Environments with embedded learning analytics offer strong potential to progress teaching and learning by providing real-time, data-driven insights into student engagement, progress, and learning behaviors. These analytics support personalized instruction, early identification of learners who need intervention, and informed instructional decision-making, allowing educators to adjust strategies and resources more effectively. However, creating and implementing such environments also presents challenges, including concerns about data privacy and ethical use of student information, technical and financial constraints, and the need for teacher training to accurately interpret and act on analytic data.

One example of a learning and assessment environment that uses embedded learning analytics is the Stanford Mobile Inquiry-based Learning Environment (SMILE). SMILE works by allowing students to create, share, and evaluate questions using mobile devices, while the system collects data on participation, question quality, and interaction patterns. These analytics help teachers monitor student engagement and higher-order thinking in real time, enabling more responsive instruction. Studies have shown that SMILE’s use of learning analytics promotes active learning, increases student participation, and supports deeper understanding, though its effectiveness depends on teachers’ ability to use the data meaningfully and responsibly.