Description
Design an action research study to address a problem of practice in your organization or that someone could apply in another organization
After considering what you learned while conducting the literature review and the questions that were raised about the practical application of the findings, you will use these resources and your knowledge of your own site to conceive and design an action research study that could be implemented in your organization or another organization to address a problem of practice. The action research study you propose will be based on your area or interest and thus based on a real-world problem for your organization; however, the proposed study will be hypothetical. You will not carry out the action research study; you will simply propose it. You will use a detailed action research design template and other resources as guides when you complete this assessment. In this assessment, you will consider what you learned from the literature review about your organizational issue and the questions raised about the practical application of the findings. Use these resources and knowledge of your own site to conceive and design an action research study to address a problem of practice that you might implement in your organization or that someone could apply in another organization. Although the problem you are addressing in this assessment is a real-world organizational problem or process, the proposal with its intervention and data collection and data analysis plan are hypothetical. To complete this assessment: You need to have a good grasp of the Cycle of Inquiry within the Applied Improvement Process. Also, be sure to read information about action research in Given’s (2008) The SAGE Encyclopedia of Qualitative Research Methods. Develop your assessment using the Annotated Template With Instructions for u06a1 [DOCX] Download Annotated Template With Instructions for u06a1 [DOCX].
Complete and submit your assessment on the Assessment Template for u06a1 [DOCX] Download Assessment Template for u06a1 [DOCX].
Your hypothetical action research proposal will follow and present a plan for completing the first five steps of the 10-step Cycle of Inquiry as illustrated in Applied Improvement Process: Using a Cycle of Inquiry to Plan, Implement, and Evaluate Improvement.
Steps 1 and 2: For step 1, “Diagnose the Problem,” and step 2, “Generate Alternatives,” if the corresponding data are not available to you, you will need to have already hypothetically collected the data you need via a needs assessment and collaborated with your organizational leaders to generate alternatives.
Step 3: In step 3, “Design Action Plan,” you will propose an intervention based on your understanding of the organizational problem or process and your review of the literature. Step 4: In step 4, “Implement Action Plan,” you will create a plan to implement the intervention. Step 5: In the “Collect and Analyze Data” step, you will include a data collection plan and a data analysis plan.
Step 6: The final component, “Dialogue About Process,” will conclude with a discussion of: How the action research proposal relates to your specialization within the EdD program. The ethical considerations for the action research study.
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Annotated Bibliography
David A Braden
School of Public Service and Education, Capella University
EDD8040: Research Design for Practitioners
Dr. Cheryl Bullock
January 9, 2024
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Annotated Bibliography
Quantitative Study 1
Latino, C. A., Stegmann, G., Radunzel, J., Way, J. D., Sanchez, E., & Casillas, A. (2020).
Reducing gaps in first-year outcomes between Hispanic first-generation college students
and their peers: The role of accelerated learning and financial aid. Journal of College
Student Retention: Research, Theory & Practice, 22(3), 441-463.
https://journals.sagepub.com/doi/abs/10.1177/1521025118768055
Purpose and Main Results
Latino et al.’s (2020) study examined the relationship between accelerated learning in
high school, financial aid, and academic outcomes for Hispanic FGCS and non-FGCs at a fouryear postsecondary institution. This aims to treat disparities in first-year outcomes between these
two groups, specifically on first-year students’ grade point average (GPA) and retention from
Year 1 to Year 2. This study, surveyed with a sample of 2,499 respondents, is designed to
investigate if environmental supports, including accelerated learning and financial assistance, can
lessen the achievement gap in GPA and persistence rates for Hispanic FGCS. The general results
show that Hispanic FGCS perform poorer in first-year GPA and retention than non-FGCS
counterparts. However, upon controlling academic, nonacademic, and demographic variables,
the study concludes that accelerated learning promotes diminished achievement gaps in first-year
GPAs. In addition, financial aid is linked to higher retention rates for Hispanic FGCS without
achievement gaps. Based on the study, it is proposed that environmental supports such as quick
learning and financial assistance significantly promoted better GPA results or retention outcomes
for Hispanic FGCS.
