{"id":4753,"date":"2025-12-10T14:17:01","date_gmt":"2025-12-10T14:17:01","guid":{"rendered":"https:\/\/education.ufl.edu\/research-evaluation-methods\/?p=4753"},"modified":"2025-12-10T14:21:59","modified_gmt":"2025-12-10T14:21:59","slug":"edf-7469c-ai-for-evaluation-in-educational-environments","status":"publish","type":"post","link":"https:\/\/education.ufl.edu\/research-evaluation-methods\/2025\/12\/10\/edf-7469c-ai-for-evaluation-in-educational-environments\/","title":{"rendered":"EDF 7469C \u2013 AI for Evaluation in Educational Environments"},"content":{"rendered":"

[et_pb_section fb_built=”1″ theme_builder_area=”post_content” _builder_version=”4.24.0″ _module_preset=”default”][et_pb_row _builder_version=”4.24.0″ _module_preset=”default” theme_builder_area=”post_content”][et_pb_column _builder_version=”4.24.0″ _module_preset=”default” type=”4_4″ theme_builder_area=”post_content”][et_pb_text _builder_version=”4.24.0″ _module_preset=”default” hover_enabled=”0″ global_colors_info=”{}” theme_builder_area=”post_content” locked=”off” sticky_enabled=”0″]<\/p>\n

EDF 7469C \u2013 AI for Evaluation in Educational Environments<\/h2>\n

Spring 2025
Professor: Dr. Walter Leite
E-mail: walter.leite@coe.ufl.edu<\/a><\/p>\n

Course Description<\/h3>\n

This course provides knowledge and skills to implement AI methods to evaluate the effectiveness of educational programs and policies, investigate variability in program effects across groups and contexts, and optimize the matching of educational experiences to students.<\/p>\n

Objectives<\/h3>\n

The objectives of this course are: 1) provide students with the knowledge and experience in applying AI methods to estimate the effects of educational programs 2) Enable students to use AI to identify variation of the effects of educational programs across groups and settings; 3) Enable students to optimize the matching of educational experiences to students.<\/p>\n

Student Learning Outcomes:<\/h3>\n
    \n
  1. Students will understand the process of propensity score analysis.<\/li>\n
  2. Students will implement multiple machine learning methods to estimate propensity scores.<\/li>\n
  3. Students will evaluate covariate balance.<\/li>\n
  4. Students will estimate conditional average treatment effects with multiple machine learning methods.<\/li>\n
  5. Students will evaluate relationships between covariates and conditional average treatment effects.<\/li>\n
  6. Students will implement model-based clustering methods.<\/li>\n
  7. Students will estimate causal effects of cluster membership.<\/li>\n
  8. Students will estimate and evaluate optimal treatment regimes.<\/li>\n<\/ol>\n

    Pre-requisite:<\/h3>\n
    \n
    \n
    \n

    EDF 7405 \u2013 Advanced Quantitative Foundations of Educational Research <\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n

     <\/p>\n

    Software:<\/h3>\n

    I will use R and RStudio software for statistical analyses.<\/p>\n

    Recommended website<\/a> for survey data analysis with R.<\/p>\n

     <\/p>\n

    Topics<\/h3>\n
    \n
    \n
    \n
      \n
    1. Quasi-experimental designs for evaluation: Applications of supervised learning <\/span><\/li>\n
    2. Detection of treatment effect heterogeneity: Applications of supervised learning <\/span><\/li>\n
    3. Detection of treatment effect heterogeneity: Applications of unsupervised learning <\/span><\/li>\n
    4. Dynamic treatment regimes: Applications of reinforcement learning <\/span><\/li>\n<\/ol>\n<\/div>\n<\/div>\n<\/div>\n

       <\/p>\n

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