|Instruction, Classroom Management, Precision Teaching, and Coaching with the Morningside Model of Generative Instruction|
|Sunday, May 24, 2020|
|5:00 PM–6:50 PM |
|Area: EDC/OBM; Domain: Translational|
|Chair: Andrew Robert Kieta (Morningside Academy)|
|Discussant: Andrew Kieta (Morningside Academy)|
|CE Instructor: Andrew Kieta, Ph.D.|
The Morningside Model of Generative Instruction is based on five pillars: Assessment, Curriculum, Instruction, Precision Teaching, and Generative Responding.
Nicole Erickson will detail how a teacher, working within a homogeneously achievement grouped classroom, uses a package of instruction strategies, Precision Teaching practices, and further assessment, to continuously evaluate and refine the homogeneity.
Kathy Fox will detail how coaches at Haugland Learner Center have developed a school-wide, systematic modification of the Good Behavior Game to improve student academic and social-emotional behavior outcomes.
Jill Hunt will describe how the Judge Rotenberg Center has worked with coaches from Morningside Teachers' Academy to develop a staff coaching model that focuses on effective classroom management and Precision Teaching procedures to improve student outcomes and shift the educational culture.
Andrew Bulla will present a study focused on effective practices in instruction and Precision Teaching, specifically a comparison of free operant acquisition and frequency building procedures versus restricted operant procedures, such as discrete trial training (DTT).
|Target Audience: |
Behavior Analysts, Teachers, Psychologists
Differentiating Instruction Within Homogeneous Achievement Groups: A Year in the Life of a Morningside Teacher
|NICOLE ERICKSON (Morningside Academy)|
One of the five pillars of the Morningside Model of Generative Instruction is homogeneous achievement grouping, wherein students with similar academic repertoires are placed together to foster the most effective instruction. While students complete a wide range of macro assessments – standardized, norm-referenced achievement tests – those assessments are designed to show growth over the course of year, not for use in homogeneous achievement grouping. Instead, results from a battery of curriculum placement tests are used to create the most homogeneous instructional groups. However, while students are placed homogeneously according to their overall average strengths and weaknesses, they do not show up in the classroom as homogeneous in each specific area of strength and weakness related to curricula. Within a given classroom, several areas of variance are evident, such as specific learning and organizational skills. As effective instructional practices turn student weaknesses into strengths, the teacher must continuously reassess and regroup students to maintain homogeneity. The never-ending job of the classroom teacher is to analyze multiple levels of assessment data to accommodate the different types of deficits that learners present with, and to effectively differentiate instruction and practice opportunities to an ever-changing diverse set of homogeneous learners. Data will be presented that demonstrate how this differentiation is done to produce successful learner outcomes.
A Systematic School-Wide Implementation of a Modified Good Behavior Game With Children With Autism
|KATHY FOX (Haugland Learning Center), Patrick Billman (Haugland Learning Center), Jason Guild (Haugland Learning Center)|
Good classroom management is a key factor in student success in all settings but can be especially important in classrooms that serve students with special needs. The Good Behavior Game is widely recognized as an evidence- based classroom management strategy. Haugland Learning Center(HLC), based in Columbus, Ohio, serves students with autism and other disabilities and uses variations of the Good Behavior Game to set students in a variety of classroom settings up for behavioral and academic success. This presentation will discuss how the use of the Good Behavior Game affects progress and outcomes, how HLC trains and coaches staff to implement effective classroom management strategies using the Good Behavior Game and how data are monitored to ensure continuous progress for individual students, classroom groups, and teachers. Our data indicate that students and staff perform better and reach more optimal academic and behavior outcomes when the Good Behavior Game is used consistently and reliably. Specific examples of student, classroom, staff and school academic and behavior data will be analyzed and discussed.
The Impact of the Morningside Model of Generative Instruction on Student Engagement, Classroom Management, and Staff Coaching at the Judge Rotenberg Educational Center
|JILL HUNT (Judge Rotenberg Educational Center), Justin Halton (Judge Rotenberg Educational Center)|
The Judge Rotenberg Education Center(JRC) is a residential school for students with severe disabilities. For the last two and a half years, JRC has had the privilege of learning from Morningside Teachers Academy(MTA) via onsite vists from MTA consultants. Work with MTA has focused on the Morningside Math Facts program, classroom management, and staff coaching. After the introduction of the Morningside Math Facts program, data demonstrated grade level equivalency gains of 1.8 years growth during the first 8 months. Additionally, staff coaching data show improved classroom management and increased student participation in the Morningside Math Facts program. Data collected during coaching sessions in the classroom have shown an increase in the amount of group responses and teacher praise statements and many staff and students report a pleasant change in the classroom environment. This presentation aims to discuss how the use of well- sequenced learning materials combined with application of good classroom management strategies inspired change in our educational department and continues to lead to better outcomes for our students and the lessons we've learned along the way.
|Comparing the Effects of Restricted Operant and Free Operant Teaching Paradigms on Students’ Learning Pictures|
|ANDREW BULLA (Georgia Southern University - Armstrong ), Jennifer Wertalik (Georgia Southern University - Armstrong), Thea Schmidt (Georgia Southern University - Armstrong)|
|Abstract: In applied behavior analysis, two training techniques for learning new material include frequency building and discrete trial training (DTT). Frequency building is a free operant teaching paradigm where instruction moves at the pace of the learner under a timed condition in order to build the frequency of correct responses. DTT is a restricted operant paradigm where the frequency of responding is under the control of the instructor, with a distinct start and end to each trial to build the number of correct responses. Despite to effectiveness of both procedures, few studies have compared the two techniques and assessed the effects on the learning patterns produced. The current study extends the research to typically developing college students to directly compare frequency building and DTT. Numerals 0-10 in unknown foreign languages (i.e., Mandarin, Arabic, and Hindi) were taught to participants using both procedures. The number of practice trials and frequency of reinforcement were controlled for throughout. Learning pictures for both teaching techniques will be shared, as well as generativity probes for numerals 11-20.|