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Self‐Monitoring for Students With Challenging Behavior:Technological Innovations, Research, and Real‐world Examples
DR. HOWARD WILLS, UNIVERSITY OF KANSAS
DR. ALLISON BRUHN, UNIVERSITY OF IOWA
ASHLEY RILA, UNIVERSITY OF IOWA
SARA ESTRAPALA, UNIVERSITY OF IOWA
The Marshmallow Test
Self‐Regulation
Unfortunately, many students with challenging behavior lack self‐regulations skills◦ These skills are critical for academic success and developing positive social relationships (Cameto, Levine, Wagner, & Marder, 2004; Carter, Lane Pierson, & Glaser, 2006)
What does it mean to be a self‐regulated learner? (Arslan, 2014)
Establish goal
Determine strategies that support progress toward that goal
Apply strategies
Monitor progress toward goal
A focus on self‐regulation/self‐management
Self‐De
term
ination
Choice‐Making
Decision‐Making
Problem‐Solving
Self‐Regulation/Self‐Management
Goal‐Setting
Self‐Instruction
Self‐Monitoring
Self‐Evaluation
Strategy instruction
Self‐Advocacy
Self‐Efficacy
Self‐Knowledge
What strategies do you use in your own life?
How do you help your students self‐manage?
What strategies do you use in your own life?
How do you help your students self‐manage?
What is self‐monitoring?
A meta‐cognitive skill that involves:(a) teaching students to be aware of their behavior, and then
(b) students recording whether or not the behavior occurred
A research‐based intervention demonstrating
positive effects across age, gender,
disability, and setting
A research‐based intervention demonstrating
positive effects across age, gender,
disability, and setting
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Example of “in the moment” self‐monitoring: Are you in your seat right now?
Interval Yes No1:00 X2:00 X3:00 X4:00 X5:00 X6:00 X7:00 X8:00 X9:00 X10:00 XTotal 80% 20%
Behavior: In Seat
Goal: During math class, Juan will be in his seat for 80% of intervals each day for a week.
Example of “retrospective” self‐monitoring: Did you meet classroom expectations during whole group instruction?
Bruhn, McDaniel, & Kreigh, 2015The Role of TechnologyPrompting devices: ◦ helpful for cueing, but not collecting data
We use technology to self‐monitor so many other things…why not behavior in schools?
Technology for Self‐Monitoring in Education: Early Examples
Gulchak, 2008
Bedesem & Dieker, 2012
Szwed & Bouck, 2013
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What can we learn from the medical field?
Technology-based self-recording interventions, involving making an observation and recording a behavior, have been used in applications focusing on…
Weight LossTurner-McGrievy, Beets, Moore, Kaczynski, Barr-Anderson, Tate, 2013
Diabetes ManagementLevine, Burns, Whittle, Fleming, Knudson, Flax, & Leventhal, 2016
Mental HealthKauer, Reid, Crooke, Khor, Hearps, Jorm, & Patton, 2012
Physical Activity and Health Records(Burke, Wang, & Sevick, 2011) (Häyrinen, Saranto, & Nykänen, 2008).
BENEFITSHealth Care Professionals Patients
Real‐time patient care data Real‐time feedback
Another means of communicating…
Reminders
Data over time Goal‐tracking
Increased Patient Accountability
Accountable and Supported
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WEARABLE DEVICES
Real-time feedbackPrompts to get up and move Upload activity data to the web and produce simple graphs and charts for users to monitor progressConnect! (Share and Join a Community)
Learning from the Medical and Health Fields
Piette (2007) Recommendations:“Look before you leap (but do not forget to leap)” (p. 2428). “One size does not fit all” (p. 2428). “Beware of “cool apps” (applications)” (p. 2428). TBSM, “is most effective when it supports human contact” (p. 2428).
Emerging TBSM in Education
Award: H327S170001
Stepping Up Technology Enabled Self-Monitoring for High SchoolStudents with Disabilities.
Office of Special Education and Rehabilitative Services
I-Connect
www.iconnect.ku.eduI-Connect Some studies supporting the use of I‐Connect
Clemons, L. L., Mason, B. A., Garrison‐Kane, L., & Wills, H. P. (2016). Self‐monitoring for high school students with disabilities: A cross‐categorical investigation of I‐Connect. Journal of Positive Behavior Interventions, 18(3), 145‐155.
Rosenbloom, R., Mason, R. A., Wills, H. P., & Mason, B. A. (2016). Technology delivered self‐ monitoring applicationto promote successful inclusion of an elementary student with autism. Assistive Technology, 28(1), 9‐16.
Crutchfield, S. A., Mason, R. A., Chambers, A., Wills, H. P., & Mason, B. A. (2015). Use of a self‐ monitoring application to reduce stereotypic behavior in adolescents with autism: A preliminary investigation of I‐Connect. Journal of Autism and Developmental Disorders, 45(5), 1146–1155.
Wills, H. P., & Mason, B. A. (2014). Implementation of a self‐monitoring application to improve on‐ task behavior: Ahigh school pilot study. Journal of Behavioral Education, 23(4), 421‐434.
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Rosenbloom, R., Wills, H. P., & Mason, R. A.
