Co-teaching brings powerful benefits to inclusive classrooms: two professionals with complementary expertise, more eyes on students, and richer instructional options. But when it comes to behavior data collection, that second set of eyes can create confusion. Who tracks what? How do you avoid duplicate entries? What happens when both teachers observe the same incident differently? This guide gives co-teaching teams a practical framework for dividing data responsibilities so nothing falls through the cracks.
The Co-Teaching Data Challenge
Research shows that data reliability drops when multiple observers record the same behavior without clear protocols. Either both teachers log the same incident (inflating counts) or each assumes the other is tracking (missing data entirely). The solution is not less collaboration. It is clearer ownership.
Understanding Co-Teaching Models and Data Implications
Different co-teaching models create different data collection opportunities. Your model for any given lesson should inform who tracks behavior data during that time.
| Model | Description | Recommended Data Owner |
|---|---|---|
| One Teach, One Observe | One teacher leads instruction while the other observes | Observing teacher tracks all behavior data |
| One Teach, One Assist | One leads while the other circulates and provides support | Assisting teacher tracks target students they support directly |
| Station Teaching | Students rotate through teacher-led stations | Each teacher tracks students at their station |
| Parallel Teaching | Class split in half, both teachers teach same content | Each teacher tracks their half of the class |
| Alternative Teaching | One works with small group, other with large group | Small group teacher often tracks target students |
| Team Teaching | Both teachers share instruction equally | Pre-assign specific students or behaviors to each teacher |
Key Principle: Proximity Determines Ownership
In most models, the teacher physically closest to the target student should own their data for that period. This ensures the observer sees antecedents, subtle behaviors, and consequences that a teacher across the room might miss.
Creating Your Shared Responsibility Matrix
A shared responsibility matrix eliminates daily guesswork. It documents who tracks what, when, and how. Here is how to build one:
1 Option A: Divide by Student
Assign specific students to each co-teacher. Teacher A owns data for Marcus, Jayden, and Aaliyah. Teacher B owns data for Emma, Carlos, and Destiny.
- Pros: Clear ownership, deep knowledge of individual students
- Cons: May miss data if assigned teacher is absent or occupied
2 Option B: Divide by Time Block
Alternate data collection by period. Teacher A tracks all target behaviors during periods 1, 3, and 5. Teacher B covers periods 2, 4, and 6.
- Pros: Fair workload distribution, natural backup coverage
- Cons: Less continuity for individual student patterns
3 Option C: Divide by Behavior Type
Teacher A tracks all instances of the target behavior (verbal disruptions). Teacher B tracks all instances of the replacement behavior (hand raising).
- Pros: Specialized focus, less cognitive load per teacher
- Cons: Requires coordination to link target and replacement data
4 Option D: Divide by Data Type
Teacher A does quick frequency counts (tallies). Teacher B captures detailed ABC narratives for significant incidents.
- Pros: Plays to different strengths, provides both breadth and depth
- Cons: Requires end-of-day alignment to connect counts with context
Sample Shared Responsibility Matrix
Target Students: Marcus (BIP), Emma (monitoring phase)
Ms. Rodriguez (Special Ed): All ABC data for Marcus; frequency tallies during small group instruction; owns IEP progress documentation
Mr. Chen (General Ed): Frequency tallies during whole-group instruction; all data for Emma; owns daily summary to parents
Communication Protocols for Co-Teachers
Even with clear data ownership, co-teachers need communication systems to stay aligned. Here are essential protocols:
Before students arrive, align on three questions:
- Which teaching model are we using each period today?
- Any setting events to watch for? (rough morning, missed medication, etc.)
- Are we adjusting data collection focus based on yesterday?
Develop nonverbal signals for real-time coordination:
- Tap clipboard twice: "I logged this incident, you do not need to"
- Hold up one finger: "Can you take over data for a moment?"
- Subtle thumbs up: "Replacement behavior just happened, did you catch it?"
- Point to watch: "Note the time, this is significant"
Before leaving, share and align:
- Total counts for target behaviors (do they align with your observations?)
- Any significant incidents that need ABC documentation completed
- Emerging patterns: "Did you notice Marcus escalated twice right before lunch?"
