Training Paraprofessionals to Make Data-Based Training Strategy Decisions (1986)
©1986, 2013 by Dallas Denny
Source: Denny, Dallas. (1986). Training paraprofessionals to make data-based training strategy decisions with and without the aid of a microcomputer: Effect on the performance of institutionalized severely and profoundly retarded persons. Paper for Richard Shores and Herb Reith, Department of Special Education, George Peabody College of Vanderbilt University.
Training Paraprofessionals to Make Data-Based Training Strategy Decisions
With and Without the Aid of a Microcomputer
Effect on the Performance of Institutionalized
Severely and Profoundly Mentally Retarded Persons
By Dallas Denny
For
Dr. Richard Shores and Dr. Herb Reith
Special Education 3937
March 10, 1986
Abstract
A number of studies have shown teachers can be successfully trained to collect and analyze data daily using trend and error analysis techniques and to make resulting educational decisions that lead to improved student performance. Effects have tended to be positive across a variety of academic tasks and a variety of handicapping conditions, as well as across a range of educators and a variety of settings. However, the use of data-based instructional decision-making has not been widespread. Study 1 is an attempt to extend these findings to a residential facility for the mentally retarded, using paraprofessional trainers as subjects. Study 2 is an attempt to determine whether the findings of study 1 can be extended by training instructors to use a microcomputer program which will graph daily data and indicate when instructional decisions are needed.
With the impending enactment of the Gramm-Rudman-Hollings legislation, which will result in severely reduced funding for most human service programs (Blustein, 1985; Lytle, 1986), teachers will be under increased pressure to demonstrate that their instructional techniques are effective. Already, documentation of teacher effectiveness is considered of paramount importance (Sykes, 1983); this is especially true in special education, where P.L. 94-142 has already resulted in an increased pressure for accountability in schools (Fuchs, Deno, & Mirkin, 1983 Wesson, Tindal, Fuchs, Mirkin, & Deno, 1986). Many educators have realized that “the goodness of any instructional decision must ultimately be judged by its impact on child performance” (Gable & Hendrickson, 1979, p. 29). However, empirical evidence of the effectiveness of teaching strategies is often lacking, (Lovitt & Smith, 1972; Shores, Roberts & Nelson, 1976).
Two traditional ways of judging the effectiveness of instruction are the administration of posttest only and the administration of both pre and post tests (White & Liberty, 1976). These types of evaluation have been described as both indirect and infrequent (Lovitt, Schaaf, & Sayre, 1986). Problems with such infrequent measurement include insensitivity to needs of individuals, the inability to revise curriculum efficiently, and probable wastage of time (Lovitt, Schaaf, & Sayre, 1986; White & Liberty, 1976). With these approaches, it is not possible to demonstrate a relationship between day to day changes in teaching strategy and pupil performance. Also, improvement in the day to day performance of the individual student can go undetected (Lovitt, Shaaf, & Sayre, 1986).
Since Lindsley (1964, 1971), special educators have become increasingly aware that a technology of teaching requires an empirical technology of measuring both pupil and teacher behavior (Fredericks, Baldwin, Moore, Moore, & Furey, 1978; Gerber & Kauffman, 1979; Stowitschek, Lewis, Shores, & Ezzell, 1978). Frequent measurement of child performance is critical for both timely and effective revisions of curriculum and for providing teacher feedback (Lovitt, Schaaf, & Sayre, 1986). Performance data should be collected continuously or at least twice a week (Cooper, 1981; Fuchs, 1986; Hasselbring & Hamlett, 1984a, 1984b; Reith, Polsgrove, & Semmel, 1981; Stowitschek et al., 1978; White & Haring, 1980; White & Liberty, 1976).
