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NURS FPX 6111 Assessment 3 : Course Evaluation Template

Assessment 03: Course Evaluation Template

Name

Capella University

FPX-6111

Dr. Name

 March, 2024

Course Evaluation Template

The course instructor distributes an assessment questionnaire to students to gather insights on their learning experiences and outcomes in Nursing Informatics. This questionnaire is critical for understanding students’ perceptions and experiences, enabling the instructor to refine and enhance future course offerings. The instrument of choice for this evaluation is the Likert scale, renowned for its reliability and validity in measuring attitudes and opinions. The Likert scale used in this context is a five-point scale carefully designed to capture the varying degrees of student agreement or disagreement (Batran et al., 2022). Each level on the scale represents a distinct response: the first level indicates strong agreement, the second signals agreement, the third represents a neutral stance, the fourth expresses disagreement, and the fifth signifies strong disagreement. 

Additionally, the scale includes a specialized category for responses that reflect a neutral position, accommodating those who neither agree nor disagree. Employed frequently in surveys, the Likert scale offers a nuanced view of individual responses. It provides multiple points, each denoting a different level of agreement or disagreement. This range allows for a detailed interpretation of student feedback, mapping their understanding from a spectrum of “strong agreement” to “strong disagreement.” Specifically, in the context of a session on Nursing Informatics, the scale effectively captures students’ levels of comprehension and engagement with the material (Reid et al., 2021).


Instructions:
This survey is designed to evaluate your level of agreement or disagreement with the effectiveness of the current curriculum. It aims to assess whether the curriculum meets its intended outcomes, especially in terms of functionality. Please allocate sufficient time to thoroughly review and answer all the questions in this survey. Select the option that best reflects your opinion for each statement, ensuring your answers are independent and authentic. Remember, only one choice per statement is allowed, based on your personal judgment without external influences.

Course Title: Nursing Informatics
Faculty Name:
Date:

Assessment of Learning Outcomes:

Learning OutcomesStrongly DisagreeDisagreeNeutralAgreeStrongly Agree
Proficiency in assessing clinical and statistical results in bioinformatics.
Ability to identify effective strategies for patient disease prevention using statistical analysis.
Confidence in advocating for the integration of technology in medical practice.
Enhanced communication and goal achievement in interdisciplinary teams through advanced technology.

Connection Between Learning Outcomes and Evaluation Table

A learning outcome is a declarative statement that outlines the knowledge and skills students are expected to acquire by the end of a course. This outcome is assessed through surveys, providing a measure of the respondent’s learned competencies. In this particular program, the Likert scale is employed as a tool for course evaluation, aiming to assess students’ comprehension levels post-course completion. Students express their perspectives through a range of responses: strongly agree, agree, neutral, disagree, or strongly disagree. This feedback is instrumental in evaluating not just their understanding, but also their confidence in the subject matter. After completing the course, students’ responses to these surveys are critical in determining their readiness and enthusiasm for applying healthcare technology in their practice. These insights are valuable in understanding the efficacy of the course in imparting relevant skills and knowledge, particularly in the realm of healthcare technology integration.

Assessment Strategies

The program is designed to offer students a comprehensive reassessment of their learning experience. This involves presenting a variety of questions that delve deep into the course’s objectives and the learning outcomes. The questionnaire aims to uncover not only what the students were expected to learn but also the extent to which these expectations were met. By examining these facets, the survey seeks to understand the alignment between the course’s intended goals and the actual learning outcomes as perceived by the students (Pescaroli et al., 2020). This in-depth evaluation process is crucial in identifying the strengths and weaknesses of the course structure and content. The survey questions are thoughtfully curated to capture a wide range of student responses, offering insights into their comprehension, the effectiveness of teaching methods, and the applicability of the course material in real-world scenarios. Furthermore, this approach aids in assessing whether the course has successfully equipped students with the necessary skills and knowledge, particularly in the context of healthcare technology integration, and how confident they feel in applying what they have learned.

