GUEST POST: Equivalence-based instruction in the classroom

GUEST POST: Equivalence-based instruction in the classroom

Swisher EBI photo.JPG

By Melissa Swisher

Dr. Melissa Swisher is a lecturer in Psychological Sciences at Purdue University.  She studies stimulus equivalence in pigeons and people and would like to see this research benefit more learners.  She tweets @eabswisher.

Have you ever noticed how children learn to read? Typically, they learn by seeing pictures of animals and hearing a parent say the animal name aloud. Next, children repeat the name and look at the picture. Then, children point to the picture or say the name out loud when they hear a parent read the written animal name aloud. We can essentially use a more precise version of this procedure—equivalence-based instruction—to teach lots of different skills, including reading, to people of all ages.

What is equivalence-based instruction?

Equivalence-based instruction is an evidence-based method for efficiently and effectively teaching material to students. It’s efficient because we can teach a little and get a lot of learning in return (1). It’s as effective and well-liked by students as traditional lecture (2). Equivalence-based instruction has only been around for the past 15 years or so, but we’ve been doing research on stimulus equivalence—the basis for equivalence-based instruction—since Sidman discovered it in the 1970s and used it to teach adults with developmental disabilities to read (3), (4).

Teaching and testing in stimulus equivalence is exactly what we do in classroom instruction and examination. However, what we test in stimulus equivalence is different than what we regularly test in the classroom. We often want students to learn the basics as well as infer new relationships among those ideas we present in class; equivalence-based instruction allows students to learn these new relationships without direct instruction (see (2)). For example, we want to teach undergraduate students how to interpret graphs in a research methods class (see (5)). We can show students slides with terms on the left side and graphs illustrating examples of those terms on the right side (see top row Figure 1). We can also show students slides with descriptions of those terms on the left side and graphs illustrating examples of those descriptions on the right side (see bottom row Figure 1). These term " graph and description " graph associations are our directly taught associations.

Figure 1. How we can create slides in the classroom to teach associations between terms, graphs, and their descriptions.

Figure 1. How we can create slides in the classroom to teach associations between terms, graphs, and their descriptions.

According to set theory, students should be able to demonstrate reflexivity, symmetry, and transitivity after learning a few associations (6). Reflexivity means that each item is associated with itself (e.g., term " term, graph " graph, and description " description). We typically assume that older students have mastered reflexivity and don’t test for it. Symmetry means that each trained association is reversible (e.g., graph " term and graph " description). Transitivity means that ideas associated with one common item are also interchangeable (e.g., term " description and description " term; they have graph in common). Examples of symmetry and transitivity are shown in Figure 2 (notice the reversed positions of items), and how we can assess these associations on an exam is shown in Figure 3. This design is essentially what (7) and (2) used to teach brain functions and experimental designs to undergraduate students, respectively.

Figure 2. The untaught associations we can test between terms, graphs, and their descriptions.

Figure 2. The untaught associations we can test between terms, graphs, and their descriptions.

Figure 3. How we can test students on exams to see if they made new associations between terms, graphs, and their descriptions. The correct answers are bolded.

Figure 3. How we can test students on exams to see if they made new associations between terms, graphs, and their descriptions. The correct answers are bolded.

In our example of teaching six research methods associations, students should be able to form concepts of main effects, interactions, and situations with no effects. That includes six new symmetrical and transitive associations (and nine reflexive ones) that should help students interpret graphic results using proper terminology. We can also teach and test with slightly different descriptions and graphs to ensure better generalization (or transfer) across new examples (e.g., (8); (9); (10)). This is how we help students apply what they know to unique situations.

How can we use equivalence-based instruction in class?

Many different participants and content areas have been tested for stimulus equivalence. So far, equivalence-based instruction—applied behavior analytic technology—has been used to teach preschool children (11), elementary school children (12), and undergraduate and graduate students (5); (13) in content areas like spelling (14), geography (15), statistics (16), and drug names for pharmacology (1). This technology has also been used with children with auditory (17) and visual impairments (18) as well as children with intellectual disabilities like autism (see (19); (20) for reviews). Obviously, equivalence-based instruction can incorporate many kinds of material for learners of all ages and skill sets.

We can use equivalence-based instruction as part of or supplemental to classroom instruction, and the following are some suggestions for incorporating equivalence-based instruction. They work for teaching both simple (e.g., numbers) and complex (e.g., experimental designs) concepts.

