Harnessing Learning Analytics to Advance Health Professions Education

Emerging technologies have expanded the opportunity to collect, manage, synthesize, and visualize educational “big data”. With the goal of maximizing learning, analytics are being used to provide feedback and foster the development of learners’ self-regulated learning skills, support programmatic assessment and precision education, and facilitate coaching and data-driven decision-making within educational programs.

The literature describing use of learning analytics dashboards (LADs), descriptive, predictive, and prescriptive analytics in health professions education is evolving. Questions remain about the potential benefits and unintended consequences related to the use of learning analytics in competency-based education. Additional evidence is needed to define best practices, provide equitable access to these tools, and ensure responsible stewardship of data. Importantly, as the technological tools mature, the ways in which the use of learning analytics advance teaching and learning and ultimately, enhance patient care outcomes must continue to be explored.

The goal of this collection is to enable authors to share their experiences in implementing and evaluating the use of learning analytics in health professions education programs. Through sharing outcomes, collected using quantitative and qualitative methodologies, the collection will advance what is currently known and also shape questions for future research, stimulating conversation and advancing collective knowledge.

Submissions related to the use of learning analytics in competency-based education and assessment programs involving students, postgraduate trainees, and in continuing professional development of health professionals are welcomed. More specifically, this collection will consider scholarly evaluation and research related to:

• the implementation of predictive and prescriptive analytics,
• use of tools for data visualization i.e. dashboards,
• preparation of factors impacting engagement and use by stakeholders,
• use of analytics in programmatic assessment/assessment of competence, and
• use of analytics to inform program improvement and evaluate program effectiveness.

Submissions may describe use of artificial intelligence tools as well as other technologies. Articles can be in the form of original research, brief research reports, curriculum, instruction, pedagogy articles describing preparation of stakeholders, systematic reviews, mini-reviews and perspectives.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Community Case Study
• Curriculum, Instruction, and Pedagogy
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Community Case Study
• Curriculum, Instruction, and Pedagogy
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: learning analytics, dashboards, predictive analytics, assessment, healthcare professionals, medical education, health professions education

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.