Collaborative ideation tools like digital whiteboards are widely used by designers, academics, and creative practitioners; yet most ideation tools are inaccessible to blind or low vision (BLV) users. Informed by prior work on whiteboarding challenges encountered by BLV users and our formative study with eight sighted whiteboard users, we built Idea11y, a whiteboard plug-in that provides a hierarchical, editable text outline of board content, augmented with audio cues and voice coding. Findings from evaluation with thirteen BLV screen reader users revealed how Idea11y supported BLV users’ understanding of clustering structure and streamlined their process to author, synthesize, and prioritize ideas on the board. Collaborative ideation sessions with six BLV-sighted dyads demonstrated how BLV users used Idea11y to develop collaboration awareness and coordinate actions with sighted collaborators. Drawing on this, we discuss ways to move beyond implicit visual norms in established ideation frameworks and practical considerations for future accessible ideation systems.

The COVID-19 pandemic has generated massive data in the healthcare sector in recent years, encouraging researchers and scientists to uncover the underlying facts. Mining interesting patterns in the large COVID-19 corpora is very important and useful for the decision makers. This paper presents a novel approach for uncovering interesting insights in large datasets using ontologies and BERT models. The research proposes a framework for extracting semantically rich facts from data by incorporating domain knowledge into the data mining process through the use of ontologies. An improved Apriori algorithm is employed for mining semantic association rules, while the interestingness of the rules is evaluated using BERT models for semantic richness. The results of the proposed framework are compared with state-of-the-art methods and evaluated using a combination of domain expert evaluation and statistical significance testing. The study offers a promising solution for finding meaningful relationships and facts in large datasets, particularly in the healthcare sector.