Respond to 2 students discussion using the rise Model
Due Friday November 17, 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- Guadalupe
Use an example of statistical data you are interpreting to indicate probability, correlation coefficient, and the type of analysis used.
- Example: examining the relationship between student attendance and academic performance- to examine the relationship between student attendance and academic performance, statistical data interpretation can involve the calculation of probability, correlation coefficient, and using specific analysis methods. Statistical data analysis is a procedure of performing various operations to summarize and analyze data. Statistical techniques provide a better understanding of the meaning of numbers (McMillan, 2021).
- probability: can be used to determine the likelihood of certain events occurring in relation to student attendance and academic performance. For example, data can be collected from a sample of 200 students to track their attendance and grades. If 90% of attendance is tacked the data can determine the probability that attendance has a positive correlation with academic performance.
- correlation coefficient: helps measure the strength and direction of the linear relationship between two variables (McMillan, 2021). If the data is from the sample of 200 students and their attendance rates and average grades are a percentage calculating the correlation coefficient, it can determine the strength and direction of the relationship between attendance and grades. The analysis may reveal a positive correlation coefficient of 0.75. The correlation coefficient can assess the degree of association between student attendance and academic performance.
- analysis methods: a regression analysis can determine the relationship between attendance and academic performance while considering other factors that may influence performance, such as study habits or socioeconomic status. Another analysis method that can be utilized is the chi-square test which helps to determine the association between attendance and academic performance. This type of analysis is a procedure that uses frequencies to determine whether there is a relationship between the variables (McMillan, 2021).
Reference
McMillan, J. H. (2021). Educational research: Fundamental principles and methods (8th ed.). Pearson.
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Reponse 2- Ashley
Example Scenario: A statistical analysis is conducted in order to assess the impact that an existing school counseling program has on students’ academic success. The researchers collect data on the number of counseling sessions attended by students and their corresponding grade point average at the end of the academic year to see if attending a higher number of sessions with the school counselor influenced the students’ academic performance.
Probability: Probability could be used to estimate the likelihood of a student achieving a higher GPA given their participation in counseling sessions. For example, the probability of a student having a GPA above 3.0 if they attended five or more counseling sessions throughout the school year.
Correlation Coefficient: The correlation coefficient can indicate the strength and direction of the relationship between the number of counseling sessions a student attended and their resulting GPA at the end of the year. For example, the researchers might conduct this analysis and find a correlation coefficient of 0.70. This positive correlation suggests that there is a moderately strong positive relationship between the number of counseling sessions attended and their GPA. In other words, as the number of counseling sessions increases, there is a tendency for GPAs to also increase.
Type of Analysis Used: The researchers could use the chi-square procedure to analyze their results and determine if there is a significant difference in terms of GPA for students who attended five or more sessions with their counselor compared to those who attended fewer or no meetings during the year. Chi-square analysis is used by researchers to test frequency counts in different categories (McMillan, 2016).
Reference
McMillan, J. H. (2015). Educational Research: Fundamentals for the Consumer (7th ed.). Pearson.