Rad N Bad: The Middle Ground of NET — Escaping the Babysitting Trap with Rigorous Natural Environment Teaching
Presented by Sean Yocum, MA, BCBA, LBA & Michael Carrero, BCBA, LBA
As the field has moved away from discrete-trial training, natural environment teaching (NET) has become a default, but it is not always implemented as teaching. In this episode, hosts Sean Yocum and Michael Carrero describe the 'babysitting trap,' in which child-led play without planned learning opportunities is presented as intensive, medically necessary intervention. They define rigorous NET as real-time manipulation of establishing operations, planned and contrived learning opportunities in natural contexts, rapid prompt fading, continuous differential reinforcement, and natural reinforcers functionally tied to the response, such as a request for a push on a swing producing the push. They discuss how token systems often fail to function as generalized conditioned reinforcers, and how NET's complexity makes skill deficits harder to detect than in DTT. They make the case that entry-level staff need extensive behavioral skills training and procedural fidelity monitoring to implement NET well. The hosts frame DTT and NET as complementary: DTT builds foundational mechanics, and NET makes skills functional. Participants will leave with a NET fidelity checklist.
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About this CEU
Natural environment teaching isn't sitting on a mat and following the child's lead. Done right, it's high-skill teaching: real-time EO manipulation, fast prompt fading, and natural reinforcers tied to the response. Mike and I lay out what rigorous NET looks like, and what it takes to train staff to do it. Disclosures & Publication Info No commercial products discussed. Both instructors are employed in ABA service delivery. Published: 2026-10-16 Listening to the podcast episode does not earn CEUs; credit is earned only by completing this OpenCEU event.
Abstract
As the field has moved away from discrete-trial training, natural environment teaching (NET) has become a default, but it is not always implemented as teaching. In this episode, hosts Sean Yocum and Michael Carrero describe the 'babysitting trap,' in which child-led play without planned learning opportunities is presented as intensive, medically necessary intervention. They define rigorous NET as real-time manipulation of establishing operations, planned and contrived learning opportunities in natural contexts, rapid prompt fading, continuous differential reinforcement, and natural reinforcers functionally tied to the response, such as a request for a push on a swing producing the push. They discuss how token systems often fail to function as generalized conditioned reinforcers, and how NET's complexity makes skill deficits harder to detect than in DTT. They make the case that entry-level staff need extensive behavioral skills training and procedural fidelity monitoring to implement NET well. The hosts frame DTT and NET as complementary: DTT builds foundational mechanics, and NET makes skills functional. Participants will leave with a NET fidelity checklist.
Credits
1.5 CEUs (1 learning, 0.5 supervision) · 80 min
From the talk
What was covered
Rigorous NET is not child led play: building motivation on purpose, natural reinforcers, trial density, staff training, and honest data.
- Plan each NET session on paper first: the target, the moment you will create, and the natural payoff.
- Build the want instead of waiting for it. Lock the container, hold the swing, or hand over a juice box with no straw.
- Tie the reward to the response. A request for a push should produce the push, before you reach for a token board.
- Test a token against a neutral item in a side by side choice before you call the board a reinforcer.
- Pick the measure when you write the goal: cold probes, first trial data, or requests per minute. Never fill a box from memory.
- Train staff with behavioral skills training and on floor coaching. Then check fidelity instead of relying on an online course alone.
The Babysitting Trap: When Play Gets Billed as Teaching
Sean Yocum opens with a blunt claim about where the field landed. Teams ran hard away from discrete trial training (teaching in short, planned trials). They ran straight into natural environment teaching (teaching inside real play and routines). The move itself was fine, but the teaching often did not follow it.
He paints a scene most of us have walked past. A technician sits on a foam mat and stares into the middle distance. The child watches wheels spin on a screen for the rest of the hour. No target gets run, and no real data gets taken. The session is still written up as intensive, medically needed care.
