An Architecture for Formative Assessment Analytics of Multimodal Artefacts in ePortfolios Supported by Artificial Intelligence

A key objective of higher education is to promote deeper learning strategies. Complex process-oriented teaching-learning methods, such as inverted classrooms, portfolios or blog writing, help students to actively engage with academic content. Learning in these settings is highly dependent on timely formative assessment and highly informative feedback to guide students’ learning efforts. A major challenge to the successful implementation of such settings is the lack of time resources for teachers to provide such feedback to a larger group of students. In the case of process portfolios, for instance, students design digital portfolios that incorporate multiple pages and a range of multimodal artefacts, such as text, concept maps, images, presentations, documents, audio recordings and videos. In this chapter, we design a high-level solution architecture using both rule-based and machine-learning modules. Our aim is to analyse the various modalities of produced multimodal content, such as ePortfolios, and to provide teachers with explainable metrics that represent human assessment rubrics in order to generate personalised feedback. To demonstrate the feasibility of the architecture, we present an example using produced ePortfolio data from a teacher training course, outlining the different steps to create quality indicators for a specific rubric and derive scores to support the final stage of feedback generation. Additionally, we explore potential refinements and implementation steps for the architecture.