PROJECT TITLE :

Uncertainty Representation in Visualizations of Learning Analytics for Learners: Current Approaches and Opportunities

ABSTRACT:

Adding uncertainty information to visualizations is becoming increasingly common across domains since its addition helps ensure that informed choices are made. This work has shown the difficulty that's inherent to representing uncertainty. Moreover, the representation of uncertainty has nonetheless to be completely explored in instructional domains while visualizations are typically utilized in educational reporting. We analyzed fifty uncertainty-augmented visualizations from varied disciplines to map out how uncertainty has been represented. We then analyzed 106 visualizations from instructional reporting systems where the learner will see the visualization; these visualizations offer learners with information regarding several factors together with their information, performance, and abilities. This analysis mapped the planning area that has been used to speak a learner's skills, information, and interests. It additionally revealed several opportunities for the inclusion of uncertainty information among visualizations of academic data. We describe how uncertainty data will be added to visualizations of instructional data and illustrate these opportunities by augmenting many of the varieties of visualizations that are found in existing learning analytics reports. The definition of this design space, based mostly on a survey of the literature, can enable the systematic exploration of how totally different design selections have an effect on learner trust, understanding, and decision creating.