In ACE Lab, we conduct a variety of studies that evaluate behavior analytic instructional interventions. Click below to learn more about some of the specific interventions:
Equivalence Based Instruction
Technology is rapidly changing the face of education. Technology has the potential to deliver individualized, effective instruction. This is not a new idea. In fact, Fred Keller proposed individualized systems of instruction in 1968. Now, with the proliferation of computers we have a means to disseminate this technology. The basic principles are straight forward. Effective instruction should: 1) Break information into small units, 2) Require mastery of one unit before proceeding to next unit, 3) Allow for frequent opportunities to respond to material, 4) Provide specific feedback on learning, and 5) Allow learners to move through material at his or her own pace.
Fienup, D. M., Mylan, S. E., Brodsky, J., & Pytte, C. (2016). From the laboratory to the classroom: The effects of equivalence-based instruction on neuroanatomy classes. Journal of Behavioral Education, 25, 143-165. doi: 10.1007/s10864-015-9241-0 [link]
Fienup, D. M., Wright, N. A., & Fields, L. (2015). Optimizing equivalence based instruction: Effects of training protocols on equivalence class formation. Journal of Applied Behavior Analysis, 48, 613-631. doi: 10.1002/jaba.234 [link]
Reyes-Giordano, K., & Fienup, D. M. (2015). Emergence of topographical responding following equivalence based neuroanatomy instruction. The Psychological Record. Advanced online publication. doi: 10.1007/s40732-015-0125-4 [link]
Critchfield, T. S., & Fienup, D. M. (2013). A “happy hour effect” in translational stimulus relations research. Experimental Analysis of Human Behavior Bulletin, 29, 2-7. [link]
Pytte, C. L., & Fienup, D. M. (2012). Using equivalence-based instruction to increase efficiency in teaching neuroanatomy. The Journal of Undergraduate Neuroscience Education (JUNE), 10, 125-131. [article]
Fienup, D. M., & Critchfield, T. S. (2011). Transportability of equivalence-based programmed instruction: Efficacy and efficiency in a college classroom setting. Journal of Applied Behavior Analysis, 44, 435-450. doi: 10.1901/jaba.2011.44-435 [article]
Fienup, D. M., Hamelin, J., Reyes-Giordano, K., & Falcomata, T. S. (2011). College-level instruction: Derived relations and programmed instruction. Journal of Applied Behavior Analysis, 44, 413-416. doi: 10.1901/jaba.2011.44-413 [article]
Critchfield, T. S., & Fienup, D. M. (2010). Using stimulus equivalence technology to teach about statistical inference in a group setting. Journal of Applied Behavior Analysis, 43, 763-768. doi: 10.1901/jaba.2010.43-763 [article]
Fienup, D. M., & Critchfield, T. S. (2010). Efficiently establishing concepts of inferential statistics and hypothesis decision making through contextually-controlled equivalence classes. Journal of Applied Behavior Analysis, 43, 437-462. doi: 10.1901/jaba.2010.43-437 [article]
Fienup, D. M., Covey, D. P., & Critchfield, T. S. (2010). Teaching brain-behavior relationships economically with stimulus equivalence technology. Journal of Applied Behavior Analysis, 43, 19-33. doi: 10.1901/jaba.2010.43-19 [article]
Fienup, D.M., Critchfield, T.S., & Covey, D.P. (2009). Building contextually-controlled equivalence classes to teach about inferential statistics: A Preliminary Demonstration.Experimental Analysis of Human Behavior Bulletin, 30, 1-10. [link]
Critchfield, T. S. & Fienup, D. M. (2008). Stimulus equivalence. In S.F. Davis & W.F. Buskist (Eds.), 21st Century Psychology (pp. 360-372). Thousand Oaks, CA: Sage. [link]
Fienup, D. M., & Dixon, M. R. (2006). Acquisition and maintenance of visual-visual and visual-olfactory equivalence classes. European Journal of Behavior Analysis, 7, 87-98. [link]
Discrete Trial Learning Arrangements
In recent years, there has been considerable research looking at different learning arrangement for children with autism and developmental disabilities. Researchers have investigated variables such as the distribution of trials across sessions (Haq & Kodak, 2015), inter-trial intervals (Majdalany, Wilder, Greif, Mathisen, & Saini, 2014), reinforcer magnitude (Paden & Kodak, 2015), the distributions of trials and reinforcers (Kocher, Howard, & Fienup, 2015), and delays to reinforcement (Majdalany, Wilder, Smeltz, & Lipshultz, 2016).
At ACE Lab, we have investigated how the distribution of trials and reinforcers in a teaching session affect learner performance. To get an idea of this research area, answer the following question. Which do you prefer: Small reinforcers after a few responses or large reinforcers after a large number of responses? This is one of the questions that drives our research in the area of response-reinforcer arrangements. Many children with developmental disabilities have skill acquisition programs that deliver reinforcers after a specific number of responses. Our research in this area has found that different arrangements of responses and reinforcers are preferred by children and produce different effects on performance.
More recently, we have begun examining mastery criteria and asking questions about what are appropriate levels of performance to promote the maintenance and generalization of academic responding.
Fuller, J., & Fienup, D. M. (under review). A parametric analysis of mastery criterion level: Effects on maintenance of academic responding.
Fienup, D. M., & Brodsky, J. (accepted). Effects of mastery criterion on the emergence of derived equivalence relations. Journal of Applied Behavior Analysis.
