NURS FPX 6116 Assessment 3 Criteria and Rubric Development

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Capella university

NURS-FPX6116 Nursing Education Assessment and Evaluation

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Date

Criteria and Rubric Development

The telehealth nursing course assessment at Riverside Community Hospital is developed to measure the learners’ abilities to deliver virtual healthcare. It is in tandem with the course’s goals in combining theoretical and practical competencies. By focusing on all three domains of cognition, psychomotor, and affective, this assessment offers an all-around evaluation of whether students can implement telehealth knowledge, perform technical skills, and display cultural sensitivity. This document describes the assessment strategy, reason, and the grading rubric to clarify expectations for the learners and the faculty.

Part One – Assessment Description and Rationale

Assessment Description 

The assessment for this telehealth nursing course from Riverside Community Hospital is an all-around assessment that gauges different learning outcomes specific to telehealth capabilities. It is made up of three stages, and each targets a different learning domain. 

• Cognitive Domain: 20 items, a multiple-choice exam testing theoretical knowledge of principles of tele-health, their technologies, and means of patient communication. 
• Psychomotor Domain: A simulated telehealth consultation involving technical competencies like telehealth platform use and virtual patient assessments. 
• Affective Domain: A reflective essay aimed at having learners express their attitudes and beliefs concerning cultural competence in telehealth and assessed on the depth of insight and fidelity to the code of ethics. 

These elements guarantee that learners can apply knowledge of telehealth, learn practical skills, and demonstrate professional attitudes. The assessment is taken when the course ends: exam and simulation take place in a controlled class environment, whereas the essay is submitted via the web.

NURS FPX 6116 Assessment 3 Criteria and Rubric Development

Rationale

This assessment strategy is consistent with the outlined MSN Practicum Conference Call document’s course objectives, prioritizing the integration of the theoretical and practical telehealth components and considering cultural competence. The multiple-choice exam (quizzes) tests cognitive mastery necessary for a telehealth-based framework comprehension. A study by Zheng et al. (2022) states that deep learning-enhanced teaching improved student outcomes in critical thinking and clinical decision-making compared to traditional methods. The simulation applies psychomotor skills for real-world telehealth practice to ensure learners can effectively work with technology (Changiz et al., 2021). The reflective essay promotes the affective learning process that underlines self-awareness and ethical reflection, which is crucial in culturally sensitive care.

Research by Wei and Peng (2025) recommends using quizzes for lower-level objectives and case-based discussions for higher-level cognitive skills, with performance measured via rubrics. With the integration of all three domains, through the assessment, diverse needs of learners are catered for, which is also in line with AACN (American Association of Colleges of Nursing) Essentials and QSEN (Quality and Safety Education for Nurses) competencies, meaning that there is a thorough evaluation. In order to increase validity, the exam is peer reviewed by faculty, and the simulation is piloted on a small group to establish reliability. The student feedback surveys will improve the assessment’s efficiency (Imanipour et al., 2023).

Part Two – Grading Rubric

Course Title: Telehealth Nursing Course

Learner: Esohe

Faculty: Carie Braun

Date: May 2025

NURS FPX 6116 Assessment 3 Criteria and Rubric Development

C#	Criteria	Non-Performance	Basic	Proficient	Distinguished
C1	Case Study: Application of Evidence-Based Telehealth Practice	Does not apply evidence-based strategies to the case or fails to submit the assignment.	Applies irrelevant or unsupported strategies in a telehealth context. Limited explanation provided.	Applies evidence-based strategies to the case study and explains how telehealth supports care delivery.	Applies clearly defined, evidence-based strategies aligned with the patient’s needs and telehealth best practices. Provides a comprehensive analysis of care delivery supported by scholarly evidence.
C2	Virtual Simulation: Patient Assessment & Technology Use	Does not participate in the virtual simulation or demonstrates unsafe practices.	Participates but demonstrates frequent errors in virtual assessment or telehealth platform navigation.	Performs a virtual assessment using telehealth tools with minimal errors. Demonstrates safe and effective care.	Performs a comprehensive, accurate virtual assessment using telehealth tools. Demonstrates advanced clinical decision-making and platform fluency. Provides detailed self-assessment or instructor-reviewed performance.
C3	Reflective Journal: Cultural Competence and Ethical Practice	Does not submit a reflection or fails to address cultural or ethical dimensions.	Submits a reflection with general or surface-level insights related to ethics or culture.	Reflects on both cultural and ethical considerations in a thoughtful and coherent manner.	Provides a deeply reflective and personal account of cultural and ethical issues encountered or anticipated in telehealth. Connects insights to professional standards and real-world patient care scenarios.
C4	Role-Play: Interprofessional Communication Skills	Does not participate in the communication role-play or demonstrates a lack of collaboration.	Participates in the activity with unclear communication or limited demonstration of teamwork.	Communicates clearly and respectfully with team members. Demonstrates understanding of professional roles in virtual care.	Demonstrates highly effective, patient-centered communication in a collaborative virtual setting. Provides realistic dialogue and feedback grounded in interprofessional standards and telehealth communication principles.
C5	Alignment With Course and Program Outcomes	Fails to align assessment content with course objectives or program outcomes.	Attempts to align work with learning outcomes, but alignment lacks clarity or relevance.	Clearly aligns content with course objectives and demonstrates understanding of expected program competencies.	Thoroughly aligns all aspects of performance with course objectives, BSN program outcomes, and external standards (AACN, QSEN, ANA). Demonstrates ability to integrate learning into professional nursing practice.

Conclusion

The assessment provides a robust framework for evaluating telehealth nursing competencies, ensuring learners are well-prepared for real-world practice. Addressing cognitive, psychomotor, and affective domains fosters comprehensive skill development and cultural sensitivity. Ongoing feedback and validation processes will continue to refine their effectiveness, supporting the course’s alignment with professional standards.

References

Changiz, T., Amouzeshi, Z., Najimi, A., & Adibi, P. (2021). A narrative review of psychomotor abilities in medical sciences: Definition, categorization, tests, and training. Journal of Research in Medical Sciences: The Official Journal of Isfahan University of Medical Sciences, 26, 69. https://doi.org/10.4103/jrms.JRMS_965_19 

Imanipour, M., Mirzaeipour, F., & Hazaryan, M. (2023). Effectiveness of feedback type on performance quality and satisfaction of nursing students: A comparative interventional study. Journal of Education and Health Promotion, 12(1), 324. https://doi.org/10.4103/jehp.jehp_1178_22 

NURS FPX 6116 Assessment 3 Criteria and Rubric Development

Wei, Y., & Peng, Z. (2025). Application of the flipped classroom model based on Bloom’s Taxonomy of Educational Objectives in endodontics education for undergraduate dental students. PeerJ, 13, e18843. https://doi.org/10.7717/peerj.18843 

Zheng, J., Tayag, J., Cui, Y., & Chen, J. (2022). Bloom’s Classification of Educational Objectives based on deep learning theory, teaching design of nursing specialty. Computational Intelligence and Neuroscience, 2022(1), 1–8. https://doi.org/10.1155/2022/3324477