Evaluation
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The study shows overall quality by addressing a substantial issue in the literature about
Hispanic FGCS. By addressing the unique nexus of being a FGCS and an ethnic or racial
minority, this work provides valuable information. Two thousand four hundred ninety-nine
participants obtained from the large-scale sample increase the reliability and generalizability of
this study. The presence of accelerated learning and financial aid as variables aligns with the
overall scope this study has taken to understand environmental aspects influencing Hispanic
FGCS. In addition, a bias is that some variables are obtained through self-reporting, which can
create response bias. Furthermore, the study considers the first-year outcomes and thus presents a
snapshot rather than a longitudinal view. The research might be advanced by studying the longterm effects to determine how accelerated learning and financial aid impacted Hispanic FGCS
throughout their college experience.
The study successfully situates itself in the broader scope of growing significance
attributed to having a bachelor’s degree and its impacts on FGCS and racial or ethnic
minorities. The theoretical framework is also consistent with prior research on the effects of fasttrack programs and financial assistance in college results, developing further by focusing on how
these concepts apply to Hispanic FGCS. The study also has a large sample size, which increases
its statistical power and enables sound analyses. However, the article may provide more
information on how sampling was conducted to guarantee a transparent method and the
possibility of replication. There is clarity in the presentation of results with a systematic analysis
of all variables, which enhances overall coherence to any given study. Again, the findings of this
research have considerable implications since they suggest that environmental facilitators,
including accelerated learning and financial aid, can enhance Hispanic FGCS’ first-year
results. These results align with what is already known about the efficacy of such programs but
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offer distinct insights unique to Hispanic FGCS. Based on the study’s relevance to potential
action research proposals and the Applied Improvement Project, practical approaches can be
proposed regarding Hispanic FGCS achievement gaps through interventions in accelerated
learning and financial aid. Similar strategies to be implemented in the proposed projects might
help close gaps in first-year results.
Quantitative Study 2
Palacios, C. A., Reyes-Suárez, J. A., Bearzotti, L. A., Leiva, V., & Marchant, C. (2021).
Knowledge discovery for higher education student retention based on data mining:
Machine learning algorithms and case study in Chile. Entropy, 23(4), 485.
https://www.mdpi.com/1099-4300/23/4/485
Purpose and Main Results
Palacios et al. (2021) conducted a study that used data mining and machine learning
algorithms to predict student retention in higher education. The purpose was to develop models
that would be accurate in predicting students’ retention levels at different points during their first,
second, and third years of study. This study aimed to utilize applicable variables in higher
education data for better discovery of knowledge and to inform institutions about
dropouts. Machine learning algorithms like decision trees, k-nearest neighbors, and logistic
regression, to mention, were used, and the random forest technique proved to be the most
efficient. The study identified secondary educational scores and the community poverty index as
important predictive variables previously unreported in similar academic studies. The models
were highly accurate, over 80% in all scenarios, allowing institutions to predict how high a
dropout level may be.
Evaluation
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The study has a number of strengths, including the innovative application of data mining
and machine learning in addressing a principal issue within higher education: student
retention. Including multiple machine learning algorithms offers a comprehensive approach
while employing an actual case study strengthens the pragmatic potential of its outcomes. The
open-access nature of this study promotes knowledge dissemination. Aside from the robustness
of the study, potential biases could also result from relying on specific variables with their
predictive power varied by different educational contexts. Furthermore, the extent to which
findings could be generalized beyond institutions outside of this Chilean case should also be
approached with caution due to variations in educational systems and demographics. In addition,
the research is firmly rooted in data mining and machine learning, as it aligns with a growing
tendency to use innovative analytics for educational research. The literature review establishes
the need for predictive models in higher education; however, discussing current effective models
and methodologies would strengthen that section.
