In Behavior Analysis, we have a saying, “the learner is always right.” We don’t mean this in the sense of giving the correct answer. Of course, learners will make mistakes. We also don’t mean this in the way that businesses mean it as in, “the customer is always right.” We mean it in terms of data. Really, the full saying (perhaps not as catchy) is the “The learner’s data are always right.”
In other words, no matter how well designed the program or how superb the teacher, if the learner didn’t learn, the problem is not with the learner, it is in the instruction. Too often, teachers (and yes, even some BCBAs) will blame the learner: “He is just not getting it;” “He doesn’t generalize;” “He has over-selectivity;” Etc. etc.
Of course, this is in large part the reason why we take data in the first place. These types of data help us make decisions. I think this is one of the reasons we love behavior analysis. We try something that doesn’t work, look at the data, do an analysis, figure out the cause of the problem, make a revision, and finally see the learner succeed based on our efforts.
But… I think we sometimes take this beautiful concept too far. Why?
As one small example, consider the abysmal pass rates of the BCBA exams–approximately 1 out of 3 fail on the first attempt. These people spent years completing all the required courses, devoted enormous amount of time in supervision, and still fail? Is graduate school instruction and supervision so poor that 1 out of 3 people who complete all the requirements fail the exam? Well, probably. No matter how good you are, there is room for Poogi. No doubt, there is room for massive improvement in both graduate school instruction and supervision. I’ve taught graduate school classes in behavior analysis. I’ve supervised many people for their BCBAs. I spent years improving instructional design and supervision, and I see clearly that better teaching leads to better outcomes. At this point, I now feel very confident in my ability to help someone learn the concepts in behavior analysis. But not always. There is another problem besides the quality of instruction and supervision.
If you are in graduate school, there are certain prerequisite skills you should have before you begin. But I’ve seen situations where it looked like people somehow made it into graduate school without possessing the prerequisite skills needed to be successful. For example, if the average high school student skipped algebra and went straight to calculus, we wouldn’t expect them to be successful even if they had the best calculus teacher in the world. In a similar manner, it may not be possible for a graduate school professor or BCBA assigned to provide supervision to help a student or supervisee catch up on the all the prerequisite skills they need. Not that it is the learner’s fault. The learner is still right. Just the prescription might not always be better teaching, it might be building prerequisite skills.
The learner is always right attitude is the way to become a better teacher, regardless of whether you teach children with autism, graduate students, or others. It never makes sense to blame the learner for failing to acquire a skill. The literature is filled with examples of people whom everyone originally thought couldn’t learn a particular concept. But with excellent teaching, they were able to learn. Though if the learner needs to build the prerequisite skills, I think it is reasonable for teachers to ask if they are the right person to do that. This applies to graduate school professors, BCBA’s conducting supervision, and the general education teacher that has a student three years behind grade level included in his or her class.