NURS FPX 6111 Assessment 4 Program Effectiveness Presentation
NURS FPX 6111 Assessment 4 Program Effectiveness Presentation
Name
Capella university
NURS-FPX 6111 Assessment and Evaluation in Nursing Education
Prof. Name
Date
Program Effectiveness Presentation
Greetings, esteemed audience. I am _. In today’s discussion, we will explore the efficacy of the Bachelor of Science in Nursing (BSN) curriculum, focusing particularly on the implementation of the “Healthcare Technology Management” (HTM) course.
The core objective of this presentation is to evaluate the effectiveness of the BSN program and propose significant improvements for the seamless integration of the HTM course into the nursing curriculum. Before incorporating any new course, a comprehensive assessment is vital to ensure continuous progress and adaptation. Evaluating the program’s effectiveness helps identify both strengths and areas for improvement, ensuring alignment with evolving healthcare standards. This process fosters better student outcomes, ensures compliance with management criteria, and facilitates adaptation to emerging trends in nursing practice. A data-driven approach is critical for ensuring that any new course integrates smoothly and enhances the overall curriculum. This evaluation approach also nurtures a learning environment conducive to self-driven learning, encouraging nursing students to remain open and responsive to changes (Oermann et al., 2024).
Purpose
This presentation will proceed in the following manner:
- Exploring various philosophical approaches to evaluation and scrutinizing the evidence employed for clarification.
- Introducing the steps involved in the program evaluation process and examining their interconnected limitations.
- Outlining an evaluation framework/design for program assessment and delving into the associated constraints.
This section will detail how data analysis plays a pivotal role in continuous program evaluation, identifying knowledge gaps and addressing uncertainties that may require additional information.
Evaluation of Philosophical Approaches
The evaluation of the HTM course incorporates several philosophical approaches, including:
Benner’s Model:
Benner’s Model categorizes learners into novice, advanced beginner, competent, proficient, and expert stages. This model evaluates the learner’s ability to understand, acquire, and address challenges based on their proficiency with HTM. It suggests that learners, starting as novices, gradually develop their skills and practical understanding, particularly in integrating technology with healthcare services (Murray et al., 2019).
DIKW Theory:
The Data, Information, Knowledge, and Wisdom (DIKW) theory is essential in the HTM course. This theory begins with gathering raw data, synthesizing it to generate information, and then analyzing it alongside existing evidence to produce knowledge. Ultimately, the goal is to enhance students’ understanding of integrating technology with healthcare services (Peters et al., 2024).
Summative and Formative Assessments:
Formative assessments measure the learner’s ability to apply integrated healthcare and technological advancements during the course. Summative assessments, conversely, assess students’ ability to apply the knowledge acquired after completing the HTM course. Both assessments are crucial for evaluating students’ capacity to effectively use the knowledge they have gained throughout the course (Arrogante et al., 2021).
Supporting Evidence for the Explanation
The DIKW theory, Benner’s model, and the use of formative and summative assessments all play significant roles in helping students integrate Information Technology (IT) with healthcare services (Murray et al., 2019). These models focus on teaching students how to utilize simulation-based healthcare services, Artificial Intelligence (AI), automated IV pumps, EMR, and remote monitoring technologies. Their purpose is to assess student progress during and after the HTM course (Peters et al., 2024).
Process of Evaluating the Program
The HTM course evaluation process is structured as follows:
Evaluation:
In this stage, data is collected from HTM students through questionnaires and surveys, ensuring anonymity. The data collection process involves structured, closed-ended questions to gather specific feedback.
Analysis:
Once collected, the data is synthesized to extract valuable insights regarding the HTM course’s effectiveness. The goal is to evaluate its relevance in healthcare education and its ability to nurture technological competencies for effective healthcare service delivery (Oermann et al., 2024).
Strategizing:
The insights gained from the analysis phase are used to evaluate the course’s success in promoting technology use among HTM students, ensuring the delivery of safe and high-quality patient care.
Execution:
During this stage, necessary adjustments are made to the course structure to address deficiencies, focusing on enhancing students’ skills in the cognitive, affective, and psychomotor domains.
Assessment:
Students’ progress is assessed through the distribution of questionnaires and surveys to gauge the effectiveness of any changes implemented in the HTM course. The Likert Scale is used as an evaluation tool, given its high reliability score of 94% (Jowsey et al., 2020).
Limitations of the Process Steps
The limitations of this evaluation process include:
- Insufficient data availability due to student non-participation.
- Errors during survey collection, leading to data mismanagement.
- Incorrect application of technology during data collection, which results in inappropriate question formats.
- Data analysis errors due to improper analytical methods.
- Inadequate skills in evaluating data, including improper timing for assessments like formative and summative evaluations (Jowsey et al., 2020).
