CEP822- Literature Review

Impact of SMART Boards on student engagement, behavior,
and self-confidence within a Section 22 Behavior classroom:

A Review of Literature

• Topic

Adolescence, the time of life between puberty and adulthood, is a transitional phase filled with highs and lows. With each year of development, new responsibilities are dictated and society’s social expectations increase. For most adolescents, the acquisition of these new behaviors is part of a natural progression that includes recognizing and mirroring social norms in their environment (Laursen & Collins, 1988).

Contrary to these norms, students with emotional behaviour disorder (E/BD) may not experience this natural growth process due to the nature of their disability. This difference is then magnified by their inability to fit in socially in the general school setting. Due to this social inadequacy, some of these students face isolation and rejection which can oftentimes lead to aggressive behaviour (Dunlap & Childs, 1996).

Since the introduction of special education in 1975, federal legislation has mandated that all students with special needs be educated in the least restrictive environment (Eber, Nelson, & Miles, 1997). Since the authors of this legislation failed to define the "least restrictive environment," the interpretation and implementation of the mandate has been left up to administrators, and parents. Typically, identified behavior students are placed in self-contained classrooms where they are given extra attention and support while developing coping skills and preparing for integration into mainstream educational settings.

Many identified behaviour students rely heavily on visual aids when studying for tests and when trying to recognize the relationship between ideas. A software program that allows these students to work in a hands-on manner with a visual aid could benefit them by helping them to stay organized, while also providing creative and fun ways to learn information and remember it (Murray, 2003). These students require training programs that enable them to appropriately interact with people outside of the special education setting (MacMillan, Gresham, & Forness, 1996). One of the primary goals of such a program is to help these students to build self-confidence, become actively engaged in their learning, and begin to interact appropriately with their peers in social situations.

The educational need or opportunity that SMART board software seeks to address is the need for many students to have a visual aid and actively participate when trying to learn and understand the information being presented to them in a variety of subject areas (Murray, 2003). Thus the question remains: How will implementing interactive SMART Boards software across the curriculum in a Section 22 Behavior classroom impact student engagement, behavior, and self-confidence?

• General overview of the literature

Assistive technology is any technology that allows one to increase, maintain, or improve the functional capabilities of an individual with special learning needs. Its applications and adaptations can help open doors to previously inaccessible learning opportunities for many students with special needs (Burrello, 2001).

According to Sue Murray’s article, “Mapping the Mind,” what we know about the way students learn started back in the early 1900s with Piaget’s model of development and the way children learn. Piaget recognized that the developing child builds cognitive structures such as mental maps, schemes, or networked concepts, for understanding and responding to physical experiences within their environment. Today we refer to these cognitive structures as Concept Mapping, or Mind Mapping (Murray, 2003). Recognizing this idea, many schools are now considering the use of SMART board software.

One potential limitation of such use might be that some teachers and students are intimidated by technology and may not be comfortable using the tool in an effective manner. There would definitely be a learning curve for most new users to overcome. Furthermore, some students may struggle to remain on- task and might lose valuable time playing with features instead of using the technology to complete assigned work. In addition, access to SMART boards is sometimes limited which could mean that certain groups of students have limited opportunities to employ the Smart board technology.

• Rationale

The following will be an overview of the literature considered while exploring SMART Board effects on the student engagement, behavior, and self-confidence within the Section 22 Behavior classroom. Findings will be presented in the order of the most recent publication examining:

I. the effectiveness of SMART boards as assistive technology and some potential pitfalls

II. characteristics of students with Emotional Behavior Disorder

• Kinds of work reviewed

SMART board as assistive technology and potential pitfalls

There are many features inherent in interactive whiteboards that have a statistically significant relationship with student achievement as was evident in Marzano’s study (2009). His study showed that interactive whiteboards such as SMART Boards have a great potential to enhance teaching practices and student achievement. This was evident in his results illustrating a 23% gain in student achievement in classrooms where these tools were used. Though these results were encouraging, the fact remains that this increase is only possible if teachers are given proper training on SMART board use and ways to effectively implement the technology into their curriculum to increase student engagement.

