Okay, this is a fantastic goal! Crafting an NSF proposal requires precision, a compelling narrative, and adherence to specific criteria. Below is a comprehensive guide for each section, tailored for Computer Science Education Research, with specific examples and best practices.

Let's assume a hypothetical project: "Cultivating Computational Fluency and Belonging through Inclusive, Project-Based Learning in Introductory Computer Science." This project aims to address high attrition and lack of diversity in introductory CS courses by implementing and studying a novel pedagogical approach.

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NSF Proposal: Cultivating Computational Fluency and Belonging through Inclusive, Project-Based Learning in Introductory Computer Science

1. Project Summary (1 Page Overview)

(Target: ~500 words)

Project Title: Cultivating Computational Fluency and Belonging through Inclusive, Project-Based Learning in Introductory Computer Science

PI: [Your Name, Affiliation] | Co-PI(s): [Names, Affiliations]

Summary:
The rapid growth of the computing sector demands a diverse and skilled workforce, yet introductory computer science (CS) courses often face significant challenges in student retention, particularly for women and underrepresented minority (URM) students. Traditional lecture-based approaches can foster an exclusionary environment, leading to decreased self-efficacy and a diminished sense of belonging, which are critical predictors of persistence in STEM. This project addresses these challenges by developing, implementing, and rigorously evaluating an innovative, inclusive, project-based learning (iPBL) pedagogical model for introductory CS courses (CS1/CS2 equivalents).

The proposed iPBL model integrates collaborative, real-world projects with explicit instruction in inclusive practices, growth mindset, and social-emotional learning, designed to foster a more equitable and engaging learning environment. We hypothesize that students in iPBL sections will demonstrate significantly higher learning gains, increased self-efficacy and sense of belonging, and improved retention rates in subsequent CS courses compared to students in traditional lecture-based sections.

This project will employ a mixed-methods, quasi-experimental design. Over three years, we will compare outcomes for students in iPBL-implemented sections at [Your University] with control groups using a comprehensive suite of quantitative and qualitative measures. Quantitative data will include pre/post CS concept inventories, validated self-efficacy and belonging surveys, and institutional retention data. Qualitative data will be gathered through student focus groups, instructor interviews, and classroom observations to capture nuanced experiences and inform iterative model refinement.

Intellectual Merit: This research will advance the theoretical understanding of how project-based learning, specifically when infused with intentional inclusive practices, impacts diverse students' computational fluency, motivation, and identity formation in foundational CS courses. It will contribute new knowledge regarding the mechanisms by which pedagogical interventions foster a sense of belonging and mitigate imposter syndrome for URM students in STEM. Findings will inform the refinement of existing theories of self-efficacy and situated learning within the context of CS education. The development and validation of an iPBL curriculum model and associated assessment tools will provide generalizable resources for the broader CS education community.

Broader Impacts: This project has the potential for transformative societal benefits by significantly increasing the participation and success of women and URM students in computing, thereby diversifying the future technology workforce. By developing a scalable and adaptable iPBL model, the project will provide practical, evidence-based pedagogical strategies that can be adopted by institutions nationwide, particularly those serving diverse student populations. Dissemination will include peer-reviewed publications, national conference presentations, open educational resources (OER) for curriculum sharing, and faculty development workshops. Increased retention in CS will lead to a more robust, innovative, and representative computing profession, addressing a critical national need. This work directly aligns with NSF's strategic goals to promote a diverse STEM workforce and improve STEM education across all levels.

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2. Intellectual Merit Argument (Advance Knowledge)

(Target: ~3-5 pages in the full proposal)

The intellectual merit of this project lies in its potential to significantly advance fundamental knowledge and theoretical understanding within computer science education research, particularly concerning effective pedagogy for diverse learners and the cultivation of an inclusive learning environment. While project-based learning (PBL) has shown promise in various educational contexts, its specific mechanisms for fostering computational fluency, self-efficacy, and a sense of belonging among historically underrepresented groups (HUGs) in introductory CS remain underexplored.

Foundation in Educational Theory:
Our work is grounded in several key educational and psychological theories:

• Self-Determination Theory (SDT): This theory posits that intrinsic motivation is enhanced when individuals experience autonomy, competence, and relatedness. Our iPBL model directly addresses these needs by offering student choice in projects (autonomy), providing scaffolded challenges and mastery opportunities (competence), and fostering collaborative teamwork (relatedness). We aim to provide empirical evidence for how iPBL specifically cultivates these three psychological needs in a computing context, especially for students who might initially feel marginalized.


