Finding the Needle in the Haystack: Can Natural Language Processing of Students' Evaluations of Teachers Identify Teaching Concerns?

Background: Institutions rely on student evaluations of teaching (SET) to ascertain teaching quality. Manual review of narrative comments can identify faculty with teaching concerns but can be resource and time-intensive.

Aim: To determine if natural language processing (NLP) of SET comments completed by learners on clinical rotations can identify teaching quality concerns.

Natural Language Processing of Learners' Evaluations of Attendings to Identify Professionalism Lapses.

Unprofessional faculty behaviors negatively impact the well-being of trainees yet are infrequently reported through established reporting systems. Manual review of narrative faculty evaluations provides an additional avenue for identifying unprofessional behavior but is time- and resource-intensive, and therefore of limited value for identifying and remediating faculty with professionalism concerns. Natural language processing (NLP) techniques may provide a mechanism for streamlining manual review processes to identify faculty professionalism lapses.

Analysis of Professionalism Themes Raised in Evaluations of Faculty.

This qualitative study examines unprofessional behavior by medical center faculty according to narrative evaluations.

Twelve tips for embedding assessment and learning practices in a programmatic assessment system.

Programmatic assessment supports the evolution from assessment learning to fostering assessment learning and learning practices. A well-designed programmatic assessment system aligns educational objectives, learning opportunities, and assessments with the goals of supporting student learning, making decisions about student competence and promotion decisions, and supporting curriculum evaluation. We present evidence-based guidance for implementing assessment and learning practices in the pre-clinical knowledge assessment system to help students learn, synthesize, master and retain content for the long-term so that they can apply knowledge to patient care.

PBL and beyond: trends in collaborative learning.

Building upon the disruption to lecture-based methods triggered by the introduction of problem-based learning, approaches to promote collaborative learning are becoming increasingly diverse, widespread and generally well accepted within medical education. Examples of relatively new, structured collaborative learning methods include team-based learning and just-in-time teaching. Examples of less structured approaches include think-pair share, case discussions, and the flipped classroom.