Mike Carrero answers the challenge with one plain line about long term outcomes. The question is whether you want to pad a wallet or pad skills and independence. That test runs through the whole episode. Real NET is not the soft option here. The hosts call it elite work, then spend an hour proving it.
The babysitting trap is not really about lazy people. It is about a setup where play looks like therapy and nobody checks the difference. Everything that follows is a way to check it.
That's the world's most aggressively overbuilt babysitting gig.
From the talk — Sean Yocum, MA, BCBA, LBA & Michael Carrero, BCBA, LBA
Engineering the Moment: How Rigorous NET Creates Learning Chances
Mike defines a motivating operation (what makes a reward matter now) in plain words. An establishing operation (a setup that raises value) makes a thing worth more right then. An abolishing operation (a setup that lowers value) drops it back down. Rigorous NET lives on that dial all session long. You teach when the want is hot, not when the schedule says so.
The hosts are blunt about waiting for a want to appear. Some clinicians stand there and hope motivation falls out of the sky. Mike says the moment is gone by the time you spot it. So you build the moment yourself. Lock the bubble container tight. Keep the swing two feet out of reach. Hand over the juice box with no straw. Give the train track with the bridge piece missing.
They also name the fake version of this work. Playing with farm toys while you drill names of trucks is not NET. That is DTT on the floor with a nicer label. Real teaching rides the activity the learner already chose. Mike makes the wrong animal sound on purpose so the child corrects him. No demand is placed, and the learning still happens.
Shoe tying is their favorite example of a skill taught in a bubble. Pulling a shoe off at a table to put it back on teaches very little. There is a trampoline that needs shoes off and a playground door that needs them on. Skip the giant parka and the random wooden practice shoe. Run the goal in the season and the place where it has to work. That builds generalization (using a skill in new places) from the first trial.
You set the forest on fire so communication is the only extinguisher they've got.
From the talk — Sean Yocum, MA, BCBA, LBA & Michael Carrero, BCBA, LBA
Natural Reinforcers Beat Token Boards That Were Never Conditioned
The third strength of NET is the payoff itself. In real NET the reward is tied to the response. You ask for the heavy door, and the door opens. You ask for a push, and you get the speed of the swing. The learner finds out that words, signs, and devices move the world.
Mike adds a shaping tool most teams skip. Change the size of the payoff to match the response. One word gets a small push. Three words send them into the solar system. That is differential reinforcement (better tries get a better payoff) running inside real play.
Now compare that with a token economy (chips traded later for a reward). Sean argues most boards never became generalized conditioned reinforcers (a reward that covers many things). Teams skip the pairing phase. They skip the free trades at zero effort. They skip the value test, where a token sits beside a paper clip to see which one the learner picks.
Mike pushes on the science behind the board. Few teams run a reinforcer check before they build one. Fewer train staff on when to use the board and when to drop it. He also asks why so many learners sit on the same fixed ratio. He asks the same about breaks, whether they run five minutes or fifteen. Find the point where the work per reward gets too heavy, then thin the schedule on purpose. A general education classroom will be far thinner than your room.
It's just a visual countdown timer for when the kid gets to escape the clinician.
From the talk — Sean Yocum, MA, BCBA, LBA & Michael Carrero, BCBA, LBA
The Trial Count Math: What Loose NET Costs a Learner in a Year
Now the swamp. Tight, well run DTT can produce 60 to 100 trials in a hundred minute block. Loose, unplanned NET can drop to four trials in a whole hour. Sean puts a year of math on that gap. A well run blend can hit 40 to 60 trials an hour, which stacks into tens of thousands of chances across a year.
Loose NET can land near five thousand chances in that same year. The hosts call that roughly an eighty five percent drop in trial density (teaching tries packed into an hour). Sean guesses half to three quarters of a session can go dead. Mike's guess runs higher, at eighty percent or more.
