Philosophical Approaches to Evaluation

There are multiple philosophical approaches to the evaluation of nursing informatics courses. They are as follows:

The DIKW Framework

The DIKW framework, as its title suggests, encompasses four elements: data, information, knowledge, and wisdom. The foundation of this framework is the start with capturing data. Data is then processed into information that is structured and organized to be comprehensible.

This information undergoes verification against accepted truths to generate knowledge. Ultimately, the knowledge that is acquired culminates in wisdom, which is deeper insight into the concept/subject (Dammann, 2019).

In nursing informatics, the DIKW framework is used to teach students the proper use of technology in healthcare. It demonstrates the journey starting from mere data gathering to making evidence-based decisions. Following this framework empowers the students to not only understand the fundamentals of technology but know how to use it in a way that enhances patient care.

This approach is useful because it promotes gradual growth in learning. Using a predefined framework, it first teaches students how to extract value from basic data and eliminates the extraneous elements starting from the raw data.

Moving from data to information, to knowledge and finally wisdom, students appreciate the role technology can play in improving the services offered in the healthcare industry. This complete approach also assists in developing a better learning comprehension for effective clinical decisions.

Benner’s Model of Skill Acquisition

This model by Benner’s is equally helpful where he classifies learners into five levels: novice, advanced beginner, competent, proficient, and expert. Most learners will come into class as novices and progress through the course at different speeds.

At the start of the course, several students sit in the novice category, which is characterized by a lack of prior experience in Nursing informatics. Through the duration of the course, students will slowly progress through the stages with the accumulating experiences and procedural skills they acquire (Paul et al. 2019).

Benner’s model helps us understand the progression of learning. Initially, students pay attention to simple tasks and rules. As they continue to get exposed and practice, they begin to appreciate more intricate patterns and improve their ability to solve problems.

The intention is that towards the end of the program students will ideally be able to manipulate and use technology proficiently and safely in real-life healthcare scenarios. This progression ensures that every learner is working at their level and has a clear framework for development throughout the learning journey.

NURS FPX 6111 Assessment 4 Program Effectiveness Presentation

Formative and summative assessments are the two most important assessments within a course. Formative assessments are carried out during the learning process. They serve the purpose of tracking progress and providing feedback. These assessments enable instructors to determine whether students are implementing and progressing through the course as intended.

Conversely, the summative assessment occurs at the end of a course. They assess the extent of students’ learning as well as their ability to put their knowledge into practice (Tseng et al., 2021). These two forms of assessments provide a closure to both the ongoing and final evaluations. Collectively, they present a full spectrum of a learner’s achievement, thereby establishing whether the course was able to adequately equip the learner with the requisite skills for the application of technology in healthcare.

Evidence to Support the Explanation

A marked synergy is created with the combination of DIKW framework and Benner’s Model with the two assessment types to evaluate the course. These approaches capture critical insights on students’ technology interactions, but also offer useful direction to faculty about teaching improvement.

For example, the evaluation methodologies incorporate the use of simulation-based teaching that exposes students to real-life scenarios within a controlled environment. Moreover, to properly orient students to contemporary healthcare instruments, topics like artificial intelligence, automated IV pumps, electronic medical records (EMR), and remote monitoring systems are also taught (Tseng et al., 2021).

This application of different models and methods of evaluation assists educators in knowing what processes are effective and which ones need adjustment. With multiple perspectives, instructors can pay attention to the entire scope of the course from introducing the concepts to their practical application in the field.

The Program Evaluation Process

A clear process is essential for a successful evaluation. The following steps break down how the course is reviewed and improved over time:

1. Assessment

Data collection begins with students participating in evaluations. Most of the time, the information is gotten with the help of rank and file surveys. Asking multiple-choice questions allows for easier analysis of responses because there is a greater likelihood of obtaining specific answers. Student anonymity assures validity of the data as devoided of feedback, which means the data collected is representative of the student views.

2. Diagnosis

The subsequent step’s objective is to analyze the collected information with the aim of determining the strong and weak points of the course. One of the primary assessment steps is verifying the realization of set objectives for the course, especially the integrated teaching of critical technological skills for efficient healthcare. With the understanding of what works, instructors are able to create a diagnosis of the improvements that need to be made and what doesn’t work.

3. Planning

A planning session is held to fix any problems that have been detected. This is a very important and perhaps the most important planning stage, for it explains how the course will be altered towards achieving the educational goals set. For instance, in cases where students do not have sufficient exposure to practical experiences, the plan may suggest an increased number of simulation sessions or exercises.

