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The Effects of Self-Monitoring of Academic Performance on Students with LearningDisabilities and ADD/ADHDAuthor(s): Serena M. Shimabukuro, Mary Anne Prater, Amelia Jenkins and PatriciaEdelen-SmithSource: Education and Treatment of Children, Vol. 22, No. 4 (NOVEMBER 1999), pp. 397-414Published by: West Virginia University PressStable URL: http://www.jstor.org/stable/42899585Accessed: 16-05-2018 22:18 UTC

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EDUCATION AND TREATMENT OF CHILDREN Vol. 22, No. 4, NOVEMBER 1999

The Effects of Self-Monitoring of Academic Performance on Students with Learning

Disabilities and ADD/ADHD

Serena M. Shimabukuro

Mary Anne Prater Amelia Jenkins

Patricia Edelen-Smith

University of Hawaii at Manoa

Abstract

This study investigated the effects of self-monitoring of academic productivity and accuracy on the academic performance and on-task behavior of three male students with both learning disabilities (LD) and attention deficit disorder/ attention deficit hyperactivity disorder (ADD /ADHD). The students were taught to self-monitor and self-graph their academic performance for reading comprehension, mathematics, and written expression. On- task behaviors were observed and recorded by the teacher. A single group, multiple baseline design across three academic areas was used to assess the effectiveness of the intervention. The three students made gains in academic productivity and accuracy, and their on- task behaviors improved across all academic areas. The results of this study indicate that self-monitoring is an effective procedure for helping students improve their academic performance and attentional behaviors.

★ ★ ★

Students with learning disabilities may experience a wide range of problems with learning or performing academic skills in classroom envi- ronments. These difficulties may be exacerbated by attention deficits and inadequate self-management skills. Researchers have found that students with learning disabilities often have low levels of attention to task and are inattentive and easily distracted (Harris, Graham, Reid, McElroy, & Hamby, 1994; Lloyd, Hallahan, Kosiewicz, & Kneedler, 1982; Reid & Harris, 1993). The lack of attentional skills is further associated with inadequate independent work habits and the inability to manage one's behaviors (Hughes, Ruhl, & Peterson, 1988; Reid & Harris, 1993). Attention deficit disorder /attention deficit hyperactivity disorder

(ADD /ADHD) is a medically diagnosed disorder characterized by inat- tention, hyperactivity, and impulsivity which occur across home, school, work, and other social settings (Silver, 1995). Many students with ADD/ ADHD experience difficulties with learning and academic performance.

Address: Serena Shimabukuro, College of Education, University of Hawaii at Manoa, 1776 University Avenue, Honolulu, Hawaii 96822. EMail: [email protected].

Pages 397-414


398 SHIMABUKURO et al.

Although medication is the most common intervention for students with attention problems (Mathes & Bender, 1997), educational interventions including instruction in organizational strategies, self-monitoring, self- evaluation, and self-instruction procedures can also help these students to perform better in general and special education settings (Barkley, 1990; Fowler, 1991). The co-occurrence of learning disabilities and ADD/ ADHD is significant. According to Silver (1995), as many as 10 to 20% of children and adolescents have learning disabilities, and about 20 to 25% of them also have ADD/ ADHD. Many of these students receive special education ser- vices, but increasing numbers of them are being placed in general education settings and need to acquire independent learning skills and strategies.

The results of numerous studies indicate that self-management procedures, such as self-monitoring of attention, are effective in increasing time on-task for students with learning disabilities and other mild disabilities (Blick & Test, 1987; Dunlap, Dunlap, Koegel, & Koegel, 1991; Hal- lahan & Sapona, 1983; McLaughlin, 1983; Prater, Joy, Chilman, Temple, & Miller, 1991; Rooney, Hallahan, & Lloyd, 1984; Rooney, Polloway, & Hallahan, 1985; Webber, Scheuermann, McCall, & Coleman, 1993). Some researchers examined the effects of self-monitoring of attention on students with ADD/ ADHD and found that self-monitoring in combination with pharmacological or behavioral interventions may enhance students' on-task behavior (Hinshaw & Melnick, 1992; Mathes & Bender, 1997). Other researchers demonstrated that self-monitoring of attention also produces positive effects on academic productivity for students with learning disabilities, emotional disabilities, and attentional and academic difficulties (Blick & Test, 1987; DiGangi, Maag, & Rutherford, 1991; Ed- wards, Salant, Howard, Brougher, & McLaughlin, 1995; Hallahan & Sa- pona, 1983; Maag, Rutherford, & DiGangi, 1992; McLaughlin, 1984; McLaughlin, Burgess, & Sackville-West, 1981; Prater, Hogan, & Miller, 1992).

