Association for Behavior Analysis International

The Association for Behavior Analysis International® (ABAI) is a nonprofit membership organization with the mission to contribute to the well-being of society by developing, enhancing, and supporting the growth and vitality of the science of behavior analysis through research, education, and practice.

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50th Annual Convention; Philadelphia, PA; 2024

Event Details


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Symposium #427
CE Offered: BACB
Teaching Self-Management and Simple Generative Responding Repertoires With the Morningside Model of Generative Instruction
Monday, May 27, 2024
10:00 AM–10:50 AM
Marriott Downtown, Level 5, Grand Ballroom Salon F
Area: EDC/CSS; Domain: Translational
Chair: Andrew Robert Kieta (Morningside Academy; The Wing Institute)
Discussant: Andrew Bulla (Georgia Southern University - Armstrong )
CE Instructor: Andrew Bulla, Ph.D.
Abstract:

Successful learners apply what they’ve learned to a variety of new contexts. Even full grasp of the skills, concepts, and principles taught during one’s K-12 education are insufficient in teaching everything learners need to know. Thus, it’s essential that education settings teach students how to take ownership of their own learning, by teaching specific self-management strategies for application to a variety of classroom learning environments, and for application to real-world contexts. This symposium will feature two presentations detailing how students can learn to be better learners and to use what they’ve learned outside of the classroom setting. First, Ky’Aria Moses will describe a systematic literature review of instruction of self-management strategies with low-income students. Next, Bailee Scheuffele will explain how she used Morningside’s simple generative responding technology to design a year long project aimed at teaching students to apply computation skills learned in math class to meaningful, real-world challenges outside of the classroom.

Instruction Level: Intermediate
Keyword(s): Executive Functioning, Generalization, Generative Responding, Self-Management
Target Audience:

Professionals interested in behavioral education, direct instruction, Precision teaching/frequency building, Response to Intervention/Multi-Tier System of Supports, executive functioning, self-management, communication, and designing for and teaching towards generalization. Audience should have a basic understanding of applied behavior analysis as applied to academic learning behavior.

Learning Objectives: 1. list and describe at least three variations of self-management strategies which are supported in schools, 2. list and describe Morningside's five ingredients for simple generative responding, 3. describe how instruction is used during initial instruction and during instruction for application to increase the likelihood of simple generative responding.
 
A Review of Self-Management Strategies for Struggling Learners
(Applied Research)
KY'ARIA MOSES (Western Michigan University ), Jessica E. Van Stratton (Western Michigan University)
Abstract: Poverty continues to pose a threat to children’s development of behavioral regulation skills, which can impact students’ academic readiness and achievement (Engle & Black, 2008; McKenzie, 2019). Self-management has been studied throughout the literature to teach student independence and self-regulation skills, both of which are critical for learning in the classroom (Fantuzzo, et al., 1988; Briesch et al., 2019). To date, there has been no systematic review of self-management strategies for low-income students in general education settings. Thus, the purpose of this review was to examine the efficacy of self-management strategies with this population. A systematic review of the literature identified 10 studies that implemented self-management strategies with low-income students. Results support the use of several variations of self-management in general education settings and highlight essential features when designing self-management strategies to promote academic achievement and regulation of classroom behaviors. This presentation will review common self-management strategies including self-monitoring, graphing, error correction, and self-evaluation and provide a case example of teaching students to self-graph on the Standard Celeration Chart.
 
Towards a Technology of Generalization: Simple Generative Responding of Mathematics Computation Skills to Real-World Contexts
(Service Delivery)
BAILEE SCHEUFFELE (Morningside Academy), Andrew Robert Kieta (Morningside Academy; The Wing Institute), Kent Johnson (Morningside Academy)
Abstract: An unequivocal example of effective teaching—and learning—is when learners engage in previously taught behaviors under more varied contexts than those presented in the classroom. As such, educators must provide instruction on how, and guided opportunities to, practice direct application of skills and concepts to real-world circumstances. We systematically employed the Morningside Model of Generative Instruction, moving through explicit instruction to less-and-less structured forms of application. During teacher-guided application, students were given a designated budget and a list of items to purchase. Learners engaged in activities that reinforce basic arithmetic operations such as addition, subtraction, multiplication, and division, like calculating and comparing prices to make informed purchasing decisions. Over time, teacher involvement was slowly faded out, until our learners independently applied the skills taught in math class in novel environments, as reported by self or parent. Our engineered application of various aspects of mathematics in real-world environments promotes the development of essential skills, including problem-solving, critical thinking, and mathematical reasoning. It provides students with an opportunity to apply mathematical knowledge to real-life situations, thereby making abstract concepts more tangible and relevant.
 

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