Codix was designed to support video coding analysis using the creative sense-making and observable creative sense-making cognitive frameworks. The user uploads a video, selects either the CSM framework or the OCSM framework, and plays the video to start the coding process. The slider is used to adjust the code value, which highlights the corresponding selection in the coding scheme at the top right. The code count is cumulatively displayed to the right of the code chart. The user can add actions that appear on a timeline at the bottom right.
Codix was developed by Nicholas Davis, PhD at Co-Creative AI Consulting. It is a video analysis software platform that enables users to code video using the creative sense-making (CSM) and observable creative sense-making (OCSM) coding techniques. See the references at the bottom left of the page for more information on these coding techniques.
This tool can be used to code the interaction dynamics and behavior of participants in experimental studies, user studies, and ethnographic fieldwork. In particular, it is well suited to study improvisational domains, such as dance, drawing, pretend play, and music. The variety of coding schemes, and the fact they are editable, enables rich qualitative data collection in a quantitative manner.
Both the CSM and OCSM coding techniques employ continuous data collection processes, where the user selects a code with a slider and the system records that code once every second. The system highlights the selected code value in the coding scheme table to signify which code is selected.
The system dynamically updates the charts based on the continuous coding process. The code application chart under the video displays the coded values through time. The code count chart displays how many codes of each type were applied throughout the session. The creative sense-making curve displays the cumulative sum of code scores through time.
The timeline shows the events that have been added to the analysis process. This feature can be used for event-based coding of qualitative events that happened during the video session. This would be useful for identifying categories and themes in the video data, which would aid a Grounded Theory or Thematic Analysis methodological approach.
Davis, N., Hsiao, C. P., Singh, K. Y., Lin, B., & Magerko, B. (2017, June). Creative sense-making: Quantifying interaction dynamics in co-creation. In Proceedings of the 2017 ACM SIGCHI Conference on Creativity and Cognition (pp. 356-366). [PDF]
Davis, N. (2024) Creative Sense-Making: A Cognitive Framework for Quantifying Co-Creative AI. To appear in eds. Tigre-Moura, F., AI, Co-Creativity, Creativity. Routledge.
Deshpande, M., Trajkova, M., Knowlton, A., & Magerko, B. (2023, June). Observable Creative Sense-Making (OCSM): A Method For Quantifying Improvisational Co-Creative Interaction. In Proceedings of the 15th Conference on Creativity and Cognition (pp. 103-115). [PDF]