Mike then reframes the problem for the whole episode. The flaw sits in the way NET gets run, not in the design. A learner with a large language delay needs many reinforced repetitions. Casual play will not close that gap before kindergarten. Repetition only counts when the skill is usable outside the room.
He also names the pressure behind the numbers. Funders want clean counts on a page, since counts look like proof of need. So teams chase the count and skip the setting where the skill has to work. Flip that for mand goals (asking for what you want). A mand goal run well should produce a very high count, not ten in a day.
You cannot replace a high-volume clinical repetition with good intentions and expect the rate of skill acquisition to just magically stay the same.
From the talk — Sean Yocum, MA, BCBA, LBA & Michael Carrero, BCBA, LBA
Why Weak Skills Hide in NET and Show Up Fast in DTT
DTT is easy to hand to a new technician. The notes say what to present, how to prompt, and what to reward. A green tech can follow that and still look clean on video. NET asks for several hard things at once. You catch the shift in want, set a barrier, and fade the prompt fast (drop help as skill grows). You also have to pay the right try, all while holding a toy train.
Then look at what the field actually gives staff. A forty hour online course, a playroom rug, and a cheerful go. Mike refuses to put that on the technician. He does not think you need an expert to run NET. But you need one to teach it and keep it alive.
That means behavioral skills training (tell, show, practice, then coach) and repeated feedback. It also means checks on procedural fidelity (running the plan the right way). Mike compares DTT to following a cake recipe with the steps written out. NET is the cooking show where the ingredients change on you mid task. Staff freeze and snap back into demands because nobody taught them how to pivot.
Sean adds the part that stings. Weak DTT shows itself in thirty seconds of bad timing and bad pacing. Weak NET can hide behind a pile of blocks for two hours. A parent at the glass sees warm play either way. That is form versus function (what a session looks like versus what it actually teaches). Leaders often judge the skill by how the session looks.
We are saying that you have not been given the quality support and tools on the majority of instances from what we've experienced to do your job well.
From the talk — Sean Yocum, MA, BCBA, LBA & Michael Carrero, BCBA, LBA
Measuring NET Without Turning Data Into Creative Writing
Data is where honest NET gets hard. The technician is spotting a swing, moving materials, and watching the room. The tablet sits on a shelf across the gym. At the end of the day, someone types eighty percent from memory. Sean calls that clinical creative writing instead of science.
Mike draws a hard line around the ethics of this. If you cannot say for sure that it happened, do not record it. Do not guess a count because the box wants a number. Guessing puts you in conflict with your ethics code. He would rather read an honest gap than a tidy fiction.
He also puts weight on the system around the technician. Records built for rigid fields push people toward numbers that fit the form. Leaders have to make it safe to say that the data is slipping. Then they have to fix the method instead of scolding the person.
The fixes the hosts name are concrete. Use cold probes (one check before any help). Take first trial data. Measure the rate of requests per minute during a set play routine. Pick the measure when you write the goal, not at the end of the day. Supervisors should spend real hours beside staff teaching counts, not watch a muted screen from a laptop. Both hosts liked charting the rate of learning over time. It shows how fast a skill is truly coming.
If you can't measure whether the learner is acquiring the cusp in the natural setting, you aren't doing science.
From the talk — Sean Yocum, MA, BCBA, LBA & Michael Carrero, BCBA, LBA
Assent Without Surrender: Where Child Led Stops Being Teaching
The last trap they name is assent washing (using fake consent to excuse skipping teaching). Assent (the learner agreeing to take part) is real and it matters. The hosts warn about what it can turn into in practice. Any friction becomes a reason to stop teaching for the rest of the day. A child loops the same short clip for hours and the note says child led.
Mike starts the line with safety and human rights. Keep people safe first, because furniture can be replaced and people cannot. Then run a simple cost and benefit check on the moment. Does this choice help the person live a fuller life later? He wants shaping rather than a demand for perfect skills today.
