Send the link below via email or IMCopy
Present to your audienceStart remote presentation
- Invited audience members will follow you as you navigate and present
- People invited to a presentation do not need a Prezi account
- This link expires 10 minutes after you close the presentation
- A maximum of 30 users can follow your presentation
- Learn more about this feature in our knowledge base article
The Effects of Behavioral Skill Training package on Rule-Gov
Transcript of The Effects of Behavioral Skill Training package on Rule-Gov
Discussion & Future Research
, Ph.D., CABAS® SBA, Asst. RS, BCBA-D
, Ph.D., CABAS® SBA, Assoc. RS, BCBA-D
, M.A., CABAS® Teacher I, BCBA
, M.A., CABAS® Teacher II, BCBA
, Ph.D., CABAS® Assoc. BA, BCBA-D
Applied behavior analysis based educational programming is becoming increasingly common in school settings.
A critical staff behavior necessary for quality ABA-based educational programming involves using student educational data to guide instructional programming (Martens, Eckert, & Bradley, & Ardoin, 1999; Daly, Witt, Martens, & Dool, 1997).
There are many challenges to delivering individualized ABA-based instructional programming in a classroom setting.
One issue is that teachers and/or behavior analysts are typically responsible for multiple students, and each student often has multiple educational programs carried out daily.
Thus, it may be difficult for professionals to review the large amounts of data generated by the educational programs on an ongoing basis.
One way to address this limitation is to train paraprofessionals and/or teaching assistants (TA's) to conduct preliminary analysis of graphed data, identify decision opportunities that suggest a potential program change is needed, and alert the teacher/behavior analyst to review those data sets.
in correct responding demonstrating steady progress indicate the teacher should continue with the current program.
Variability or downward trends
indicating poor or inconsistent performance might suggest the teacher should either initiate an alternative teaching tactic, or obtain additional information to analyze the learning problem.
Alternatively, data patterning showing
(e.g., stable performance above 90%) indicates that it is time to shift to the next learning objective (e.g., establish new response requirements, decrease reinforcement, etc).
Identifying these patterns and making appropriate changes in a timely fashion makes programming more responsive to student progress and can avoid continued presentations of instructions that have been mastered or are too difficult for the student to complete.
Behavioral Skill Training (BST)
is a widely used method of training incorporating a combination of verbal instruction, modeling, practice and feedback toward the acquisition of a particular skill:
(1) a picture exchange communication system (Rosales, Stone, & Rehfeldt, 2009)
(2) paired-choice stimulus preference assessments (Lavie & Sturmey, 2002),
(3) discrete trials (Sarokoff & Sturmey, 2004).
Keohane and Greer (2005)
tested the effects of instructing teachers in the use of a verbally governed algorithm to solve students’ learning problems.
The teachers were taught to analyze students’ responses to instruction using a strategic protocol (i.e., decision making) to determine whether the instructional method would affect the number of verbally governed decisions which the teachers made as well as the number of academic objectives achieved by the teachers’ students.
The decision making algorithm required teachers to make different levels of complexity of verbally governed decisions on a task analysis of a hierarchy.
The results indicated that the teachers’ students achieved significantly more learning objectives when they used a verbally governed algorithm to solve instructional problems.
In two experiments, we tested the effects of a Behavioral Skill Training (BST) procedure on paraprofessionals’ rule governed decision makings.
Behavioral Skill Training
vs. Didactic training
Ranging in age from 22 to 39 years old
Employed from 1 week to 1 year
At least a high school diploma
80 hours of training prior to working independently with students
Responsible for working 1:1 with students and implementing written educational programs, collecting student educational data, and graphing data
Pre-Test/Post-Test Control Group Design
Graphs depicting correct student responses to educational programming (actual student graphs)
Simulated data depicting what would be found on educational graphs (simulated graphs for training purposes)
Decision Tree Protocol, including list of decision-making rules (Greer, 2002; Keohane & Greer, 2005)
Rule-identification was evaluated using 20 simulated data sets (each data set contained one decision opportunity).
The simulated data sets were presented.
“Was the decision made correctly?" Yes or No
“What rule from the decision protocol was applied?” the name of the rule corresponding to the decision.
Rule-following was evaluated using both simulated data and educational data.
20 simulated data sets were presented.
Participants were presented student educational data sets, which contained a total of 30 decision opportunities across each group.
The participants were required to make decisions per the decision rules.
This group training session took approximately 45 minutes. In this group, participants attended a lecture together where they were presented with the decision rules and examples of graphs demonstrating the use of each rule.
