Respond to 2 students discussion using the rise Model
Due Saturday November 18, 2023 by 11:00 pm
Must Read Everything:
Reply to at least two classmate’s posts, applying the RISE Model for Meaningful Feedback
I will also show an example below of how the response needs to be addressed.
Here’s an example of how the response should look. Please don’t copy it.
The response to the classmate need to be just like this.
Example Response (Response Needs to be writen just like the response below No copying)
RISE Feedback:
REFLECT: I concur with “Action plans should reflect the type of services that are needed and have an idea of the expected outcome of the services” because it is in line with Hatch and Hartline’s intentional school counseling guidelines in regards to determining students needs.
INQUIRE: Can you further explain what “closing-the-gap action plans” are?
SUGGEST: I encourage you to revisit Hatch and Hartline’s MTMDSS tier interventions in order to add a citation that would illustrate your example on bullying prevention efforts.
ELEVATE: What if you re-purposed “For example, after a needs assessment, the school is having problems with bullying” as “Following Trish Hatch’s MTMDSS tier based interventions, if the school is having problems with bullying, after a needs assessment, we could… citation…” for a more weighted argument?
ReferencesHatch, T., & Hartline, J. (2022). The use of data in school counseling: Hatching results (and so much more) for students, programs and the profession (2nd Ed.). Corwin.
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See below for the two classmate discussion post that you will need to respond to
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Response 1- Niccole
How would inferential statistics be used in experimental design?
In experimental design, inferential statistics play a crucial role in drawing conclusions about a population based on a sample from that population. Researchers use inferential statistics to make predictions, test hypotheses, and generalize findings beyond the specific group studied. For instance, if an educational researcher conducts an experiment to evaluate the effectiveness of a new teaching method on student performance, inferential statistics can be employed to determine whether any observed differences are likely to be representative of the broader student population. Common inferential statistical techniques include t-tests, analysis of variance (ANOVA), regression analysis, and chi-square tests, all of which help researchers make inferences about the relationships and effects within the population based on the data obtained from the sample.
Give examples of when parametric statistics are used versus nonparametric statistics in educational research.
Parametric statistics are typically used when certain assumptions about the data distribution are met, such as normality and homogeneity of variances. Examples of parametric tests include t-tests, ANOVA, and Pearson correlation. These tests are powerful and provide precise estimates when the assumptions are satisfied. In educational research, parametric statistics might be applied when comparing the mean scores of two groups or assessing the relationship between two continuous variables.
On the other hand, nonparametric statistics are preferred when the assumptions of parametric tests cannot be met or when dealing with ordinal or nominal data. Examples include the Mann-Whitney U test, the Kruskal-Wallis test, and the Spearman correlation. Nonparametric tests are more robust in the presence of skewed or non-normally distributed data. In educational research, nonparametric statistics might be used when comparing median scores or analyzing data that do not meet the assumptions of parametric tests.
From this week’s article, what are the similarities and differences for students with ADHD on a 504 Plan or IEP?
The article outlines several similarities and differences for students with ADHD on a 504 Plan or an Individualized Education Program (IEP). Similarities include both plans providing services and accommodations to meet the needs of students with ADHD, with the 504 plan having a broader definition of disability. Differences arise in the eligibility criteria, law applications, creation of the plans, content, and review processes. The IEP is governed by the federal special education law (IDEA), requiring specific disabilities that affect educational performance. At the same time, the 504 plan operates under Section 504 of the Rehabilitation Act of 1973 and accommodates students who do not qualify for an IEP. The comparison also highlights distinctions in the composition of the teams creating the plans, the nature of the plans as written documents, and the dispute resolution processes, reflecting the nuanced support structures for students with ADHD in K-12 education.
Reference
“The Difference Between IEPs and 504 Plans.” Understood.Org, California SCDD. Accessed 16 Nov. 2023.
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Response 2- Courtney
How would inferential statistics be used in experimental design?
Inferential statistics are procedures and techniques used to make inferences about the larger population based on the sample data gathered in research (McMillan, 2016). In an experimental design, inferential statistics consider errors in the data set and allow researchers to determine statistical differences, indicate true values, and make predictions about the sample population (McMillan, 2016). Some of these inferential statistical procedures include hypothesis testing, confidence intervals, regression analysis, analysis of variance (ANOVA), t-tests, and chi-square tests (McMillan, 2016).
Give examples of when parametric statistics are used versus nonparametric statistics in educational research.
Parametric statistics are procedures used when the data meets certain assumptions, such as normal distribution within the population (forming a bell curve), variances of the data in different groups are equal, and when the data are measured at the interval level. If assumptions are not met, nonparametric statistics may be used (McMillan, 2016). Nonparametric statistics are used when the data are not normally distributed or when working with ordinal or nominal variables such as categories and groups as opposed to numerical measurements (McMillan, 2016).
From this week’s article, what are the similarities and differences for students with ADHD on a 504 Plan or IEP?
IEPs and 504 plans are designed to ensure all students can access their education regardless of disabilities. Both are under federal law, requiring schools to provide services, free to parents, to meet the needs of individual students in the classroom (State Council on Developmental Disabilities [SCDD], n.d.). A student with ADHD may qualify for an IEP if it impacts their academic performance and ability to learn from the general educational program, requiring individualized instruction (SCDD, n.d.). However, if a student with ADHD does not qualify for an IEP, they may be eligible for a 504 plan, allowing for certain accommodations within the classroom (SCDD, n.d.). While a student with ADHD can receive accommodations under an IEP and 504 plan, only under an IEP will they be able to receive modifications. These two plans have several similarities, including the requirement for parent consent to evaluate a child, parent notice of changes to the plan, and a three-year reevaluation cycle (SCDD, n.d.). However, significant differences exist. For example, an IEP requires written documentation regarding the plan and implementation of specialized services, as well as written parent consent/notice of changes. (SCDD, n.d.). On the other hand, a 504 plan, which is more generalized, doesn’t require written documentation of a plan or written parent consent/notice of changes, and primarily focuses on removing barriers by providing accommodations in the learning environment. (SCDD, n.d.). Overall, while an IEP is more comprehensive, both plans are set up to support students with disabilities in school.
References
McMillan, J. H. (2016). Fundamentals of educational research (7th ed.). Pearson.