Can social semantic web techniques foster collaborative curriculum mapping in medicine?

Development and implementation of an online platform for curriculum mapping in medical education

Objectives

Nowadays universities face ever-increasing demands on quality of education, which is crucial from perspective of future graduates. In face of the need of constant quality improvements of medical curricula, it is important to seek strategies for their efficient management. The general trend is to develop electronic support tools to streamline the curricular design, analysis and harmonization.

Methods

Based on the requirements we have identified by the needs analysis among curriculum designers, teachers and managers at five universities involved in the Building Curriculum Infrastructure in Medical Education (BCIME) project, and evidence published in literature on curriculum development, we have developed methodological guidelines on curriculum innovations and a software-based tools that help manage, map and analyse curricula in the medical and healthcare study fields.

Results

In this paper, we share our experiences with building and implementation of EDUportfolio, an online platform developed within our consortium and intended to facilitate harmonisation and optimisation of medical outcome-based curricula. Its functionalities and outputs were verified by pilot mapping of Anatomy curricula as taught at partner universities in five European countries.

Conclusions

The visualisation and the analysis of described curriculum data using natural language processing techniques revealed both the hidden relations between curriculum building blocks and a set of overlaps and gaps in curricula. In addition, we demonstrate both the usability of the platform in the context of the involved academic environments and the capability to map and compare curricula across different institutions and different countries.

Technical infrastructure for curriculum mapping in medical education: a narrative review

Curriculum mapping is the process of designing a multidimensional model of an educational programme for a complete, more transparent and better-integrated learning experience. Many universities worldwide are building or expanding their technical infrastructure to manage their curricula. Our aim was to deliver a synopsis of current practices and describe the focus of research interest in implementing curriculum mapping tools for medical education. As part of the Building Curriculum Infrastructure in Medical Education (BCIME) project, we conducted a state-of-the-art narrative review of the literature. A systematised search of the PubMed/MEDLINE database for the years 2013–2019 resulted in 352 abstracts, from which 23 full-text papers were included in the final review. From these, we extracted guidance on 12 key characteristics of curriculum mapping tools. The collected experiences formed four thematic categories: visualisations, text descriptions and analysis, the outcome-based approach and adaptability in curriculum mapping. As result of the review, we summarised topics regarding ways of: implementating new competency-based catalogues (like NKLM) in curriculum mapping software (e. g., using dynamic checklists), methods of streamlining the authoring process (e. g., by automatic detection and alignment of action verbs in learning objectives descriptions) and graphical forms of presenting curriculum data (e. g., network visualisations using automatic clustering of related parts of a curriculum based on similarities between textual descriptions). We expect further developments in text-mining methods and visual/learning analytics in curriculum mapping. The collected data informed the design of a new curriculum management system called EduPortfolio, which is currently being implemented by the BCIME project.

Do they teach what they need to? An analysis of the impact of curriculum mapping on the learning objectives taught in a lecture series in surgery

Introduction: Curriculum mapping shows concordances and differences between the intended and the taught curriculum. To our knowledge, no previous studies describe the effects that this mapping has on the curriculum. The aim of the present study is to map the content of a lecture series in surgery to the National Catalogue of Learning Objectives in Surgery and analyze the effects this mapping has on the content of the following lecture series. Methods: All lecturers in the lecture series were directly observed by a minimum of two reviewers and learning objectives and the level of competence were documented. After the lecture series, the results were visualized within the catalog of learning objectives and were sent to the lecturers. In the following lecture series, learning objectives were documented correspondingly. Results: In the first lecture series, 47% of the learning objectives were taught. After the mapping, the number of learning objectives that were taught increased to 59% (p < 0.001). The increase was found in all surgical disciplines and in all levels of competences without any changes in the average duration of the lectures. Conclusions: The presented method for mapping a curriculum effectively increased the number of taught learning objectives without requiring longer lecture durations.

How we avoid patient shortage with an integrated analysis of learning objectives and clinical data during development of undergraduate medical curricula

Access to patients is a crucial factor for student-centred medical education. However, increasing numbers of students, teacher shortage, a patient spectrum consisting of rarer diseases, and quicker discharges limit this necessary access, and therefore pose a challenge for curriculum designers. The herein presented algorithm improves access to patients in four steps by using routinely available electronic patient data already during curriculum development. Step I: Learning objectives are mapped to appropriate ICD-10 (International Statistical Classification of Diseases) codes. Step II: It is determined which learning opportunities need to be considered first for patient allocation in order to maximise overall benefit. Step III: Hospital's departments with the highest expertise on respective learning objectives are assessed and selected for teaching. Step IV: Patients of the chosen department that present the best match for a given learning opportunity are assigned to participation. This integrated analysis of learning objectives and existing clinical data during curriculum development is a well-structured method to maximise access to patients. Furthermore, this algorithm identifies learning objectives of a curriculum that do not correspond well to the spectrum of patients of the respective teaching hospital and which should therefore be taught in learning formats without patient contact.