Treiber and Lahey (1983) indicated that behavioral interventions to de- crease disruptive behaviors do not necessarily result in improved academic performance. They proposed that interventions should focus on improving academic behaviors rather than on inattentiveness, disrup- tiveness, and excessive motor activities that are incompatible with learning. Reid and Harris (1993) found that both self-monitoring of attention and self-monitoring of academic performance may result in increased levels of attention, but they also suggested that on-task behaviors are not necessarily correlated with active academic engagement.

Self-monitoring of academic performance is effective in increasing academic productivity (completion and /or rate of completion), accuracy, or use of strategies for students with learning disabilities, attentional difficulties, and behavioral disabilities (Miller, Miller, Wheeler, & Selinger, 1989; Olympia, Sheridan, Jenson, & Andrews, 1994; Trammel, Schloss, &


SELF-MONITORING OF ACADEMIC PERFORMANCE 399

Alper, 1994). The effects of self-monitoring of academic performance on on-task behavior have also been investigated in several studies. The findings of these studies indicate that self-monitoring of academic performance is likely to result in improved on-task behavior for students with learning disabilities, attentional difficulties, and behavioral disorders (Carr & Punzo, 1993; Harris, 1986; Lloyd et al., 1982; Maag, Reid, & Di- Gangi, 1993; Reid, 1996; Reid & Harris, 1993). Previous studies have examined the effects of self-monitoring of aca-

demic performance on students with learning disabilities, behavioral difficulties, and ADD/ ADHD), but fewer studies have focused on students with both learning disabilities and ADD /ADHD). In addition, few studies have implemented self-monitoring of accuracy and productivity across three academic areas using curricular materials already in use in a classroom setting to examine the effects on accuracy, productivity, and on-task behavior. The purpose of this study was to investigate the effects of self-monitoring of academic productivity and accuracy on the academic performance of students with learning disabilities and ADD /ADHD during independent class assignments using curricular materials already in place and to determine the effects of academic self-monitoring on attentional behaviors. The self-monitoring intervention was implemented using a single group, multiple baseline design across three academic areas.

Method

Participants and Setting

Participants included three male students, one sixth grader and two seventh graders. All three students (a) performed within average range of intelligence on individualized school evaluations; (b) were identified by a multidisciplinary team as having a learning disability; (c) were diagnosed medically as having ADD /ADHD; (d) were able to perform academic tasks or skills during academic instruction and independent practice as determined by teacher administered curriculum based assessments; (e) demonstrated a history of academic deficiencies (i.e., work not completed or inaccurately completed); and (f) had problematic attentional behaviors during academic periods.

Additional characteristics for Glen, Manny, and Nelson are presented in Table 1. Participants were members of the same reading and math groups which consisted of a total of seven and six members respectively. While all members of the two groups participated in the self-monitoring procedures, only target students' data were collected. Other members of the group were not selected for the study because they either were not diagnosed for ADD /ADHD or did not exhibit problematic attentional behaviors.



Table 1

Characteristics of Productivity Students

Functional Levels

Rdg Writ Student Age Sex Disability Grade Comp Math Expr

Glen 12-0 M SLD/ADHD 6 3-4 5 50 Manny 13-1 M SLD/ADHD 7 4-5 4-5 84 Nelson 12-10 M SLD/ADHD 7 4 6 37

Note. Functional grade levels for Reading Comprehension were based on achievement scores on the Wechsler Individual Achievement Test (WIAT), Woodcock Reading Mastery Tests, Revised (WRMT-R), and Stanford Achievement Test (SAT), and school performance. Functional grade levels for Math were based on achievement scores on the WIAT, Wide Range Achievement Test-3 (WRAT-3), and SAT, and school performance. Percentile scores for thematic maturity (content sophistication) for Written Expression were based on spon- taneous writing samples for the Test of Written Language-2 (TOWL-2).

