 As we've moved through Module 4, the message has been clear. It's all about the feedback. The presenters in this final module have stressed how essential feedback is for deep learning. They've described feedback as a constant cycle, working towards closing the gap between actual performance and intended performance, and that our definition of feedback is dependent on whose perspective we're taking. Learner, teacher or peer. Reflecting on her travels in Hamburg, Marilyn Goose suggested that deep learning can be seen as connected ideas and reasoning that moves you towards something new. On her journey, Marilyn needed to know, how am I travelling and what do I need to do next to get to where I'm going? Making a crucial connection to deep learning, John Hattie introduced us to the three essential feedback questions. Where am I going? How am I going? And where to next? John then related these three questions to the types and purposes of feedback and how well the feedback is received by learners. Each of these questions relates to the three levels of feedback. At the task level, the learner is concentrating on what content to learn, deciding what prior knowledge exists and where to go for more information. The teacher is heavily involved in this surface level learning. At the process level, the learner is making decisions about strategies and techniques to be used, but it's the self-level that we really want our learners to be at. At this level, the teacher is minimally involved as the learner engages in self-directed error detection and strategic thinking for deep learning. John stressed that the overall purpose of these questions and levels is to help the students interpret the feedback given. With this in mind, it's essential that we have a better understanding of what constitutes effective feedback for both the learner and teacher. We explored several principles of effective feedback and saw these in action in the classroom. These include feedback that is goal-referenced, tangible and transparent, provides actionable information, is user-friendly, is delivered in a timely manner, is ongoing, consistent, accurate and trustworthy. As John Hattie explains, the most powerful feedback is that which is provided by the learner to the teacher. Cameron Books reminded us that most feedback given to learners is rarely used and that is why teachers need to focus on both the nature of effective feedback, but most importantly on how feedback is received. We saw Cameron providing feedback to teachers on the feedback they had provided to their own students. To add to our knowledge, Cameron gave us four conditions for effective feedback. One, clarifying expectations and standards for the learner. Two, scheduling ongoing targeted feedback within the learning period. Three, fostering practices to develop self-assessment and four, providing feed-forward opportunities to close the feedback loop. We were introduced to Cameron's effective feedback matrix that further explored the three levels of feedback and how these relate to visible learning. John Hattie differentiated feedback for surface learning and deep learning and with the example of space learning in mathematics, we made connections to cognitive load introduced to us in module one. Melissa Kane and John Hattie then explored the benefits of learners providing peer feedback and how this ultimately assists with self-assessment and moving learners to the self level where they engage in self-directed strategic thinking for deep learning. One of the foci of this module has been stressing the importance of recognizing that puzzlement and error making is of the greatest importance. Error is not a dirty word. At the surface level, teachers do need to provide the knowledge and guidance but at the deep level, puzzlement is actually very healthy. The feedback received through experiencing puzzlement and making errors stimulates learners to ask questions, explore concepts, engage with challenges and experiment with possibilities. John Hattie reminds us that it's okay to wallow in the confusion. He suggests that this is when we actually learn more interesting things. We were then introduced to questioning as an important technique for deep learning. In general, teachers ask many more questions than are necessary. 95% of questions are about surface level knowledge and include questions that both the students and teacher already know the answer to and are answered in several seconds. Our focus should be on the nature of student questions about their work for deep learning. For this, we need to create an atmosphere of trust and respect so that learners will ask important questions, think out loud and engage in self-questioning. And finally, Punkage Sa gave us a peek into the future by explaining a neuroscience perspective on feedback. Wearable technologies are already providing us with important information about how learners respond to feedback and it's clear we will see much more of this in the classrooms of tomorrow. Feedback is a pivotal element in deep learning. We hope that you found this module informative and that some of the concepts explored will prompt you to give further thought to the purpose of feedback for deep learning.