- Adjustments needed for tomorrow
When to Split vs. Share Data Collection
Not every data collection task needs to be split. Some situations call for both teachers to observe and compare. Here is a decision framework:
| Situation | Split Data Duties | Both Observe (IOA) |
|---|---|---|
| Daily routine data collection | Yes | No |
| New BIP first two weeks | No | Yes, for reliability |
| Behavior with unclear definition | No | Yes, then calibrate |
| Data collection for IEP progress report | Yes | No |
| Preparing for FBA team meeting | Partially | Yes, for key periods |
| Training new co-teacher | No | Yes, then compare and coach |
What Is IOA?
Interobserver Agreement (IOA) is when two people independently observe the same behavior and compare results. It is a research standard for data reliability. In schools, it is most useful when establishing baselines, calibrating new data collectors, or verifying that a behavior definition is clear enough.
Tools for Real-Time Coordination
The right tools make co-teaching data collection seamless. Here are options that work:
A behavior tracking app like Classroom Pulse that syncs in real time. Both teachers see the same data, and the system prevents duplicate entries.
- Real-time sync across devices
- Automatic timestamps
- One source of truth for the team
A simple, free option. Create a shared sheet with columns for time, behavior, observer initials, and notes. Both teachers have edit access.
- Easy to customize
- Accessible from any device
- Requires manual deduplication
Each teacher has a clipboard with the same data sheet. At day end, one teacher transfers data to the master record while the other verifies.
- No tech required
- Works during outages
- Requires daily consolidation time
Free counter apps on phones that can be shared or synced. One tap per incident. Perfect for frequency counts during instruction.
- Minimal disruption to teaching
- Easy to use while circulating
- Limited context capture
Handling Disagreements About What You Observed
Sometimes co-teachers see the same incident differently. This is normal and actually useful. Here is how to handle it:
Use It as a Calibration Opportunity
If you disagree about whether something counted as the target behavior, revisit the operational definition together. Refine it until you both would code the same incident the same way.
Default to the Closer Observer
When in doubt, trust the teacher who was physically closer to the student. They had a better view of the antecedent and the behavior itself.
Document Both Perspectives for Major Incidents
For significant events, capture both viewpoints in the notes. "Teacher A observed X; Teacher B noted Y." This adds context for later analysis.
Never Argue in Front of the Student
Save data discussions for planning time. Students should not see adults disagreeing about whether their behavior "counted."
Sample Week: Putting It All Together
Here is how a co-teaching pair might apply these principles across a typical week:
Monday
Morning huddle: Review responsibility matrix. Ms. Lee takes Marcus data during math (station teaching). Mr. Patel takes Marcus data during ELA (one teach, one assist). Both log to shared Classroom Pulse account.
Tuesday
BCBA requested IOA data. Both teachers independently track Marcus during period 3. End-of-day sync: compare counts, discuss one incident coded differently, clarify definition of "verbal disruption."
Wednesday
Mr. Patel absent. Ms. Lee covers all data collection. Leaves detailed notes for Mr. Patel in shared doc. Notices spike in behavior right before lunch.
Thursday
Morning huddle: Ms. Lee shares lunch pattern observation. They adjust: Mr. Patel will track ABC data specifically during the 10 minutes before lunch to investigate.
Friday
End-of-week review: Data suggests Marcus is avoiding cafeteria transitions. Team decides to share finding with BCBA at next week's meeting. Ms. Lee drafts summary; Mr. Patel reviews.
Common Pitfalls and How to Avoid Them
Without clear ownership, both teachers may assume the other is logging, resulting in missing data.
Fix: Document who owns what on your shared responsibility matrix. Review it every Monday.
Without signals, both teachers log the same behavior, inflating frequency counts.
Fix: Use nonverbal signals. The first teacher to log taps their clipboard twice.
Busy days mean end-of-day syncs get skipped. Data drift accumulates.
Fix: Protect 5 minutes. Put it in the schedule. Even a quick hallway check-in helps.
Teacher A counts eye-rolling as defiance; Teacher B does not. Data becomes unreliable.
Fix: Do periodic IOA checks. If agreement drops below 80%, recalibrate the definition.
Your Next Step
Effective co-teaching data collection starts with one conversation. This week:
Today: Schedule a 15-minute planning session with your co-teacher.
This week: Draft your shared responsibility matrix based on your current teaching models.
Next week: Pilot the matrix and nonverbal signals. Adjust after three days.
The Collaboration Advantage
Co-teaching done well gives students the best of two professionals. When data collection is coordinated, it gives behavior teams the complete picture they need to support every student effectively. The extra planning time is worth it.
References
Briesch, A. M., Chafouleas, S. M., & Riley-Tillman, T. C. (2016). Direct behavior rating: Linking assessment, communication, and intervention. Guilford Press.