The presentation of results is also clear, with a detailed analysis of machine learning
algorithms and their performance, making the study coherent. Also, the study’s consequences are
weighty for higher education institutions worldwide, particularly those with issues like those in
the Chilean case. Predictive models, if they are suited and shown to function for a range of
circumstances, could provide institutions with the ability to institute preventative programs to
raise retention rates. Focusing on variables, including secondary educational scores and the
community poverty index, brings new dimensions to dropout forecasting. The study provides a
valuable methodological approach for action research proposals and Applied Improvement
Projects. It demonstrates that advanced analytics could be beneficial in addressing issues of
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student retention efforts. However, careful consideration of contextual differences in the
proposed projects is necessary to make sure that the models and findings applied are applicable.
Qualitative Study 1
Cuellar, M. G., & Gándara, P. (2021). Are you promoting access and equity for underrepresented
racial minorities? An examination of policies and practices in community college
baccalaureate programs. Community College Review, 49(1), 52-75.
https://journals.sagepub.com/doi/abs/10.1177/0091552120964877
Purpose and Main Findings
Cuellar and Gándara (2021) focus on analyzing the connection between CCB programs in
community colleges and facilitating accessibility and equal opportunities for URMs. The
research questions are two-fold: first, finding out how administrators signify the objective of
CCBs as related to URM equity, and second, understanding what policies and practices are
adopted by CCB programs to facilitate equitable access or success among relevant students. The
study employs a multiple-case design using three states with different demographic compositions
and CCB offerings. Insights into the views and approach to CCB programs are obtained from
interviews with administrators in two colleges of each state. The main findings suggest that
administrators view CCBs as mechanisms for improving socioeconomic mobility, especially
among low-income students, indirectly contributing to returning equity. However, the
consideration assigned to delivering access for URMs depends on how well-represented those
are in the local community and feeder programs. The study reveals no specific outreach efforts
and support services aimed at the URMs, indicating a gap in intentional moves to address
disparities. It concludes that while CCBs might be a policy needed to help narrow the gap in
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education, deliberate outreach and support for URMs are also necessary as they drive the best
results.
Evaluation
This study shows significant strengths in focusing on CCB programs and their influence
on URMs. Using a multiple-case study design, this research covers diversity across states and
colleges, increasing generalizability levels. This emphasis on administrator perspectives offers
considerable insight into the underlying impetus and difficulties associated with CCB
programs. However, certain limitations warrant consideration. This study recognizes the
importance of URM representation in the community, but analyzing this variable more
profoundly could improve the depth of findings on program outcomes. Further, the absence of
apparent outreach efforts and support services directed at URMs is outlined; however, this study
does not explore how these gaps can be eliminated through potential strategies or
recommendations. About the theoretical framework and literature review, this study successfully
situates itself in a broader sense of educational disparities, even in community colleges. It aligns
with the literature to explore CCB programs as a solution to closing gaps in educational
attainment for URM. While the results report is easy to read and arrange, potential implications
for this study could be discussed more thoroughly. The study could have been more substantial if
the authors had provided a more robust discussion on the empirical implications for
policymakers, educators, and administrators and potential strategies to improve equity in CCB
programs.
Qualitative Study 2
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Bzour, M., Zuki, F. M., & Mispan, M. (2022). Causes and remedies for secondary school
dropout in Palestine. Improving Schools, 25(1), 52-64.
https://journals.sagepub.com/doi/abs/10.1177/13654802211004067
Purpose and Main Findings
In their research, Bzour et al. (2022), the research questions focused on determining the
reasons behind school dropout, analyzing each of these causes separately, and finally evaluating
actions and processes to reduce such rates. Factors influencing the dropout included family
background, teachers, school environment, and students’ roles. These multiple factors were also
incorporated into the conceptual model for student dropout. The article suggested that
individuals, schools, communities, and families should be involved in this process to prevent
early school leaving. Some specific recommended actions included fighting illiteracy, promoting
positive interaction between teachers and students, and promoting community involvement in
education initiatives.
Evaluation
The study’s strengths start with its relevance to a critical issue in the Palestinian education
system. The study’s depth can be observed in the identification of multifactorial causes, the
incorporation of a conceptual model, and the recommendations made to adopt a holistic
approach. Community and family involvement, emphasized in the program, is suitable for the
social culture of Palestinian society. Further, the study recognizes the intricacy of dropout
reasons; however, it may not detail each peculiarity of every contributing factor. Additionally,
the study could be enhanced by a deeper analysis of the socio-political environment in Palestine
as outside forces and occupation play their role in the difficulties education faces.