Addressing Appropriate Cognitive, Psychomotor, and Affective Domains

Development AreasStrongly DisagreeDisagreePartialAgreeStrongly Agree
Cognitive Domain
Enhanced decision-making behavior
Improved critical thinking in healthcare
Developed strong reasoning abilities
Application of learned skills in practice
Psychomotor Domain
Confidence in using wireless scales
Proficiency in operating electronic health records
Ability to utilize virtual stethoscopes
Competence in using blood pressure apparatus
Affective Domain
Effective evaluation of patient perspectives
Confidence in meeting patient expectations
Skillful understanding of patients’ views

Assumptions

The program’s evaluation is guided by several key hypotheses:

  • Application of Learning Across Technological Interfaces: The program seeks to test students’ ability to apply their learning both with and without the aid of technology, particularly in enhancing the efficacy of treatment. This assessment spans various critical disciplines within the curriculum, encompassing areas such as diagnosis, prevention, identification, and treatment. The hypothesis posits that the integration and application of technological tools and platforms in these areas can significantly impact the effectiveness of healthcare practices.
  • Correlation between Educator Effort and Educational Quality: Another fundamental hypothesis of the program is the direct relationship between the quality of education and the level of effort put forth by educators. This principle suggests that the dedication and engagement of educators in the learning process are crucial determinants of educational outcomes. The program aims to examine how educator involvement, teaching methods, and the commitment to student learning correlate with the overall quality of the educational experience. This includes evaluating the effectiveness of teaching strategies, the depth of knowledge imparted, and the degree to which students feel supported and challenged in their learning journey.

Evaluation Format that Rates Learning Outcomes

Outcome of the ProgramStrongly DisagreeDisagreePartialAgreeStrongly Agree
Multidisciplinary collaboration aided in executing technological advancements.
Commentative investigation of clinical and statistical research papers.
Activity plan for improved patient prevention from illnesses.
Describing the medical procedure based on nursing informatics technology.

The Likert scale serves as a vital tool to juxtapose the actual outcomes of the program with the anticipated ones. Its application in this study facilitates a deeper exploration into the impact of the program on student perceptions and attitudes. The insights gathered from this scale are instrumental in shedding light on the learners’ capabilities and their progress in acquiring new knowledge and skills. Moreover, the findings obtained from the Likert scale assessments offer valuable information regarding areas where learners have shown significant growth or need further improvement. This nuanced understanding aids in identifying specific aspects of the program that may require refinement or enhancement (Peltonen et al., 2021).

The evaluation, incorporating feedback from educators, plays a crucial role in pinpointing the areas in need of development. Educators’ perspectives are essential in this process, as they provide an expert view on the effectiveness of teaching methodologies, curriculum relevance, and the overall educational environment. By analyzing educators’ responses, the program can be tailored more effectively to address both educational goals and student needs, ultimately leading to a more effective and enriching learning experience.

Criteria Used to Evaluate the Format

The Likert Scale is employed as a method to ascertain the students’ evaluation of the educational program’s outcomes. It offers a spectrum of five choices, enabling participants to articulate their viewpoints with clarity. The options range from “strongly agree” to “strongly disagree,” including intermediate responses such as “agree,” “disagree,” and a neutral “neither agree nor disagree.” This range of choices allows students to precisely convey their level of concurrence or objection to specific statements related to the program. The scale’s design is intended to capture detailed feedback on various elements, including the course content, teaching methodologies, and overall educational impact. This detailed feedback is essential for understanding the effectiveness of the program from the student perspective and identifying areas for improvement.

Validity and Reliability of Evaluation Methods Used in Course Evaluation Form

Reliability in research refers to the consistency of results when a study is replicated under similar conditions. In contrast, validity pertains to the accuracy with which a study’s findings reflect its intended objectives. The Likert scale, a popular tool in surveys and research, often utilizes Cronbach’s alpha as a key indicator of its reliability. This statistical measure assesses the internal consistency of the scale, determining how closely related a set of items are as a group. Empirical evidence supports the high reliability of Likert-type scales, with reported reliability coefficients of 90%, 89%, and 88% in various studies. These percentages indicate a strong level of consistency in the responses gathered using the Likert scale. High reliability coefficients suggest that the scale produces stable and consistent results across different instances of the same survey. Furthermore, such high levels of reliability contribute positively to the scale’s overall validity, reinforcing the argument that the Likert scale is an effective tool for accurately measuring attitudes, perceptions, and opinions in research contexts (Milner & Zadinsky, 2022).

Strengths of the Likert Scale:

  • Ease of Use: The Likert scale is straightforward for respondents to understand and for researchers to implement, making it a practical choice for surveys and questionnaires.
  • Versatility: It is adaptable to various types of questions and can be used in diverse fields, from market research to psychological assessments.
  • Quantifiable Data: The scale provides quantitative data from qualitative opinions, allowing for easier analysis and interpretation of results.
  • Subtlety in Responses: By offering multiple degrees of agreement or disagreement, the scale captures nuances in attitudes and opinions that yes/no questions cannot.
  • Comparative Analysis: The standardized response options facilitate the comparison of data across different groups or time periods.
  • High Sensitivity: The scale can detect even small changes in opinions or attitudes over time or in response to interventions.

