Teacher Lens for Digital Relevance (TLDR) Model

About the TLDR Model

As part of an ongoing research project, students and faculty from the Learning Sciences and Technologies program at the University of Pennsylvania developed the Teacher Lens for Digital Relevance (TLDR) model to evaluate educational video games based on player experience, design for learning, and culturally relevant instruction. Our inspiration for this model came from a number of scholars, including JLG Sanchez, James Gee, Gloria Ladson-Billings, Geneva Gay, and Rebecca Powell. Currently, we are refining the model through collaboration with teachers.

This model can be used by teachers, researchers, and game designers to analyze existing educational video games or inform the design of new ones. Our teams' analysis showed it to be particularly helpful for teachers who would like to approach game evaluation in a systematic way and identify areas to support with out-of-game instruction.

There are persistent issues with balancing play and learning in educational video games. Immersive games are best suited for instruction when deep play and cultural affirmation are priorities.

Use the TLDR Model

Try it out! You can download a copy of the TLDR Model as a spreadsheet here (v 4.0) or the streamlined version here (v 5.0).

Publications

Duvall, M., Li, H., Li, L., Liu, R., Shah, H., & Zhong, L. (2025.) Comparing award-winning educational games: Description versus experience. To be presented at the 2025 International Conference of the Learning Sciences.

Duvall, M. (2025.) Creating an open educational resource to support teachers in digital game-based learning. Presented at The American Educational Research Association 2025 annual conference.

Duvall, M. & Shah, H. (2024). Do Award-Winning Serious Games Meet Benchmarks for Quality Learning Tools?. In Lindgren, R., Asino, T. I., Kyza, E. A., Looi, C. K., Keifert, D. T., & Suárez, E. (Eds.), Proceedings of the 18th International Conference of the Learning Sciences - ICLS 2024 (pp. 2121-2122). International Society of the Learning Sciences.

Duvall, M., Chen, J., He, A., Lou, V., Shah, H., Shi, Y., Shuai, Y., & Tian, M. (2024). Using Teacher Feedback to Refine an Educational Video Game Evaluation Model. In Lindgren, R., Asino, T. I., Kyza, E. A., Looi, C. K., Keifert, D. T., & Suárez, E. (Eds.), Proceedings of the 18th International Conference of the Learning Sciences - ICLS 2024 (pp. 2279-2280). International Society of the Learning Sciences.

Duvall, M., Chai, L., Deng, X., Harpi, S., Lee, K., Wang, Y., Xia, K., & Zhou, Y. (2023). Evaluating educational video games for playability, learning, and culturally relevant instruction. Presented to The American Educational Research Association 2023 annual conference. https://doi.org/10.3102/2007032

Duvall, M., Chai, L., Deng, X., Harpi, S., Lee, K., Wang, Y., Xia, K., & Zhou, Y. (In submission.) The TLDR Model for evaluating educational video games for playability, learning, and culturally responsive instruction..

Chai, L., & Duvall, M. (In submission.) Analyzing the playability and educational content in commercial video games from a culturally responsive perspective.

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