Similarly, Burkett’s research (2007) outlined that technology was one of the proven best practices that could assist in meeting the needs of diverse learners, especially those who tended to be divergent. Burkett defined divergent learners as those who characteristically disliked repetitive practices and could benefit from chunking larger amounts of information. For these reasons, he felt that technology use had been proven to be successful. Using SMART board technology was found to provide learners with a rich learning experience that appealed to their needs and allowed them to participate in hands-on instruction. Burkett’sresearch found that using a SMART board in the classroom assisted divergent math students in increasing their scores during a unit on division. Students were actively engaged at the SMART board, working on the problems created by the teacher as well as visiting additional websites for further explanation and practice.

By infusing technology into the daily lessons, divergent learners’ needs can evidently be met through hands-on application and creativity. Burkett’s research (2007) revealed that important strategies, like technology, helped to ensure that all students, especially divergent learners, were engaged in the classroom setting. In addition, an article written by Delaney (2007) provided further evidence to prove that students are actively engaged when at the SMART board, working on problems created by the teacher as well as having the opportunity to visit additional websites for further explanation and practice. This type of success was confirmed by a study conducted by the Smarter Kids Foundation.

According to Sue Murray (2003), the key to success is to teach in a way that is based upon what we already know about the way students learn best. The article discussed the benefits of supporting learning skills with mind and concept mapping techniques and thus, making use of ICT tools. Piaget’s model of child development and the way that children learn was of central focus since Piaget recognized that the developing child builds cognitive structures or mental ‘maps’ when trying to understand and respond to experiences within their environment. For this reason, SMART technologies such as the interactive SMART board were considered to be viable teaching and learning tools, provided they were used effectively.

The idea that providing assistive technology in educational settings could be seen as a positive educational intervention was reiterated by Smith (2000). However, he shed additional light on the reality that it is often difficult to measure assistive technology outcomes. This result could be due to interventions possessing multiple attributes, outcomes affecting numerous domains, and few measurement instruments being available. The paper reviewed relevant theory and the practical implications of measuring assistive technology outcomes.

Emotional Behavior Disorder

Moving away from assistive technology for a moment, the topic of self-efficacy was discussed by Bandura (1997) as something that has emerged during recent decades a highly effective predictor of students' motivation and learning. Self-efficacy was described as a performance-based measure of perceived capability and one that researchers have verified as being valid when predicting motivational outcomes, and emotional reactions. Self-efficacy beliefs have been found to be sensitive to subtle changes in students' performance context, to interact with self-regulated learning processes, and to mediate students' academic achievement.

Historically, educational programs for this student population have not been associated with generally positive outcomes. Eber and Nelson (1997) addressed the idea of trying to meet the complex needs of students with emotional and behavioral disabilities (EBD). Excessive dropout rates, high rates of academic failure and poor achievement test scores, low graduation rates have been noted consistently among students with EBD. A national study of school programs, indicated that lack of appropriate services, little coordination or integration with other provider agencies, and limited support for families contributed to these poor outcomes. On the one hand, the educational system bears the mandate to support programming under Individuals with Disabilities Education Act (IDEA) that enables students with disabilities to receive a free and appropriate education. On the other hand, education, mental health, social service, and other providers are struggling with lack of agreement on prioritized target populations, financial challenges, liability, and coordination of resources.

In an influential study conducted by Dunlop and Childs (1996),evidence does suggest that programs do exist that could support students with E/BD, not all research is conclusive. The study surveyed 12 journals in the field of emotional and behavior disorders to explore trends in five dimensions of research: (1) subject characteristics; (2) settings; (3) research design; (4) dependent variables; and (5) independent variables (interventions). Findings were of so little consequence as to warrant little or no attention regarding the trends found and few studies reported interventions that were individualized on the basis of assessment data.

Evidence does exist though contradicting the position that "more restrictive" placements were never beneficial for students with Emotional Behavior Disorder. An article by MacMillian and Gresham (1996) examined empirical evidence and arguments for full inclusion of students with emotional and behavioral disorders. Their report mentioned a lack of empirical evidence supporting inclusion of this population and identified problems in the arguments of full inclusion proponents. McMahon, Wacker, Sasso, and Melloy (1994) evaluated multiple effects of a social skills intervention with three elementary-school children who had behavioral and learning disorders that resulted in increasing student acquisition of targeted social behaviours. This in turn led to lengthier peer interactions with some increases in non-targeted social responses demonstrated as well.