Chafouleas, S. M., Kilgus, S. P., Riley-Tillman, T. C., Jaffery, R., Christ, T. J., Briesch, A. M., Chanese, J. A. M., & Kalymon, K. M. (2013). An evaluation of the generalizability of direct behavior rating single-item scales to measure academic engagement across raters and observations. School Psychology Review, 42(4), 407–421.
Volpe, R. J., & Briesch, A. M. (2012). Generalizability and dependability of single-item and multiple-item direct behavior rating scales for engagement and disruptive behavior. School Psychology Review, 41(3), 246–261.
Smith, T. E., Thompson, A. M., & Maynard, B. R. (2022). Self-management interventions for reducing challenging behaviors among school-age students: A systematic review. Campbell Systematic Reviews, 18(1), e1223. https://doi.org/10.1002/cl2.1223
Ruble, L. A., McGrew, J. H., Wong, W. H., & Missall, K. N. (2018). Special education teachers' perceptions and intentions toward data collection. Journal of Early Intervention, 40(2), 177–191. https://doi.org/10.1177/1053815118771391
Simonsen, B., Fairbanks, S., Briesch, A., Myers, D., & Sugai, G. (2008). Evidence-based practices in classroom management: Considerations for research to practice. Education and Treatment of Children, 31(3), 351–380. https://doi.org/10.1353/etc.0.0007
Stormont, M., Reinke, W. M., Newcomer, L., Marchese, D., & Lewis, C. (2015). Coaching teachers’ use of social behavior interventions to improve children’s outcomes: A review of the literature. Journal of Positive Behavior Interventions, 17(2), 69–82. https://doi.org/10.1177/1098300714550657
Carr, E. G., Dunlap, G., Horner, R. H., Koegel, R. L., Turnbull, A. P., Sailor, W., Anderson, J. L., Albin, R. W., Koegel, L. K., & Fox, L. (2002). Positive behavior support: Evolution of an applied science. Journal of Positive Behavior Interventions, 4(1), 4–16. https://doi.org/10.1177/109830070200400102
Sugai, G., & Horner, R. H. (2020). Sustaining and scaling positive behavioral interventions and supports: Implementation drivers, outcomes, and considerations. Exceptional Children, 86(2), 120–136. https://doi.org/10.1177/0014402919855331
Kearns, D. M., Feinberg, N. J., & Anderson, L. J. (2021). Implementation of data-based decision-making: Linking research from the special series to practice. Journal of Learning Disabilities, 54(5), 365–372. https://doi.org/10.1177/00222194211032403
U.S. Department of Education, Privacy Technical Assistance Center. (2015). Data governance checklist. https://studentprivacy.ed.gov/resources/data-governance-checklist
U.S. Department of Education. (2021). FERPA general guidance for parents and eligible students. https://studentprivacy.ed.gov/
Take Action
Put what you've learned into practice with these resources.
Key Takeaways
- Clear data ownership prevents duplicate entries and missed observations in co-taught classrooms
- Use a shared responsibility matrix to assign specific behaviors, times, or students to each co-teacher
- Brief daily huddles (2-3 minutes) align priorities and catch emerging patterns early
- Split data duties by strength: one tracks frequency while the other captures ABC context
- Digital tools with real-time sync eliminate end-of-day reconciliation headaches
Tags:
Ready to Transform Your Classroom?
See how Classroom Pulse can help you streamline behavior data collection and support student outcomes.
Try Classroom Pulse FreeFree for up to 3 students • No credit card required
About the Author
The Classroom Pulse Team consists of former Special Education Teachers and BCBAs who are passionate about leveraging technology to reduce teacher burnout and improve student outcomes.
Related Articles
Monthly Behavior Data Reviews: A Principal's Guide
A comprehensive guide for school administrators on conducting effective monthly behavior data reviews. Learn what data to pull, key questions to ask, red flags to watch for, and how to translate insights into action items that improve campus culture.
5 Ways Paraprofessionals Can Improve Behavior Data Quality
Practical tips for paraprofessionals to collect more accurate, useful behavior data. Learn how clear definitions, quick codes, context notes, communication, and consistency make your observations invaluable to the team.
Data Collection for Paras: What to Track and How
A practical, jargon-free guide for paraprofessionals on collecting behavior data. Learn what to track, how to use simple tally systems, ABC recording, and tips for communicating with your lead teacher.