Proceedings of the Annual Meeting of the Cognitive Science Society

Conversation-Based Assessment (CBA) represents a relatively new method of measuring student cognitive skills(e.g., science reasoning) through adaptive dialogues with automated characters. This approach leverages the openness ofnatural language with the interactivity of spoken dialogue to engage students in verbal reasoning and constructive processes(i.e., cognition). These two dimensions differentiate CBA from other assessment items (e.g., multiple choice and essays) byallowing for more freedom in responses along with the ability to adapt and follow-up on particular threads of information.The conversational exchange affords a rich data stream that can provide additional explanatory evidence of students’ cognitionexhibited through conversational content and dialogue paths. The current work, built on the AutoTutor dialogue engine, willdiscuss the affordances and constraints of CBA along with how this approach may complement and enhance other methods ofmeasuring of cognitive skills.