The three students were members of a self-contained, mixed grade class in a private school for students with learning disabilities. The class included 17 students in grades six through eight and was self-contained for all subjects, including the three targeted academic periods for reading, mathematics, and written expression. Each instructional period last- ed 45 minutes, and instruction was provided by one certified special education teacher and one assistant teacher.

Tasks and Materials

The study was implemented during reading, mathematics, and written expression instruction. The instruction and assignments in all three academic areas were designed to help students develop academic and skill proficiencies based on their Individualized Educational Plan (IEP) goals and objectives and present levels of performance.

Following group or class instruction during each of the three academic periods, the students were allowed 10 to 15 minutes to complete their independent practice assignments. They corrected their own work in groups and discussed any questions to obtain feedback on their performance. All students instructed in self-monitoring recorded their completion and accuracy percentages on their progress graphs at the end of the academic period.

Reading comprehension. Here, instruction and practice focused on reading comprehension skills in a variety of subject and interest areas, using instructional materials selected from Jamestown Publishers' Comprehen- sion Skills Series, Educators Publishing Service's Reading Comprehension in Varied Subject Matter, and Barnell Loft's Cloze Connections. The objective question formats included fill-in-the-blank, numerical sequencing, true-



false, short questions, multiple choice, matching, and underlining. Mathematics. The mathematics materials included textbook exercises,

worksheets, and manipulative activities from the Addison-Wesley basal text, Pre-Algebra. The sequence of content in the text guided instruction and activities for the group, and supplemental worksheets were used for independent practice. Written expression. The entire class, as opposed to small groups, was in-

volved in the written expression instruction. However, the self- monitoring instruction for this academic area was provided only to the three students during separate sessions. The materials for written expression during independent practice were adapted primarily from The Sen - tence Writing Strategy (Sheldon & Schumaker, 1985).

Dependent Variables

Three dependent variables were assessed: (a) academic accuracy, (b) academic productivity, and (c) on-task behavior. The students self- monitored for academic accuracy and productivity, and the teacher observed and recorded the students' on-task behavior.

Academic accuracy. At the end of each independent work period for the three targeted academic areas, the students self-corrected their own assignments. They counted the total number of items in each assignment, the number of items completed, and the number of items correctly answered. Using calculators, they computed their accuracy scores by dividing the number of correct items by the number of items completed, multiplied by 100; their scores were expressed as percentages. They then recorded and plotted their accuracy scores on a graph.

Academic productivity. The students self-monitored their own academic productivity, the measure of the number of items completed relative to the number of items assigned. For each assignment, they counted the total number of items in the assignment and the number of items completed. They computed their academic productivity scores as percentages by dividing the number of items completed by the number of items assigned, multiplied by 100. They finally recorded and plotted their productivity scores.

On-task behavior. The classroom teacher and assistant teacher observed

and rated the students' on-task behavior using a 10-second time sam- pling procedure for 10 minutes during three academic periods (i.e., reading, math, and written expression). Using a wall clock with a second hand to synchronize their observations, they systematically observed and recorded the students' on-task behavior at 10-second intervals, rotat- ing from one student to the next. On-task behavior was defined as: (a) seated at own seat; (b) writing supplies and work materials on desk in front of student; (c) eyes on teacher, board, or own work; (d) reading or working on assignment; and (e) asking relevant questions of teacher or neighbor, as appropriate.



Procedures

A multiple baseline design across three academic areas was used to assess the effects of self-monitoring on academic performance in reading, math, and written expression for each of the three students. This single group design was used to determine whether the intervention would be effective for a small group of students, as well as for individual students (i.e., single cases) within the group. Data collection. Baseline data were collected over four days for each academic area. During baseline, the teacher computed and recorded the academic accuracy and productivity scores for independent work completed by the three students during the three academic periods. The teacher and assistant teacher also observed and recorded their on-task behavior

on the behavioral recording sheets over four days to establish baseline data for the three academic periods.

