From literature to patterns: a scoping review of blended learning assessment

Blended learning (BL) has gained widespread adoption in higher education due to its flexibility in accommodating diverse student needs. However, there is a lack of theoretical frameworks to inform the design of formative assessment strategies within BL environments. This scoping review investigates how formative assessment strategies are implemented across different BL approaches in higher education, utilizing generative AI tools alongside traditional methods. By systematically analysing 32 studies published between 2014-2024, the review explores formative assessment tools and methods used in transmissive (student-content), active (student-teacher), and collaborative (student-student) BL models. Findings reveal self-assessment as the most prevalent formative evaluation method, with limited utilization of feedback mechanisms like peer and teacher feedback. The review highlights opportunities for leveraging digital tools to enhance personalized feedback, engagement tracking, and adaptive learning pathways. While machine-led assessments are prominent, collaborative BL emphasizes peer assessment and human-centered evaluation. This mapping of current practices aims to inform educators and researchers in developing assessment strategies aligned with modern pedagogical approaches in BL environments.