The primary advantage of frequent measures of performance is the opportunity for ongoing analysis of performance, with timely modifications of curriculum and teacher behavior based on the data (Alberto & Troutman, 1983 Fuchs, Deno, & Mirkin, 1983; White & Liberty, 1976). Unfortunately, data is not always collected, and simply collecting data does not insure that good data-based instructional decisions will be made (Fuchs, Deno, & Mirkin, 1983; Reith, 1982). Also, visual inspection of data by teachers can be idiosyncratic, leading to poor instructional decisions (Haring, Liberty, & White, 1980a).
A number of studies have shown that training teachers to collect and analyze data daily using trend and error analysis techniques (White, 1971, and Cox, 1975, respectively), and to make resulting educational decisions can lead to improved student performance (Bohannon, 1979; Burney & Shores, 1979; Haring, Liberty, & White, 1980a; Kerr & Strain, 1978; Stowitschek, et al., 1978; and White & Liberty, 1978). In a meta-analysis, Fuchs & Fuchs (in press) reported that systematic formative evaluation (an instructional strategy characterized by ongoing evaluation and modification of programs) resulted in increased learner performance. Effects have tended to be positive across a variety of academic tasks (reading, spelling, and arithmetic), and a variety of handicapping conditions (behaviorally disordered children 10 to 12 years of age, trainable mentally retarded children, adolescents with behavior disorders, a boy in need of remedial reading training, mildly handicapped and learning disabled persons, and severely and profoundly mentally retarded individuals, some of whom were multiply handicapped). Positive effects were found across a range of educators, including rural special education teachers, teacher trainees, and psychiatric teacher-counselors, and in a variety of settings including rural schools and a residential psychiatric hospital.
Kerr & Strain (1978) additionally found in the instructional setting a positive relationship between the use of precision teaching strategies and the amount of contingent praise delivered to students by teachers, and Burney & Shores (1979) found a shift from teacher antecedent to teacher consequent events.
Bohannon (1975) found that the use of daily charting and trend analysis procedures by teachers accelerated the progress of learning disabled children in grades 1-3. After one month, these reading-delayed children reached the level of performance of their non-delayed peers. Bohannon concluded that daily assessment, regular charting, and the use of program change rules were responsible for the improved performance.
Stowitschek, et al. (1978) used a multiple baseline across teachers (teacher-counselors) to determine whether consistent use of a 14-step planning strategy would result in increased mathematics performance by behaviorally disturbed teenagers. The strategies were based on the analysis of performance trends used by Haring, Liberty, & White (1980a, 1980b). Four of the five students showed significant performance gains when teachers used the planning strategies. The teachers used significantly more planning strategies after training than they had before training.
In a study using a multiple baseline across subjects and two academic areas, Kerr & Strain (1978) taught teacher trainees a 9-step precision teaching packing, including trend analysis which was an elaboration of Lindsley (1964) and Young (1972)’s teaching package. Findings were (1) that the intervention procedures used resulted in a dramatic and consistent increase in the use of precision teaching tactics by the teacher trainees; (2) this increase occurred over all students and across the academic areas of math and reading; and (3) the use of contingent praise by the teacher trainees increased with increased use of precision teaching strategies; and (4) pupil performance in all classrooms showed improvement upon implementation of the planning procedures by the teacher trainees.
Burney & Shores (1979) used a multiple baseline design across teachers and their students to determine the relationship between the use of a procedure to train data-based decision-making skills, teacher-pupil interaction during instruction, and performance of mentally retarded children on a mathematics task. They found that two of three teachers used the decision-making skills more often after training; that during instruction, teachers exhibited fewer teacher antecedent events (questions, instructions, or models), and more consequent events after training; and that pupil performance accelerated with use of the planning techniques.
In a three year study of severely handicapped students, Haring, Liberty, & White (1980a, 1980b) found that teachers who used data-based decision-making procedures made effective changes (changes resulting in improved student performance) 62 percent of the time. During the two years before intervention, only 33% and 41% of instructional strategy changes resulted in improved performance.
