NURS FPX 6111 Assessment 4 Program Effectiveness Presentation

Name

Capella University

NURS-FPX 6111 Assessment and Evaluation in Nursing Education

Prof. Name

Date

Greetings, esteemed audience. I am_____. In today’s discussion, we shall explore the efficacy of the BSN curriculum, focusing on the implementation of the “Healthcare Technology Management” (HTM) course.

Program Effectiveness Presentation

The core objective of this presentation is to shed light on the efficacy of the BSN program and to propose significant enhancements for seamlessly integrating the proposed course into the nursing curriculum. This assessment is crucial before the integration of any new course to ensure continual advancement. Evaluating program effectiveness aids in identifying strengths and areas for improvement. This evaluation ensures curriculum alignment with evolving healthcare standards. This process facilitates the enhancement of student outcomes, compliance with management criteria, and adaptation of the curriculum to emerging trends in nursing practice. This systematic assessment guarantees that the introduction of any new course is grounded in data-driven decision-making. This assessment raises an environment conducive to self-driven learning and receptiveness among aspiring nurses (Oermann et al., 2024).

Purpose

The presentation will proceed as follows:

Exploring various philosophical approaches to evaluation and scrutinizing the evidence employed for clarification.
Introducing the steps of the program evaluation process and examining the interconnected limitations.
Outlining an evaluation framework/design for program assessment and delving into its associated constraints.
This section details how data analysis facilitates continuous program evaluation and addresses knowledge gaps and uncertainties that require additional information.

Evaluation of philosophical approaches

Evaluation of the HTM course encompasses various philosophical approaches, including:

Benner’s Model

This model categorizes learners into novice, advanced beginners, competent, proficient, and expert. It evaluates the learner’s capacity to comprehend, acquire, and tackle issues based on their proficiency in the HTM. The model suggests that individuals starting as novices with little to no knowledge of the HTM progressively enhance their skills and practical understanding throughout the course, particularly in integrating technology with healthcare services (Murray et al., 2019).

DIKW Theory

The Data, Information, Knowledge, and Wisdom (DIKW) theory involves gathering raw data, which is then synthesized to generate information. This information is analyzed alongside existing evidence to extract knowledge, enhancing understanding of the subject matter. In the HTM course, DIKW facilitates the provision of data, information, knowledge, and wisdom to students, intending to augment their understanding of technology integration within healthcare services (Peters et al., 2024).

Summative and Formative Assessments

Formative assessments gauge the learner’s proficiency in employing integrated healthcare with technological advancements during the study period. On the other hand, summative assessments evaluate the learner’s capacity to apply acquired knowledge after completing the HTM course. Both assessments are instrumental in assessing students’ aptitude to effectively apply the gained knowledge throughout the course (Arrogante et al., 2021).

Supporting Evidence For The Explanation

The DIKW Theory, Benner’s Model, and Formative and Summative assessments seek to gather, analyze, and extract knowledge concerning integrating Information Technology (IT) with healthcare services (Murray et al., 2019). These models aim to teach students about using simulation-based healthcare services, Artificial Intelligence (AI), and other technologies like automated IV pumps, EMR, and remote monitoring services. They aim to evaluate students’ learning progress during and after the HTM course (Peters et al., 2024).

Process of Evaluating Program

The HTM course evaluation process is structured as follows:

Evaluation

Data is gathered from HTM students via questionnaires and survey forms during this stage, ensuring anonymity. The collected data follows a structured approach with closed-ended questions, facilitating specific responses.

Analysis

Subsequently, the collected data is synthesized to extract precise perceptivities regarding the HTM course. This data furnishes details on the pertinence of healthcare education and the course’s efficacy in nurturing technological competencies for delivering efficient healthcare services (Oermann et al., 2024).

Strategizing

Insights from the analysis phase are utilized to assess the course’s effectiveness, specifically in cultivating the utilization of technology among HTM students to ensure the delivery of safe and high-quality patient care.

Execution

During the implementation stage, targeted adjustments are integrated into the course structure to address any deficiencies, aiming to enhance nurses’ proficiency across cognitive, affective, and psychomotor domains.

Assessment

Students were assessed through the distribution of questionnaires and survey forms to gauge the effectiveness of the changes implemented in the HTM course. The evaluation sought to determine whether the modifications added value to the course. The Likert Scale was employed as the evaluation instrument due to its high-reliability score of 94% (Jowsey et al., 2020).

Limitation of Steps in Process

The limitations of the process steps encompass the following:

Insufficient data availability stemming from student non-participation.
Errors during the collection of survey forms led to data mismanagement.
Incorrect application of technology during data collection, resulting in the use of inappropriate question formats.
Errors in data analysis due to the use of improper analytical methods.
Subpar skills employed in data evaluation, including improper timing for conducting program assessments, such as formative and summative evaluations (Jowsey et al., 2020).

Model for Program Enhancement

The enhancing HTM program model incorporates the Plan-Do-Study-Act (PDSA) cycle. This renowned approach analyzes necessary changes within the program, particularly in the context of HTM coursework. The plan is designed to evaluate the alignment between the course’s learning objectives and actual outcomes, focusing on enhancing students’ understanding of technology integration in healthcare services (Mukwato, 2020). Additionally, the model evaluates the effectiveness of simulation-based learning and evidence-based practices in HTM education, ensuring students can proficiently apply cognitive, psychomotor, and affective skills to deliver safe, high-quality patient care (Mukwato, 2020).

The collected data will undergo analysis to determine if HTM students have acquired, applied, and proficiently utilized technology integrated with healthcare services in patient scenarios. If discrepancies arise between the course’s learning objectives and actual outcomes, a corrective plan will be developed to address these gaps. Subsequently, the plan will be implemented, and three months later, a re-evaluation will be conducted to assess the effectiveness of gap bridging. Questionnaires will be used for data collection, and evaluation will be performed using the Likert Scale to evaluate students’ learning in simulation-based scenarios (Joyce et al., 2019).

Limitations of the PDSA cycle:

Limitations associated with the PDSA cycle comprise:

Infrequent data collection
Timing issues in data collection (Joyce et al., 2019)

Data Analysis for Continuous Program Enhancement

Data analysis informs ongoing improvements in the HTM education program. This analysis evaluates the ongoing examination’s efficacy, with frequent data collection ensuring the authenticity of program success assessment via the Likert Scale. Continuous data analysis throughout the program offers insights into its success status, facilitating timely adjustments if necessary (Rouleau et al., 2019). This ensures uninterrupted student education and continual effective learning of technology integrated with healthcare services. Furthermore, ongoing program analysis assesses its effectiveness in student learning and evaluates its ability to deliver healthcare services effectively through technology utilization (Rouleau et al., 2019).

Knowledge Gaps

Utilizing closed-ended questions instead of open-ended ones limits the richness of student responses. By restricting responses, questionnaires fail to capture comprehensive information, hindering thorough data evaluation. Consequently, student inquiries and issues can remain unaddressed, impeding the implementation of necessary course improvements (Spurlock et al., 2019).

Conclusion

In conclusion, ongoing evaluation and refinement of the HTM program are essential for ensuring its relevance and effectiveness. Despite challenges in data collection and timing, continuous improvement efforts are crucial. By leveraging evaluation insights, the program can better prepare students for success in integrating technology with healthcare services, thereby meeting 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

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

NURS FPX 6111 Assessment 4 Program Effectiveness Presentation

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

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