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The Behavioral View Episode 5.1: Visual Analysis & AI: A Conversation with Rick Kubina

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Manage episode 463650022 series 2921302
Content provided by CentralReach. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by CentralReach or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://staging.podcastplayer.com/legal.

This Season 5 Premier explores the current state and future possibilities of visual analysis in behavior analysis, with particular focus on how artificial intelligence may enhance these practices. Dr. Kubina discusses limitations in current visual analysis practices, including inconsistent application of analysis techniques, lack of standardization in graph construction, and reliability issues in interpretation. The discussion extends to how AI tools might support more comprehensive and consistent visual analysis while maintaining the essential role of human judgment. The conversation includes practical considerations for implementing AI tools in clinical practice while maintaining ethical standards and professional competence.

To earn CEUs for listening, click here, log in or sign up, pay the CEU fee, + take the attendance verification to generate your certificate! Don’t forget to subscribe and follow and leave us a rating and review.

Show Notes

References:

Datchuk, S. M., & Kubina, R. M. (2011). Communicating experimental findings in single case design research: How to use celeration values and celeration multipliers to measure direction, magnitude, and change of slope. Journal of Precision Teaching & Celeration, 27, 3-17.

Kahng, S., Chung, K.-M., Gutshall, K., Pitts, S. C., Kao, J., & Girolami, K. (2010). Consistent visual analyses of intrasubject data. Journal of Applied Behavior Analysis, 43(1), 35–45.

Kubina, R. M., Kostewicz, D. E., Brennan, K. M., & King, S. A. (2017). A Critical Review of Line Graphs in Behavior Analytic Journals. Educational Psychology Review, 29, 583-598.

Vanselow, N. R., Thompson, R., & Karsina, A. (2011). Data-based decision making: The impact of data variability, training, and context. Journal of Applied Behavior Analysis, 44(4), 767-780.

Resources:

  continue reading

53 episodes

Artwork
iconShare
 
Manage episode 463650022 series 2921302
Content provided by CentralReach. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by CentralReach or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://staging.podcastplayer.com/legal.

This Season 5 Premier explores the current state and future possibilities of visual analysis in behavior analysis, with particular focus on how artificial intelligence may enhance these practices. Dr. Kubina discusses limitations in current visual analysis practices, including inconsistent application of analysis techniques, lack of standardization in graph construction, and reliability issues in interpretation. The discussion extends to how AI tools might support more comprehensive and consistent visual analysis while maintaining the essential role of human judgment. The conversation includes practical considerations for implementing AI tools in clinical practice while maintaining ethical standards and professional competence.

To earn CEUs for listening, click here, log in or sign up, pay the CEU fee, + take the attendance verification to generate your certificate! Don’t forget to subscribe and follow and leave us a rating and review.

Show Notes

References:

Datchuk, S. M., & Kubina, R. M. (2011). Communicating experimental findings in single case design research: How to use celeration values and celeration multipliers to measure direction, magnitude, and change of slope. Journal of Precision Teaching & Celeration, 27, 3-17.

Kahng, S., Chung, K.-M., Gutshall, K., Pitts, S. C., Kao, J., & Girolami, K. (2010). Consistent visual analyses of intrasubject data. Journal of Applied Behavior Analysis, 43(1), 35–45.

Kubina, R. M., Kostewicz, D. E., Brennan, K. M., & King, S. A. (2017). A Critical Review of Line Graphs in Behavior Analytic Journals. Educational Psychology Review, 29, 583-598.

Vanselow, N. R., Thompson, R., & Karsina, A. (2011). Data-based decision making: The impact of data variability, training, and context. Journal of Applied Behavior Analysis, 44(4), 767-780.

Resources:

  continue reading

53 episodes

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