The first step in using equivalence-based instruction is to decide what concepts we want to teach students—like the numbers 1-10. Each number is a concept: [one, 1, one shape], [two, 2, two shapes], [three, 3, three shapes], etc. and what our mastery criterion will be for the directly taught associations prior to testing new relations: 80%, 90%, or 95% accuracy. It’s best to have several associations for each concept: four " 4 is one association and four " four shapes is another (see Figure 4). It’s unclear how many associations a person can learn at one time, but it seems that 15 individual ideas for two concepts is the known maximum; that’s 209 unique associations (21). At minimum, we want to have at least three associations for each concept. We use a mastery criterion because we want students to know this information well before we test what they’ve learned, and it’s most important for an online or tabletop format.

The second step is to decide what format we will use to teach our concepts: online via a custom program or Blackboard, in lecture for the entire class, or in person one-on-one with pictures/words printed on cards. As previously mentioned, we can show slides in class of the associations we want students to learn (Figures 1 and 2). Alternatively, we can program those associations (Visual Basic software is the most popular) for presentation on a computer (Figure 4), use Blackboard’s quiz feature with multiple-choice or fill-in-the-blank questions (Figure 3), or do a tabletop version of trials by arranging stimuli in front of a student (also Figure 4). Whatever presentation method we use, we want students:  to choose between one correct answer and several (more than two) incorrect answers, to see potential answers in random order to avoid position biases, and to experience a 2-4 s delay from when they see a sample stimulus or question to when they see comparison stimuli or potential answers (see Figure 4). We also might want students to learn all associations of each type before they are intermixed. Specifically, they should learn all numbers in words " symbol (e.g., one " 1, two " 2, etc.) and then learn all numbers in words " number of shapes (e.g., one " one shape, two " two shapes, etc.). This is especially important for younger learners. It’s also extremely important to give feedback while teaching the basic associations. Depending upon learners and their preferences, we can program stars and chimes, points, short videos, pictures, or praise for correct answers. Basically, our feedback should be something each learner likes.

Figure 4. Two math trials (top and bottom rows) with one sample stimulus (middle text on left side) and four comparison stimuli (symbols and shapes on outer edges of the right side). The sample stimuli are not seen on a computer screen at the same time as the comparison stimuli.

Figure 4. Two math trials (top and bottom rows) with one sample stimulus (middle text on left side) and four comparison stimuli (symbols and shapes on outer edges of the right side). The sample stimuli are not seen on a computer screen at the same time as the comparison stimuli.

If we follow these equivalence-based instruction recommendations, then it is likely that students will learn some basic associations (e.g., numbers in words " symbols and numbers in words " number of shapes) after a few hours, at most, and demonstrate new, untaught associations (e.g., symbols " numbers in words, number of shapes " numbers in words, symbols " number of shapes, and number of shapes " symbols). Students should remember these concepts for at least a few months without additional training (22), which is great for any semester-long course.

Equivalence-based instruction is a great addition to any existing educational approach. It can be used to review material from prerequisite courses at the beginning of the semester or for any material that students generally find difficult (e.g., interactions, interference, classical conditioning, etc.). With just a few considerations, any teacher can provide evidence-based individualized instruction to all students.


References

(1) Zinn, T. E., Newland, M. C., Ritchie, K. E. (2015). The efficiency and efficacy of equivalence-based learning: A randomized controlled trial. Journal of Applied Behavior Analysis, 48, 865-882. doi: 10.1002/jaba.258

(2) Lovett, S., Rehfeldt, R. A., Garcia, Y., & Dunning, J. (2011). Comparison of a stimulus equivalence protocol and traditional lecture for teaching single-subject designs. Journal of Applied Behavior Analysis, 44, 819-833. 10.1901/jaba.2011.44-819

(3) Sidman, M. (2007). The analysis of behavior: What’s in it for us? Journal of the Experimental Analysis of Behavior, 87, 309-316. doi: 10.1901/jeab.2007.82-06

(4) Sidman, M. (2009). Equivalence relations and behavior: An introductory tutorial. The Analysis of Verbal Behavior, 25, 5-17.

(5) Walker, B. D., & Rehfeldt, R. A. (2012). An evaluation of the stimulus equivalence paradigm to teach single subject design to distance education. Journal of Applied Behavior Analysis, 45, 329-344. doi: 10.1901/jaba.2012.45-329

(6) Sidman, M., & Tailby, W. (1982). Conditional discrimination vs. matching to sample: An expansion of the testing paradigm. Journal of the Experimental Analysis of Behavior, 37, 5-22. doi: 10.1901/jeab.1982.37-5

(7) Pytte, C. L., & Fienup, D. M. (2012). Using equivalence-based instruction to increase efficiency in teaching neuroanatomy. The Journal of Undergraduate Neuroscience Education, 10, A125-A131.

