Abstract

This study examined the effectiveness of using a technology-based self-monitoring intervention called Monitoring Behavior on the Go (MoBeGo). On-task behavior for students with behavioral issues was the primary dependent variable in the study. The researcher employed a single-subject withdrawal design (ABAB) with two generalization phases (C-D) to investigate the ability of MoBeGo to generalize the results to a different setting. Visual analysis of graphs revealed the participants had a clear functional relationship between MoBeGo and percentage of on-task behavior. The finding illustrated on-task behaviors in a different setting did not increase without using MoBeGo and therefore no automatic generalization occurred in different settings. A replicated phase (D) was conducted to confirm the finding, and the results showed the percentage of on-task behavior increased in math and science classes which used MoBeGo and did not increase in reading/writing which did not use MoBeGo. Also, the outcomes showed MoBeGo has a high level of acceptability among teachers who participated in the study. The researcher evaluated this single-subject withdrawal design (ABABCD) by using the What Works Clearinghouse (WWC) evidence standards. In addition, the researcher utilized the Single-Case Analysis and Review Framework (SCARF) to evaluate the study outcomes. The evaluation results of using WWC and SCARF are discussed in Chapter 4. The researcher discussed major lessons learned and some limitations of using technology based self-monitoring (TBSM). In addition, implications for practitioners, researchers, and application developers were included as future directions for using TBSM. Moreover, the researcher discussed the potential role of self-monitoring-based artificial intelligence (SMBAI) in education, and the use of artificial intelligence (AI), large language models (LLMs), or machine learning (ML) with self-monitoring apps. Finally, some important questions were raised about protecting privacy and minimizing the risk of data breaches for individuals, and how to ensure the security of individuals' data.

Notes

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Graduation Date

2023

Semester

Summer

Advisor

Vasquez, Trey

Degree

Doctor of Philosophy (Ph.D.)

College

College of Community Innovation and Education

Department

School of Teacher Education

Degree Program

Education; Exceptional Education

Identifier

CFE0009685; DP0027792

URL

https://purls.library.ucf.edu/go/DP0027792

Language

English

Release Date

August 2026

Length of Campus-only Access

3 years

Access Status

Doctoral Dissertation (Campus-only Access)

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