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Overview:

In week 1, you selected a topic and developed a research question for that topic. Then, you developed a data gathering instrument to measure the question either quantitatively or qualitatively. Now that you have had the opportunity to read how scholarly methodologies are written, you will write a condensed 3-4 page methodology section for your research question using the required headings from the University of the Cumberlands Dissertation Handbook. Like we discuss in class, each university has unique parameters for what they expect in chapter 3, so you may see papers from other universities that look slightly different. The importance here is to focus on the content, not necessarily the organization. This assignment will help determine your readiness to write a full-length chapter three.

Directions:

1. Review your notes from class on the different methodologies and instruments used to measure. Also, review the examples:

Approaches Expectations.docx

Approaches Expectations.docx – Alternative Formats

Meets Expectations.docx

Meets Expectations.docx – Alternative Formats

Exceeds Expectations.docx

Exceeds Expectations.docx – Alternative Formats

Finally, review the rubric:

Rubric for Methodology.docx

Rubric for Methodology.docx – Alternative Formats

2. Develop a 3-4 page (more is fine) methodology section that includes the following:

Introduction

Research Paradigm (qualitative or quantitative)

Notes:

Choose Qualitative or Quantitative based on what methodology you plan to use for your actual dissertation. You may not choose to do both qualitative and quantitative (mixed-methods)

Research- or project- Design

Sampling Procedures and

Data Collection Sources

Statistical Tests Summary (quantitative) OR Data Organization Plan (Qualitative).

Notes:

If you chose a quantitative research paradigm, you must choose a quantitative statistical test summary option in this section. If you chose a qualitative research paradigm, you must choose the qualitative organization plan option in this section.