His example is a learner pushing a hand away instead of hitting. That is better, so start there and build the next step from it. Saying no in a clear way is a skill, not a failure. Pulling back from a moment does not mean every demand disappears forever.
Sean lands the point on freedom. A child whose only exit from a room is biting does not have autonomy. Teach a clear way to ask, whether that is a card, a sign, a word, or a device. Honor it fast every time, then build waiting and coping on top of it. The gut check is simple. Did backing off today make this person more able tomorrow, or just make my afternoon easier?
Autonomy is having the functional skills to influence your own life.
From the talk — Sean Yocum, MA, BCBA, LBA & Michael Carrero, BCBA, LBA
The Middle Ground: Build It at the Desk, Prove It on the Floor
The hosts refuse the fight between the two camps. Mike reaches for music to explain the point. You do not join a band and tour on the first day. You practice, you play somewhere real, then you raise the bar again. DTT builds the mechanics, and NET gives those mechanics a reason to exist.
Sean turns that into a loop you can run. Build fluency (fast, accurate responding) with fast, thick, high energy trials when a learner needs them. The second the form is there, push the skill into the messy room. Stop early and you get a skill that only works at a table. Skip the first half and the learner has nothing to generalize.
The balance is not fifty fifty, and it is not the same for two learners. Some need more structure because the natural moment moves too fast for them. Some need more real routines because the set up version never transfers. What matters most is that everyone running the plan knows why and how.
That is the spirit of the NET fidelity checklist this episode leaves you with. A real want that you created or caught. A planned chance to teach inside the activity. Fast prompt fading and a payoff tied to the response. And a count you could defend to anyone who asks.
First, you earn the floor at the desk and you prove the desk on the floor, if that makes sense.
From the talk — Sean Yocum, MA, BCBA, LBA & Michael Carrero, BCBA, LBA
Common questions
What is the difference between NET and just following the child's lead?▾
Following the lead only picks the activity. NET uses that activity to run planned targets. You create or catch the want, prompt, fade help fast, and pay off with something tied to the response. If no target is running and nothing is measured, it is play, not teaching.
How do you contrive a learning chance without being coercive?▾
You change the setup, not the person. Lock a container, hold the swing at the top, or leave out the straw, then teach the ask and deliver right away. The hosts stress that the payoff must be what the learner wanted. Safety and the learner's right to say no still come first.
Why do the hosts say most token boards do not work?▾
They argue the boards were never conditioned. Teams skip the pairing, the easy trades, and the test where the token sits next to a neutral item. Without that work, the board is a countdown to escape rather than a reward. A payoff tied to the response does the job better.
How can a technician collect usable data during fast moving NET?▾
Pick the measure when the goal is written. Cold probes, first trial data, and rate of requests per minute all survive a messy room. Never fill in a number from memory at the end of the day. If the method does not fit the setting, change the method instead of the data.
Can I earn CEUs by listening to the podcast episode?▾
No. Listening to the episode on its own does not earn credit. Credit is earned only by completing this OpenCEU event, which includes the NET fidelity checklist.
About the speaker
Sean Yocum, MA, BCBA, LBA and Michael Carrero, BCBA, LBA host the Rad N Bad podcast from Hickory Learning Group, where they talk about what actually happens on the floor during therapy. In this episode they draw on supervising staff and inheriting cases to describe what rigorous natural environment teaching looks like in practice. Michael also references teaching research methods and design, which shows up in his push for honest measurement and clear data.
This summary was generated from the recording’s transcript. Quotes are taken word for word from the talk.
What you'll learn
- 1Distinguish rigorous natural environment teaching from unstructured, child-led play. Describe how to manipulate establishing operations and contrive learning opportunities within natural routines. Explain why natural reinforcers functionally tied to the response promote generalization more than arbitrary token systems that are not functioning as conditioned reinforcers. Describe the training and procedural fidelity monitoring staff need to implement NET effectively.
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