|Adaptations of the Morningside Model of Generative Instruction to Online and Other Alternative Learning Environments|
|Saturday, May 29, 2021|
|11:00 AM–12:50 PM |
|Area: EDC/TBA; Domain: Translational|
|Chair: Kent Johnson (Morningside Academy)|
|Discussant: Kent Johnson (Morningside Academy)|
|CE Instructor: Jennifer Wertalik, Ph.D.|
|Abstract: Due to the Covid-19 pandemic, Morningside Academy and colleagues who implement the Morningside Model of Generative Instruction have been forced to adapt their practices to unique learning environments, including fully online, remote instruction and live instruction in student living quarters at a residential special education school. First, Morningside teacher Nicole Erickson will describe the process of assessment, selection, and subsequent instruction of learning, organization, and technology skills necessary for students to learn reading, writing, and math in an online environment. Second, Morningside teacher Hannah Jenkins will describe how mathetics-based instruction facilitates meaningful active student responding during online learning. Then, Judge Rotenberg Center Special Education Director Justin Halton will describe how MMGI was adapted to deliver instruction in the living and dining rooms of on campus student residences. Finally, Georgia Southern University - Armstrong professor Dr. Andrew Bulla will present best practices in instructional design strategies for teaching behavior analysis to college students preparing for the BCaBA exam.|
|Instruction Level: Intermediate|
|Keyword(s): Assessment, Basic Skills, Instruction, Online-learning|
|Target Audience: The audience should be aware of basic principles of behavioral education, and be familiar with terms such as fluency, Precision Teaching, and direct instruction.|
|Learning Objectives: At the conclusion of the presentation, participants will be able to define three behaviors categorized as learning skills.
At the conclusion of the presentation, participants will be able to discriminate instances of meaningful active student responding from instances of active student responding that are not meaningful.
At the conclusion of the presentation, participants will be able to describe how one effective instructional strategy can be modified to online learning platforms.|
Assessing and Teaching Learning Skills in Online Environments With the Morningside Model of Generative Instruction
|NICOLE ERICKSON (Morningside Academy), Andrew Robert Kieta (Morningside Academy)|
A central component of the Morningside Model of Generative Instruction is teaching students specific repertoires, called “learning skills”, that allow them to more effective and more confident learners in the classroom. Online learning created many challenges for teachers and students, particularly in the area of learning skills instruction. Morningside teachers had to assess, then teach, not only the specific skills needed to effectively learn in the remote setting, but also those that would be necessary for in-person setting. This presentation will focus on that assessment, instruction, and measurement process. With the students working at home, amidst several distractions, online learning created a unique opportunity to teach the students to advocate for themselves and to take control of their own learning. To develop these independent learning repertoires, students were taught to identify when they were confused, what part of the instruction confused them, and how to ask specific questions to get the information necessary to be successful. Students were then coached to track these behaviors using Morningside’s Daily Support Card and the Standard Celeration Chart, to set goals to decrease the amount of time between activities and the number of prompts given in each instructional period.
Creating Meaningful Student Responding, Errorless Learning, and Immediate Feedback With Generative Instruction in Online Environments
|HANNAH JENKINS (Morningside Academy), Joanne K. Robbins (Morningside Academy)|
As the world of remote learning unfolds, old habits and patterns must be adjusted for technological advances. Markle describes good instructional design as having meaningful active responding, errorless learning, and immediate feedback. Donald Cook observed, “...the principles of active response and low error rate were widely cited, but they were often misunderstood and misapplied.” Often overlooked is the word meaningful; being active is not enough. Responding that is a function of prompting, copying, or echoing, are all active, yet all should be avoided when teaching cognitive tasks. When Morningside Academy moved to online learning, faculty had to stay true to the model. The author will provide examples of response requests that require active responding, how a mathetics approach limits errors and facilitates an error analysis, and new ways to to provide immediate feedback.
High Rate Responding and Academic Performance With Morningside’s Generative Instruction Model During a Pandemic
|JUSTIN HALTON (Judge Rotenberg Center)|
Learning at the Judge Rotenberg Center shifted from the school buildings to individual residences. Dining rooms and living areas became classrooms and teachers were tasked with instructing a new group of students organized by residential assignment to accommodate the school health and safety plan. High rate responding activities during ELA instruction occurred at the school daily before moving to in-person instruction from the residences. From March-June, students continued to engage in high rate responding activities during ELA instruction, from their residence opposed to the structure of the classroom. This presentation aims to share data from one classroom/residential group of students diagnosed with severe disabilities and between the ages of 15-19, before, during, and after initial pandemic response adaptations to learning environments within the Judge Rotenberg Center. This Presentation will also detail steps taken to deliver in-person instruction from the residential environment. Data presented was collected using the IReady Assessment tool for both Reading and Math for each student.
Applying Instructional Design Principles and Evidence-Based Teaching Strategies to the Online Classroom
|JENNIFER WERTALIK (Georgia Southern University), Andrew Bulla (Georgia Southern University - Armstrong )|
Behavior analysis offers a variety of instructional technologies to teach all types of students a variety of skills. Many college-level instructors have incorporated behavior analytic techniques to the college classroom successfully to improve learning outcomes across subject areas. While there are a plethora of data available on the effectiveness of these techniques in face-to-face classroom, several issues arise regarding the practicality of these techniques in an online classroom. The current presentation highlights the literature on instructional design and evidence-based instructional strategies as it applies to virtual learning. Additionally, the presenters offer practical recommendations with associated examples on how to extend additional strategies to meet the demand of virtual learning.