Weaknesses of the Likert Scale:

  • Central Tendency Bias: Respondents might avoid extreme responses and tend to choose middle options, leading to skewed data.
  • Acquiescence Bias: There’s a tendency for some respondents to agree with statements as presented, regardless of their actual opinion.
  • Limited Depth: The scale may not capture the complexity or depth of respondents’ true feelings and can oversimplify nuanced opinions.
  • Interpretation Variances: Different respondents might interpret the scale points differently, affecting the consistency of responses.
  • Scale-Length Sensitivity: The number of points on the scale (e.g., 5-point, 7-point) can influence the responses, potentially impacting the data’s reliability and validity.
  • Not Suitable for Complex Concepts: The Likert scale may be inadequate for exploring deeply complex or multifaceted topics

Executive Summary

This report provides a comprehensive analysis of the Likert Scale, a widely used tool for measuring attitudes and opinions in research and surveys. The Likert Scale is characterized by its simplicity, versatility, and ability to convert qualitative opinions into quantitative data, making it a valuable instrument in various fields such as market research, psychology, and social sciences. The Likert Scale’s format of multiple-choice responses ranging from strong agreement to strong disagreement allows for nuanced data collection. It is user-friendly and easily interpretable, both for respondents and researchers (Kleib et al., 2021). Quantitative data generated are conducive to statistical analysis, facilitating clear, objective conclusions.

The evaluation closely aligns the program and curriculum objectives with the assessment outcomes, conducting a thorough analysis of the course by examining how the program goals intersect with student learning achievements. This involves gauging relevant skills, knowledge, and attitudes as key components of the desired learning outcomes. The evaluation process, employing the Likert scale, effectively measures the extent to which program objectives are met. This scale enables a detailed understanding of students’ comprehension and learning outcomes, integrating their feedback into the curriculum design and teaching strategies. The evaluation’s findings inform necessary adjustments and highlight both the educational impacts envisioned by instructors and the achievements of the students. Cronbach’s alpha, used to validate the Likert scale, confirms its reliability with high scores of 90, 89, and 88 percent, underscoring the method’s robustness in ensuring both validity and reliability in measuring educational outcomes.

NURS FPX 6111 Assessment 3 : Course Evaluation Template References :

Batran, A., Al-Humran, S. M., Malak, M. Z., & Ayed, A. (2022). The relationship between nursing informatics competency and clinical decision-making among nurses in west Bank, Palestine. Computers, Informatics, Nursing : CIN, 40(8), 547–553. https://doi.org/10.1097/CIN.0000000000000890

Kleib, M., Chauvette, A., Furlong, K., Nagle, L., Slater, L., & McCloskey, R. (2021). Approaches for defining and assessing nursing informatics competencies: a scoping review. JBI Evidence Synthesis, 19(4), 794–841. https://doi.org/10.11124/JBIES-20-00100

Milner, J. J., & Zadinsky, J. K. (2022). Nursing informatics and epigenetics: An interdisciplinary approach to patient-focused research. Computers, Informatics, Nursing : CIN, 40(8), 515–520. https://doi.org/10.1097/CIN.0000000000000922

Peltonen, L. M., Nibber, R., Block, L., Ronquillo, C., Lozada Perezmitre, E., Lewis, A., Alhuwail, D., Ali, S., Georgsson, M., Jeon, E., Tayaben, J. L., Lee, Y. L., Kuo, C. H., Shu, S. H., Hsu, H., Sommer, J., Sarmiento, R. F. R., Jung, H., Eler, G. J., Badger, M. K.,  Pruinelli, L. (2021). Nursing informatics research trends: Findings from an international survey. Studies in Health Technology and Informatics, 284, 344–349. https://doi.org/10.3233/SHTI210741

Pescaroli, G., Velazquez, O., Alcántara-Ayala, I., Galasso, C., Kostkova, P., & Alexander, D. (2020). A Likert Scale-based model for benchmarking operational capacity, organizational resilience, and disaster risk reduction. International Journal of Disaster Risk Science, 11(3), 404–409. https://doi.org/10.1007/s13753-020-00276-9

Reid, L., Maeder, A., Button, D., Breaden, K., & Brommeyer, M. (2021). Defining nursing informatics: A narrative review. Studies in Health Technology and Informatics, 284, 108–112. https://doi.org/10.3233/SHTI210680

Schrum, M. L., Johnson, M., Ghuy, M., & Gombolay, M. C. (2020). Four years in review: Statistical practices of Likert Scales in human-robot interaction studies. Companion of the 2020 ACM/IEEE. International Conference on Human-Robot Interaction. https://doi.org/10.1145/3371382.3380739

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