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Additionally, the study offers a short but sufficient presentation of education’s
importance for social development. Second, the article does not provide thorough information
regarding the specific sampling procedures implemented in this study, making it difficult to
evaluate if the sample is representative of their population. Findings are presented clearly, with
an excellent causal analysis and recommendations for cure that help the coherence of this
study. Finally, the study’s implications are essential for increasing dropout statistics in
Palestine. Consequently, the proposed multi-level intervention is consistent with good practices
in education. However, these remedies should be evaluated in light of Palestinian socio-political
peculiarities. The study provides valuable insights into the multifaceted nature of school dropout
and underlines that comprehensive, community-centered interventions are needed to address it
when considering potential action research proposals or Applied Improvement Projects. It can be
used to build strategies considering a range of factors contributing to students’ disengagement
from education.
Action Research Study 1
Canty, A. J., Chase, J., Hingston, M., Greenwood, M., Bainbridge, C. P., & Skalicky, J. (2020).
Addressing student attrition within higher education online programs through a
collaborative community of practice. Journal of Applied Learning and Teaching.
https://rune.une.edu.au/web/handle/1959.11/53529
Purpose and Main Findings
The research by Canty et al. (2020) focuses on the urgent problem of student attrition in
online higher education programs. The study’s objective is to determine factors that influence
student success, create effective strategies, and address issues faced by distance students. An
analysis of attrition at the University of Tasmania, with a high rate for commencing bachelor
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students, 28%, shows that there is a lot to be learned about how challenges faced by ‘at-risk’
distance become overcome. The study introduces a Community of Practice approach, identifying
four key challenges: students, good data dependability, a sense of ‘belonging,’ and student access
to known academics. The key findings highlight that such a problem lacks a unique solution,
offering several approaches of tailored and synergistic early interventions. These interventions
seek to assist students, improve their learning process, and contribute towards reducing dropouts.
Evaluation
The study’s strengths are multiple, starting from identifying student attrition as a strategic
problem that affects students, university funding, and reputation. A necessary strength is the
emphasis on finding challenges for distance students and suggesting a collaborative Community
of Practice approach. The recognition that there are many different challenges and that we cannot
have a one-size-fits-all solution shows an understanding of the complexities of student
attrition. In addition, the fact that this study acknowledges problems associated with distance
education would be more insightful if they looked at socio-economic, cultural, and regional
factors leading to students’ attrition. The study recognizes the problems of obtaining reliable
data, but more detailed discussions on overcoming these challenges can add some
completeness. In addition, the study offers a detailed overview of Australia’s higher education
landscape and identifies transformative changes that have taken place over the last five
years. Incorporating theories such as Tinto’s Student Integration Model and Lizzio and Wilson’s
five senses of successful transition improve the theoretical framework. A more thorough
literature review of challenges associated with distance education and student attrition could
provide a more robust study.
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Nonetheless, the study must present detailed information on how samples were taken to
reach representatives. The presentation of findings is clear; the paper identifies four significant
challenges and lists interventions. Thus, the implications of this study are essential in dealing
with emerging issues of student attrition in online programs. The proposed interventions meet the
needs of distance learners and the dynamic nature of online learning. The study provides
valuable information for potential action research proposals and Applied Improvement Projects
on collaborative approaches, underlining the need for individualized support and engagement
strategies. It gives a solid platform for designing interventions that consider distance students’
specific issues, thus increasing the likelihood of success in various online learning
initiatives. This study applies not only to the University of Tasmania but also brings forth
guidelines that will be useful for all institutions worldwide when dealing with student attrition in
online higher education programs.
