About this paper

Appears in:
Pages: 2629-2638
Publication year: 2017
ISBN: 978-84-617-8491-2
ISSN: 2340-1079
doi: 10.21125/inted.2017.0729

Conference name: 11th International Technology, Education and Development Conference
Dates: 6-8 March, 2017
Location: Valencia, Spain


K. Plummer, R. Swan, N. Lush

Brigham Young University (UNITED STATES)
This paper introduces the theoretical foundations for an innovative pedagogy called Decision-Based Learning (DBL). This pedagogy addresses a challenge that plagues many problem-based learning methods. Kirschner, Sweller, & Clark (2006) claim that a critical flaw with problem-based approaches is that students are unable to develop a viable way to structure their knowledge. In the case of both problem-based and traditional teaching methods students struggle organizing their knowledge around what Bransford, Brown, & Cocking (1999) call the big ideas of a discipline. Additionally, research has shown that students struggle connecting the big ideas with the computational skills used to solve problems particularly in the hard sciences (See Hestenes, D., Wells, M., & Swackhamer, 1992; Stone, Allen, Rhoads, Murphy, Shehab, & Saha, 2003; Libarkin & Anderson, 2005; Garvin-Doxas, & Klymkowsky, 2008).

Decision-Based Learning is a pedagogy that organizes instruction around the decisions an expert makes to solve problems in a given domain of learning. With Decision-Based Learning the instructional focus is, first and foremost on the interrelated decisions experts make to frame problems. These decisions fan out like a decision tree, with general, high-level decisions at the beginning and then increasingly detailed decisions toward the end. As students take problems through the decision tree or model they are provided with just-in-time / just enough instruction so as to learn the concepts and/or procedures necessary to make each decision. In this way, concepts and procedures are taught when they are needed as students try to make sense of each problem from multiple angles. Over time, the decision model is slowly removed or fades so as to help students internalize it and use it flexibly do deal with problems they have not seen before.

This ability to frame problems through a decision-making model draws on a knowledge type called conditional knowledge. If conceptual knowledge deals with the knowledge of “the what” and “the why” and procedural knowledge deals with the knowledge of "the how to do something”, then conditional knowledge is the knowledge of the “when” or “under what conditions” that knowledge should be used in the first place. Whitehead (1929), Bransford et al. (1999), and Gobet (2005) assert that without conditional knowledge conceptual and procedural knowledge would remain inert and inactive. Biggs (2011) calls this type of knowledge “functional knowledge” because it makes a person’s knowledge functional or practical in the real world. Furthermore, all of these knowledge types must be learned schematically so as to understand how they all fit together in a broader structure. Researchers over the years have observed that both schema building and conditional knowledge acquisition are both learned tacitly through life experience and not explicitly in formal academic settings. (Reinking, Mealey, & Reidgeway, 1993; Marshall, 1995; McCormick, 1997, Gobet, 2005).

We assert that methodologies like Decision-Based Learning show promise in imbuing both conditional and a schematic understanding of a discipline. In this paper we will discuss the theoretical foundations and implications of this approach for learning, teaching and instructional design.
author = {Plummer, K. and Swan, R. and Lush, N.},
series = {11th International Technology, Education and Development Conference},
booktitle = {INTED2017 Proceedings},
isbn = {978-84-617-8491-2},
issn = {2340-1079},
doi = {10.21125/inted.2017.0729},
url = {http://dx.doi.org/10.21125/inted.2017.0729},
publisher = {IATED},
location = {Valencia, Spain},
month = {6-8 March, 2017},
year = {2017},
pages = {2629-2638}}
AU - K. Plummer AU - R. Swan AU - N. Lush
SN - 978-84-617-8491-2/2340-1079
DO - 10.21125/inted.2017.0729
PY - 2017
Y1 - 6-8 March, 2017
CI - Valencia, Spain
JO - 11th International Technology, Education and Development Conference
JA - INTED2017 Proceedings
SP - 2629
EP - 2638
ER -
K. Plummer, R. Swan, N. Lush (2017) INTRODUCTION TO DECISION BASED LEARNING, INTED2017 Proceedings, pp. 2629-2638.