• Social Cognitive Theory (SCT): SCT emphasizes the role of observational learning, self-efficacy, and outcome expectations. Our model will investigate how observing peers succeed, receiving constructive feedback, and successfully completing projects contribute to students' self-efficacy beliefs in computing. We will specifically explore how iPBL promotes vicarious learning and mastery experiences that are particularly critical for students lacking prior exposure to computing or facing stereotype threat.


• Situated Learning Theory: This theory suggests that learning is most effective when embedded in authentic, real-world contexts and social interaction. iPBL naturally aligns with this by placing students in team-based projects that mirror professional practice. Our research will explore how the authenticity of iPBL projects influences the transfer of learning and the development of professional identities for diverse students.


• Stereotype Threat Theory: We acknowledge that HUGs often face stereotype threat in STEM fields. Our iPBL model includes explicit inclusive practices (e.g., affirming belonging, emphasizing growth mindset, diverse role models) designed to mitigate these threats. This research will contribute novel insights into the efficacy of such explicit interventions within a PBL framework.

Addressing Knowledge Gaps:
Current literature lacks comprehensive studies that:

• **Isolate the impact of intentionally inclusive practices within a PBL framework** on the learning and affective outcomes of diverse students in introductory CS. Most PBL research focuses on its general effectiveness but doesn't deeply examine how specific inclusivity components (e.g., explicit discussions of imposter syndrome, diverse team formation strategies, project contextualization for social good) differentially impact HUGs.


• Provide robust, mixed-methods evidence of the mechanisms by which iPBL influences students' sense of belonging and identity formation in computing. While quantitative measures of belonging exist, qualitative insights into how students perceive and internalize these experiences within an iPBL setting are scarce.


• Offer a detailed, adaptable, and validated iPBL curriculum model and associated assessment tools that are specifically designed for foundational CS courses and prioritize diversity and inclusion.

Novelty and Contribution:
This project will generate significant new knowledge by:

• Developing and validating the iPBL framework: This includes specific curriculum modules, instructor training materials, and inclusive pedagogical strategies that are empirically tested and iteratively refined. This framework will serve as a generalizable model for CS education.


• Elucidating the impact of iPBL on specific affective outcomes: We will move beyond general engagement metrics to deeply analyze changes in self-efficacy, belonging, imposter syndrome, and growth mindset, particularly for women and URM students.


• Providing a robust evidence base: Through rigorous mixed-methods research, we will offer statistically significant quantitative data coupled with rich qualitative insights, providing a nuanced understanding of "what works, for whom, and why" in inclusive CS education.


• Contributing to theoretical refinement: Our findings will either support or challenge existing theories (SDT, SCT, Situated Learning) in the context of CS education, potentially leading to new theoretical propositions about inclusive learning environments.


• Developing open educational resources (OER): The iPBL curriculum, assessment rubrics, and instructor guides will be openly shared, enabling other institutions to adopt and adapt the model, accelerating the translation of research into practice.

By filling these critical knowledge gaps, this project will significantly advance the field of computer science education, providing a clearer understanding of how to cultivate not only computational skills but also a diverse and inclusive talent pipeline for the future.

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3. Broader Impacts (Societal Benefit, Diversity)

(Target: ~3-5 pages in the full proposal)

The proposed project holds substantial potential for transformative broader impacts, aligning directly with NSF's mission to promote the progress of science and advance the national health, prosperity, and welfare. Our work specifically targets critical national needs: increasing the diversity of the STEM workforce, enhancing the quality of STEM education, and fostering innovation.

1. Increasing Diversity and Inclusion in Computing:

• Direct Impact on Underrepresented Groups (URG): The primary broader impact is to significantly increase the participation, retention, and success of women and URM students in introductory CS courses and subsequently in computing majors and careers. By intentionally designing an inclusive pedagogical approach, we aim to dismantle barriers that traditionally alienate these groups, such as competitive environments, lack of relatable contexts, and insufficient support for self-efficacy and belonging.


• Diversifying the Computing Workforce: A more diverse talent pool in computing leads to more innovative solutions, products, and services that reflect the needs of a diverse society. By improving the retention pipeline from foundational CS, this project contributes directly to developing a workforce that is more representative of the U.S. population, addressing a long-standing challenge in the technology sector.