Action Research Study 2
Nikolaidis, P., Ismail, M., Shuib, L., Khan, S., & Dhiman, G. (2022). Predicting student attrition
in higher education through the determinants of learning progress: A structural equation
modeling approach. Sustainability, 14(20), 13584. https://www.mdpi.com/20711050/14/20/13584
Purpose and Main Findings
The study by Nikolaidis et al. (2022) aims to forecast student attrition in higher education
by examining factors affecting those students’ achievements during their studies. Using Tinto’s
integration theory and Bean’s attrition model, researchers suggest a model that would help
identify students at risk for dropping out from their studies by developing an academic selfanalysis using factors affecting their learning progress. There are several leading indicators of
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learning progress, such as student interactions with fellows and teachers, teaching effectiveness,
test results, etc. The researchers conducted an exploratory and confirmatory factor analysis on a
sample of 530 undergraduate students, finding that the indicators of learning progress could be
classified into two constructs. Notably, teacher effectiveness and learning materials emerged as
the most critical factors to foster progress in learning. Learning progress variables significantly
impacted students’ attrition status, as revealed by structural equation modeling. Moreover, a
multi-group analysis revealed the moderating role of academic semesters in mediated effects
involving students’ GPAs.
Evaluation
The study has several strengths that enhance its quality overall. First, the research model
is theoretically developed based on Tinto’s integration theory and Bean’s attrition model,
because of which student dropout can be predicted. Structural equation modeling improves the
strength of the analysis and thoroughly investigates relations between variables. The study also
establishes methodological rigor because it applies exploratory and confirmatory factor analysis
on a large sample of 530 undergraduate students. This approach allows for a deep dive into the
indicators of learning success and their subsequent influence on attrition.
The study chose a particular cultural and educational setting (Malaysian higher
education), which may limit the transferability of findings to other settings. Furthermore, using
self-reported data increases the opportunity for social desirability bias in replies. Finally, from a
pragmatic point of view, the study results are helpful to organizations interested in dealing with
attrition issues by improving teaching skills and optimizing course resources. The focus on the
progress of learning as a precursor matches with increasing interest in student-centered
approaches and interventions.
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Methodological Comparison
The two quantitative studies, Latino et al. (2020) and Palacios et al. (2018), used similar
methodological approaches. Both studies are quantitative, using statistical methods to investigate
relationships and forecast effects. Palacios et al., on the other hand, employ data mining and
machine learning algorithms such as decision trees, logistic regression, and random forest to
predict student retention in higher-degree studies. Where methodological approaches are
concerned, the types of quantitative data sets and statistical procedures also vary. Latino et al.’s
research uses the regression technique conducted on survey data collected from 2,499
respondents to investigate relationships between variables. On the other hand, Palacios et al.’s
work concerns knowledge discovery based on data mining methods; he used a dataset with
variables, namely secondary educational scores and community poverty index, to predict student
retention. Regarding action research studies, Canty et al., 2020 Nikolaidis et al. Both studies
refer to the problem of student attrition in higher education. Canty et al. use the Community of
Practice (CoP) approach to resolve issues that distance students encounter and decrease drop-out
rates by working with them.
Despite such similarities, methodological differences occur. Canty et al.’s AR includes
qualitative data collection methods, such as interviews and focus groups, which help identify the
problem areas and co-develop interventions. On the other hand, Nikolaidis et al. utilize
quantitative methods such as exploratory and confirmatory factor analysis alongside structural
equation modeling on self-reported data collected from 530 undergraduate students.
Turning to the qualitative studies, Cuellar and Gándara’s (2021) study and Bzour et
al. Cuellar and Gándara analyze the community college baccalaureate (CCB) programs using a
multiple-case study method, interviewing administrators to shed light on policies and
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practices. Bzour et al. explore the reasons for dropping out of secondary school in Palestine by
using qualitative approaches focused on different factors analyzed through interviews and
discussion. Cuellar and Gándara use a multiple-case study design, allowing in-depth analysis
across various states and colleges. On the other hand, Bzour et al.’s study offers an in-depth
analysis of why students drop out of secondary school and what can be done without taking a
multiple-case approach style.