Ward-Horner, J. C., Cengher, M., Ross, R. K., & Fienup, D. M. (in press). Arranging work requirements and the distribution of reinforcers: A brief review of preference and performance outcomes. Journal of Applied Behavior Analysis.
Kocher, C. P., Howard, M. R., & Fienup, D. M. (2015). The effects of work-reinforce schedules on skill acquisition for children with Autism. Behavior Modification, 39, 600-621. doi: 10.1177/0145445515583246 [link]
Bukala, M., Hu, M. Y., Lee, R., Ward-Horner, J. W., & Fienup, D. M. (2015). The effects of work schedules on performance and preference in students with Autism. Journal of Applied Behavior Analysis, 48, 215-220. doi: 10.1002/jaba.188 [link]
Ward-Horner, J. C., Pittenger, A., Pace, G., & Fienup, D. M. (2014). Effects of reinforcer magnitude and distribution on preference for work schedules. Journal of Applied Behavior Analysis, 47, 623-627. doi: 10.1002/jaba.133 [link]
Fienup, D. M., Ahlers, A. A., & Pace, G. (2011). Preference for fluent v. disfluent work schedules. Journal of Applied Behavior Analysis, 44, 847-858. doi: 10.1901/jaba.2011.44-847 [article]
Task Analysis Instruction
Task analyses - or orders sequences of responses in a behavior chain - are ubiquitous to behavior analytic interventions. Behavior analysts develop task analyses to teach multi-step behaviors to our clients (e.g., vocational tasks or grooming tasks like brushing teeth) or to teach staff how to implement intervention procedures. Now, the guidance for developing task analyses are somewhat vague (e.g., engage in the behavior yourself, then develop the task analysis). In response to this, our lab has examined variables (topography of instruction, types of stimuli included in instruction) that affect task analysis instruction.
Tyner, B. C., & Fienup, D. M. (2015, online first). A comparison of task analysis with and without descriptions of relevant cues and performance criteria for creating reversal design graphs in Microsoft Excel. Journal of Behavioral Education. doi: 10.1007/s10864-015-9242-z [link]
Tyner, B. C., & Fienup, D. M. (2015). A comparison of video modeling, text-based, and no instruction for creating multiple baseline graphs in Microsoft Excel. Journal of Applied Behavior Analysis, 48, 701-706. doi: 10.1002/jaba.223 [link]
Tyner, B. C., & Fienup, D. M. (2014). Adapting user research methodology for behavior analytic instructional design. Behavior Analysis and Technology, ABAI Special Interest Group. Retrieved from http://batechsig.com/2014/12/01/adapting-user-research-methodology-for-behavior-analytic-instruction-design/
Functional Analysis of Academic Skill Deficits
A learner may struggle to accurately or fluently complete academic tasks. One strategy to increase academic performance is to investigate empirically supported academic interventions and use the intervention with your particular learner. An alternative strategy is to investigate the specific challenges your learner is experiencing and intervene on the specific variables you uncover.
Daly III, Witt, Martens, and Dool (1997) proposed a model for conducting a functional analysis of academic skill deficits. A functional analysis is when multiple treatments or hypotheses are evaluated in order to determine what affects behavior the most. In the case of academic skill deficits, one would examine how various treatments differentially affect academic behavior.
Fienup, D. M., Reyes-Giordano, K., Wolosik, K., Aghjayan, A., & Chacko, A. (2015). Brief experimental analysis of reading deficits for children with Attention Deficit/Hyperactivity disorder. Behavior Modification, 39, 191-214. doi: 10.1177/0145445514550393 [link]
Fienup, D. M., Mudgal, D., & Pace, G. P. (2013). Increasing money counting skills with a student with brain injury: Skill and performance deficits. Brain Injury, 27, 366-376. doi: 10.3109/02699052.2012.743176 [link]
Baraneck, A., Fienup, D. M., & Pace, G. (2011). Brief experimental analysis of sight word interventions: A comparison of acquisition and maintenance of detected interventions. Behavior Modification, 35, 78-94. doi: 10.1177/0145445510391242 [article]
Motivating Operations and Language
Motivating operations affect the momentary effectiveness of rewards (Mayer, Sulzer-Azaroff, & Wallace, 2012). Deprivation, or when you have not had access to rewards for a period of time, can increase the value of that reward and make it a more effective reinforcer. Conversely, satiation, or when you have had free access to reward for a period of time, can decrease the value of that object and makes it less a effective reinforcer. Motivating operations are important when teaching language. One strategy a therapist might use is to deprive a child of rewards they are teaching the child to request (also called manding). This makes requesting more likely and receiving that reward very reinforcing! Research supports this notion (e.g., O’Reilly et al., 2012) and this strategy is frequently used in therapeutic settings.
Cengher, M., Jones, E. A., & Fienup, D. M. (2014). The effects of presession attention on tacting. Journal of Applied Behavior Analysis, 47, 176-180. doi: 10.1002/jaba.83 [link]
Contingencies are relations between behaviors and consequences, such as raising one's hand in class (behavior) and being called on by the instructor (this may function as positive reinforcement). Within the entirety of a course, there are many behaviors a student engages in, such as reading book chapters or articles, sitting in a desk during class, listening to a professor, reading PowerPoint slides, studying for exams, and taking exams. There was been some research demonstrating that putting contingencies in place for course-related behaviors affects the frequency of those behaviors.