The general population for this study is the community of paraeducators and general education teachers who interact directly with paraeducators in their work practices. Paraeducators are additional school professionals who provide support to teachers and children with special learning needs (Mauro, 2021). In fact, paraeducators in this study will refer to mediating staff members who play a vital role in the effective adjustment of students with special learning needs. In terms of general educators, the general population includes those professionals who have had or have experience collaborating with paraeducators and students with IEPs. The geographic profile of the population potentially includes the entire relevant community in the United States, but the sample will be collected in Northern California. Due to geographic dispersion, no specific ethnic characteristics of the target population can be outlined. The ability to extrapolate sample results is due to common educational practices, including the existence of the paraeducational profession in various states and IEP programs. The age characteristics include the boundaries outlined for the sample population, namely the age of professionals under 90 years old and the availability of professional experience in inclusive school groups of one year or more. Since it is not possible to conduct a study for the entire general population due to limited resources, creating a representative sample that meets the geographic, professional, and age criteria of the general population is a good practice.
Sample Formation Procedure
This paper proposes the use of purposive nonprobability sampling, which optimizes the participant recruitment process. A limitation of such a sample is that members of the general population do not have an equal chance to participate in the study, thus increasing the bias of the results. On the other hand, due to resource and time constraints, the use of purposive convenience sampling is appropriate because, in this case, the researcher invites those respondents who have proven to be most available to participate (McCombes, 2022). To that end, invitations to participate will be sent to teachers and paraeducators who meet the inclusion criteria described. Invitations will be distributed via email, either directly or through the principal of the educational institution. The motivation for choosing this method of sampling is due to the optimization of effort combined with significant savings in resources and the increased availability of communication with specific respondents.
Units of Analysis
The specific units of analysis for this research paper are learning professionals, including elementary education teachers and paraeducators. Each of these professionals is viewed as a distinct unit that provides valuable data for further analysis. The differentiation of the research group includes a breakdown into two cohorts, namely several elementary teachers who work directly with the inclusive classroom and paraeducators whose job function is to assist the teacher.
The free G*Power software was used to calculate the sample size that would be necessary and sufficient to form a normal distribution and thus extrapolate the results to the general population. The need to calculate sample size is primarily driven by the desire to optimize the use of resources and achieve meaningful results: it is known that samples that are too small can be unrepresentative and produce no actual results, while samples that are too large can lead to errors of the significance of really insignificant results (Kyriazos, 2018). A one-way ANOVA test was chosen to determine the sample size in G*Power, meeting the research requirements for the nature of the variables and detection of the desired effect. Because the experience of teachers collaborating with paraeducators in an IEP classroom is expected to increase the achievement and satisfaction of students with special learning requirements, the effect size was assigned as 0.8, or a high effect size (McLeod, 2019). The effect size was chosen as 0.80, at the 5% significance level of the experiment; finally, the number of groups was chosen to be about the same for the different criterion. For age, ethnic group, years of teaching, experience with an IEP, as well as an inclusive classroom, and experience with paraeducators as independent factors, the number of internal subgroups would be defined as 5. In this case, G*Power calculates the final sample size as 25, which means at least 25 respondents are needed for a statistically valid study, which includes both elementary teachers and paraeducators.
Since a one-way ANOVA was chosen as the statistical tool, the use of subgroups is critical to meet all requirements in the analysis. More specifically, Table 1 below shows the differentiation of each categorical independent factor into subgroups, the comparison of trends between which will be part of the statistical study. The inferential test will determine if there are significant differences between the subgroups, so focusing on the designated cohorts is a targeted part of the current study.
|Age (years)|| |
|Ethnic affiliation|| |
|Academic degree|| |
|Years of teaching|| |
|Experience with IEPs classes (years)|| |
|Current class|| |
|Experience working with paraeducators (years)|| |
Table 1. Subgroups relevant to the ANOVA analysis.
Statement of the Problem
From a quantitative perspective, students in inclusive classrooms may experience a drop in academic performance when there is a lack of inclusion in the academic setting; thus, the current research problem determines the numerical effects of teacher-paraeducator collaboration in an inclusive classroom. The purpose of this paper is to determine the numerical statistical effects of teacher-paraeducator collaboration on the satisfaction and achievement rates of students in a school requiring special learning needs; for example, to determine if it makes any meaningful sense for the class.