Practical Application
The knowledge acquired from the studies plays a significant role in informing and
improving the practical use of my action research proposal. I use the results of quantitative
studies by Latino et al. (2020) and Palacios et al. (2020). Motivated by Latino et al.’s ideas on
environmental supports such as fast learning and financial aid, my action research proposal will
consider personalized interventions for these sources to assist Hispanic FGCS. My proposal is
influenced by Canty et al.’s Community of Practice approach in that it involves a collaborative
effort to improve support for distance students or, more broadly speaking. The structural
equation modeling approach offered by Nikolaidis et al. provides a framework that can help
explain the intricate relationships leading to student attrition, which will support the analytical
dimensions of my action research. The learnings synthesized above will inform my action
research proposal, which would encompass specific interventions based on quantitative and
qualitative insights of the action research to enhance outcomes for Hispanic FGCS in an
electronic higher education setting. The collaboration and data-driven approaches will be the
primary features, corresponding with the best practices discovered in the reviewed research.
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References
Bzour, M., Zuki, F. M., & Mispan, M. (2022). Causes and remedies for secondary school
dropout in Palestine. Improving Schools, 25(1), 52-64.
https://journals.sagepub.com/doi/abs/10.1177/13654802211004067
Canty, A. J., Chase, J., Hingston, M., Greenwood, M., Bainbridge, C. P., & Skalicky, J. (2020).
Addressing student attrition within higher education online programs through a
collaborative community of practice. Journal of Applied Learning and Teaching.
https://rune.une.edu.au/web/handle/1959.11/53529
Cuellar, M. G., & Gándara, P. (2021). Are you promoting access and equity for underrepresented
racial minorities? An examination of policies and practices in community college
baccalaureate programs. Community College Review, 49(1), 52-75.
https://journals.sagepub.com/doi/abs/10.1177/0091552120964877
Latino, C. A., Stegmann, G., Radunzel, J., Way, J. D., Sanchez, E., & Casillas, A. (2020).
Reducing gaps in first-year outcomes between Hispanic first-generation college students
and their peers: The role of accelerated learning and financial aid. Journal of College
Student Retention: Research, Theory & Practice, 22(3), 441-463.
https://journals.sagepub.com/doi/abs/10.1177/1521025118768055
Nikolaidis, P., Ismail, M., Shuib, L., Khan, S., & Dhiman, G. (2022). Predicting student attrition
in higher education through the determinants of learning progress: A structural equation
modeling approach. Sustainability, 14(20), 13584. https://www.mdpi.com/20711050/14/20/13584
Palacios, C. A., Reyes-Suárez, J. A., Bearzotti, L. A., Leiva, V., & Marchant, C. (2021).
Knowledge discovery for higher education student retention based on data mining:
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Machine learning algorithms and case study in Chile. Entropy, 23(4), 485.
https://www.mdpi.com/1099-4300/23/4/485
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Action Research Design
Insert your Name Here
School of Public Service and Education, Capella University
EDD-FPX8040: Research Design for Practitioners
Insert the Instructor’s Name Here
Insert the Due Date Here (Month, Day, Year)
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Important Writing Instructions
[This assignment and the entire course project needs be written in the third person voice.
Do not write in the first-person voice (I . . .). There should be none of you and your voice in this
assignment or the course project. Do not use awkward language such as The researcher . . . or
The learner when referring to yourself. Do not refer to yourself. Do not write in the second
person voice (writing that uses the language you or your). This assignment does not need to
include a long paper; your writing should be clear, precise, and concise. The assignment should
be no more than 8 to 12 pages of text.]
[Use direct quotes sparingly. At the doctoral level your writing should be comprised
primarily of summarizing and paraphrasing. Ensure your paragraphs have at least three
sentences. There are no single sentence paragraphs in either scholarly or basic writing.
The word data are the plural form of the singular word datum, which is rarely used because
datum is a single bit of data. Although in conversational language we treat the word data as
singular, in scholarly writing you must treat data as a plural (e.g., the data are, the data were, the
data indicate, etc.). Similarly, we use double verbs in conversational writing for emphasis but
not in scholarly writing. For emphasis use italics (refer to APA 7th ed. pp. 170-171). There are
no contractions in scholarly writing. Because you are proposing a study, use future tense verbs
for proposing. When reviewing the literature, present studies using past tense verbs (refer to
APA 7th ed. pp. 117-118 section 4.12 Verb Tense).]