Carl
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Score It & MoBeGo
Acknowledgements
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ReferencesBruhn, A. L., Woods‐Groves, S., Fernando, J., Choi, T., & Troughton, L. (2017). Evaluating technology‐based self‐monitoring as a tier 2 intervention across middle school settings. Behavioral Disorders, 42(3) 119‐131. Vogelgesang, K., Bruhn, A. L., Coghill‐Behrends, W., Kern, M., & Troughton, L. (2016). A single subject study of a technology‐based self‐monitoring intervention. Journal of Behavioral Education, 25, 478‐497. Bruhn, A. L., Vogelgesang, K., Fernando, J., & Lugo, W. (2016). Using data to individualize a multi‐component, technology‐based self‐monitoring intervention. Journal of Special Education Technology, 31(2), 63‐76.
Bruhn, A. L., Waller, L., & Hasselbring, T. (2016). Tweets, texts, and tablets: The emergence of technology‐based self‐monitoring. Intervention in School and Clinic, 51(3), 157‐162.
Bruhn, A. L., Vogelgesang, K., Schabilion, K., Waller, L., & Fernando, J (2015). I don’t like being good! Changing behavior with technology‐based self‐monitoring. Journal of Special Education Technology, 30, 133‐144.
www.scoreit.info
Participants
Student eligibility◦ High rates of off-task behavior & poor academic performance◦ ODR or Screen score◦ IEP behavioral goal◦ EBD diagnosis
Teachers (n=13) Students (n=13)
SPED (n=2) Gen Ed (n=11 SPED (n=7) Gen Ed (n=6)
Female (n=11) Male (n=2) Female (n=2) Male (n=11)
Professional Development 5‐part Professional Development Series◦ Introduction to Self‐Monitoring◦ Introduction to Data‐Based Individualization (DBI)◦ Baseline Data and Intervention Implementation◦ Intervention Data Analysis◦ Final Analysis
DBI
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Baseline Data and Intervention Implementation
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General Recommendations:• Initial Goal = No more than 10% above
baseline mean• Look at data every 3‐5 days and make
decision• Change 1 variable at a time
General Recommendations:• Initial Goal = No more than 10% above
baseline mean• Look at data every 3‐5 days and make
decision• Change 1 variable at a time
Intervention Implementation
Intervention Data Analysis
Is Allison responding? How do you know?
Intervention Data Analysis
Is Allison responding? How do you know?
Intervention Data Analysis
Is Allison responding? How do you know?
Intervention Data Analysis
Is Allison responding? How do you know?
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Overall Outcomes for Behavior
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p < .00010
10
20
30
40
50
60
70
80
90
100
Baseline Intervention
Percen
tage
of P
ositive Beh
avior 81.1%
61.7%
Individual Outcomes
44
0
10
20
30
40
50
60
70
80
90
100
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
Percen
tage
of Positive Beh
avior
Session
Baseline 50%(3 min) 60% (3 min) 70% (3 min) 80% (3 min) 80% (5min)• Behaviors Monitored: Be On‐
Task, Be Productive• Feedback at the end of each
session• “I think my secret was the student. It
was the perfect intervention for her. Her mom at conferences mentioned that her daughter explained how she would always check to see how much time was left before needing to score. She mentioned how her daughter mentioned she would space off, touch the iPad and realize she needed to get back on task. So I attribute it to a student that just needed a visual reminder. Having the iPad on her desk gave her ownership in monitoring her time on task.”
Year 1
•Plot digitizing and data analysis of 80 self‐monitoring studies•Develop decision rules and apply them to existing data•Teacher focus groups•Reprogramming of app into “expert system”
Year 2• Usability & feasibility testing across sites• Adjustments to decision rules/programming
Year 3• Randomized control trial across sites• Adjustments to decision rules/programming
Project SCORE IT: Developing and Evaluating Interactive Technology to Support Self‐Monitoring and Data‐Based Decision‐Making
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MoBeGo Prototype Screen Shots
Student Settings Teacher Ratings Student Ratings & Comparison
Interval by Interval Line Graphs
MoBeGo Graph Screen Shot
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Ask the AudienceWould you be likely to follow the recommendations provided by the app? Why or why not?
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Preliminary Feedback How did you feel about the recommendations provided by the app?
“Great! I like that is scores baseline and comes up with goal and suggests a new goal when met. (The less I have to think the better).”
“I generally agreed with the recommendations and thought they matched the data.”
“I was happy when it said it wanted to bump it up and disheartened when the goal needed decreased.”
How did using the app fit into the structure and flow of your classroom? “My other students ignored it after the first few days. It was easy to run and the
student was not bothered by doing it. He rather enjoyed it.”
What did you enjoy or find useful about the app? “I liked being able to talk to [the] student about why he chose the
score he gave and what I chose.”
Future DirectionsResearch◦Can we expect differences in fidelity, sustainability, and social validity (as compared to traditional paper/pencil methods?◦Are commercially‐available apps supported by rigorous research?◦ Look at app store and developer websites
Future DirectionsData◦Capitalizing on efficiency and accuracy for data collection, storage, graphing, and adapting◦Creating “expert systems”
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Future DirectionsHuman Interaction◦Do not neglect, nor replace the human element of self‐monitoring◦Use data as a touchstone for feedback
Time for QuestionsHOWARD WILLS
University of Kansas
ALLISON BRUHN
University of Iowa
Allison‐[email protected]