Specific research questions arising from the statement of purpose are as follows:
- Is there a relationship between teacher and paraeducator collaboration and student achievement in an inclusive classroom?
- Is there a relationship between teacher and paraeducator collaboration and student satisfaction in an inclusive classroom?
- Do additional sociodemographic variables affect student achievement in an inclusive classroom?
- Do additional sociodemographic parameters affect student satisfaction in an inclusive classroom?
- The first null hypothesis postulates that there is no difference in the mean values of student achievement in an inclusive classroom as a function of teacher-paraeducator collaboration experience.
- The second null hypothesis postulates that there is no difference in the mean values of student satisfaction in an inclusive classroom as a function of the teacher’s experience of collaboration with the paraeducator.
- The first alternative hypothesis postulates that there is a difference in the mean values of student achievement in an inclusive classroom as a function of the teacher’s experience of collaboration with the paraeducator.
- The second alternative hypothesis postulates that there is a difference in the mean values of student satisfaction in an inclusive classroom as a function of the teacher’s experience of collaboration with the paraeducator.
Level of Significance
The significance level for this study, as for most sociological studies, was set as 0.05. In other words, there is a 5% chance that the null hypothesis will be rejected even though it is true. In addition, the SPSS used for statistical analysis automatically allows statistical significance to be calculated at less conservative significance levels, such as 0.01.
Improving the performance of elementary English teachers is an important goal of academic discourse, so finding possible predictors of this growth is essential, especially in inclusive classrooms. The use of a statistical test in this study will identify critical patterns of difference and assess the relationship of teacher-paraeducator collaboration with dependent variables. The study is expected to make valuable practical contributions to the understanding of mechanisms that improve teacher performance.
The academic search identifies key findings shaped by previous research. For example, it has already been reported that the use of multiple tactics by paraeducators significantly increases student engagement in the educational process (Joyal, 2020). Parametric tests have already been used and have shown that collaboration between physical education teachers and paraeducators makes sense to improve students’ academic performance (Swenson & Haegele, 2020). It has also been reported that paraeducators must possess specific skill sets and competencies to influence student outcomes effectively; otherwise, it can be disruptive (Gasca, 2020). Thus, the literature search reveals a dearth of helpful knowledge on this topic, but the available evidence points to improved academic outcomes for students in an inclusive classroom when teacher-paraeducator collaboration is developed.
The concept used as the research paradigm for this work is that increased professional engagement with students’ problems has the potential to increase their motivation to learn. This postulates that the more intensely teachers and assistants interact with students, the higher their academic performance (Tucker, 2021). In addition, the conceptual basis was that the use of paraeducators in an inclusive classroom was a necessary guarantee of increased academic achievement and satisfaction for students with special needs (Garcia, 2021). Based on the two theoretical concepts described, the research design was developed, and hypotheses postulated.
Garcia, A. (2021). New study reveals positive impacts of paraeducators on student achievement. New America. Web.
Gasca, G. C. (2020). Best practices for middle school teachers working with special education paraeducators: A training manual [PDF document]. Web.
Joyal, S. (2020). Addressing IEP goals through the arts and increasing engagement in students, teachers, and paraeducators with universal design for learning [PDF document]. Web.
Kyriazos, T. A. (2018). Applied psychometrics: Sample size and sample power considerations in factor analysis (EFA, CFA) and SEM in general. Psychology, 9(08), 1-24. Web.
Mauro, T. (2021). Understanding the paraprofessional’s role in schools. Very Well Family. Web.
McCombes, S. (2022). Sampling methods | types, techniques & examples. Scribbr. Web.
McLeod, S. (2019). What does effect size tell you? SimplyPsychology. Web.
Swenson, T. G., & Haegele, J. A. (2022). Examining the role clarity, ability, and training needs of paraeducators supporting students with disabilities in physical education settings. The Physical Educator, 79(1), 15-37.
Tucker, C. (2021). Understanding teacher engagement in blended learning environments. Dr. Catlin Tucker. Web.