2. Enhancing STEM Education Quality and Innovation:

• Scalable and Adaptable Pedagogical Model: The iPBL model and associated curriculum developed and validated through this research will be designed for scalability and adaptability across various institutional contexts (e.g., community colleges, liberal arts colleges, large research universities). This provides a proven, evidence-based approach for improving the quality of foundational CS education beyond our institution.


• Faculty Development: The project includes plans for developing and disseminating professional development materials for instructors on implementing inclusive PBL. This will enhance pedagogical expertise among CS faculty nationally, fostering a culture of continuous improvement in teaching practices.


• Improved Student Learning Outcomes: Beyond diversity, iPBL is designed to enhance computational fluency, problem-solving skills, and critical thinking for all students, leading to a more highly skilled and adaptable computing workforce.

3. Strengthening Educational Infrastructure and Capacity:

• Open Educational Resources (OER): The iPBL curriculum modules, project prompts, assessment rubrics, and instructor guides will be released as OER. This will lower barriers for other institutions to adopt effective, research-backed pedagogical innovations, particularly for institutions with limited resources for curriculum development.


• Building Community of Practice: Through dissemination activities (workshops, conference presentations), we will foster a community of practice among CS educators interested in inclusive PBL, facilitating knowledge exchange and collaborative development.


• Research-Practice Integration: This project serves as an exemplar of how educational research can directly inform and improve teaching practice, bridging the gap between theory and application in CS education.

4. Public Engagement and Outreach:

• K-12 Pathways: While focused on higher education, the principles and practices of inclusive PBL can inform and inspire K-12 CS education initiatives, potentially influencing curriculum development for early exposure to computing and fostering interest among younger students, particularly girls and URM students.


• Dissemination to Broader Audiences: Findings will be translated into accessible formats for non-academic audiences (e.g., university news, social media, industry reports) to highlight the importance of inclusive computing education and inspire public interest in CS.

By systematically addressing the intertwined issues of student engagement, belonging, and learning outcomes through an innovative and evidence-based pedagogical approach, this project will contribute to a more diverse, equitable, and excellent computing ecosystem, benefiting both the individual students and the nation as a whole.

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4. Research Plan with Clear Hypotheses

(Target: ~7-10 pages in the full proposal)

A. Research Questions (RQs):
Our research is guided by the following overarching questions:

• RQ1: To what extent does the Inclusive Project-Based Learning (iPBL) model impact students' computational fluency and technical skill development in introductory CS courses compared to traditional lecture-based instruction?


• RQ2: How does the iPBL model influence students' self-efficacy, motivation, and sense of belonging in CS, particularly for women and underrepresented minority (URM) students?


• RQ3: What are the long-term effects of participating in iPBL on students' persistence and retention in CS majors and subsequent courses?


• RQ4: What are students' and instructors' lived experiences and perceptions of the iPBL model, and what factors contribute to its effectiveness or challenges?

B. Hypotheses:

Based on our RQs and theoretical framework, we hypothesize the following:

• H1 (Learning Outcomes): Students enrolled in iPBL sections will demonstrate significantly higher learning gains in core CS concepts (e.g., algorithmic thinking, data structures, programming paradigms) and produce higher quality project work compared to students in traditional lecture-based control sections.


• H2 (Affective Outcomes): Students in iPBL sections, particularly women and URM students, will report significantly higher self-efficacy in computing, greater intrinsic motivation, and a stronger sense of belonging in the CS community compared to their counterparts in control sections.


• H3 (Retention): Students who participate in iPBL sections will exhibit higher rates of persistence in subsequent CS courses and declaration of a CS major compared to students in control sections.


• H4 (Qualitative Impact): Qualitative data will reveal that iPBL fosters deeper conceptual understanding, more positive attitudes towards collaboration, and a greater connection to the relevance of CS for diverse students, attributes that are less pronounced in traditional settings.

C. Overall Research Design: Mixed-Methods Quasi-Experimental Study

This project will employ a mixed-methods (explanatory sequential) quasi-experimental design over three academic years, comparing student outcomes in iPBL sections with those in traditional lecture-based control sections. This approach allows for the rigorous quantitative assessment of treatment effects while providing rich qualitative data to explain why and how these effects occur.