Finally, Fuchs, Tindal, & Fuchs (in press) found that low-achieving first grade students whose progress was frequently measured and who received frequent corrective feedback scored higher than low-functioning first grade students who were measured and achieved corrective feedback less often. This effect was not found for high-achieving students.
Two western states, Utah and Montana, have mandated use of precision teaching models in all schools (R. Gall, personal communication, 24 February, 1986). Despite this, and despite the demonstration that decisions made in the absence of data are often faulty (White & Haring, 1980) the use of data-based instructional decision-making has not been widespread (Hasselbring & Hamlett, 1984a Howell, Kaplan, & O’Connell, 1979; Wehman, 1979). The time and energy required for the processes of collecting and analyzing data and making the resulting instructional decisions may be part of the problem (Brady & Langford, 1984; Mirkin, Fuchs, Tindal, & Deno, 1981). The process has been described as impractical for teachers with limited support systems (Howell, Kaplan, & O’Connell, 1979; Wehman, 1979). However, speed increases with practice (Fuchs, Wesson, Tindal, Mirkin, & Deno, 1982; Reith, 1982; R.E. Shores, personal communication, 16 January, 1986; Wesson, et al., 1986), and it is likely that other factors, such as lack of support from other school personnel, are involved when teachers trained to collect and analyze data stop doing so (H.J. Reith, personal communication, 17 February, 1986). Without further research in this area, it is unlikely that a clear picture of these factors will emerge.
Study 1
This study will be an attempt to extend findings that training teachers use data-based decision-making techniques leads to increased learner performance to a different setting (a residential facility for the developmentally disabled), and to a different population of instructors (paraprofessional trainers). The specific research questions asked are: (1) Will it be possible to demonstrate that the training procedure used results in increased use of data-based instructional decision-making by instructors; and (2) Will there be a resulting change in the performance of pupils of the instructors?
Despite a growing trend toward deinstitutionalization, residential institutions for the mentally retarded exist in all fifty states. In Tennessee, these institutions are called Developmental Centers, and exist for the purpose of training mentally retarded persons in skills important for living and working in society (Dokecki & Mashburn, 1984). Because of the high costs and limited bed space of the Developmental Centers, most admissions are for six-month periods. Obviously, then, effective training techniques are of considerable importance in these centers.
Public Law 94-142 requires the provision Education Plans (IEPs) for individuals under 21 years of age, and to be eligible for AC MRDD accreditation, Individual Program Plans are required for all individuals regardless of age in residential facilities (AC MRDD, 1978). AC MRDD guidelines require daily training for all individuals, with collection of data; additionally, the Tennessee Mental Health, Mental Retardation, and Autism maintains a Goal Planning System which requires the statement of goals in behavioral terms and daily collection of data. Consequently, staff at all levels are accustomed to collecting behavioral data and evaluating these data in order to see if the predetermined goal has been met. However, goal-setting is done by the Interdisciplinary Team at the yearly IEP meeting, and even though AC MPDD mandates data-based decision making, it is unlikely that staff at any level make decisions based on error analysis (Cox, 1975; Young, 1972) or trend analysis (White, 1971).
In the chosen setting, trainers include certified teachers, developmental technicians, and Counselor Associates. The instructors selected for this study are Counselor Associates; they were chosen because they do a great deal of individual training and because the time constraints on them are less rigid than those of the developmental technicians and teachers. Previous studies have used as subjects teachers, teacher-trainees, and psychiatric teacher-counselors. Counselor Associates differ from previous subjects in that they are paraprofessional personnel with limited training in education and the other behavioral sciences.
Method
Setting
The study will be conducted at Green Valley Developmental Center, a state-operated residential developmental center located near Greeneville, Tennessee. The facility houses over 700 individuals, most of whom are severely and profoundly mentally retarded. Permission of the facility’s Committee for Research, Publicity, and Training will be sought.
