Providing narrative feedback is an ongoing and time-consuming process, so it’s understandable that many teachers are looking for solutions to streamline it. Before we explore emerging tools that could support educators, it’s important to clarify what feedback should encompass.
John Hattie, a leading authority on feedback, offers a structured framework that enhances student learning through various feedback levels. Each level serves a unique purpose, from assessing task completion to fostering self-directed learning. This approach allows educators to customize their feedback, promoting critical thinking, encouraging autonomy, and motivating students. By integrating these feedback levels, teachers can cultivate a more effective learning environment that supports student growth and academic performance.
Effective feedback operates at multiple levels, each serving a distinct purpose in supporting student learning and development. Task Level feedback addresses the accuracy of specific tasks, providing clear but limited guidance unless paired with deeper strategies. Process Level feedback refines students' methods, enhancing problem-solving and skill development. In other words, for Task Level feedback to be beneficial, it should include suggestions or strategies that help the leader understand why something was correct or incorrect and how they can improve in the future. The Self-Regulation Level empowers students to take control of their learning through self-monitoring, goal-setting, and resourcefulness. Lastly, Self Level feedback often includes praise, boosting motivation, but it is less effective for skill improvement since it focuses on personal attributes rather than actionable guidance.
AI can effectively deliver feedback at the Task Level by determining whether a student's response meets specific criteria. While AI can also provide some Process Level feedback, it may lack the nuance and comprehensiveness of human feedback, which considers a wider range of contextual factors and learning behaviors. When it comes to Self-Regulation, AI struggles to offer meaningful feedback on self-assessment, motivation, or goal-setting, areas where human teachers excel. Furthermore, AI's generic responses for Self-Level feedback may fail to resonate with students or foster the growth mindset necessary for effective learning.
In the future, advancements may improve AI's capabilities. Currently, it is primarily designed for analyzing text-based submissions, making it less effective at handling more complex activities, projects, and student interactions that would be warranted for most types of effective feedback.
Other Blog Topics In This Series
Comments