Interobserver reliability. The primary and secondary observers of the students' on-task behavior throughout the experiment were the teacher and assistant teacher, respectively. The primary observer was trained through university courses, and she, in turn, trained the secondary observer. Interobserver reliability was calculated by dividing the number of agreements on occurrences of on-task behavior by the total number of observations, multiplied by 100. Interobserver agreement was assessed for every fourth day of the experiment and ranged from 86 to 100% with a mean of 94%.

Instruction of self-monitoring. To introduce the procedures for self- monitoring, the teacher discussed with the students the importance of ac- tively participating in group instruction and independent practice activities for the academic area. They also discussed the importance of com- pleting independent assignments, as well as striving for mastery or accuracy in their work efforts, to emphasize that academic performance is evaluated in terms of both quantity and quality. The teacher then suggested that students could learn to manage their independent work and introduced the progress graph forms and self-monitoring procedures.

Using an enlarged progress graph form on the chalk board, the teacher demonstrated how students would compute the scores for academic completion. Given several examples of possible student scores, the teacher and students used calculators to compute each completion score as a percentage. They then recorded and graphed a sequence of completion scores on the enlarged form, using bullets (•), and drew lines to connect the scores to establish a trend. The teacher and students then used the

same scores to compute each accuracy score as a percentage. They recorded and graphed the sequence of accuracy scores on the enlarged form, using x's, and drew lines to connect the scores.

Accuracy of student self-monitoring. During intervention, the students corrected their completed work as the teacher read the correct responses aloud to the group. They then computed their accuracy and productivity



scores and recorded and graphed those scores on progress graphs for each of the targeted academic periods. The teacher circulated among the small groups during each academic period to oversee each student's accuracy in scoring, recording, and graphing to ensure the accuracy of student self-monitoring.

Results

The students' productivity and accuracy for reading comprehension, math, and written expression are presented in the multiple baseline graphs for each of the three students in Figures 1, 2, and 3. Self- monitoring of academic performance yielded greater positive effects for productivity than for accuracy for reading comprehension and math for all three students, and productivity scores approached or maintained at or above 90%. While there were increased levels of productivity and accuracy with self-monitoring for written expression for all students, the levels of academic performance for this academic area were generally below 90%.

On the average, all three students scored above 90% for the productivity scores for reading comprehension and math with self-monitoring, indicating that they were able to complete all or most of their independent assignments for these two areas. Similarly, there were overall improvements in the accuracy scores for both reading comprehension and math, although these scores did not reach 90%.

Gains in Productivity with Self-Monitoring

The means, standard deviations, medians, and ranges for the scores achieved by the three students for productivity for reading comprehension, math, and written expression are displayed in Table 2. The improvements achieved by the three students for self-monitoring of productivity for all academic areas indicated that the students completed more of their assignments during independent practice with self- monitoring.

Although the mean baseline scores were relatively high for productivity in math, all three students achieved increases with self-monitoring, ranging from 34 to 39 percentage points. On average, with self- monitoring of academic performance, they had completed all or most of the math problems assigned for independent practice. The mean baseline scores for productivity for reading comprehension were below 45% for all three students; however, the gains raised their productivity scores to the 94 to 98% range with self-monitoring. The students improved in productivity with self-monitoring for written expression, but the average mean scores for all three students remained at or below 80%.



Figure 1. Measures of academic productivity and accuracy for baseline and self-monitoring across three academic areas for Glen.



Figure 2. Measures oř academic productivity and accuracy for baseline and self-monitoring across three academic areas for Manny.



Figure 3. Measures of academic productivity and accuracy for baseline and self-monitoring across three academic areas for Nelson.