Adolescence can be seen as a pivotal transition phase, often recognized as one that includes both good times and bad. Laursen and Collins (1988) recognized that as developmental milestones and new responsibilities were met during adolescence that society’s social expectations increased. They stressed the reality that for most adolescents, the acquisition of new social behaviors was part of the natural growth process that included adaptation to the social milieu. This however, was not always the case for students with emotional behaviour disorder.

Self-efficacy refers to personal judgments of one's capability to organize and implement behaviors in specific situations. Schunk (1984) examined the idea that perceived self-efficacy was an important variable in understanding achievement behavior. Students gain information about their level of self-efficacy from self-performances, vicarious experiences, verbal persuasion, and physiological indices. In forming efficacy judgments, people take into account factors such as perceived ability, task difficulty, effort expenditure, performance aids, and outcome patterns. Even when students acquire efficacy information from self-performances, efficacy judgments are not mere reflections of those performances because educational practices differ in the type of information they convey about students' capabilities.

• How my work was informed by the work of others

Although the implementation of technology within classrooms continues to generate controversy, researchers have demonstrated over the past 25 years that technology, when used correctly, can lead to promising results. The question of whether or not proper implementation of SMART board technology into a Section 22 Behavior classroom will positively affect student behaviour, engagement and self-confidence remains. It was encouraging to examine the Marzano’s findings (2009) indicating that effective teacher training and subsequent implementation did have a significant impact on student achievement. Also of great value when seeking to examine the population of a self-contained E/BD classroom, were MacMillan et al. (1996), findings reporting a lack of empirical evidence supporting inclusion of this population and identifying problems in the arguments of full inclusion proponents.

References

1. Bandura, A. (1997). Self-Efficacy: The Exercise of Control. New York: Freeman
2. Burkett, Christopher. The Neglected Majority: Recognizing Divergent Learners in the Middle School Classroom. South Carolina Middle School Association Journal, Winter 2007
3. Bullis, M. & Davis, C. (1996). Further Examination of Job-Related Social Skills Measures forAdolescents and Young Adults with Emotional and Behavioral Disorders. Behavioral Disorders, 21 (2), p. 160-171.
4. Burrello, L. (2001). Educating All Students Together: How School Leaders Create Unified Systems. Thousand Oakes, California: Corwin Press.
5. ConnectAbility. (2008). Using Visuals. Retrieved July, 2010, from ConnectAbility Web site:
http://www.connectability.ca/connectability/library/documents/using_visuals
6. Delaney, M. (2000). Lines, curves, and graphs. Smarter Kids Foundation. Retrieved July, 2010 from http://smarterkids.org/research/librar y_subject.asp
7. Dunlop, G. & Childs, K. E. (1996). Intervention Research in Emotional and Behavioral Disorders: An Analysis of Studies from 1980-1993. Behavioral Disorders, 21 (2), 125-136.
8. Eber, L., Nelson, C. M., & Miles, P. (1997). School-based Wraparound for Students with
Emotional and Behavioral Challenges. Exceptional Children, 63 (4), 539-555.
9. Heacox, D. (2002). Differentiating instruction in the regular classroom. Minneapolis, MN: Free Spirit Publishing.
10. Laursen, B., & Collins, W. A. (1988). Conceptual Changes during Adolescence and Effects upon Parent-Child Relationships. Journal of Adolescent Research, 3 (2), 119-139.
11. MacMillan, D., Gresham, F. M., & Forness, S. R. (1996). Full Inclusion: An Empirical Perspective. Behavioral Disorders, 21 (2), 145-159.
12. Marzano, R.J. (2009). Teaching with interactive whiteboards. Educational Leadership, 67(3), 80.
13. McMahon, C.M., Wacker, D. P., Sasso, G. M., & Melloy, K. J. (1994). Evaluation of Multiple
Effects of a Social Skills Intervention. Behavioral Disorders, 20 (1), 35-50.
14. Murray, Sue. (2003, March/April) Mapping the Mind. InteracTIVE, 17-18.
15. Schunk, Dale. (1984, Winter). .Self-efficacy Perspective on Achievement Behavior. Educational Psychologist, 19 (1), 48-58.
16. Smith, Roger. (2000). Measuring Assistive Technology Outcomes in Education Assessment for Effective Intervention, 25: 273-290.