Subjects
Four subjects will be chosen from a pool of Counselor Associates. Counselor Associates are paraprofessional persons who are under the direct supervision of psychological examiners. Average education of the current Counselor Associates is high school plus one or two years of college. The Counselor Associates have some familiarity with principles of applied behavior analysis, and are accustomed to collecting and graphing data on a daily basis. It is hoped that the Counselor Associates will voluntarily participate in the study.
Pupils
Each Counselor Associate will select three adult residents of the facility as pupils. Most of the residents of the facility are severely or profoundly retarded, as determined by their performance on standardized intelligence tests, and many are multiply handicapped. Facility policy requires that all residents have six active goals, stated in behavioral terms. Goals may be in any of the following areas: self-help skills, discrimination training (often this includes discrimination of shapes, colors, or sizes), vocational activities, cottage-living skills, communication, or reduction of inappropriate behavior. Training usually occurs at regularly scheduled times throughout the week; data is recorded daily. Counselor Associates are often the person responsible for carrying out training.
Criteria for inclusion of pupils in the study will be based on (1) age over 18, (2) functioning level of severe or profound retardation, and (3) a history of failure to meet criteria for successful completion of discrimination learning goals such as sorting or shape, color, or number recognition (over 60% failure rate).
Learning Tasks
The learning tasks selected are tasks which are commonly taught at Greene Valley. Because of the widely differing intellectual and performance abilities of the pupils, two different learning tasks were chosen: (1) a simple sorting task; and (2) a (presumably more difficult) sign recognition task. Pupils who can successfully sort objects will be given the sign recognition task. The Counselor Associates will be the trainers for the tasks. The sign learning task is socially relevant because it will train recognition of courtesy, traffic, and warning signs commonly seen around the facility and in the community. The sorting task is socially relevant because at Green Valley, the ability to sort and discriminate items is a prerequisite for placement in vocational workshops. Each task will train a skill which is important for maximal functioning in the community and at Green Valley. For example, the sorting task will involve parts or apparatus used in actual contracts in the vocational workshop, and the sign recognition task will teach signs the pupil will see on the grounds or on trips to the community. Session length and criteria for successful completion of the goals will be the same for all subjects.
Pupils will be assigned Task 1 or Task 2 by the following procedure: Pupils will be pre-tested on both tasks. Pupils who perform at more than 60% of criterion level on the sign recognition task will be excluded from the study. Pupils who perform at more than 60% criterion level on the sorting task will be given the sign recognition task during the study.
Criterion for successful completion of Task 1 will be successfully placing 10 bolts of two different sizes into the correct bins (100% success for three different trials). Criterion for successful completion of Task 2 will be successfully discriminating 5 of 6 signs during three successive trials.
Data Collection
Data collection will consist of (1) examination of daily data sheets, (2) daily interviews with Counselor Associates with discussion of the planning techniques used that day, and (3) direct observation of the training session. A small percentage (5-10%) of the training sessions will be videotaped.
(1) Data will be recorded on the forms which are normally used at the facility. The experimenter will gather data sheets daily after all training sessions are complete, examine them, and plot correct and incorrect responses of the pupil in graphic form.
(2) Counselor Associates will be interviewed by the experimenter daily after all training sessions are complete. The purpose of the interview will be to determine the specific planning strategies the Counselor Associates used. Specific questions will be asked about each strategy. A separate interview will be conducted for each pupil. The interviews will be videotaped. For reliability purposes, a second observer will be present during at least one-third of the interviews, or the observer will later view the videotape. Reliability will be calculated by the formula: (agreements/(agreements + disagreements)) * 100% = P% (Araujo & Born, 1985).
(3) The experimenter or an associate will directly observe all training sessions. Primary data taken will be of student performance (correct and incorrect responses, time to respond during each trial, and total time of the training session). Observations will be recorded on a Radio Shack Model 100 portable computer, using the program Behavioral Observation System– COmputerized (BOSCO, Denny, Fox, & McEvoy, 1985).