Chapter Three: Procedures and Methodology
Introduction
The goal of education is to increase student achievement and knowledge of the material
being taught. It is imperative that, if this is the real goal of education, search for best practices
that assist in increasing student achievement. While many different aspects impact student
achievement, expanding the practice efforts of educators to help in the classroom is beneficial
(Tucker & Strange, 2020). The idea for the study focused around the theory, while it may be
considered old by many in education today, from Benjamin Bloom and mastery learning and the
utilization of formative assessments and individualized learning to drive instruction (Guskey,
2010). The purpose of this quantitative study was to determine the strength and nature of the
relationship between the level of implementation of the diagnostic assessment software
PowerSchool and student achievement on the eighth-grade mathematics TCAP test in a semirural system in northeast Tennessee.
The target system for this study served a total enrollment of 5,290 students, grades pre-k
through grade 12, and consists of 15 schools and one alternative placement setting. While many
of the schools from the target district are considered to perform at proficient levels for student
achievement, others in the system are or are in danger of becoming target schools by the
Tennessee Department of Education based on student achievement. As with all public schools in
the state of Tennessee, all third through eighth-grade students in the target district partake in
yearly TCAP testing in ELA, mathematics, science, and social studies. As discussed in Chapter
2, TCAP is a criterion-referenced assessment that, when coupled with TVAAS and value-added,
is a reliable and valuable source of data for educators statewide. Chapter 3 discusses the
methodology of the research as well as the utilization of the TCAP and TVAAS as sources of
data. The chapter begins with an introduction and research paradigm and design before moving
through the sampling procedures, data collection sources, statistical tests being utilized, and a
summary of the chapter.
Research Paradigm
The review of the literature discussed in Chapter two explained how the use of formative
assessments and mastery learning could be used to increase student achievement. Furthermore,
as mentioned in Chapter One, the state of Tennessee, as well as the nation, is facing a crisis with
a large percentage of today’s students performing below grade-level expectations. For this
reason, systems nationwide have implemented programs specifically for assisting in increasing
student achievement in mathematics such as Response to Intervention (RTI), and continual
search for programs that can further help in this goal of improving student achievement and
understanding in mathematics.
The goal of this study was to investigate the relationship between the level of
implementation of the diagnostic assessment software PowerSchool and student achievement in
eighth-grade mathematics in a semi-rural northeast Tennessee school system. A quantitative
study was chosen for the study since quantitative research “entails the collection of numerical
data and exhibiting the view of the relationship between theory and research as deductive, a
predilection for natural science approach, and as having an objectivist conception of social
reality” (Bryman & Bell, 2015, p. 160). The dependent variables for this quantitative study
focused on the student different data on achievement results based on the eighth grade Tennessee
Comprehensive Assessment Program (TCAP) performed in the spring semester of the 2020-2021
school year. The independent variable was based on the different levels of implementation of the
diagnostic assessment software PowerSchool during the 2020-2021 school year in a school
district in semi-rural northeast Tennessee.
Research Design
The research design for this study includes a correlation design utilizing the independent
samples t-test and Chi-square to measure the strength and nature of the relationship between
student achievement and the level of implementation of PowerSchool. A correlation design was
chosen due to the desire to realize if and how strong of a relationship exists between the level of
implementation and student achievement. One group of classes uses PowerSchool as merely a
benchmark testing, making up less than five percent of the time spent utilizing PowerSchool for
instructional purposes. In contrast, the second group not only uses PowerSchool for the systemwide benchmark testing but also weekly as formative assessments to drive the daily instruction,
making up 50% or more of instructional time spent utilizing PowerSchool for instructional
purposes. The research used the PowerSchool program, including criterion-referenced
benchmark exams based on Tennessee state standards provided through the program, as well as a
second criterion-based test in the Tennessee Comprehensive Assessment Program (TCAP)
during the 2020-2021 school year. PowerSchool was implemented through an online platform
inside the individual classrooms as well as home access provided at home. The TCAP test was
presented in the paper-pencil format during Spring 2021 by exiting eighth-grade students in
Northeastern Tennessee.
The researcher chose three different sources to serve as dependent variables, one for each
research question, and one for the independent variable for each. The dependent variables were