Discussion about the rules was guided and followed by a quiz to assess both rule-identification (in response to the presentation of graphs), and rule-following (which involved emitting the appropriate responses “check and initial”, and “flag”).
The quiz contained 5 questions and, in addition to feedback on quizzes, participants received opportunities to respond when the graphs were reviewed with the group by the trainer.
Behavioral Skill Training
Three participants received training individually for approximately 30 minutes using BST in which they received specific instructions based on the decision errors he/she made during baseline with the simulated data sets.
That is, the trainer presented the participant with a set of data, asked the individual to identify the decision opportunity and the corresponding rule.
Participant responses to those stimuli resulted in positive or corrective feedback by the trainer. If an error was made, the participant was guided to emit the appropriate responses, which involved marking the graph as described in the decision rules. These opportunities were presented as learn units and each participant completed 20 during the BST training.
Three participants in the control group did not receive any training apart from what all teaching staff were exposed to as a part of orientation, regular in-servicing, and ongoing supervision.
Rule-identification was not assessed in the control group.
Only student educational data were used to evaluate rule following.
Rule-following was evaluated at two points in time: prior to the other groups receiving training (baseline), and after the other groups completed training (post training).
Participants were presented student educational data sets that contained a total of 30 decision opportunities across each group (averaging 10 decision opportunities per participant; range 9 to 13 opportunities.
Pre and post data using actual graphs were collected for 6 months up to the intervention and for up to 6 months immediately following the intervention.
Data were examined using a combination of visual analysis and statistical analysis.
One-way ANOVAs were performed on difference scores to determine if groups showed different levels of improvement. When the ANVOAs revealed significant differences between three groups (BST, DI, and control), Tukey HSD post hoc analys was conducted to identify which groups had significantly different improvement.
Percentage of correct rule-following and rule-identification was used as the primary outcome measure.
Simulated graphs – Rule identification and Rule-following
Actual graphs – Rule-following
We tested the effects of a behavioral skill training (BST) procedure by using a multiple baseline design across participants.
Pre- and Post-
First, all participants received traditional didactic instruction about the data analysis rules. Then, the participants were required to apply the data analysis rules.
The participants ran individualized student instructional programs while using the decision-making protocol.
When three instances of decision opportunities occurred, the percentage of participant’s correct responses was calculated and graphed as one data point.
Experimenters scored permanent products using graphed student data.
Behavioral Skill Training
using simulated graphs
Verbal Instruction (30 min)
Modeling (15 min)
Practice Quiz (i.e., a quiz consisted of 24 practice graphs) with which the participants were required to apply the decision analysis rules correctly.
Feedback (after quiz)
This procedure continued until the participant achieved mastery criterion set at 100% accuracy (i.e., the rules were correctly selected and applied across 24 of 24 sets).
Multiple baseline across participants design
3 novel paraprofessionals (new TA)
Thus, the future research should investigate the effects of in-situ BST on different levels of decision makings emitted by participants and their students’ instructional outcomes (STO, learn units to criterion, etc.).
Group 1 –
Three provisional teachers (college degree, provisional teacher license, eligible for teacher license)
Group 2 –
Senior level paraprofessionals with a college school degree (e.g., head TA, team leader)
Group 3 –
Senior level paraprofessionals without a college school degree (e.g., head TA, team leader)
The current two experiments showed that the BST was effective to improve paraprofessional’s rule-governed decision making (basic level of decisions).
Different levels of complexity of decision making (using rule-governed, verbally-meditated, verbally-governed verbal behaviors) should occur to improve the quality of student’s instruction (Keohane & Greer, 2005). They also showed the functional relation between correct decision making and student’s better progress (e.g., learn units to criterion).
The BST in the current two experiments was designed to train paraprofessionals to make the rule-governed basic level of decisions.
analysis conducted on rule-following difference scores revealed a statistically significant effect of group F (1, 2) = 19.26, p = 0.002, indicating that the three groups differed.
analysis revealed that both treatment groups showed greater improvement in rule-following relative to the control group.
The greatest difference was between the BST and control group (p = 0.002), however the DI group also showed significantly greater improvement relative to the control group (p = 0.032). The BST and DI groups were not significantly different (p = 0.074); however all 3 participants in the BST group achieved mastery compared to only 1 of 3 participants in the DI group.
This paper (Matthews & Hagopian, accepted) is accepted by Journal of Organizational Behavior Management (JOBM), 2013
Experiment 1 –
we compared the effects of BST, and didactic training on correct rule governed decision makings.
Experiment 2 –
we tested the effects of a BST procedure by using a multiple baseline design across participants.