Table 2

Percentages for Productivity Across Academic Areas

Reading Written Comprehension Math Expression

BL SM BL SM BL SM

Glen Mean 30.0 94.4 60.0 98.1 40.6 80.0 SD 8.2 8.7 7.8 3.9 10.3 6.7 Median 30.0 100.0 60.0 100.0 40.0 80.0

Range 20-40 70-100 50-70 90-100 20-60 70-90

Manny Mean 40.0 97.4 51.8 90.9 39.4 72.5 SD 8.2 6.2 11.7 8.2 7.5 5.9 Median 40.0 100.0 50.0 90.0 40.0 72.5

Range 30-50 80-100 30-70 70-100 30-50 60-80

Nelson Mean 45.0 98.0 57.5 91.5 40.0 66.8 SD 5.8 5.0 6.2 9.3 9.1 5.1 Median 45.0 100.0 60.0 90.0 40.0 70.0

Range 40-50 80-100 50-70 75-100 30-60 60-75

Note. Data are presented under two conditions. BL = Baseline; SM = Self-monitoring.

Gains in Accuracy with Self-Monitoring

The means, standard deviations, medians, and ranges for the scores achieved by the three students for academic accuracy for the three academic areas are displayed in Table 3. The students' increases in accuracy with self-monitoring ranged from 22 to 31 percentage points over mean baseline scores for reading comprehension, math, and written expression. The mean baseline scores for accuracy for reading comprehension and written expression were between 47 and 57%, indicating that these were difficult areas for all three students. The mean baseline scores for

math were slightly higher, between 61 and 67%, which suggested that the students performed relatively better in this academic area than in the other two areas.

With self-monitoring, all of the students' mean scores for accuracy were within the 71 to 89% range, with the highest mean scores achieved in math. The lowest mean scores for accuracy with self-monitoring were for written expression.



Table 3

Percentages for Accuracy Across Academic Areas

Reading Written Comprehension Math Expression

BL SM BL SM BL SM

Glen Mean 50.0 81.6 67.0 89.2 51.9 76.2 SD 13.9 6.4 7.1 5.8 10.7 5.8 Median 50.0 80.0 67.0 89.5 50.0 75.5 Range 33-67 67-90 60-83 80-100 33-67 64-83

Manny Mean 56.8 88.4 61.4 87.4 49.6 78.4 SD 8.3 8.9 9.4 5.5 12.3 7.1 Median 55.0 90.0 60.0 89.0 50.0 80.0 Range 50-67 65-100 50-80 75-94 33-75 67-87

Nelson Mean 50.0 79.6 61.8 87.1 46.6 70.8 SD 8.2 10.8 8.2 5.7 11.6 5.5 Median 50.0 80.0 63.5 89.0 50.0 71.0 Range 40-60 50-90 50-71 75-94 20-67 64-83

Note. Data are presented under two conditions. BL = Baseline; SM = Self-monitoring.

Table 4

Percentages of On-Task Behavior Across Academic Areas

Reading Comprehension Math Written Expression

Glen Manny Nelson Glen Manny Nelson Glen Manny Nelson

50 40 30 40 50 45 45 40 60 50 60 40 50 45 55 60 55 50 60 50 35 55 50 45 45 50 45 50 50 40 60 55 50 60 55 50

80* 60» ~ 55 45 - 50 60 80» 90 70 50 60 55 50 60 55 90 90 80 60 45 50 60 40 50 85 100 75 80» 90» 75» 45 50 55 85 98 80 90 85 95 50 55 40 90 90 - 85 90 - 60 50 90 100 85 95 90 100 70» 65» 80

»85 - 90 100 - 95 65 - 75 85 100 95 100 95 90 70 60 80 90 100 85 95 100 100 60 75 70

Note. Asterisks (*) indicate introduction of intervention. Dotted lines ( - ) indicate student absences.



On-Task Behavior

All students demonstrated consistent improvement of on-task behavior with self-monitoring during the reading comprehension period (see Figure 4). The highest levels of attentional behavior were achieved by all three students during the math periods. The least gains in percentage of on-task behavior were for the written expression periods, when students participated in whole class instruction rather than small group instruction. Overall, however, all three students increased their levels of attention to task.

The students' on-task behaviors during baseline ranged from 30 to 60% for reading comprehension, 40 to 60% for math, and 40 to 60% for written expression. Self-monitoring of academic performance resulted in improvements of on-task behavior for the three students in all academic areas; however, the results were more pronounced in some academic areas than in others. The percentages of on-task behavior for the three students during observations are presented in Table 4.