D. Intervention Description: The iPBL Model

Our iPBL model for introductory CS (targeting CS1 and CS2 equivalent courses) will incorporate the following key features:

• Authentic, Real-World Projects: Projects will be designed around engaging, relevant problems (e.g., social good, interdisciplinary applications) that encourage creativity and provide a sense of purpose beyond abstract coding exercises.


• Team-Based Collaboration: Students will work in carefully constructed teams (balanced for diversity in background, experience, and demographics) to promote peer learning, communication skills, and a sense of shared responsibility. Explicit training in effective teamwork, conflict resolution, and inclusive communication will be provided.


• Scaffolded Support and Iteration: Projects will be broken into smaller milestones with clear rubrics and regular feedback loops. Students will have opportunities for peer review and multiple iterations, de-emphasizing a single "correct" answer and fostering a growth mindset.


• Instructor-as-Facilitator: Instructors will shift from being primary knowledge dispensers to facilitators, guiding student inquiry, providing resources, and fostering an environment of active learning and problem-solving.


• Explicit Inclusive Practices:


• Belonging Cues: Instructors will explicitly communicate that all students belong and are capable of success.


• Growth Mindset: Emphasis on learning from mistakes and the development of skills over innate talent.


• Stereotype Threat Mitigation: Open discussions about common challenges faced by HUGs in CS, providing counter-stereotypical role models, and attributing struggles to learning challenges rather than fixed ability.


• Ethical Computing: Projects will incorporate discussions of ethical implications, bias in algorithms, and the societal impact of computing to broaden the relevance of CS.


• Diverse Project Contexts: Projects will be designed to resonate with a wider range of interests, moving beyond traditionally male-dominated examples.

E. Participants and Recruitment:

• Setting: [Your University]'s Department of Computer Science, a [type of institution, e.g., large public research university] serving a diverse student body.


• Participants: All students enrolled in introductory CS courses (CS1 and CS2 equivalents) will be invited to participate. Over the project's duration, we anticipate involving approximately [e.g., 600-800] students across multiple sections.


• Group Assignment: Due to institutional constraints, random assignment of individual students is not feasible. We will use a quasi-experimental design where entire course sections are assigned to either the iPBL intervention or the traditional control group. We will use propensity score matching or ANCOVA to statistically control for pre-existing differences between groups (e.g., prior programming experience, incoming GPA, demographics).


• Instructor Selection: A core group of experienced CS instructors will be recruited to implement the iPBL model. They will receive intensive professional development and ongoing support. Control sections will be taught by other instructors using standard lecture-based methods.


• Ethical Considerations: The study will adhere strictly to ethical guidelines, obtaining Institutional Review Board (IRB) approval. All participants will provide informed consent. Data will be de-identified and anonymized where appropriate.

F. Data Collection (See Section 5 for detailed measures):

• Quantitative Data:


• Pre-Post CS Concept Inventories: To measure learning gains.


• Validated Surveys: For self-efficacy, motivation, sense of belonging, imposter syndrome, and growth mindset.


• Course Grades & Project Rubrics: To assess performance on specific course components.


• Institutional Data: Enrollment in subsequent CS courses, major declaration rates, retention in the university.


• Qualitative Data:


• Student Focus Groups/Interviews: To explore experiences, perceptions of inclusivity, project engagement, challenges, and evolving identities.


• Instructor Interviews: To understand implementation fidelity, perceived challenges, pedagogical shifts, and student responses.


• Classroom Observations: To document implementation of iPBL elements, instructor-student interactions, and student engagement.


• Open-ended Survey Questions: To gather additional insights.

G. Data Analysis:

• Quantitative Analysis:


• Descriptive Statistics: For all variables.


• Inferential Statistics: Independent samples t-tests, ANOVA, ANCOVA, and multiple regression analyses will be used to compare outcomes between iPBL and control groups, controlling for relevant covariates.


• Chi-square tests: To analyze differences in retention rates.


• Hierarchical Linear Modeling (HLM): If data structure warrants, to account for nesting of students within sections and sections within instructors.


• Qualitative Analysis:


• Thematic Analysis: Interview and focus group transcripts, and observation notes will be systematically coded and analyzed using established qualitative methods (e.g., NVivo) to identify recurring themes, patterns, and emergent insights related to student and instructor experiences, challenges, and perceived impact of iPBL.


• Constant Comparative Method: To refine themes and develop theoretical explanations.


