University of York VLE Assessment Tools Expansion

Description

The University of York is looking to extend the range, and capabilities, of its "in VLE" (BlackBoard Learn) assessment capability in the most economic way in terms of resource, solutions and cost without overlapping features of existing assessment tools; which could create alternate mechanisms in operation across faculties. Lot 1: The University of York is looking to extend the range, and capabilities, of its "in VLE" (BlackBoard Learn) assessment capability in the most economic way in terms of resource, solutions and cost without overlapping features of existing assessment tools; which could create alternate mechanisms in operation across faculties. The required features additional to our current estate (BlackBoard Learn/Turnitin Feedback Studio) are: Ability to bridge the gap between handwritten (paper based) responses (either as coursework or exams) and digital marking activities, with well supported entry points for handwritten content into an online digital workflow for marking and feedback irrespective of the source of the submitted work (ie from an open coursework or a closed exam activity). Ability to use complex technical and scientific nomenclature, formulas and equations in both questions and feedback responses. This is expected to work in concert with handwritten responses, allowing questions to be posed requiring detailed handwritten answers which can then be rendered digitally and technical responses provided digitally - using scientific formulae and equations as necessary. Tools which can identify and direct specific questions within a response to specific markers who have responsibility for marking that specific question. Enabling a single multi-answer submission to be marked in parallel by multiple markers and then collating that information through a structured workflow so the student receives a single mark and set of feedback. Ability to batch mark student work (primarily short answer texts) where responses are the same or similar. Being able to group or categorise responses providing a more rapid marking capability; this may utilise machine learning in order to facilitate that batching activity. Ability to support computer code review, grading and feedback. Automation of the grading activity is also being sought in order to provide economies of scale. The delivery model needed to align to our strategy must: - be SaaS - be delivered through an LTI 1.3 connection with the VLE - production ready status with an enterprise level service. Additional information: Contract sums equate to a 4-year commitment with a growth path to extend to 24000 student users