Medical Education Online

Longitudinal assessment is increasingly becoming popular in medical education due to its potential benefits for competency-based medical education. For instance, this type of formative assessment tracks progressions and provides regular feedback to learners, allowing the learners and medical educators to identify their strengths and/or weaknesses and make necessary improvements; it supports the development of self-regulated learning skills, including self-assessment, goal-setting, and reflective practice, as well as soft skills such as communication, teamwork and other interpersonal attributes. On the other hand, its summative type enables examiners to evaluate test specifications, cut-off scores, task qualities, and other related properties in a longitudinal spectrum, making the decision-making process more scientific.

Despite the potential benefits, there are challenges in implementing longitudinal assessment. The primary one is to design tasks that may align with curriculum/training goals and provide meaningful feedback, requiring careful consideration of the content and skills being assessed and the methods used to evaluate learner performance. Another challenge is to establish appropriate scoring mechanisms that are reliable and valid, demanding developing clear rubrics that define the criteria for success and training assessors to use them consistently. In addition, there are challenges related to workload management, as providing regular feedback can be time-consuming for instructors and administrators. Research design and data analysis are also critical challenges to consider. Longitudinal assessment requires collecting and analyzing large amounts of data, which can be complex and time-consuming. There is a need for robust statistical methods to analyze longitudinal data and evaluate the effectiveness of the assessment approach. Finally, there is a concern about the potential impact of longitudinal assessment on student well-being and stress levels. Receiving regular feedback can be stressful for some learners, and there is a need to balance the benefits of feedback with the need to support student well-being.

Recent studies have explored innovative approaches to longitudinal assessment, including technology, simulation, and entrustment-based assessment. Still, there have been ongoing dialogues, discussions, and debates on different aspects of longitudinal assessment. Medical Education Online welcomes such dialogues/discussions/debates and hopes to foster these conversations. To this end, we invite research proposals and papers for a special section on Promoting Longitudinal assessment in Medical Education.

Topics that fit the journal scope and relates to the abovementioned theme will be considered. Potential topics include but are not limited to the following:

• Investigating the effectiveness of incorporating peer assessment into longitudinal assessments in medical education
• Examining the impact of longitudinal assessment on medical learners' learning and knowledge/skill retention over time
• Developing AI technologies (e.g., natural language processing, computer visions, chatbot) for longitudinal assessment in medical education using raw and process data, such as digital portfolios, videos, or e-logs
• Investigating the potential benefits of integrating longitudinal assessment with simulation-based learning activities in medical education
• Assessing medical learners’ development of medical knowledge or clinical reasoning skills using longitudinal assessment
• Investigating the impact of longitudinal assessment on medical learners' self-directed learning skills, engagements, attitudes toward learning, and/or their motivation to improve and promote lifelong learning skills
• Examining the effectiveness of longitudinal assessment in predicting medical learners' performance on high-stakes examinations, such as licensing or board exams
• Examining the potential bias, disparities, and challenges that longitudinal assessment may contribute to medical learners’ learning experience
• Designing longitudinal assessment in entrustable professional activities (EPAs)
• Synthesizing contemporary models/techniques in longitudinal assessment
• Investigating quantitative and/or qualitative feedback (or multisource feedback) in longitudinal assessment
• Longitudinal assessment and its relation to healthcare/patient outcome

Medical Education Online accepts feature articles, research articles, trend articles, review articles, rapid communications and short communications.

All manuscripts submitted to this Article Collection will undergo a full peer-review; the Guest Advisor for this collection will not be handling the manuscripts (unless they are an Editorial Board member). Please review the journal scope <link> and author submission instructions <link> prior to submitting a manuscript.

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Guest Advisors

Dr. Zhehan Jiang is an assistant professor at the Institute of Medical Education, Peking University. His scholarly interest lies in advancing the application of data science to medical education assessment. He has spanned scholarly works to a wide range of data-driven research, both methodologically and practically. He worked at the University of Alabama and Baylor College of Medicine prior to his current position.

Dr. Kuan Xing has conducted research in both medical/health professions education and psychometrics with strong research background in Educational Psychology/Psychometrics. His expertise in healthcare simulation includes assessment in simulation-based training/education, validity studies, and interrater reliability. He applies quantitative methodology to help health professions faculty/programs to assess their learners/evaluate their curriculum/make evidence-based educational/administrative decisions.

Dr. Chi Chang is an assistant professor in the Office of Medical Education Research and Development and the Department of Epidemiology and Biostatistics in the School of Human Medicine at Michigan State University. Her research interests center on classification methodologies and cognitive diagnostic assessments. She has grants, publications, and conference presentations on applying AI techniques to assess medical learners’ medical knowledge and clinical skills, diagnose learners’ cognitive skills, and explore methodologies to identify learners’ learning patterns and progress performance.

Disclosure statement: Dr.Zhehan Jiang, Dr. Kuan Xing and Dr. Chi Chang declare there is no conflict of interest.

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