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Many instructors have implemented decision-based learning (DBL) into their courses. This chapter is a careful qualitative analysis of the narratives in this book done by the editors. The author found common themes among all the narratives. The first theme was that many instructors discovered that they were missing conditional knowledge in their instruction. Second, the author found common issues around the complexity of designing an expert decision model (EDM). Included in this theme are stories about selecting problems and organizing the EDM, building the EDM around specific course learning outcomes, providing just-enough, just-in-time instruction, and introducing the decision model and software to students. Instructors also discovered that assessing the learning of students needed to go beyond traditional goals and began to include new goals related to conditional knowledge. Finally, the author describes the comments made by both faculty and students about the experience of using DBL. Several authors described the value of using DBL in the process of taking students from novice thinkers to expert thinkers. Many students expressed that they enjoyed the process that DBL presented to them and that they had a new level of confidence to be able to approach problems in the content area. Summaries and quotes from the chapters in this book are referenced by the authors’ names and the content areas they were teaching.

Graduate students often come to statistics courses with varying levels of motivation and previous academic preparation. Within the statistics education literature, there is a growing consensus to guide instructors who want to help their students gain the requisite statistical knowledge so they can conduct their own research and report their results accurately. Recommendations from the literature include using real data, showing worked-out example problems, and providing immediate feedback to allow students to reflect on the correct and incorrect decisions they made in their analyses. This chapter describes the use of expert decision models (EDMs) in two graduate-level statistics courses – multiple regression and structural equation modeling. Decision-Based learning is an effective way to support graduate students’ developing thinking about statistics. In both courses, the students encounter the EDM through a series of assignments which guides students through the process of specifying a statistical model, running that model in Statistical Package for the Social Sciences or Mplus, and interpreting the results. These assignments use real datasets whenever possible and are designed to expose students to various issues they may experience in their research (missing data, violations of assumptions, etc.) and to illustrate how an expert would have adapted to those issues to complete the analysis. The EDM, with its just-in-time, just-enough instruction, helps students navigate these obstacles through guided practice and allows them to develop the conditional knowledge to handle issues that will arise as they carry out their own research.

Current First-year Writing research seeks to address the need to help students meet the Council of Writing Program Administrators objectives on source evaluation while also changing current pedagogy methods. This chapter seeks to compare two different source evaluation pedagogies, YSearch and decision-based learning, taught by Brigham Young University’s library, to determine which one-shot library instruction session module is more effective at teaching students source evaluation skills. To answer these questions, this study uses both quantitative and qualitative methods, utilizing a quasi-experimental, pre-test/post-test design by conducting an open comparison between the two pedagogy modules. Students scored significantly higher on the post-test in both designs and differences between the two increases weren’t statistically significant, showing that both treatments are effective. Follow-up interviews explored the differences between treatments.

“When will we ever use this?” This statement is verbalized by students in middle school, high school, and even at the university level. The operative word in this statement is “When.” Most instruction focuses on the “Why” in the form of concepts, theories, and frameworks and/or the “How” in the form of formulas and step-by-step procedures, but little if no attention is given to systematically explain and practice the “When.” The “When” helps students understand those things in the environment that trigger the relevance of certain concepts and procedures as they engage in a specific task. It is one thing to know the reason behind actions in general or how to execute those very actions, but it is another thing entirely to know “the when” and “under what conditions” those “whys” and “how’s” are relevant to a specific course of action. This chapter introduces decision-based learning as an innovative teaching approach designed to help students develop what we describe as conditional knowledge, which is simply knowing the “When.”

Transitioning engineering knowledge to practice is critical for the success of a working engineer. Decision-Based learning (DBL) provides a path for developing this capability during the educational experience rather than relying on post-educational experiences in the field. In this chapter, DBL is framed in the context of an engineering educational experience, with a discussion of how certain attributes of engineering and DBL can complement each other. An overall class structure conducive to implementing DBL instruction methods is presented. A possible process for defining an expert decision model (EDM) for engineering topics is explored. This type of decision model can help communicate and teach students the small decisions that culminate to an expert conclusion on a topic, giving the student a clear way to justify analysis and design decisions. An EDM for selecting of a static failure theory with an accompanying learning module and representative assignment problems that accompany the module are presented. The implementation of a DBL approach in an engineering classroom is discussed, and reflections are given about the experience for both the instructor and the students.

This chapter is a narrative account of the implementation of decision-based learning (DBL) in an introductory religion course for the purpose of helping students acquire conditional knowledge of a scriptural text. Descriptions are offered of the instructional decisions for redesigning a portion of the course and developing an expert decision model. The narrative covers multiple semesters describing the use of PowerPoint as the primary tool for DBL and then software designed specifically for DBL. Examples of DBL assessments and assignments are also included. A simple student survey consisting of scaled and open-ended questions was administered to students to gather feedback. Finally, further possibilities for implementation and research are discussed.

In this chapter, the authors describe how instructors used decision-based learning (DBL) to teach master’s and doctoral students in qualitative research courses how to evaluate qualitative research articles and develop their own skills at communicating their own research design choices. The authors employed a unique approach to DBL by coupling it with a decision tree built on Ryan et al.’s (2007) qualitative evaluation framework and Arao and Clemens’ (2013) brave spaces model. The authors found that using the above approach helped students develop specific critiques of the articles they chose, which then aided them in developing their own research designs.

To teach principles of information literacy effectively, an instructor must convey not only the tools and methods for locating information but also the conditions under which their use is most appropriate. Similarly, a key attribute of an information-literate student is not only the ability to find information but also to assess whether it is appropriate in the context of the need and information environment. Decision-based learning provides a method for helping instructors build these types of conditional knowledge within students. The process for leveraging the affordances of this method in a library instruction setting is outlined herein, including a discussion of considerations that impact course design. Process impacts on the instructor and student are also considered, and results are provided from a multi-semester study where this method was implemented in an academic environment.