|Assessment, Instruction, and Precision Teaching With the Morningside Model of Generative Instruction
|Sunday, May 24, 2020
|4:00 PM–4:50 PM
|Marriott Marquis, Level M4, Independence D
|Area: EDC/OBM; Domain: Translational
|Chair: Andrew Robert Kieta (Morningside Academy)
|CE Instructor: Andrew Bulla, Ph.D.
The Morningside Model of Generative Instruction is based on five pillars: Assessment, Curriculum, Instruction, Precision Teaching, and Generative Responding. This symposium will focus on the development of assessment systems of different scales and how they inform instruction and Precision Teaching approaches. First, Austin Siebert will describe a one-year project to design a centralized, school-wide system that incorporates each level of Morningside Academy’s three-tiered assessment system to obtain better validity, improve the frequency and administration of progress monitoring assessments, and enhance teacher decision making. Second, 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. Lastly, Dr. 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).
|Instruction Level: Intermediate
|Keyword(s): Assessment, Instruction, Precision Teaching, Progress Monitoring
Behavior Analysts, Teachers, Psychologists
|Designing a Centralized Progress Monitoring System to Increase Effective Teacher Decision Making
|AUSTIN SEABERT (Morningside Academy), Andrew Robert Kieta (Morningside Academy), Julian Gire (Morningside Academy)
|Abstract: The Morningside Model of Generative Instruction features a three-tiered assessment system. At the Micro level, Morningside teachers use Precision Teaching to collect daily measurements on several academic pinpoints. The Meta level consists of placement tests and progress monitoring tests to validate data at the Micro level, diagnose potential obstacles to desired growth, and predict performance on end of the year tests. Those end-of-the-year assessments make up the Macro level, where standardized, norm-referenced tests are used to evaluate student growth across an entire school year. Implementing this robust system is not without difficulty. Doing so requires timely assessment administration, clear communication of results to all relevant individuals, and most importantly, effective instructional decision making based on assessment data. This has proven particularly challenging at the Meta level, prompting a one year revision project. This presentation will describe a process improvement methodology involved with the creation of a new system, including: Defining the assessment problem, outlining features and capabilities of an ideal assessment system, identifying resource limitations, system design, testing and rollout, and feedback. Data will be presented that show how and why redesign decisions were made as well as their effect in improving MMGI’s assessment system.
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.
|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.