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From Simple to Complex: Methods for Assessing and Shaping Students’ Behavior |
Monday, May 28, 2012 |
10:30 AM–11:50 AM |
616/617 (Convention Center) |
Area: EDC/DEV; Domain: Applied Research |
Chair: Richard Hennigan (Salem State University) |
CE Instructor: Marcie Desrochers, Ph.D. |
Abstract: The education and training of successful behavior analysts is important for the health and longevity of our field. It is imperative to use our science to teach our science, which includes a task analysis of the response set required for mastering the principles and procedures involved. The four presentations in this symposium cover the range of assessment of performance at the student undergraduate and graduate levels, as well as with practitioners. The first presentation covers the area of functional assessment with students and practitioners. The second with the complexity of grant writing on the part of graduate students. The third and fourth presentations address the issue of scoring and scaling the complexity of the skills required to effectively write and perform at undergraduate and graduate levels. The overall theme of student and practitioner success in these endeavors will be of interest to both academics and to supervisors who train in the field. |
Keyword(s): Development, Practitioners, Students, Task Analysis |
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Teaching Functional Assessment: The In's, the Out's, and the Arounds |
MARCIE DESROCHERS (State University of New York at Brockport) |
Abstract: Functional assessment has become the cornerstone of intervention practices in the field of applied behavior analysis. The methods by which we disseminate and train practitioners in the use of functional assessment is important for clients, students, and staff. As such, a careful consideration of learning prerequisite knowledge and skills, objectives, content, method, and outcome of efforts to teach this domain is required for anyone teaching new or developing students and practitioners. A review of the research literature and available instructional materials (e.g., textbooks, software, videos, online resources) was conducted to identify existing approaches for teaching functional assessment to psychology students and staff. A summary of the instructional methods that currently exist and the research evidence on their effectiveness will be presented and evaluated. The accomplishments, missing links and possible avenues to be explored will be discussed. |
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Exploring Student Development of Grant Proposal Review and Evaluation Skills in a Graduate-Level Grant-Writing Course |
WESLEY H. DOTSON (Texas Tech University), David M. Richman (Texas Tech University), Chrystal E.R. Jansz (Texas Tech University) |
Abstract: Graduate students hoping to secure an academic or research position face employment opportunities that increasingly require pursuit of external funds to support their work. The ability to prepare high-quality grant proposals depends on not only a deep knowledge of an area of inquiry and of research design methodologies, but also an understanding of and ability to critically evaluate proposals according to the scoring criteria against which they will be judged (e.g., how the National Institutes of Health or the Institute of Education Sciences instruct reviewers to score proposals). The purpose of the current study was to evaluate the impact of a grant-writing class on graduate student ability to critically review grant proposals according to federal guidelines. Graduate students from a College of Education reviewed and scored a grant proposal using IES guidelines on the first day of class and again on the last day of a summer-semester grant-writing class. We compared their pre-post performance along several dimensions of skill including the ability to identify issues and problems with the significance, methodology, personnel, and resources specified in the proposal. |
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Teaching by Task Analysis: Using our Science to Teach It |
DARLENE E. CRONE-TODD (Salem State University) |
Abstract: The principles and procedures in applied behavior analysis can be ordered from less to more complex tasks. One way to order these tasks is to assess them using the model of hierarchical complexity. The usefulness of scoring and then scaling the tasks is that doing so can lead to better shaping and chaining of student repertoires. This is imperative for the potential student to become an effectively functioning practitioner in the field. The principles and procedures (i.e., tasks) taught in a junior-level university applied behavior analysis course will be presented in terms of their scored and scaled orders of complexity, and student performance on those tasks. Data-based recommendations for curriculum design and training support will be discussed. In addition, some of the most complex tasks (e.g., functional assessment) will be highlighted as a potential pitfall that can be overcome. |
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Scoring Graduate-School Admissions Essays using the Model of Hierarchical Complexity |
PATRICE MARIE MILLER (Salem State University), Darlene E. Crone-Todd (Salem State University), Richard Hennigan (Salem State University), Rachel Lucas (Salem State University) |
Abstract: The Model of Hierarchical Complexity has been used extensively to study the behavioral tasks involved in critical reasoning in adults of different ages and different educational backgrounds. Previous research presented pilot data showing that the written products of prospective counseling program students demonstrated different orders of complexity, ranging from concrete to systematic, on their Graduate School admissions essays. Also, that undergraduate Honors students in psychology produced written work that is consistently scored at the formal order. The data presented here represents a subset of a current study in which graduate school admissions essays are being scored and then related to outcomes within the program. Establishing that the stage of writing in a student’s narrative statement is related to their success in a graduate program could allow for better prediction of which students are likely to be successful as well as providing some predictive validity data for the Model of Hierarchical Complexity. |
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