• Mixed Methods Integration: Quantitative findings will be used to identify areas for deeper qualitative exploration (e.g., if HUGs show greater gains in self-efficacy, qualitative data will explore why). Qualitative data will help explain the mechanisms underlying quantitative effects and identify unforeseen impacts.

H. Pilot Study (Year 1, if feasible within timeline):
A pilot implementation of iPBL will be conducted in one CS1 section. This will allow for initial testing of curriculum modules, refinement of project prompts, validation of survey instruments, and training of instructors before full-scale implementation. Feedback from the pilot will be crucial for iterative design.

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5. Evaluation Plan and Measures

(Target: ~2-3 pages in the full proposal)

The evaluation plan is integral to this research, ensuring rigor, accountability, and the ability to answer our research questions and test hypotheses. We will employ both internal and external evaluation strategies.

A. Internal Evaluation and Measures:

This will be driven by the research team and focuses on answering our RQs.

• Student Learning and Computational Fluency (RQ1, H1):


• Pre- and Post-Course Concept Inventories: We will utilize established, validated CS1/CS2 concept inventories (e.g., PPCL for introductory programming, or adapt existing ones for specific course content) administered at the beginning and end of each semester to measure learning gains.


• Project Rubrics: Standardized, detailed rubrics will be developed for all iPBL projects to assess technical correctness, design quality, code clarity, problem-solving approach, and adherence to specifications. Instructor scoring will be calibrated.


• Final Exam Scores: Comparisons of performance on common final exam questions (testing core concepts) between iPBL and control groups.


• Course Grades: Comparison of overall course grades (accounting for different grading schemes).


• Affective Outcomes: Self-Efficacy, Motivation, Belonging (RQ2, H2):


• Validated Self-Efficacy Scales: Administer pre- and post-course surveys using established scales such as the Computer Science Self-Efficacy Scale (CSSES) or adapting general academic self-efficacy scales to the CS context.


• Motivation Scales: Utilize instruments like the Intrinsic Motivation Inventory (IMI) or subscales of the Motivated Strategies for Learning Questionnaire (MSLQ) related to task value and self-regulation.


• Sense of Belonging Scales: Employ validated scales measuring students' perceived sense of belonging in their CS course and the broader CS community (e.g., measures adapted from the Higher Education Research Institute's surveys, or specific STEM belonging scales).


• Imposter Syndrome Scale: Administer a short, validated scale (e.g., Clance Imposter Phenomenon Scale) to track changes, especially for URM and women students.


• Growth Mindset Inventory: Use a brief inventory to assess students' beliefs about the malleability of intelligence and ability.


• Surveys: Administered pre- and post-intervention each semester for both iPBL and control groups.


• Persistence and Retention (RQ3, H3):


• Institutional Data Tracking: Access to anonymized student records (with IRB approval) to track:


• Enrollment in subsequent CS courses (CS2, data structures, etc.).


• Declaration of CS major or minor.


• Overall university retention rates.


• Timeframe: Data will be collected at the end of each semester and tracked for up to 1-2 years post-intervention for longitudinal analysis.


• Qualitative Insights and Process Evaluation (RQ4, H4):


• Student Focus Groups/Interviews: Conduct ~6-8 semi-structured focus groups per semester (stratified by demographic, iPBL/control) and ~10-15 individual interviews with students to gather in-depth perspectives on their learning experiences, perceptions of inclusion, team dynamics, challenges, and overall course satisfaction.


• Instructor Interviews: Conduct ~4-6 semi-structured interviews with iPBL instructors each semester to gather insights on implementation fidelity, challenges, perceived student engagement, and reflections on pedagogical shifts.


• Classroom Observations: The research team will conduct structured classroom observations (using an observation protocol) in both iPBL and control sections to document instructor behaviors, student engagement, and the presence/absence of inclusive practices.


• Open-Ended Survey Questions: Include open-ended questions in the post-course surveys to capture additional unprompted feedback.

B. External Evaluation:

A critical component of our evaluation plan is the engagement of an independent external evaluator. This ensures objectivity, provides expert guidance, and strengthens the credibility of our findings.

• Evaluator Role: The external evaluator, [Name/Affiliation, or describe desired qualifications - e.g., expert in STEM education evaluation with a strong background in mixed-methods research], will:


• Review the overall research design, data collection instruments, and analysis plan.


• Provide feedback on the project's progress and adherence to the timeline.


• Conduct independent analyses of selected data (e.g., cross-checking a subset of qualitative coding, reviewing statistical outputs).


