacher might step in here.

Visualization = Dashboard

Think of this like a report card with symbols, not just grades.
The dashboard shows green, yellow, or red next to your name or skill to quickly show how you’re doing:

Student	Reading Comprehension	Essay Writing	Grammar Usage
YOU 🧑🏽‍🎓	🟢 Trendency	🔴 Fluke	🟡 Wobble

Why it matters:

This helps a teacher quickly see where a scholar (6-12) is growing, where they need coaching, or if something unexpected happened. It’s like turning grades into a live progress report with color-coded signals.

Step 5: Generate Recommendations

Based on output:

•  Fluke → Verify conditions; no primary intervention unless repeated.

• Trend → Reinforce (positive) or intervene (negative) with scaffolded support.

• Wobble → Diagnose causes (e.g., engagement, misunderstanding, external stress), adjust pace or modality.

Optional: Learning Loop

Continuously feed back student performance and new data into the system to refine classifications using supervised learning models (e.g., decision trees or Bayesian networks).

Sample Use Case

Student A shows:

 55, 57, 56 → Negative Trend

 73, 90, 52 → Wobble

 60, 59, 94 → 94 = Fluke

Closing Thought

This algorithm allows educators to move from reactive grading to diagnostic, real-time understanding of student growth. With the FTWA in place, we can finally answer: Was that success a sign of mastery, luck, or confusion?