Instructional coaching is widely promoted as a means of enhancing elementary teacher learning and improving instructional practices. However, research reveals inconsistent outcomes. This article addresses the disparity between the substantial investment in coaching and its variable impact by advocating for the development of theories of action as a foundational step. We argue that a clearly articulated theory of action can serve as an explicit blueprint that links intentions to outcomes, which can help ensure that coaching initiatives are purposefully designed and systematically implemented. To illustrate this approach, we present a theory of action guiding a multiyear project around mathematics elementary teacher learning. We conclude by offering principles to guide the development of contextually appropriate theories of action for instructional coaching.

We propose a model for investigating the quality of mathematics teaching using learning opportunities as an alternative dependent variable to student learning outcomes. We define learning opportunities as opportunities intended by the teacher and engaged by students. We use a general learning goal—conceptual understanding—to show that sufficient empirical evidence exists to identify features of learning opportunities that reliably connect with commonly desired learning outcomes. If accepted, our model would allow teaching researchers to reallocate scarce resources to study, in more detail, the nature of teaching that yields learning opportunities with desired features engaged by students. We believe the model could clarify the criteria for justifying appropriate dependent variable selection in research designs, grant proposals, and manuscript drafts.

This book is about scientific inquiry, providing a practical manual for conducting and communicating quality research in (mathematics) education.

Preservice teachers often find it difficult to apply professional knowledge to their teaching practice. The ability to connect professional knowledge with one aspect of practice - lesson planning - was assessed with the Lesson Analysis and Plan Template in (N = 18) preservice teachers after one year of teacher preparation. Lesson plans were analysed using qualitative and quantitative analyses. The results suggest that the Lesson Analysis and Plan Template can reveal several aspects of preservice teachers' attention to connecting professional knowledge with their lesson plans as well as areas in which they would benefit from additional support. Furthermore, preservice teachers' connections were compared with their ability to connect professional knowledge with another aspect of practice - observations of teaching - using a validated instrument that assesses professional vision. The relationships found between planning and observation abilities suggest ways for teacher educationprogrammes to bring professional knowledge closer to practice.

AbstractIn this chapter we propose a way to create theories of teaching that are useful for teachers as well as researchers. Key to our proposal is a new model of teaching that treats sustained learning opportunities (SLOs) as a mediating construct that lies between teaching, on the one hand, and learning, on the other. SLOs become the proximal goal of classroom teaching. Rather than making instructional decisions based on desired learning outcomes, teachers could focus on the kinds of SLOs students need. Because learning research has established reliable links between specific types of learning opportunities and specific learning outcomes, theories of teaching no longer must connect teaching directly with learning. Instead, theories of teaching can become theories of creating SLOs linked to the outcomes teachers want their students to achieve. After presenting our rationale for moving from theories of teaching to theories of creating SLOs, we describe the benefits of such theories for researchersandteachers, explain the work needed to build such theories, and describe the conditions under which this work could be conducted. We conclude by peering into the future and acknowledging the challenges researchers would face as they develop these theories.

AbstractEvery researcher wants their study to matter—to make a positive difference for their professional communities. To ensure your study matters, you can formulate clear hypotheses and choose methods that will test them well, as described in Chaps. 1, 2, 3 and 4. You can go further, however, by considering some of the terms commonly used to describe the importance of studies, terms like significance, contributions, and implications. As you clarify for yourself the meanings of these terms, you learn that whether your study matters depends on how convincingly you can argue for its importance. Perhaps most surprising is that convincing others of its importance rests with the case you make before the data are ever gathered. The importance of your hypotheses should be apparent before you test them. Are your predictions about things the profession cares about? Can you make them with a striking degree of precision? Are the rationales that support them compelling? You are answering the “So what?” question as you formulate hypotheses and design tests of them. This means you can control the answer. You do not need to cross your fingers and hope as you collect data.

AbstractBuilding on the ideas in Chap. 1, we describe formulating, testing, and revising hypotheses as a continuing cycle of clarifying what you want to study, making predictions about what you might find together with developing your reasons for these predictions, imagining tests of these predictions, revising your predictions and rationales, and so on. Many resources feed this process, including reading what others have found about similar phenomena, talking with colleagues, conducting pilot studies, and writing drafts as you revise your thinking. Although you might think you cannot predict what you will find, it is always possible—with enough reading and conversations and pilot studies—to make some good guesses. And, once you guess what you will find and write out the reasons for these guesses you are on your way to scientific inquiry. As you refine your hypotheses, you can assess their research importance by asking how connected they are to problems your research community really wants to solve.