Discussion

The findings of this study indicate that self-monitoring of academic performance may result in increased academic productivity and accuracy, as well as improved on-task behavior during independent class work. Academic productivity and on-task behavior improved for all students in the three subject areas. Gains in productivity were greater than gains in accuracy, and productivity gains were greater for both reading comprehension and math than for written expression.

Although not examined as part of the study, the teacher reported that the intervention was easy to implement in the classroom setting with small groups, appropriate for the target behaviors, and relevant to the students' needs. This is similar to other researchers' findings (Harris et al., 1994). Also similar to other findings, we found the self-monitoring procedures were easy to learn and implement (Carr & Punzo, 1993), con- venient and feasible for classroom use (Treiber & Lahey, 1983), and did not require that the teacher spend a lot of time closely monitoring the students (McLaughlin, 1984). Further, the intervention was incorporated with the existing curriculum for each of the academic areas and did not require new instructional methods or materials.

While some researchers have used backup consequences to strengthen self-monitoring effects (Edwards et al., 1995; McLaughlin, 1984; Nelson, Smith, Young, & Dodd, 1991; Reid, 1996; Rooney et al., 1984; Salend, Whittaker, Raab, & Giek, 1991; Yeager, Royster, & McLaughlin, 1995), Hallahan and Sapona (1983) found that self-monitoring can be success- fully implemented without consequences. The results of this study indicate that general academic improvements were achieved without backup



Figure 4. Percentage of on-task behavior observed for three students across academic areas during baseline and self-monitoring.


SELF-MONITORING OF ACADEMIC PERFORMANCE 41 1

consequences; however, the use of extrinsic consequences applied con- tingently upon pre-selected increments in performance may have resulted in more consistent academic improvements for the students across all subject areas.

Self-graphing served three practical purposes in the present study. First, the clerical tasks of record keeping and monitoring of academic performance were accomplished through self-recording and self- graphing. Second, the graphs provided a visual record so that students could monitor their own academic performance during independent practice (DiCangi et al., 1991). Third, the students acquired functional skills in computing percentages and plotting and analyzing graphs. While the use of self-graphing by some students may contribute to their improved academic achievement by providing a visual stimulus which motivates them to strive for improved performance levels (Graham, Har- ris, & Reid, 1993), the students in this study were not interviewed to ex- plore the reactive effects of self-recording and self-graphing.

There were several limitations in this study. First, the intervention was not equally effective for all students in all academic areas. For example, improvements in Manny's academic performance for written expression was less significant and more gradual than for the other students. Sec- ond, the study did not examine generalization across settings because the students were taught in a self-contained special education classroom. Third, potential bias due to experimenter effects may have affected the outcomes of the present study because the teacher served as both the im- plementor and the observer. Finally, while it appeared to the investiga- tors that the students enjoyed and were motivated by the self-monitoring procedures, no formal procedures were used to measure the students' satisfaction with the intervention or to examine their perceptions of the targeted variables.

Since accuracy scores were based on a percentage correct of those at- tempted, the high accuracy scores may be misleading. For example, for a 20 item exercise for written expression, Glen could have completed 40% of the items (or eight completed responses) and correctly responded on 50% of the items (or four correct responses). His accuracy score of 50% was recorded while four correct responses of 20 possible items repre- sents only 20% accuracy of the total possible score. An alternative measure of accuracy would have computed the percent of items assigned that were correctly answered to reflect improvements in overall academic performance.

The results of this study extend past research on the use of self- monitoring procedures to improve on-task behavior and academic performance by examining the use of these procedures with students who have both LD and ADD /ADHD. This study also demonstrated concur- rent increases in academic productivity and accuracy and on-task behavior across three academic subject areas using curricular materials already adopted by the teacher. Another contribution of this study is the effective



implementation of the intervention in a classroom setting with minimal teacher preparation time and no curriculum modifications or backup consequences.

