Principles of Asking Effective Questions During Student Problem Solving

ABSTRACT Using effective teaching practices is a high priority for educators. One important pedagogical skill for computer science instructors is asking effective questions. This paper presents a set of instructional principles for effective question asking during guided problem solving. We illustrate these principles with results from classifying the questions that untrained human tutors asked while working with students solving an introductory programming problem. We contextualize the findings from the question classification study with principles found within the relevant literature. The results highlight ways that instructors can ask questions to 1) facilitate students' comprehension and decomposition of a problem, 2) encourage planning a solution before implementation, 3) promote self-explanations, and 4) reveal gaps or misconceptions in knowledge. These principles can help computer science educators ask more effective questions in a variety of instructional settings

Principles of Asking Effective Questions During Student Problem Solving

ABSTRACT Using effective teaching practices is a high priority for educators. One important pedagogical skill for computer science instructors is asking effective questions. This paper presents a set of instructional principles for effective question asking during guided problem solving. We illustrate these principles with results from classifying the questions that untrained human tutors asked while working with students solving an introductory programming problem. We contextualize the findings from the question classification study with principles found within the relevant literature. The results highlight ways that instructors can ask questions to 1) facilitate students' comprehension and decomposition of a problem, 2) encourage planning a solution before implementation, 3) promote self-explanations, and 4) reveal gaps or misconceptions in knowledge. These principles can help computer science educators ask more effective questions in a variety of instructional settings

Designing Effective Questions for Classroom Response System Teaching

Classroom response systems (CRSs) can be potent tools for teaching physics. Their efficacy, however, depends strongly on the quality of the questions used. Creating effective questions is difficult, and differs from creating exam and homework problems. Every CRS question should have an explicit pedagogic purpose consisting of a content goal, a process goal, and a metacognitive goal. Questions can be engineered to fulfil their purpose through four complementary mechanisms: directing students' attention, stimulating specific cognitive processes, communicating information to instructor and students via CRS-tabulated answer counts, and facilitating the articulation and confrontation of ideas. We identify several tactics that help in the design of potent questions, and present four "makeovers" showing how these tactics can be used to convert traditional physics questions into more powerful CRS questions.Comment: 11 pages, including 6 figures and 2 tables. Submitted (and mostly approved) to the American Journal of Physics. Based on invited talk BL05 at the 2005 Winter Meeting of the American Association of Physics Teachers (Albuquerque, NM

Team-Based Learning in Law

Used for over thirty years in a wide variety of fields, Team-Based Learning is a powerful teaching strategy that improves student learning. Used effectively, it enables students to actively engage in applying legal concepts in every class -- without sacrificing coverage. Because this teaching strategy has been used in classes with over 200 students, it also provides an efficient and affordable way to provide significant learning. Based on the principles of instructional design, Team-Based Learning has built-in student accountability, promotes independent student preparation, and fosters professional skills. This article provides an overview of Team-Based Learning, reasons to adopt this teaching strategy in light of Best Practices for Legal Education and the Carnegie and MacCrate reports, concrete methods to use Team-Based Learning in Law School, and ways to address challenges to this teaching strategy. Co-authors Sophie M. Sparrow and Margaret Sova McCabe provide examples from their years of teaching a variety of courses using Team-Based Learning

Applying science of learning in education: Infusing psychological science into the curriculum