[In proposing and describing your action research outcomes avoid using colloquial
expressions that contain the word would as in a phrase such as “The participants would be . . .”
The word would is the past tense of will. The use of phrases such as would be are colloquialisms
unless preceded or followed by the word if. Instead replace would be or similar phrases with will
be or other concrete phrases. “The participants will be . . .”]
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[Scholarly writing is meant to be read and interpreted literally. Please avoid slang,
colloquialisms, anthropomorphisms, and conversational writing (refer to APA 7th ed. pp. 116117). Instead, be clear, precise, and accurate. Use direct quotes sparingly. At the doctoral level
your writing should be comprised primarily of summarizing and paraphrasing. If you must use a
direct quote ensure the quoted text is in quotation marks followed by an in-text citation that
includes the author’s name, year and page (refer to APA 7th ed. pp. 261-267 and Table 8.1 on p.
266).]
[Double space your entire paper. That means do not add additional spaces between
sections. See APA 7th ed. p. 45, Section 2.21 Line spacing and the example student paper starting
on p. 61.]
Using Previous Coursework
[It is okay to re-use work from previous discussions and assignments if that work was
part of the course progression to the action research proposal. Do not cite yourself. For this
assignment use previous course material without citing yourself. Note that previous course work
will be flagged as a match in SafeAssign. That is okay; however, if your assignment includes
work from previous discussions and assignments, please include a comment when you submit
the assignment telling your instructor that your assignment includes some previous course work.]
Important Instructions – Understanding the Action Research Inquiry Cycle
[Although the problem is a real-world organizational problem or organizational process
that needs improving, the proposal with its intervention and data collection and data analysis
plan are hypothetical. To complete this assignment, you need to have a good grasp of the Action
Research Inquiry Cycle (review the Week 6 multimedia learning activities and download the
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Cycle of Inquiry pdf). Action Research information is available via the Given (2008) resource
included in Week 9 “What you need to know.”]
[Keep in mind that your proposed action research study is a first cycle action research
study. Focus on the short-term impact of the intervention. For example, if you intervention
includes several strategies provided to middle school teachers on how to incorporate culturally
responsive instruction, your data collection and evaluation of the intervention should focus
interviewing those middle-school teachers who received the intervention regarding the extent to
which they are incorporating those strategies in their classrooms and instructional activities.]
[Keep in mind that your hypothetical action research proposal will follow and present a
plan for completing the first 5 steps of the 10 steps inquiry cycle. For step 1, diagnosing the
problem, and step 2, generating alternatives, you will need to have hypothetically already
collected the data you need via a needs assessment and hypothetically collaborated with your
organizational leaders to generate alternatives. Based on your understanding of the
organizational problem or process your hypothetical needs assessment and hypothetical
stakeholder collaboration and your review of the literature, you will propose an intervention and
a plan to implement that intervention (steps 3 and 4). Your action research proposal will also
include a data collection plan and a data analysis plan (step 5) and will conclude with a
discussion of (a) how the action research proposal relates to your specialization of with the EDD
program, (b) the ethical considerations for the action research study, and (c) a discussion of the
potential weaknesses of the action research plan.]
Preliminary Information
[Please provide a brief overview of the organizational issue you have chosen to study in
this course. Recall you initially presented this organizational issue in your u02a1 Your Area of
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Interest assignment. Include a brief description of the organization where the issue exists with
pertinent contextual information (e.g., type of organization, purpose of organization, number of
personnel, teachers, students, etc., and other pertinent demographic information). In this paper,
use a pseudonym rather than the actual name of the organization.]
Problem
]Define the problem as it exists at your organization. The problem in an action research
study is an organizational situation, deficiency, or process that needs to be improved.
Begin with the statement: “The problem at [insert organizational pseudonym] is…” T