Future Research Directions

In future studies, researchers might employ an alternating treatments analysis to determine whether to self-monitor on-task, productivity, accuracy or a combination. In addition, future studies would be enhanced by (a) including a measure of the fidelity of implementation of the independent variable (e.g., random audiotaping or videotaping of the sessions or verification on random days by an outside observer of the experimenter and procedures used), and (b) collecting social validity data to evaluate teachers' and students' perceptions of the effectiveness of the intervention, satisfaction with the procedures and results, acceptability of self-monitoring, and personal preferences for targeted behaviors.

Other directions for future research include the investigation of (a) the relationship between subject characteristics, such as disability designa- tion or age, and the target variables; (b) the reactive effects of self- monitoring; (c) how and why self-monitoring works; (d) the mainte- nance and generalization of treatment effects of self-monitoring for students with learning disabilities and attentional deficits in inclusive settings; and (e) the use of self-monitoring with other populations. Further research is also needed to expand the use of self-monitoring of more complex skills and strategies, as well as in functional contexts, such as ca- reer-related or independent living skills, goals setting, and transition planning.

References

Barkley, R. (1990). Attention deficit hyperactivity disorder : A handbook for diagnosis and treatment. New York: Guilford Press.

Blick, D. W., & Test, D. W. (1987). Effects of self-recording on high school students' on-task behavior. Learning Disability Quarterly, 10, .203-213.

Carr, S. C, & Punzo, R. P. (1993). The effects of self-monitoring of academic accuracy and productivity on the performance of students with behavioral disorders. Behavioral Disor- ders, 18, 241-250.

DiGangi, S. A., Maag, J. W., & Rutherford, R. B., Jr. (1991). Self-graphing of on-task behavior: Enhancing the reactive effects of self-monitoring on on-task behavior and academic performance. Learning Disability Quarterly, 14, 221-230.

Dunlap, L. K., Dunlap, G., Koegel, L. K., & Koegel, R. L. (1991). Using self-monitoring to increase independence. Teaching Exceptional Children, 23, 17-22.

Edwards, L., Salant, V., Howard, V. F., Brougher, J., & McLaughlin, T. F. (1995). Effective- ness of self-management on attentional behavior and reading comprehension for children with attention deficit disorder. Child & Family Behavior Therapy, 17 (2), 1-17.

Fowler, M. (1991). Attention deficit disorder [Briefing paper]. Washington, DC: National In- formation Center for Children and Youth with Disabilities.

Graham, S., Harris, K. R., & Reid, R. (1993). Developing self-regulated learners. In E. L. Meyen, G. A. Vergason, & R. J. Whelan (Eds.), Educating students with mild disabilities (pp. 127-149). Denver: Love.


SELF-MONITORING OF ACADEMIC PERFORMANCE 41 3

Hallahan, D. P., & Sapona, R. (1983). Self-monitoring of attention with learning-disabled children: Past research and current issues. Journal of Learning Disabilities, 16, 616-620.

Harris, K. R. (1986). Self-monitoring of attentional behavior versus self-monitoring of productivity: Effects on on-task behavior and academic response rate among learning disabled children. Journal of Applied Behavior Analysis, 19, 417-423.

Harris, K. R., Graham, S., Reid, R., McElroy, K., & Hamby, R. S. (1994). Self-monitoring of attention versus self-monitoring of performance: Replication and cross-task comparison studies. Learning Disability Quarterly, 27,121-139.

Hinshaw, S. P., & Melnick, S. (1992). Self-management therapies and attention-deficit hyperactivity disorder. Behavior Modification, 16, 253-273.

Hughes, C. A., Ruhl, K. L., & Peterson, S. K. (1988). Teaching self-management skills. Teach- ing Exceptional Children, 20, 70-72.

Lloyd, J. W, Hallahan, D. P., Kosiewicz, M. M., & Kneedler, R. D. (1982). Reactive effects of self-assessment and self-recording on attention to task and academic productivity. Learning Disability Quarterly, 5, 216-227.

Maag, J. W, Reid, R., & DiGangi, S. A. (1993). Differential effects of self-monitoring attention, accuracy, and productivity. Journal of Applied Behavior Analysis, 26, 329-344.