4. Implementation

Following the preparation, it is time to execute the changes. This stage entails modifying the course materials, the teaching style, or methods of evaluation to enhance student achievement. Here, the intention is to rectify all the gaps that were noted earlier.

5. Evaluation

The last part of this process is assessing the effectiveness of the changes made. Focused interviews and new surveys are administered to evaluate if the course modifications can be associated with improved achievement levels. For this, a Likert Scale is often adopted because of its proven reliability of 94% (Hancock & Volante, 2020). This step ascertains whether the course is appropriately aligned with what it was intended to accomplish.

Challenges and Limitations

Despite the clear process, several challenges may affect the evaluation:

• Low Participation: There is a risk that some students may not complete the surveys, rendering the results unreliable.
• Data Collection Errors: The process of collecting the data may be flawed.
• Inappropriate Survey Methods: The data collection includes bad practice of open-ended questions instead of closed-ended questions.
• Analytical Mistakes: The use of incorrect data interpretation techniques leads to erroneous conclusions drawn from the data.
• Timing Issues: Conducting evaluations at unsuitable times may result in feedback that does not accurately reflect the course’s performance.

Each of these challenges can impact the quality of the evaluation and should be addressed carefully in any review process.

Improving the Program Using the PDSA Cycle

The Plan-Do-Study-Act (PDSA) cycle is utilized in enhancing the course development. This approach incorporates four steps to evaluate the actual results against the intended outcomes. The PDSA cycle evaluates both the teaching methods and the practical skills acquired by students.

It assesses the efficacy of simulation-based learning and other evidence-based practices to ensure students incorporate critical thinking, technical, and procedural skills for safe patient care (Zann et al., 2021).

Regular use of the PDSA cycle helps educators identify and rectify problems within the course promptly. This continuous improvement effort is necessary to maintain usefulness and relevance considering the dynamic nature of healthcare technology.

Ongoing Data Analysis for Continued Improvement

For an educational institution, regular analysis of data helps in understanding how a specific course is performing and what needs to be improved. The data acts as an indicator for making timely corrections. A case in point is if students are reported through several evaluations to be insufficiently exposed to new technology, simulation exercises can be increased in the course structure.

Regular analysis of data helps one to solve any issues at hand promptly, unlike in traditional systems where there is only one chance to figure out how to use a course after it is delivered. It allows a course to advance continuously and address issues with modern requirements in education.

Addressing Gaps in Student Feedback

One of the most important problems that any assessment faces is the closed ended nature of the questions, which tends to restrict student feedback, thus inevitably leaving gaps. Although these questions are easier from the viewpoint of data analysis, they do not capture most of the issues students have in relation to the course. The absence of qualitative data makes it too difficult for most students to articulate for deep detailed answers devoid of the how to pinpoint the gaps where they struggle.

There is therefore need to find a middle ground between yes/no answers, fill in the blank questions and allow students to give elaborate and deep feedback to eliminate gaping voids. That is how educators will address the need.

References

Dammann O. (2019). Data, information, evidence, and knowledge: A proposal for health informatics and data science. Online Journal of Public Health Informatics, 10(3), e224. https://doi.org/10.5210/ojphi.v10i3.9631

Hancock, P. A., & Volante, W. G. (2020). Quantifying the qualities of language. PloS One, 15(5), e0232198. https://doi.org/10.1371/journal.pone.0232198

Paul, F., Abecassis, L., Freiberger, D., Hamilton, S., Kelly, P., Klements, E., LaGrasta, C., Lemire, L., O’Donnell, E., Patisteas, E., Phinney, C., Conwell, K., Saia, T., Whelan, K., Wood, L. J., & O’Brien, P. (2019). Competency-based professional advancement model for advanced practice RNs. The Journal of Nursing Administration, 49(2), 66–72. https://doi.org/10.1097/NNA.0000000000000719

Tseng, L. P., Hou, T. H., Huang, L. P., & Ou, Y. K. (2021). Effectiveness of applying clinical simulation scenarios and integrating information technology in medical-surgical nursing and critical nursing courses. BMC Nursing, 20(1), 229. https://doi.org/10.1186/s12912-021-00744-7

Zann, A., Harwayne-Gidansky, L., & Maa, T. (2021). Incorporating simulation into your plan-do-study-act cycle. Pediatric Annals, 50(1), e25–e31. https://doi.org/10.3928/19382359-20201213-01

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