The field of specialization known as the science of learning is not, in fact, one field. Science of learning is a term that serves as an umbrella for many lines of research, theory, and application. A term with an even wider reach is Learning Sciences (Sawyer, 2006). The present book represents a sliver, albeit a substantial one, of the scholarship on the science of learning and its application in educational settings (Science of Instruction, Mayer 2011). Although much, but not all, of what is presented in this book is focused on learning in college and university settings, teachers of all academic levels may find the recommendations made by chapter authors of service. The overarching theme of this book is on the interplay between the science of learning, the science of instruction, and the science of assessment (Mayer, 2011). The science of learning is a systematic and empirical approach to understanding how people learn. More formally, Mayer (2011) defined the science of learning as the “scientific study of how people learn” (p. 3). The science of instruction (Mayer 2011), informed in part by the science of learning, is also on display throughout the book. Mayer defined the science of instruction as the “scientific study of how to help people learn” (p. 3). Finally, the assessment of student learning (e.g., learning, remembering, transferring knowledge) during and after instruction helps us determine the effectiveness of our instructional methods. Mayer defined the science of assessment as the “scientific study of how to determine what people know” (p.3). Most of the research and applications presented in this book are completed within a science of learning framework. Researchers first conducted research to understand how people learn in certain controlled contexts (i.e., in the laboratory) and then they, or others, began to consider how these understandings could be applied in educational settings. Work on the cognitive load theory of learning, which is discussed in depth in several chapters of this book (e.g., Chew; Lee and Kalyuga; Mayer; Renkl), provides an excellent example that documents how science of learning has led to valuable work on the science of instruction. Most of the work described in this book is based on theory and research in cognitive psychology. We might have selected other topics (and, thus, other authors) that have their research base in behavior analysis, computational modeling and computer science, neuroscience, etc. We made the selections we did because the work of our authors ties together nicely and seemed to us to have direct applicability in academic settings

Effect of Scaffolding on Helping Introductory Physics Students Solve Quantitative Problems Involving Strong Alternative Conceptions

It is well-known that introductory physics students often have alternative conceptions that are inconsistent with established physical principles and concepts. Invoking alternative conceptions in quantitative problem-solving process can derail the entire process. In order to help students solve quantitative problems involving strong alternative conceptions correctly, appropriate scaffolding support can be helpful. The goal of this study is to examine how different scaffolding supports involving analogical problem solving influence introductory physics students' performance on a target quantitative problem in a situation where many students' solution process is derailed due to alternative conceptions. Three different scaffolding supports were designed and implemented in calculus-based and algebra-based introductory physics courses to evaluate the level of scaffolding needed to help students learn from an analogical problem that is similar in the underlying principles but for which the problem solving process is not derailed by alternative conceptions. We found that for the quantitative problem involving strong alternative conceptions, simply guiding students to work through the solution of the analogical problem first was not enough to help most students discern the similarity between the two problems. However, if additional scaffolding supports that directly helped students examine and repair their knowledge elements involving alternative conceptions were provided, students were more likely to discern the underlying similarities between the problems and avoid getting derailed by alternative conceptions when solving the targeted problem. We also found that some scaffolding supports were more effective in the calculus-based course than in the algebra-based course. This finding emphasizes the fact that appropriate scaffolding support must be determined via research in order to be effective

Improving performance in quantum mechanics with explicit incentives to correct mistakes

An earlier investigation found that the performance of advanced students in a quantum mechanics course did not automatically improve from midterm to final exam on identical problems even when they were provided the correct solutions and their own graded exams. Here, we describe a study, which extended over four years, in which upper-level undergraduate students in a quantum physics course were given four identical problems in both the midterm exam and final exam. Approximately half of the students were given explicit incentives to correct their mistakes in the midterm exam. In particular, they could get back up to 50\% of the points lost on each midterm exam problem. The solutions to the midterm exam problems were provided to all students in both groups but those who corrected their mistakes were provided the solution after they submitted their corrections to the instructor. The performance on the same problems on the final exam suggests that students who were given incentives to correct their mistakes significantly outperformed those who were not given an incentive. The incentive to correct the mistakes had greater impact on the final exam performance of students who had not performed well on the midterm exam.Comment: accepted for publication Physical Review Physics Education Research in 2016, 20 pages, PACS: 01.40Fk,01.40.gb,01.40G-, Keywords: physics education research, learning from mistakes, pedagogy, quantum mechanics, teaching, learnin