• Review all annual reports and the final project report.


• Provide an independent perspective on the project's intellectual merit and broader impacts.


• Reporting: The external evaluator will submit annual confidential reports to the PI team and NSF, providing formative feedback and summative assessments of project outcomes.

C. Data Management and Security:

All data will be collected, stored, and managed in compliance with IRB protocols and university policies. Anonymized data will be stored on secure, password-protected university servers. Access will be limited to authorized research personnel. Quantitative data will be managed in statistical software (e.g., R, SPSS); qualitative data in qualitative analysis software (e.g., NVivo, ATLAS.ti).

This robust evaluation plan ensures that the project rigorously assesses its impact on student learning and affective outcomes, identifies factors contributing to success, and provides credible, generalizable insights for the CS education community.

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6. Timeline with Deliverables

(Target: ~1-2 pages in the full proposal, often a table or Gantt chart)

This project is planned for a three-year duration.

Phase/Year	Key Activities	Deliverables
Year 1	Planning & Development
Months 1-3	Project Team Formation, IRB Submission & Approval, External Evaluator Engagement, Detailed Research Design Refinement.	IRB Approval Letter, Signed External Evaluator Agreement, Detailed Data Management Plan, Finalized Research Protocols.
Months 4-6	Development of iPBL Curriculum (CS1), Instructor Training Materials. Selection/Adaptation of Assessment Instruments.	iPBL CS1 Curriculum Modules (draft), Instructor Training Manual (draft), Pilot Survey Instruments, Pilot Concept Inventory (selected/adapted).
Months 7-9	Pilot Study (CS1): Implementation in 1-2 sections. Initial data collection, focus groups with pilot students.	Pilot Study Report (internal), Refined iPBL CS1 Curriculum, Refined Instructor Training Manual, Refined Survey Instruments & Concept Inventory.
Months 10-12	Data Analysis from Pilot. Curriculum Refinement. Preparation for Year 2 (CS1 & CS2).	NSF Annual Report 1, Presentation at relevant conference (e.g., SIGCSE). Initial Draft of OER for CS1.
Year 2	Full Implementation & Data Collection
Months 13-15	Instructor Training for Year 2 iPBL sections (CS1 & CS2). Pre-survey/Concept Inventory Administration.	Trained Instructor Cohort, Pre-Intervention Data Report (internal).
Months 16-24	Full Implementation (CS1 & CS2): Conduct iPBL and Control sections. Continuous data collection (quan/qual).	Ongoing Data Collection Logs, Classroom Observation Reports, Transcribed Student Focus Group/Interview Data, Transcribed Instructor Interview Data.
Months 21-24	Mid-Year Data Analysis (initial trends). Preparation for Year 3.	Preliminary Findings Report (internal), Conference Abstracts Submitted (e.g., ITiCSE, FIE).
Year 3	Final Implementation, Analysis & Dissemination
Months 25-27	Instructor Training for Year 3 iPBL sections (CS1 & CS2). Final Pre-survey/Concept Inventory Administration.	Trained Instructor Cohort, Final Pre-Intervention Data Report (internal).
Months 28-36	Final Implementation (CS1 & CS2): Conduct iPBL and Control sections. Concluding data collection.	Comprehensive Data Collection Complete.
Months 30-36	Comprehensive Data Analysis (quantitative & qualitative). Draft Journal Manuscripts. Dissemination Activities.	NSF Annual Report 2, Refined OER for CS1/CS2, 1-2 Draft Journal Manuscripts Submitted, 2-3 Conference Presentations (e.g., SIGCSE, ASEE, ECEDC), Final Project Report (to NSF and publicly).
Month 36	Project Completion & Final Reporting	Final NSF Project Report, Final OER Release, Publicly Available Project Website, Plan for Sustained iPBL Implementation at [Your University].

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7. Dissemination Strategy (Publications, Conferences, Workshops)

(Target: ~1-2 pages in the full proposal)

A robust dissemination strategy is crucial to ensure that the knowledge generated by this project reaches diverse audiences, including the academic research community, CS educators and practitioners, policymakers, and the general public. Our multi-faceted plan targets several key channels:

1. Peer-Reviewed Publications:

• Journal Articles: We will publish findings in leading journals in computer science education and broader STEM education research.


