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In the early 1900s, Alfred Whitehead argued that the goals of educational reform would be met if knowledge were made functional rather than simply rearranging or privileging different forms of “inert” conceptual knowledge. And for knowledge to become functional it must be “conditionalized.” Decision-based learning (DBL) is different because it explicitly conditionalizes learning and makes knowledge functional. Moreover, DBL fits within an overall developmental progression of expertise and fills a gap often overlooked by formal education. Considerations for designing and implementing DBL are outlined.

Degree programs in Information Systems typically include a course on Systems Analysis and Design which is challenging to teach for instructors and challenging to grasp for learners. The central issue is that conditional knowledge, or when to use concepts and techniques in this domain, is seldom, if ever, taught. This chapter explains the ongoing evolution of a course on Systems Analysis and Design with the goal of developing conditional knowledge in students. It follows the process of changing course objectives, developing expert models of problem solving, experiencing challenges in delivering this kind of content, and reflecting on new insights that will improve course design in the future.

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.

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.

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.

The potential benefits of structuring first-year general chemistry courses around an expert decision model (EDM) to help students develop conceptual understanding and sustainable problem-solving strategies are discussed, as is the creation of the EDM and the challenges faced in doing so. The usefulness of categorizing the course material as either process knowledge, conditional knowledge, or conceptual knowledge is also outlined. The EDM created for the course consists of four fundamental content domains branching out with a series of hierarchal questions and actions designed to guide students toward characterizing a question or solving a problem. The key characteristics of each of these domains are described along with aspects of this EDM structure that have been implemented over the past couple of years in the author’s classes and the impact this has had on the students and the author’s teaching.

“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.