based on the different degrees of measure of student achievement on the 8th grade TCAP
mathematics test: individual composite scores, TVAAS value-added for each teacher
participating in the study based on TCAP scores, and the level of achievement of each student.
The basis for the independent variable was the two levels of implementation of the diagnostic
assessment software PowerSchool: a full implementation that was used to drive the curriculum
and implementation for benchmark testing purposes only.
Sampling Procedures
Prior to conducting this study, approval was asked for and obtained from the University
of the Cumberlands Institutional Review Board (IRB). The target school system chosen for this
study has acknowledged that a problem exists with student achievement in TCAP testing,
especially in middle school mathematics. For this reason, the system implemented the mandatory
use of benchmark testing (three total tests throughout the school year) utilizing the PowerSchool
software system-wide during the 2019-2020 school year. Permission was granted to conduct
research through the district in question by the curriculum supervisor (see Appendix A).
The targeted semi-rural district located in northeast Tennessee was relatively large for a
single district. According to data obtained from the personnel department of the target school
district, during the 2020-2021 school year, the district employed 473 professional employees: 10
supervisors, 16 principals, eight assistant principals, 18 system-wide support supervisors
(curriculum coaches, testing coordinators, etc.), and 421 classroom teacher. Furthermore, the
targeted district consists of 15 schools serving students in grades pre-kindergarten – twelfth
grade and one alternative placement school. The 15 schools served 5,290 total students
containing 756 students that qualify for special education services. The system is considered
“direct serve,” which indicates all students kindergarten – eighth grade receive free breakfast and
lunch. Each school in the system qualifies as Title 1 schools. The percentage of the ethnic
diversity of the 5,290 total students served during the 2020-2021 school year consisted of 95.7%
Caucasian, 2.28% Hispanic, and 2.02% identifying as other.
Due to the nature of the study, a non-random, convenience sampling method was chosen
for participants. Convenience samples are defined as the “non-probability sampling method that
relies on data collection from population members who are conveniently available to participate
in the study” (Convenience, 2019). Because convenience sampling was utilized for this study,
the study lacks the desired trait of randomness in sampling. However, the purpose of this study
was to identify if a relationship between the level of implementation of the diagnostic assessment
software PowerSchool in a local northeast Tennessee school system, thus the research and results
may not produce data that can be generalized to an overall population. Furthermore, including all
eighth-grade students in the targeted district helps to strengthen the validity of the study.
The targeted system consists of seven middle schools, three of which implementing full
PowerSchool (50% of instruction) classified as Group X and five only utilizing the program for
benchmark tests only ( less than 5% of instruction time) classified as Group Y. The convenience
of using all seven middle schools was appropriate. Of the 5,290 total students served by the
district, 404 students were served in eighth grade, represented 8.37 % of the population. For this
study, the eighth grades were separated into two groups: Group X consisted of 188 individual
students ( n = 188) and four teachers, and Group Y consisted of 216 individual students (n = 216)
and four teachers.
Data Collection Sources
This study based the collection of data primarily from the results of the eighth grade
Tennessee Comprehensive Assessment Program (TCAP) as well as the value-added results
formulated from TVAAS. As previously discussed, the TCAP assessment is assumed to be valid
and reliable criterion-based. The TCAP assessments will be completed during April 2021, and
results will be finalized and reported back to the system during the summer of 2021. TCAP
testing is implemented for all students grades three through eight throughout the state of
Tennessee. Once the results are reported back to the system, the system will contact the
researcher and provide access to the student’s results in coded form for each individual that are
part of the study.
The TCAP test provided the overall composite scores in the subject of mathematics for
each student as well as their individual level of achievement. Figure 1 shows an example of the
reporting data provided by the TCAP for each student.
Figure 1 Individual student TCAP report
The data received from the TCAP results, as well as the TVAAS value-added reporting for each
student and teacher involved in the study were then collected, coded, and organized.
The data was collected from the testing department of the targeted school system. In
order to prevent bias testing, the data was organized into two groups based on the level of
implementation in the study: Group X (full implementation) and Group Y (benchmark utilization
only). The testing department also provided the data in each group without the individual names
of teachers, students, schools, or any other personal data that could be used as identifying
markers. The student’s data were numbered using three-digit codes beginning with 001 for the