• Primary Target Journals: ACM Transactions on Computing Education (TOCE), Computer Science Education, Journal of Research in Science Teaching, International Journal of Computer Science Education in Schools.


• Broader Education Journals: Journal of Educational Psychology, American Educational Research Journal (for theoretical contributions).


• Practitioner-Oriented Journals: Communications of the ACM (Inroads) for discussions on pedagogical innovations and their practical implications.


• Focus Areas: Separate articles will focus on:


• The quantitative impact of iPBL on learning gains and retention.


• The influence of iPBL on self-efficacy, belonging, and motivation, particularly for URM students.


• Qualitative insights into student and instructor experiences.


• The development and validation of the iPBL framework and curriculum.

2. Conference Presentations:

• National CS Education Conferences: Regular presentations (full papers, panels, posters) at prestigious conferences will share research findings and methodological approaches.


• Primary Targets: SIGCSE Technical Symposium (ACM Special Interest Group on Computer Science Education), ITiCSE (Innovation and Technology in Computer Science Education), FIE (Frontiers in Education Conference), ASEE Annual Conference (American Society for Engineering Education), ECEDC (Educators’ Conference for the Design and Development of Computer Education).


• Broader Education Research Conferences: Presentations at conferences like AERA (American Educational Research Association) to engage with a wider educational research audience.


• Diversity in STEM Conferences: Specific presentations at conferences focused on diversity and inclusion in STEM (e.g., Grace Hopper Celebration, Tapia Celebration of Diversity in Computing) to directly engage with relevant communities.

3. Workshops and Professional Development:

• Faculty Development Workshops: We will develop and deliver hands-on workshops at national conferences (e.g., SIGCSE, ITiCSE) and potentially at other institutions to train CS faculty on how to implement the iPBL model, including strategies for inclusive pedagogy and project design.


• Online Webinars: Free, accessible webinars will be offered to provide an introduction to iPBL and share best practices, reaching a broader audience of educators who may not attend conferences.

4. Open Educational Resources (OER):

• Project Website: A dedicated, publicly accessible project website will be created to host all iPBL curriculum modules, project prompts, assessment rubrics, instructor guides, and relevant publications. This will serve as a central repository for educators globally to access and adapt our materials.


• GitHub Repository: Code examples, project templates, and other technical resources will be made available via a GitHub repository, fostering community contributions and adaptations.


• Digital Libraries: We will submit our materials to relevant digital libraries for educational resources (e.g., National Science Digital Library, ACM Digital Library).

5. Outreach to Policymakers and Practitioners:

• Policy Briefs: Summarize key findings and policy implications (e.g., the need for inclusive pedagogy to diversify the tech workforce) for state and national education policy groups and funding agencies.


• University Communications: Leverage university public relations offices to disseminate findings through press releases, university magazines, and social media to reach the broader public and potential students.


• Industry Engagement: Share findings with local and national tech companies to highlight the importance of inclusive education for workforce development and potentially explore internship or mentorship opportunities for iPBL students.

6. NSF Reporting:

• Regular annual reports and a comprehensive final report will be submitted to NSF, detailing progress, findings, and impacts, ensuring accountability and transparency.

By employing this multi-pronged dissemination strategy, we aim not only to advance academic knowledge but also to translate research into actionable practices that can transform CS education and foster a more diverse and inclusive computing future.

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8. Demonstrate Team Qualifications

(Target: ~2 pages in the full proposal)

The successful execution of this ambitious project requires a team with diverse expertise, a proven track record in research and education, and a deep commitment to broadening participation in computing. Our project team, led by [Your Name] and supported by [Co-PIs, if any], brings together the necessary blend of pedagogical knowledge, research methodology, curriculum development experience, and commitment to diversity and inclusion.

A. Principal Investigator (PI): [Your Name, Title, Institution]

• Academic Background: Ph.D. in Computer Science Education [or Computer Science with a strong focus/minor in Education], [University]. My doctoral research focused on [briefly mention relevant thesis topic, e.g., the impact of collaborative learning environments on student engagement in introductory programming].


• Research Experience: I have a strong publication record in CS education research, with [X] peer-reviewed articles in journals such as [e.g., ACM TOCE, Computer Science Education] and [Y] conference proceedings papers at venues like [e.g., SIGCSE, ITiCSE]. My research consistently addresses [e.g., pedagogical innovations, student persistence, diversity in computing]. I have served as a reviewer for [relevant journals/conferences].


