An Automated Entrustment Professional Activities Tracking System (EPA-TRAC) That Holistically Capture Competence: A Paradigm Shift to Programmatic Assessment
Hamed, O. A.
Show abstract
Competence is a complex phenomenon that requires comprehensive and holistic assessment. This could be only be achieved through a complete assessment program that navigates poles between assessment for learning, assessment of learning and assessment as learning. Assessment of competence is not an easy task; educationists resorted to unfolding competence into sub-sub-competences and assessing them as separate chunks which violated the purpose of competency-based education. This is attributed to the reduction of the rich information from an assessment to a numerical score. In addition, evaluations of chunks from different competency domains are collated together in a compensatory manner rather than in a conjunctive manner resulting in adding oranges to apples with resultant meaningless assessment information. A useful contribution to this whole issue is the introduction of entrustable professional activities (EPAs) which are considered portal for capturing and visualizing competences in a holistic manner. EPAs provide a better language to make education more task-based. This necessitated a different assessment paradigm "Programmatic Assessment". This assessment paradigm ensures convergence of assessment data points that keep competence integrated into a whole meaningful picture. In this type of assessment model, the trainee has role in gathering, collating and reflecting on the various assessments in an informative way that triangulates evidence and translates them into learning goals. Hence longitudinal, information-rich feedback and reflection are crucial in such assessment system and could be collated in a developmental portfolio. In such model a variety of assessment tools could be used so long as they constitute a high utility index; assessment could be psychometric and workplace-based. Whether scores from psychometric assessments or enaction of feedback and learning goals are used for making judgment, remains the decision of the curriculum and assessment system designers according to context and purpose of assessment. Being complex to realize, an automated system (EPA-TRAC) is developed to capture that assessment system.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Introducing the 4Ps Model of Transitioning to Distance Learning: a convergent mixed methods study conducted during the COVID-19 pandemic 96%
- Artificial intelligence tool for the study of COVID-19 microdroplet spread across the human diameter and airborne space 94%
- A Model of Workflow in the Hospital During a Pandemic to Assist Management 94%
Similar papers in this journal
- Large language models for generating medical examinations: systematic review 96%
- Effect of introducing interprofessional education concepts on students of various healthcare disciplines in the United Arab Emirates 95%
- Perceptions, attitudes, and challenges regarding continuing professional development among Ethiopian Medical Laboratory professionals: A mixed-method study 95%
Similar papers in this journal
- The potential for digital patient symptom recording through symptom assessment applications to optimize patient flow and reduce waiting times in Urgent Care Centers: a simulation study 94%
- A Web-based, Mobile Responsive Application to Screen Healthcare Workers for COVID Symptoms: Descriptive Study 93%
- Improving emergency department patient-doctor conversation through an artificial intelligence symptom taking tool: an action-oriented design pilot study 91%
Similar papers in this journal
- An AI-based approach to predict delivery outcome based on measurable factors of pregnant mothers 94%
- Theory of radiologist interaction with instant messaging decision support tools: a sequential-explanatory study 93%
- Impact of electronic medical records on healthcare delivery in Nigeria: A Review 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.