analysis of the composite scores as well as the level of achievement and value-added data. Table
1, 2, and 3 represents the manner in which the researcher organized the data.
Table 1. Group X student TCAP data for the 2020-2021 school year.
Student
Composite
Equivalent Level of
Amount of
Score
Achievement
Value-added
001
002
003
…
Table 2. Group Y student TCAP data for 2020-2021 school year.
Student
Composite
Equivalent Level of
Amount of
Score
Achievement
Value-added
001
002
003
…
Table 3. The number of students in each level of achievement for both Group X and Group Y on
the TCAP test for the 2020-2021 school year.
Level 1:
Level 2:
Level 3:
Level 4:
Below
Approaching
On Track
Mastery
Group X
Group Y
This method of data collection was chosen in hopes of maintaining confidentiality as well as
preventing any bias results from the study.
Statistical Tests
The researcher utilized descriptive and inferential data to analyze the data for this
quantitative study to determine if a significant difference exists. The researcher performed
independent sample t-tests to analyze the individual student composite scores provided by
performance on the TCAP test as well as the amount of value that was provided by the TVAAS
value-added report. According to SPSS, independent samples t-test is utilized to compare “the
means of two independent groups to determine whether there is statistical evidence that the
associated population means are significantly different” (2020). The researcher chose to perform
Chi-square to determine if a significant distance exists between the in the number of students in
each level of achievement on the 8th Grade TCAP test (Below, Approaching, On Track,
Mastered) between classes with different levels of PowerSchool implementation (full
implementation as opposed to benchmark usage only). For all three tests, the data was analyzed
with a confidence level set at p = 0.05 to determine if a significant difference exists. Table 4
represents the data collection and statistical test matrix the researcher utilized for this study.
Table 4. Data collection and statistical test matrix.
Research Question
Data Collection Sources
Statistical Test
Is there a significant difference in the
Tennessee Comprehensive Assessment
Program (TCAP) 8th Grade composite
math scores between classes with
different levels of PowerSchool
implementation (full implementation as
opposed to benchmark usage only) in a
northeastern Tennessee school district?
TCAP (Composite
Independent samples
mathematics score)
t-test
(correlation)
Is there a significant difference in the
number of students at each level of
achievement on the 8th Grade TCAP test
(Below, Approaching, On Track,
Mastered) between classes with different
levels of PowerSchool implementation
(full implementation as opposed to
benchmark usage only) in a northeastern
Tennessee school district?
TCAP (level of
Chi-square
achievement)
(correlation)
Is there a significant difference in the
amount of value-added on the 8th Grade
TCAP test among classes with different
levels of PowerSchool implementation
(full implementation as opposed to
TVAAS (amount of
Independent samples
value-added)
t-test
Research Question
Data Collection Sources
benchmark usage only) in a northeastern
Tennessee school district?
Statistical Test
(correlation)
Summary
As previously discussed in Chapter 2, an increase in accountability for student learning as
well as teacher effect on student learning has caused schools to search for systematic solutions to
assist in increasing student achievement. The system-wide implementation of the use of
PowerSchool for benchmark testing in mathematics (one given at the end of each of the first
three, nine-week grading periods) took place during the 2019-2020 school year. As per the state
of Tennessee policy, the targeted district partakes in the yearly end of the school year, criterionreferenced TCAP testing. This assessment, along with TVAAS, provides individualized student
composite scores, level of achievement (one-four), and value-added data. The entire number of
students in eighth-grade mathematics in the targeted district was utilized for this study. The
students were separated into two groups: Group X (full implementation of PowerSchool) and
Group Y (benchmark testing only).
This chapter presented the research design, sample procedures, data collection sources,
and type of statistical testing used to analyze the data. Furthermore, the research paradigm was
presented and discussed. This study’s results were obtained through quantitative data produced
from TCAP scores of eighth-grade students in a northeast Tennessee school district. The study
consisted of the three following research question:
1. Is there a significant difference in the Tennessee Comprehensive Assessment Program
(TCAP) 8th Grade composite math scores between classes with different levels of
PowerSchool implementation (full implementation as opposed to benchmark usage only)
in a northeastern Tennessee school district?
2. Is there a significant difference in the number of students at each level of achievement on
the 8th Grade TCAP test (Below, Approaching, On Track, Mastered) between classes
with different levels of PowerSchool implementation (full implementation as opposed to
benchmark usage only) in a northeastern Tennessee school district?
3. Is there a significant difference in the amount of value-added on the 8th Grade TCAP test
among classes with different levels of PowerSchool implementation (full implementation
