Assessment Data-Driven Inquiry: A Review of How to Use Assessment Results to Inform Chemistry Teaching

IntroductionBetween homework, quizzes, classroom activities, high stakes summative exams, informal classroom observations, and other inputs, high school chemistry teachers around the globe have access to a wide variety of student data. Through the analysis and interpretation of this data, teachers can uncover great amounts of information, including, but not limited to, their students' conceptions about content and the educational impact of their own instruction. With this information, teachers can tailor instruction to their classroom and even to individual students. The impact of teachers effectively using the results of their many informal and formal, summative and formative assessments on the learning of their students cannot be understated (U.S. Department of Education, 2008; 2011; Institute of Education Sciences, 2009). There is no better source of information for teachers to use to make instructional decisions than data from their own students. Every assignment, homework, quiz, test, activity, lab, in-class question, and discussion yields valuable instructional feedback to high school chemistry teachers. This is free, continuous, and customized-to-yourown-students professional development available every single day to teachers. The conglomeration of literature presented here will not detail the most effective implementation of this process, but it will portray what helpful advice is already available as well as what areas need to be understood better for the use of data to inform teaching.The United States (U.S. Department of Education, 2011), Caribbeans (Ogunkola & Archer-Bradshaw, 2013; Seecharan, 2001), Hong Kong and Singapore (Towndrow, Tan, Yung, & Cohen, 2010), and Britain (Simon, 1992), just to name a few, have shown increased focus on research of teacher assessment practices. Initially, we sought to investigate how high school chemistry teachers use the results of assessment to make datadriven decisions about their teaching. We searched for research on the use of data resulting from chemistry-specific assessments (e.g., an investigation of how teachers interpret the results from a specifi c item covering percent yield problems in stoichiometry). After finding no sources that were chemistry-specific and very little that was science-specific, we scoured the general education assessment literature only to find that these resources did not provide a satisfactory level of data-use guidelines for day-today instruction. As a result, an in-depth examination of what is available and what is missing in the data-driven inquiry literature is warranted and, therefore, is the goal of this review. It is important to apply the general process of effectively using assessment results to the context of high school chemistry, because the learning goals and modes of assessment tend to vary by discipline and educational setting. Thus, while reviewing the literature, we also illustrate some of the ideas as they apply to the specific context of high school chemistry teaching.It is important to note that this review derives from bodies of literature that range from informal, formative assessments to high-stakes, national summative assessments. Although the contexts of these assessments vary drastically, all types of assessment generate data that can serve many purposes, one of which is being a guide for instruction. In this light, the process of inquiry that describes how teachers are to inform their instructional practice differs because of differences in design, goals (objectives), and format of items and results, but is the same general process in either formative, summative, diagnostic, proximal, or distal assessments. Stated otherwise, we believe that all assessments produce data that, when analyzed considering their contexts, have the potential to inform instruction. Additionally, we use the term "assessment" in a colloquial manner. The term "assessment" as we use it implies two processes: collecting data from students and subjecting these data to criteria that implies evaluation. …