Maag, J. W, Rutherford, R. B., Jr., & DiGangi, S. A. (1992). Effects of self-monitoring and contingent reinforcement on on-task behavior and academic productivity of learning- disabled students: A social validation study. Psychology in the Schools, 29,157-172.

Mathes, M. Y., & Bender, W. N. (199 7). The effects of self-monitoring on children with attention-deficit /hyperactivity disorder who are receiving pharmacological interventions. Remedial and Special Education, 35*121-128.

McLaughlin, T. F. (1983). Effects of self-recording for on-task and academic responding: A long term analysis. Journal of Special Education Technology, 6(3), 5-12.

McLaughlin, T. F. (1984). A comparison of self-recording and self-recording plus consequences for on-task and assignment completion. Contemporary Educational Psychology, 9, 185-192.

McLaughlin, T. R, Burgess, N., & Sackville-West, L. (1981). Effects of self-recording and self-recordine + matching on academic performance. Child Behavior Therapy, 3.17-27.

Miller, M., Miller, S. R., Wheeler, J., & Selinger, J. (1989). Can a single classroom treatment approach change academic performance and behavioral characteristics in severely be- haviorally disordered adolescents: An experimental inquiry. Behavioral Disorders, 14, 215-225.

Nelson, J. R., Smith, D. J., Young, R. K., & Dodd, J. M. (1991). A review of self- management outcome research conducted with students who exhibit behavioral disorders. Behavioral

Disorders, 16*169-179. Olympia, D. E., Sheridan, S. M., Jenson, W. R., & Andrews, D. (1994). Using student-

managed interventions to increase homework completion and accuracy. Journal of Ap- plied Behavior Analysis, 27.85-99.

Prater, M. A., Hogan, S., & Miller, S. R. (1992). Using self-monitoring to improve on-task behavior and academic skills of an adolescent with mild handicaps across special and regular education settings. Education and Treatment of Children, 15*43-55.

Prater, M. A., Joy, R., Chilman, B., Temple, J., & Miller, S. R. (1991). Self-monitoring of on- task behavior by adolescents with learning disabilities. Learning Disability Quarterly, 14, 164-177.

Reid, R. (1996). Research in self-monitoring with students with learning disabilities: The present, the prospects, the pitfalls. Journal of Learning Disabilities, 29, 317-331.

Reid, R., & Harris, K. R. (1993). Self-monitoring of attention versus self-monitoring of performance: Effects on attention and academic performance. Exceptional Children, 60, 29- 40.

Rooney, K.J., Hallahan, D. P., & Lloyd, J. W. (1984). Self-recording of attention by learning disabled students in the regular classroom. Jounal of Learning Disabilities, 17, 360-364.

Rooney, K., Polloway, E. A, & Hallahan, D. P. (1985). The use of self-monitoring procedures with low IQ learning disabled students. Journal of Learning Disabilities, 18, 384-389.

Salend, S. J., Whittaker, C. R., Raab, S., & Giek, K. (1991). Using a self-evaluation system as a group contingency. Journal of School Psychology, 29, 319-329.



Sheldon, J., & Schumaker, J. B. (1985). The Sentence Writing Strategy. Lawrence, KS: Edge En- terprises, Inc.

Silver, L. B. (1995). ADHD: Attention depcity-hyperactivity disorder and learning disabilities. [Booklet]. Summit, NJ: Ciba-Geigy Corporation.

Trammel, D. L., Schloss, P. J., & Alper, S. (1994). Using self-recording, evaluation, and graphing to increase completion of homework assignments. Journal of Learning Disabili- ties, 27, 75-81.

Treiber, F. A., & Lahey, B. B. (1983). Toward a behavioral model of academic remediation with learning disabled children. Journal of Learning Disabilities, 16,111-116.

Webber, J., Scheuermann, B., McCall, C, & Coleman, M. (1993). Research on self- monitor- ing as a behavior management technique in special education classrooms: A descriptive review. Remedial and Special Education, 14, 38-56.

Yeager, C. M., Royster, A. j., & McLaughlin, T. F. (1995). Behavioral self-control in the schools: Definition, components, training, and critical issues. Corrective and Social Psychi- atry, 41(3), 52-58.


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