DNP 830 Assignments Theoretical Foundations

DNP 830 Assignments Theoretical Foundations

As discussed earlier, the prospectus is an iterative process. This assignment provides the opportunity for you to incorporate feedback and improve your prospectus.

Use the following information to ensure successful completion of the assignment:

Locate your previous draft of the prospectus template or retrieve a new copy from the DC Network (http://dc.gcu.edu). This document provides instructions and criteria to assist you in the completion of the prospectus.
Synthesize into the prospectus draft all feedback provided by the instructor on the previous draft.
Doctoral learners are required to use APA style for their writing assignments. The APA Style Guide is located in the Student Success Center.
This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.
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DNP 830 Assignments Theoretical Foundations
DNP 830 Assignments Theoretical Foundations

Complete a revised draft of the prospectus according to the instructions and criteria provided in each section of the prospectus template and previous feedback from your instructor. The draft should include revised information for the following sections of the prospectus:

Introduction
Background of the Problem
Theoretical Foundations
Review of the Literature/Themes
Problem Statement
Clinical Questions and Variables
Significance of the Project
Rationale for Methodology
Nature of the Project Design
Purpose of the Project Design
Instrumentation or Sources of Data
Data Collection Procedures
Data Analysis Procedures
Ethical Considerations
Appendix: Include the previous version of your prospectus draft, including feedback from faculty, as an appendix.

Research in theoretical foundations formally defines both the types of problems that can be solved using a computer and the quality of their solutions. Computers are limited by space and time. The optimal solution to a computational problem often lies outside these limits, thus an approximate solution must be computed. Methods developed in this area define the plausibility of an optimal solution, the quality of the approximate solution, and the resources necessary to find each, thus leading the way to better utilization of a computer’s resources or those of multiple computers in parallel. Specific research in this area encompasses a broad range of foundational topics in computer science including computational learning theory, complexity theory, algorithm and data structure design, parallel algorithms, geometric computing, cryptography, computational logic, programming languages theory, and matrix computations. Several group members are also engaged actively in leveraging their research into various application areas.