What works best in teaching and learning? That is the core question John Hattie set out to answer some 40 years ago. Since then, there has been a rapid growth in leveraging the power of meta-analyses to answer that very question. Yet, a different level of complexity has changed the discussion. With over 350 influences in the Visible Learning database, known as MetaX, (www.visiblelearningmetax.com), we could easily fall into the same difficulty that led John Hattie to utilize meta-analyses from the very start. If the idea behind using meta-analyses is to help make sense of the thousands and thousands of studies that are published each year, how are we to make sense of the growing list of influences in the Visible Learning MetaX database?
Let’s be realistic, memorizing all 350 plus influences in the database and creating a list or a rank order is not the appropriate way to make sense of this growing list. Lists can mask the complexities and the nuances of the visible learning data. For example, some of the effect sizes can be deceiving. Low effect sizes can give a false impression that somehow a particular influence is not important and should be avoided. Consider the average weighted mean effect size for well-being. This effect size is 0.24. Using the hinge point of 0.40, it would be fair to conclude that well-being has an effect size below the average. However, it is grossly inappropriate to then infer that student well-being is not important and should not be something to prioritize in our schools and classrooms. I bet that every educator believes well-being is essential in teaching and learning. Instead, we must consider the complex nature of our schools, classrooms, teachers, and students.
Well-being is a foundational component of any subsequent learning and the starting point for amplifying our impact as educators.
Another example of the danger in rank ordering influences is the list of learning strategies and teaching strategies found in the Visible Learning database. Each of these learning and teaching strategies have the potential to move learning forward, but that potential only becomes a reality if it is implemented and integrated cohesively in the overall learning experience. A poorly implemented strategy that is not integrated into the learning experience, will not be saved by high effect size generated in a meta-analysis.
If memorizing 350 plus influences and ranking them is not the way to make sense of the Visible Learning data, what is?
As we think about our own schools and classrooms, we want students’ learning experiences to form a cohesive story, seamlessly connecting concepts, skills, and understandings from the first to the last day of school. In other words, the learning progression from day one until day 180 should blend in a way that helps learners see the connections within and across the disciplines. That is exactly how we should think about Visible Learning. Visible Learning uses evidence, the meta-analyses, to tell a story about what works best in teaching and learning. The characters of this story are you and your learners. The setting is the context of your school or classroom. The plot is the journey through the learning progression.
The collective body of evidence contained in the Visible Learning database leads to four key messages that make up the main messages in the story:
- Climate First, Learning Second, Achievement Third
- Students Should Drive Their Own Learning
- Know Thy Impact
- Collective Responsibility for Learning
These four big ideas tie the research together. Let’s look at each of these ideas individually to get a better understanding of the role they each play in the story of Visible Learning.
Climate first, learning second, achievement third. If you want to amplify learning in your schools and classrooms, if you want to raise achievement for your learners, you must first foster, nurture, and sustain a climate that is conducive to those goals. School and classroom climate, classroom cohesion, a sense of belonging, and well-being our integral parts of any learning and achievement. While these effect sizes may not be the highest in the database, if these are absent from your school or classroom, the effect sizes linked to teaching and learning strategies will never come to fruition. It’s virtually impossible for a learner to engage in high leverage practices if the climate is negative, there is no classroom cohesion, learners do not feel a sense of belonging, or their well-being is in jeopardy. We look no further than the effect sizes associated with bullying, feeling disliked by the teacher, labeling, and stereotype threat. We should strive to foster a learning community, develop social, emotional, and academic skills, and maintain high expectations for all students.
Students should drive their own learning. At some point, our students will no longer be our students. What I mean is, at some point they will move on to the next grade level or graduate. This begs the question: do our students know what to do, when they don’t know what to do, and we’re no longer their teachers? We should strive to build the capacity in our learners to take ownership of and drive their own learning. We do this by advancing different types of knowledge, teaching them learning strategies, relinquishing our responsibility to them, and cultivating the skill, will, and thrill to drive their own learning decisions. Those effect sizes associated with self-questioning, self-evaluating, metacognition, and strategy monitoring have the potential to accelerate learning. These effects are well above the average of 0.40. Instead of using high leverage practices on our learners we should teach high leverage practices to our learners so that they can use them on their own.
Know thy impact. In your own school or classroom, you likely find it difficult to make decisions about where to go next if you don’t have evidence of how students are doing right now. This third message emphasizes the need to continuously generate evidence that is visible to both us and our learners so that we can truly see our impact on students and their learning journeys. The mantra of Visible Learning is and always has been, “when we see learning through the eyes of our students and students see themselves as drivers of their own learning.” This is only possible if we and the students have something to see. But generating evidence is not enough. We must use that evidence to engage in evaluative thinking, demonstrate impact, and improve our teaching and our students’ learning. This is why influences like classroom discussion, summarizing, questioning, concept mapping, reciprocal teaching, and the Jigsaw method have the greatest potential to accelerate learning. All these influences make teaching and learning visible so that we can know our impact.
Collective responsibility for learning. Last and certainly not least, we must move away from phrases like, “those students” and “they’re not my students”. We should strive to build the individual and collective efficacy of school leaders, teachers, and students. This efficacy is not just a feel good intervention but should be driven and informed by evidence that shows impact. The goal is to build structures and create relationships that support collaborations that are productive in moving learning forward. Whether this is through professional learning communities (e.g., PLC+) or other collaborative endeavors, school leaders, teachers, and students need to assume responsibility for their own learning and, this is important, the learning of others. When one of us grows, all of us grow.
The meta-analyses cannot be viewed in isolation but together they can help us understand how to write and tell that story to maximize our impact on student learning. When you need to make sense of the incredible database of Visible Learning research, start with the four key messages outlined in this article that tell a story about what works best.