A second observer will be present at least one-third of the sessions for reliability purposes. Reliability will be calculated by the formula given above.
Intervention Procedure
A baseline phase (see below) will precede the intervention. The intervention will consist of training of the Counselor Associates in precision teaching skills. The Counselor Associates will be trained separately, so as to minimize communication between them. Training will consist of the learning of the nine techniques of Burney (1976), as specified by Kerr & Strain (1978):
1. Counting of the number of correct and incorrect responses.
2. Counting of correct and incorrect responses on repetitions.
3. Comparison of responses with those on previous worksheets.
4. Analysis of attack techniques used by each pupil on each stimulus item.
5. Timing of pupil’s work to completion.
6. Notation of specific teaching techniques used.
7. Establishment of performance criteria objectives for each pupil.
8. Graphing of correct and incorrect response data.
9. Analysis of graphs for trends or relationships between correct and incorrect responses. Specific relationships defined include those indicating: (a) problems of discrimination, (b) reinforcement, and/or (c) contingent control.
( p. 93)
The experimenter will be the primary trainer. Daily one-hour training sessions will be arranged. Training will continue until each Counselor Associate is able to correctly perform all nine operations upon data with which they are presented. Criterion for “correct” performance will be 90 percent accuracy for three consecutive sessions. Following completion of training, the experimenter will meet with each Counselor Associate once a week to make sure that they are still able to complete the nine planning techniques with at least 90% accuracy.
Training of Pupils
In accordance with their active goal plans, pupils will receive daily training sessions (Monday-Friday) of 15-30 minutes in length, in a training room. The Counselor Associate will work one-on-one with the pupil during this period.
Design
Design will be a multiple baseline across subjects. There will be three phases:
(1) Baseline. During baseline phase, the Counselor Associates will be told only that they are participating in a study and that their use of planning strategies. The experimenter or an associate will observe during the daily training sessions for each goal plan for each pupil. Additionally, the Counselor Associates will be interviewed daily, as above. The intervention will begin when the rates of correct and incorrect responses of the pupils are stable.
(2) Intervention. The Counselor Associates will be taught to use the nine precision teaching strategies.
(3) Follow-up. Six months after completion of the study, direct observation of the Counselor Associates will again be made by the experimenter or an associate, and interviews will again commence. The purpose of the follow-up will be to determine whether the Counselor Associates have continued to use the planning procedures.
Analysis of Data
Pupil performance (correct and incorrect responses, average time to complete trials, and total time of sessions), as well as the use of planning techniques by the Counselor Associates will be plotted graphically (the researcher will graph the data independently of the Counselor Associates). Analysis will be by visual inspection of data.
Study 2
During the past five years, microcomputers have come into widespread use in school systems throughout the United States. Schimizzi (1983-1984), in a report of the results of a nationwide survey of 400 school systems, indicated that microcomputers were housed in 33 percent of schools. More recently, Rheinhold & Corkett (1985) reported on a survey of computer use in schools throughout the U.S. They noted that more than one-half of the states have computer literacy requirements, and that all states conduct teacher-training workshops in computer usage.
Because there have historically been problems with getting teachers and others to collect and analyze data in a timely manner and to make good instructional decisions based on that data, (Liberty, 1972), Fuchs, Deno, & Mirkin (1983 and Hasselbring & Hamlett (1984a, 1984b) have independently developed computer programs to aid teachers in making successful instructional decisions. AIMSTAR (Hasselbring & Hamlett, 1983, 1984a, 1984b) runs on the APPLE ][ series of microcomputers. AIMSTAR is interactive in nature, and easy to use. It operates by the instructional decision-making rules developed by Haring, Liberty, and White (1980a, 1980b), and notifies the user when changes in instructional strategies are needed. AIMSTAR provides on-screen or hard-copy graphs of student performance, plotted in relation to the goal (aimstar) of the student.