• Teaching Experience: I have [X] years of experience teaching introductory CS courses (CS1, CS2, etc.) at [Your University]. I have experimented with various pedagogical approaches, including active learning and small-scale project-based instruction, and have consistently received strong student evaluations, demonstrating my ability to implement innovative teaching methods effectively. I have a passion for creating inclusive learning environments.


• Curriculum Development: I have led efforts to revise curricula for [mention specific courses or programs] at [Your University], incorporating modern best practices and focusing on student learning outcomes.


• Project Management & Grants: I have successfully managed [e.g., internal grants, small external grants] and collaborated on larger projects, demonstrating my organizational skills and ability to lead research initiatives. I am adept at managing budgets, personnel, and timelines.


• Commitment to Diversity: I am a vocal advocate for diversity and inclusion in computing, actively participating in [e.g., departmental diversity committees, mentoring programs for URM students, outreach events for K-12 girls]. My personal research agenda is deeply aligned with the broader impacts of this proposal.

B. Co-Principal Investigator (Co-PI): [Name, Title, Institution] (If applicable, describe similar details, focusing on complementary expertise)

• Complementary Expertise: Dr. [Co-PI's Last Name] brings [e.g., strong quantitative research methodology skills, expertise in psychology of learning, deep domain knowledge in specific CS area, extensive experience with K-12 outreach, or a focus on specific URG populations].


• Specific Contributions to Project: Dr. [Co-PI's Last Name] will lead [e.g., the statistical analysis, the development of psychological survey instruments, the qualitative data collection and analysis, or the dissemination to K-12 audiences]. Their involvement ensures the project's methodological rigor and broad impact.


• Track Record: [Briefly mention their relevant publications, grants, and experience].

C. Key Personnel & Advisory Board:

• Graduate Research Assistants (GRAs): We anticipate hiring [e.g., two] GRAs with backgrounds in CS or education. They will assist with data collection (e.g., classroom observations, focus group facilitation), data analysis, literature reviews, and curriculum development, providing valuable training opportunities.


• Undergraduate Research Assistants (URAs): We will recruit URAs, particularly from URG backgrounds, to assist with various project tasks. This provides mentorship and research experience for aspiring students.


• External Evaluator: As detailed in the Evaluation Plan, an independent external evaluator, [Name, Affiliation, or describe desired expertise], will provide objective assessment and guidance, bringing expertise in [e.g., large-scale STEM education evaluation, mixed-methods design].


• Advisory Board: We will assemble an external advisory board comprising experts in CS education, diversity in STEM, and pedagogical innovation. This board will meet annually to review progress, provide strategic advice, and offer critical feedback. Members will include:


• [Name 1, Affiliation, Expertise (e.g., expert in PBL implementation)]


• [Name 2, Affiliation, Expertise (e.g., leader in diversity & inclusion in CS)]


• [Name 3, Affiliation, Expertise (e.g., expert in educational assessment)]

D. Institutional Support and Resources:

[Your University] is strongly committed to the success of this project.

• Department of Computer Science: The department will provide necessary teaching release for the PI/Co-PIs, access to course sections for implementation, and administrative support. It also offers a rich environment for CS education research, with [mention specific departmental initiatives, e.g., an active CS education research group, strong commitment to broadening participation].


• Office of Institutional Research: Will provide access to de-identified student data (e.g., demographic, enrollment, retention) critical for longitudinal analysis, pending IRB approval.


• Center for Teaching and Learning (CTL): The CTL offers resources for pedagogical development, workshop spaces, and expertise in inclusive teaching, which will be valuable for instructor training.


• Computing Infrastructure: The university provides robust computing facilities, secure data storage, and necessary software licenses (e.g., statistical analysis software, qualitative data analysis software) to support the research.


• Commitment to Diversity: [Your University] has a strong institutional commitment to diversity and inclusion, reflected in [mention specific university-wide initiatives or programs], providing a supportive context for this project's broader impacts.

E. Synergy of the Team:
The combined expertise of the PI, Co-PI(s), and key personnel, coupled with strong institutional support and independent external review, creates a highly capable and synergistic team. We are well-prepared to execute the proposed research plan, analyze complex data, disseminate findings effectively, and make a significant contribution to both the intellectual understanding and practical improvement of CS education.

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This framework should give you a very strong start! Remember to make it specific to your institution, your background, and your specific ideas for iPBL. Good luck!