AbstractTheoretical frameworks can be confounding. They are supposed to be very important, but it is not always clear what they are or why you need them. Using ideas from Chaps. 1 and 2, we describe them as local theories that are custom-designed for your study. Although they might use parts of larger well-known theories, they are created by individual researchers for particular studies. They are developed through the cyclic process of creating more precise and meaningful hypotheses. Building directly on constructs from the previous chapters, you can think of theoretical frameworks as equivalent to the most compelling, complete rationales you can develop for the predictions you make. Theoretical frameworks are important because they do lots of work for you. They incorporate the literature into your rationale, they explain why your study matters, they suggest how you can best test your predictions, and they help you interpret what you find. Your theoretical framework creates an essential coherence for your study and for the paper you are writing to report the study.

AbstractIf you have carefully worked through the ideas in the previous chapters, the many questions researchers often ask about what methods to use boil down to one central question: How can I best test my hypotheses? The answers to questions such as “Should I do an ethnography or an experiment?” and “Should I use qualitative data or quantitative data?” are quite clear if you make explicit predictions for what you will find and fully develop rationales for why you made these predictions. Then you need only worry about how to find out in what ways your predictions are right in what ways they are wrong. There is a lot to know about different research designs and methods because these provide the tools you can use to test your hypotheses. But as you learn these details, keep in mind they are means to an end, not an end in themselves.

Abstractspiepr Abs1Every day people do research as they gather information to learn about something of interest. In the scientific world, however, research means something different than simply gathering information. Scientific research is characterized by its careful planning and observing, by its relentless efforts to understand and explain, and by its commitment to learn from everyone else seriously engaged in research. We call this kind of research scientific inquiry and define it as “formulating, testing, and revising hypotheses.” By “hypotheses” we do not mean the hypotheses you encounter in statistics courses. We mean predictions about what you expect to find and rationales for why you made these predictions. Throughout this and the remaining chapters we make clear that the process of scientific inquiry applies to all kinds of research studies and data, both qualitative and quantitative.

We investigated how the time elementary preservice teachers (PSTs) spent studying certain mathematics topics during teacher education coursework was related to performance on teaching-related tasks administered after graduation. In two studies, participants completed tasks assessing their specialized content knowledge (SCK) for teaching 12 mathematical topics addressed to varying degrees in the preparation program. We found that instructional time was positively associated with SCK demonstrated both immediately postgraduation and 2 years later. Several possible confounding factors were assessed; one, entering PSTs’ average SCK for topics, appeared to influence the relationship. Accounting for professional learning postgraduation, such as attending professional development, did not change the underlying relationship. Considering these findings, we identify policy implications for the mathematics curriculum of PST education.

For five decades, JRME has sought to publish high-quality mathematics education research that advances the field's knowledge and has a positive impact on the teaching and learning of mathematics in the classroom. The journal's 50th anniversary represents an opportune time for the research community to take a step back, assess what progress has been made on the major problems of the field, and consider the most important problems that could orient research in the future. As we look across educational scholarship, we find that among the most robust findings from research on teaching and learning is that students'

Over the past several decades, educators have become increasingly intent on using data to inform decision-making at all levels of the educational system (Cho & Wayman, 2014; Mandinach, 2012; Means et al., 2010). The underlying reasoning is sound: Better decisions can be made with relevant data. Policymakers have reasoned that instructional decisions made by teachers that are based on data relevant to the classroom will help students to learn and achieve more. Indeed, from the introduction of No Child Left Behind (NCLB) to the current policies of the Every Student Succeeds Act (ESSA), the

Significant research in science and mathematics education should advance the field’s knowledge and understanding of the teaching and learning of science and mathematics. How, then, should the significance of a research question in science and mathematics education be assessed? And, when disseminating the findings of research, how should the significance of the research questions be communicated? In this study, we analyzed peer reviews to answer these questions. Our analysis revealed the main issues peer reviewers identify about research questions and the ways they are communicated during the dissemination of research. The findings provide insights for new and experienced researchers about communicating the significance of research questions, and they also illustrate how reviewer comments in peer-reviewed journals can provide a window into the field’s frontiers.

Recommended Citation Hiebert, J. & Berk, D. (June 2020). Foreword: Building a profession of mathematics teacher education. In A. Appova, R. M. Welder, and Z. Feldman, (Eds.), Supporting Mathematics Teacher Educators’ Knowledge and Practices for Teaching Content to Prospective (Grades K-8) Teachers. Special Issue: The Mathematics Enthusiast, ISSN 1551-3440, vol. 17, nos. 2 & 3, pp. 325–366. ScholarWorks: University of Montana. Retrieve (open access) from: https://scholarworks.umt.edu/tme