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ExamSoft Data Benefits: Accreditation, Teaching and Learning, Student Success (EAC 2021)

This Pecha Kucha session consists of three sub-sessions:

A Machine-Learning Approach to Predict NCLEX-RNA Outcomes: Harnessing the Power of ExamSoft

First-time NCLEX-RN® pass rates remain the foremost measure of quality for nursing education programs and a body of literature describes efforts to identify potential predictors or antecedents of first-time NCLEX-RN® success. We analyzed associations between score performance on exam questions from key clinical courses tagged with Bloom’s categories and other student-level academic data, and first-time NCLEX-RN® success. Multiple predictive machine-learning procedures were used to model and cross-validate models of predictors of NCLEX success. We discuss the modeling process and demonstrate how machine learning can be used to provide practitioner-friendly decision-making data.

After viewing this session you will be able to:
1. Recognize the power of using ExamSoft questions categorization data in early identification of students at risk of failure at the course level.
2. Understand how to utilize ExamSoft data to predict those students unlikely to pass NCLEX-RN® on first attempt.
3. Value the opportunity to use ExamSoft data and machine learning approaches to provide upstream student support and maximize NCLEX-RN® first time success.

Using ExamSoft to Support Accreditation Standards in a Speech-Language Pathology Graduate Program

The speech-language pathology master’s program at Harding University has used ExamSoft for three years. This presentation will show how we have used categories to support accreditation standards and student certification standards.

After viewing this session you will be able to:

  1. See application potential of ExamSoft in under-represented discipline
  2. Demonstrate software’s application for tracking professionalism standards.
  3. Understand how software is used to support departmental administrator’s accreditation reporting.

This presentation will highlight how we have used it to track learning among graduate cohorts and to support program-level accreditation and student-level certification standards.

Assessment Analysis Supports Virtual Radiology Clerkship as a Viable Alternative to an In-Person Experience

All fourth-year students participated in the radiology clerkship in-person one year and virtually the next due to the COVID pandemic. ExamSoft was used to test pre- and post-instruction knowledge and compare differences between the two classes. For both classes, there was a significant improvement in knowledge without a significant difference between either group. When in-person instruction is precluded, virtual delivery remains an effective alternative.

After viewing this session, you will be able to:

  • Learn how ExamSoft can facilitate the successful integration of a niche curriculum, whether it be radiology or other disciplines, within a broader medical school curriculum
  • Understand how an online platform can allow particularly large institutions to conduct assessment seamlessly across their many satellite campuses
  • Use different folders to organize exam questions and generate image-based pre- and post-test assessments
  • Use assessment analytics to generate an objective measure of student competency
  • Learn how assessment analysis can be used to compare two different cohorts using a quasi-experimental design
  • List some of the most critical medical diagnoses that are important for graduating medical students to be familiar with

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