Model for Program Enhancement
The HTM program enhancement model incorporates the Plan-Do-Study-Act (PDSA) cycle. This model helps analyze necessary changes within the program, particularly in the context of HTM coursework. The “Plan” phase evaluates the alignment between the course’s learning objectives and the actual outcomes, focusing on enhancing students’ understanding of technology integration in healthcare services (Mukwato, 2020). It also assesses the effectiveness of simulation-based learning and evidence-based practices in HTM education, ensuring that students can proficiently apply skills in cognitive, psychomotor, and affective domains to deliver safe, high-quality patient care (Mukwato, 2020).
The collected data will be analyzed to determine if HTM students have successfully acquired, applied, and utilized technology integrated with healthcare services in patient care scenarios. If discrepancies between the course’s learning objectives and actual outcomes are found, corrective measures will be implemented. Three months later, re-evaluation will take place to assess the effectiveness of bridging the identified gaps. Evaluation will be performed using the Likert Scale to gauge students’ learning in simulation-based scenarios (Joyce et al., 2019).
Limitations of the PDSA Cycle:
The limitations associated with the PDSA cycle include:
- Infrequent data collection.
- Timing issues in data collection (Joyce et al., 2019).
Data Analysis for Continuous Program Enhancement
Data analysis plays a pivotal role in continuous improvement of the HTM education program. Regular data collection ensures the accuracy and reliability of program assessments using the Likert Scale. Continuous data analysis enables timely adjustments, ensuring uninterrupted student education and ongoing learning of technology integration in healthcare services (Rouleau et al., 2019). This process helps assess the program’s effectiveness in terms of student learning outcomes and the ability to deliver healthcare services effectively through technology utilization (Rouleau et al., 2019).
Knowledge Gaps
The use of closed-ended questions rather than open-ended ones limits the richness of student responses. This restriction fails to capture comprehensive information, hindering effective data analysis. As a result, student inquiries and concerns may remain unaddressed, preventing necessary improvements in the course (Spurlock et al., 2019).
Conclusion
In conclusion, the ongoing evaluation and refinement of the HTM program are crucial for maintaining its relevance and effectiveness. While challenges such as data collection and timing issues exist, continuous improvement efforts are essential. By leveraging evaluation insights, the program can better prepare students for successful integration of technology in healthcare services, thereby addressing the evolving needs of the healthcare industry.
References
Arrogante, O., Romero, G. M. G., Torre, E. M. L., García, L. C., & Polo, A. (2021). Comparing formative and summative simulation-based assessment in undergraduate nursing students: Nursing competency acquisition and clinical simulation satisfaction. BMC Nursing, 20(1). https://doi.org/10.1186/s12912-021-00614-2
Jowsey, T., Foster, G., Ioelu, P. C., & Jacobs, S. (2020). Blended learning via distance in pre-registration nursing education: A scoping review. Nurse Education in Practice, 44, 102775. https://doi.org/10.1016/j.nepr.2020.102775
Joyce, B. L., Harmon, M. J., Johnson, R. (Gina) H., Hicks, V., Schott, N. B., & Pilling, L. B. (2019). Using a quality improvement model to enhance community/public health nursing education. Public Health Nursing, 36(6), 847–855. https://doi.org/10.1111/phn.12656
NURS FPX 6111 Assessment 4 Program Effectiveness Presentation
Mukwato, P. K. (2020). Implementing evidence based practice nursing using the PDSA model: Process, lessons and implications. International Journal of Africa Nursing Sciences, 14, 100261. https://doi.org/10.1016/j.ijans.2020.100261
Murray, M., Sundin, D., & Cope, V. (2019). Benner’s model and Duchscher’s theory: Providing the framework for understanding new graduate nurses’ transition to practice. Nurse Education in Practice, 34(1), 199–203. https://doi.org/10.1016/j.nepr.2018.12.003
Oermann, M. H., Gaberson, K. B., & De, J. C. (2024). Evaluation and testing in nursing education (7th ed., p. 460). Springer Publishing Company. https://books.google.com.pk/books?hl=en&lr=&id=jPHbEAAAQBAJ&oi=fnd&pg=PP1&dq=education+program+evaluation+benefits,+nursing+education+&ots=_M1t3UoEYh&sig=jBaYgSi2maNxDorD27jxwNLm1VE&redir_esc=y#v=onepage&q=education%20program%20evaluation%20benefits%2C%20nursing%20education&f=false
Peters, M. A., Jandrić, P., & Green, B. J. (2024). The DIKW model in the age of artificial intelligence. Postdigital Science and Education. https://doi.org/10.1007/s42438-024-00462-8
Rouleau, G., Gagnon, M.-P., Côté, J., Gagnon, J. P., Hudson, E., Dubois, C.-A., & Picasso, J. B. (2019). Effects of e-learning in a continuing education context on nursing care: Systematic review of systematic qualitative, quantitative, and mixed-studies reviews. Journal of Medical Internet Research, 21(10), e15118. https://doi.org/10.2196/15118
NURS FPX 6111 Assessment 4 Program Effectiveness Presentation
Spurlock, D. R., Patterson, B. J., & Colby, N. (2019). Gender differences and similarities in accelerated nursing education programs. Nursing Education Perspectives, 40(6), 343–351. https://doi.org/10.1097/01.nep.0000000000000508