AIMSTAR is of great potential benefit as a tool to aid in the instructional decision-making process. However, there has not to date been a demonstration that use of AIMSTAR by teachers will lead to improved performance by students. A number of questions arise: Will teachers use the program, or will they resist? Will they prefer use of the program to the use of paper-and-pencil? Do teachers who use the program make educational decisions which lead to increased learning by students? Should the computer program be used instead of the paper-and-pencil method used in previous studies, or should it be used instead to supplement the paper-and-pencil approach? Is an understanding of error analysis and trend analysis procedures a necessary skill in making instructional decisions? Is use of the computer program less time consuming than the paper-and-pencil method? Does use of the computer program result in better instructional decisions (i.e. greater student performance) than use of the paper-and-pencil method?
The second study will be an attempt to answer some of these questions: Can instructional personnel be trained to operate the AIMSTAR program, and will they use the program to make data-based instructional decisions? What is the effect, if any, of the use of the program on the performance of pupils? The specific research questions will be: (1) Will it be possible to demonstrate that the training procedure used results in increased use of data-based instructional decision-making by instructors; (2) Will the instructors use AIMSTAP in preference to paper-and-pencil methods; and (3) Will there be a resulting change in the performance of pupils of the instructors with use of AIMSTAR?
Method
Setting
The setting will be the same as for Study 1, above.
Subjects
Subjects will be four Counselor Associates not used in the previous study. In the event that four Counselor Associates cannot be found, Cottage Trainers will be used as subjects. Cottage Trainers fill a role similar to that of the Counselor Associates. Their level of education and familiarity with data-collection techniques are also similar.
Pupils
Pupils will be drawn from the same population as for Study 1, with the constraint that pupils who participated in Study 1 will not participate in Study 2.
Learning Tasks
Learning tasks will be the same as for Study 1.
Data Collection
Data collection will proceed as in Study 1, with the exception that printouts from the AIMSTAR program will also be gathered and analyzed.
Intervention Procedure
The intervention will consist of training the Counselor Associates in precision teaching skills. The Counselor Associates will be trained separately, so as to minimize communication between them. Training will consist of the learning of the nine techniques of Burney (1976), as specified by Kerr & Strain (1978). The Counselor Associates will then be trained to operate the AIMSTAR program for the Apple It computer (Hasselbring & Hamlett, 1983, 1984a, 1984b), or for the IBM personal computer. Daily one-hour training sessions will be arranged. Training will continue until each Counselor Associate is able to correctly operate the program by entering data with which they are presented. Following completion of training, the experimenter will meet with each Counselor Associate once a week to make sure that they are still able to complete the nine planning techniques and to correctly use the AIMSTAR program.
Training of Pupils
Training of Pupils will proceed as in Study 1.
Design
Design will be a multiple baseline across subjects. There will be three phases:
(1) Baseline. During baseline phase, the Counselor Associates will be told only that they are participating in a study to determine their use of planning strategies in the classroom. The experimenter or an associate will observe during the daily training sessions for each goal plan for each pupil. Additionally, the Counselor Associates will be interviewed daily, as above. The intervention will begin when the rates of correct and incorrect responses of the pupils are stable.
(2) Intervention. The Counselor Associates will be taught to use the nine precision teaching strategies..
(3) Follow-up. As with Study 1, there will a brief follow-up period six months after completion of the intervention phase.
Analysis of Data
Data will be collected and analyzed as in Study 1.
Discussion
While the above studies may demonstrate that training paraprofessional trainers to make data-based instructional decisions will result in improved performance by severely and profoundly mentally retarded individuals in a residential setting, a number of important questions will remain unanswered. Most importantly, the design of the studies provides no means for comparing the relative effectiveness of using AIMSTAR vs. using paper-and-pencil methods of making data-based decisions. Additional studies will be needed in order to determine which of the two methods: (1) is less time-consuming; (2) leads to more effective instructional decisions; and (3) is more likely to be used over time. It would be of use also to extend the findings to untrained direct care staff.
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