(8) Carr, D. (2003). Effects of exemplar training in exclusion responding on auditory-visual discrimination tasks with children with autism. Journal of Applied Behavior Analysis, 36, 507-524. doi: 10.1901/jaba.2003.36-507

(9) Fields, L., & Moss, P. (2008). Formation of partially and fully elaborated generalized equivalence classes. Journal of the Experimental Analysis of Behavior, 90, 135-168. doi: 10.1901/jeab.2008.90-135

(10) Luciano, C., Becerra, I. G., & Valverde, M. R. (2007). The role of multiple-exemplar training and naming in establishing derived equivalence in an infant. Journal of the Experimental Analysis of Behavior, 87, 349-365. doi: 10.1901/jeab.2007.08-06

(11) Haegele, K. M., McComas, J. J., Dixon, M., & Burns, M. K. (2011). Using a stimulus equivalence paradigm to teach numerals, English words, and Native American words to preschool-age children. Journal of Behavioral Education, 20, 283-296. doi: 10.1007/s10864-011-9134-9

(12) de Rose, J. C., de Souza, D. G., & Hanna, E. S. (1996). Teaching reading and spelling: Exclusion and stimulus equivalence. Journal of Applied Behavior Analysis, 29, 451-469. doi: 10.1901/jaba.1996.29-451

(13) Walker, B. D., Rehfeldt, R. A., & Ninness, C. (2010). Using the stimulus equivalence paradigm to teach course material in an undergraduate rehabilitation course. Journal of Applied Behavior Analysis, 43, 615-633. doi: 10.1901/jaba.2010.43-615

(14) Dube, W. V., McDonald, S. J., McIlvane, W. J., & Mackay, H. A. (1991). Constructed-response matching to sample and spelling instruction. Journal of Applied Behavior Analysis, 24, 305-317. doi: 10.1901/jaba.1991.24-305

(15) LeBlanc, L. A., Miguel, C. F., Cummings, A. R., Goldsmith, T. R., & Carr, J. E. (2003). The effects of three stimulus-equivalence testing conditions on emergent US geography relations of children diagnosed with autism. Behavioral Interventions, 18, 279-289. doi: 10.1002/bin.144

(16) Fields, L., Travis, R., Roy, D., Yadlovker, E., de Aguiar-Rocha, L., & Sturmey, P. (2009). Equivalence class formation: A method for teaching statistical interactions. Journal of Applied Behavior Analysis, 42, 575-593. doi: 10.1901/jaba.2009.42-575

(17) Almeida-Verdu, A. C., Huziwara, E. M., de Souza, D. G., de Rose, J. C., Bevilacqua, M. C., Lopes, J., Alves, C. O., & McIlvane, W. J. (2008). Relational learning in children with deafness and cochlear implants. Journal of the Experimental Analysis of Behavior, 89, 407-424. 10.1901/jeab.2008-89-407

(18) Toussaint, K. A., & Tiger, J. H. (2010). Teaching early braille literacy skills within a stimulus equivalence paradigm to children with degenerative visual impairments. Journal of Applied Behavior Analysis, 43, 181-194. doi: 10.1901/jaba.2010.43-181

(19) Green, G. (2001). Behavior analytic instruction for learners with autism: Advances in stimulus control technology. Focus on Autism and Other Developmental Disabilities, 16, 72-85. https://doi.org/10.1177/108835760101600203

(20) Rehfeldt, R. A. (2011). Toward a technology of derived stimulus relations: An analysis of articles published in the Journal of Applied Behavior Analysis, 1992-2009. Journal of Applied Behavior Analysis, 44, 109-119. doi: 10.1901/jaba.2011/44-109

(21) Saunders, R. R., Saunders, K. J., Kirby, K. C., & Spradlin, J. E. (1988). The merger and development of equivalence classes by unreinforced conditional selection of comparison stimuli. Journal of the Experimental Analysis of Behavior, 50, 145-162. doi: 10.1901/jeab.1988.50-145

(22) Rehfeldt, R. A., & Hayes, L. J. (2000). The long-term retention of generalized equivalence classes. The Psychological Record, 50, 405-428.

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