as opposed to benchmark usage only) in a northeastern Tennessee school district?
The analysis of the data collected from the study is contained in Chapter 4 through explanation
of how the independent sample t-tests as well as the Chi-square were utilized as well as
representation of the process and data provided during the study. The results will be compared
used a standard of p = 0.05 to determine if a significant difference exists.
References
Bryman, A., & Bell, E. (2015). Business Research Methods (4th ed., p. 160). Oxford, England:
Oxford University Press.
Convenience sampling . (2019). In Research Methodology. Retrieved from https://researchmethodology.net/sampling-in-primary-data-collection/convenience-sampling/
Guide to test interpretation 2019-2020 TCAP assessment. (2019). In Tennessee Department of
Education. Retrieved from https://www.tn.gov/content/dam/tn/education/testing/
TN1124053_TCAP_EOC_GTI_WEBTAG.pdf
Guskey, T. R. (2010, October). Lessons of mastery learning. Educational Leadership, 68(2), 5257.
SPSS tutorials: independent samples t-test. (2020, March 24). In Kent State University:
University Libraries. Retrieved fromhttps://libguides.library.kent.edu/SPSS/Independent
TTest
Tucker, P. D., & Strange, J. H. (2020). Linking teacher evaluations and student learning.
In ASDC. Retrieved from http://www.ascd.org/publications/books/104136/chapters/ThePower-of-an-Effective-Teacher-and-Why-We-Should-Assess-It.aspx
Using Data science techniques to enhance data security`
For this dissertation, my topic will be: “Enhancing Data Security Using Data Science
Techniques.” Over the past decade, data generation has grown exponentially. With the advent of
technology, the rise of big data is primarily attributed to the need to assess and enhance the value
of a company. The proliferation in the use of big data has raised concerns about the issue of data
security and possible threats attributed to data mining and machine learning(Ajikumar, 2017)..
Data security entails protecting data from unauthorized access by a third party. The issue of data
security has been linked with cybersecurity as an emerging security threat that relate to attacks on
information infrastructure and algorithms. Technological advancement in developed country
makes cybersecurity a potential threat to the countries’ economy, infrastructures and
citizens(Ajikumar, 2017).
Big data analysis refers to the process of assessment of large data sets and also creates a framework
that can be used to observe the pattern and relationship among the data sets (Lekhrajani &
Samdani, 2018). The increased adoption of big data is due to the availability of this information
and the advancement of the techniques used in the collection of the data. Data analytic as a
precursor of data mining process entail collection, selection and pre-processing of information
(Yashasree Tummala, 2018). However, the proliferation of big data and the use of the internet has
raised the issue of data security attributed to cybercrimes. The paper, therefore, addresses the
advances in data science and machine learning and the role of technology in curbing data
insecurities (Ajikumar, 2017). The development of data science has enhanced human capabilities
in analyzing and understanding the large data that is increasing every single day (Ajikumar, 2017).
Research questions
1. What data science algorithms can be used to curb data insecurities
2. Can machine learning or artificial intelligence play a part in reducing cyber insecurities,
if yes, how?
3. What data driven models can provide insights to data concerns in cyber security patterns
Study Design
The study will be undertaken using qualitative and quantitative methods. Qualitative
methods will involve a study of academic journals and articles. The qualitative method will
involve using a grounded theory of approach to understanding what data science algorithms can
be used to curb data insecurities, and what literature can be found on this. Then, we will analyze
what role machine learning or artificial intelligence play a part in reducing cyber insecurities.
With a detailed literature review on what worked, what didn’t and why.
The second method will involve quantitative research using current data. A well-known
organization will be selected and data mining algorithm studies. After the data will be collected
analysis will be conducted using statistical software such as Microsoft Excel or IBM SPSS. The
statistical analysis will include means, medians, distribution, and analysis of means such as
ANOVA. Finally, the discussion and conclusion of the results be presented accompanied by
charts and graphs.
References
Ajikumar, J. M. (2017). Data Mining and Machine Learning Methods for CyberSecurity Intrusion
Detection. International Journal of Scientific Research in Computer Science, Engineering
and
Information
Technology,
2(2),
2456-3307.
Retrieved
from
https://www.academia.edu/33112124/Data_Mining_and_Machine_Learning_Methods_fo
r_Cyber_Security_Intrusion_Detection
Lekhrajani, S., & Samdani, K. (2018). . A Review of Implementation of Deep Learning in Big
Data Analysis. In 2018 8th International Conference on Cloud Computing, Data Science
& Engineering (Confluence) (pp. 14-15). IEEE.
Yashasree Tummala, K. H. (2018). A review on Data Mining & Big Data Analytics. International
Journal
of
Engineering
&
Technology,
92-94.
Retrieved
from
https://www.researchgate.net/publication/329014011_A_review_on_Data_Mining_Big_
Data_Analytics

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