This article describes a new form of instruction called elegant instruction. Generally speaking, educational technologists assert that instruction ought to be effective and efficient. Some educational technologists might add that instruction should also satisfy learners. Incorporating the standard requirements and going beyond satisfaction, elegant instruction is effective, efficient, and inspiring. Elegant instruction arouses an appreciation for exceptional insight, creativity, economy, or beauty, which, combined with perceived applicability, inspires learners and compels them to greater achievement, to apply what they have learned, and to learn more. To ensure efficiency, elegant instruction is 60 minutes at most. Preliminary research into elegant instruction is encouraging; further research continues. After introducing elegant instruction and discussing its theoretical foundations, this article discusses two examples of elegant instruction for inspiring learners to use intriguing computer techniques. A third example seeks to convey the elegance—succinctness, applicability, and wonder—of mathematics.
BarrowsH. S. (1996). Problem-based learning in medicine and beyond: A brief overview. New Directions for Teaching and Learning, 68, 3–11.
5.
BoydD.CrawfordK. (2011). Six provocations for big data. Paper presented at Oxford Internet Institute's “A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society.” Retrieved November 9, 2012 from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1926431
6.
BrownA.GreenT. D. (2011). The essentials of instructional design: Connecting fundamental principles with process and practice (2nd ed.). Boston, MA: Allyn & Bacon.
7.
DickW.CareyL.CareyJ. (2009). The systematic design of instruction (7th ed.). Upper Saddle River, NJ: Merrill.
8.
DuffyT. M.JonnasenD. H. (Eds.). (1992). Constructivism and the technology of instruction: A conversation. Hillsdale, NJ: Lawrence Erlbaum Associates.
9.
GagnéR. M. (1965). The conditions of learning. New York, NY: Holt, Rinehart & Winston.
KellerJ. M. (1987a). Strategies for stimulating the motivation to learn. Performance & Instruction, 26(8), 1–7.
13.
KellerJ. M. (1987b). The systematic process of motivational design. Performance & Instruction, 26(9), 1–8.
14.
KellerJ. M. (2010). Motivational design for learning and performance: The ARCS model approach. New York, NY: Springer.
15.
KirschnerP. A.SwellerJ.ClarkR. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41(2), 75–86. Retrieved November 9, 2012 from http://www.cogtech.usc.edu/publications/kirschner_Sweller_Clark.pdf
LuterbachK. J.RodriguezD.LoveL. (2012). Toward effective and compelling instruction for high school eCommerce students: Results from a small field study. TechTrends, 56(6), 59–63.
18.
ManovichL. (2012). Trending: The promises and the challenges of big social data. In GoldM. K. (Ed.), Debates in the digital humanities (pp. 460–475). Minneapolis, MN: The University of Minnesota Press.
19.
MerrillM. D. (1983). Component display theory. In ReigeluthC. M. (Ed.), Instructional design theories and models: An overview of their current status (pp. 279–333). Hillsdale, NJ: Lawrence Erlbaum Associates.
OreyM.JonesS. A.BranchR. M. (2011). Educational media and technology yearbook (Vol. 36). New York, NY: Springer.
25.
ReigeluthC. M. (Ed.). (1983). Instructional design theories and models: An overview of their current status. Hillsdale, NJ: Lawrence Erlbaum Associates.
26.
ReigeluthC. M. (Ed.). (1999). Instructional design theories and models: A new paradigm of instructional theory (Vol. II). Mahwah, NJ: Lawrence Erlbaum Associates.
27.
ReigeluthC. M.Carr-ChellmanA. (Eds.). (2009). Instructional design theories and models. Volume III: Building a common knowledge base. New York, NY: Routledge.
28.
ReiserR. A. (2012). A history of instructional design and technology. In ReiserR. A.DempseyJ. V. (Eds.), Trends and issues in instructional design and technology (3rd ed., pp. 17–34). Boston, MA: Allyn & Bacon.
29.
ReiserR. A.DempseyJ. V. (Eds.). (2012). Trends and issues in instructional design and technology (3rd ed.). Boston, MA: Allyn & Bacon.
30.
ScrivenM. (1967). The methodology of evaluation. In StakeR. E. (Ed.), Perspectives of curriculum evaluation (Vol. 1, pp. 39–55). Chicago, IL: Rand McNally.
31.
ShrockS. A. (2011). A brief history of instructional development. In AnglinG. J. (Ed.), Instructional technology: Past present and future (3rd ed., pp. 69–77). Englewood, CO: Libraries Unlimited.
32.
SmaldinoS. E.LowtherD. L.RussellJ. D. (2012). Instructional technology and media for learning (10th ed.). Boston, MA: Pearson.
33.
SpectorJ. M.MerrillM. D.van MerriënboerJ.DriscollM. P. (2008). Handbook of research on educational communications and technology (3rd ed.). New York, NY: Taylor & Francis.
34.
TobiasS.DuffyT. M. (Eds.). (2009). Constructivist instruction: Success or failure?New York, NY: Taylor & Francis.
35.
TrefilJ. (2008). Science education for everyone: Why and what?Liberal Education, 94(1), 6–11.
36.
van der MeijH.CarrollJ. M. (1995). Principles and heuristics in designing minimalist instruction. Technical Communication, 42(2), 243–261.
37.
van MerriënboerJ. J. G. (1997). Training complex cognitive skills: A four-component instructional design model for technical training. Englewood Cliffs, NJ: Educational Technology Publications.
38.
van MerriënboerJ. J. G.KirschnerP. A. (2012). Ten steps to complex learning: A systematic approach to four-component instructional design (2nd ed.). New York, NY: Routledge.
39.
WittenI. H.FrankE.HallM. A. (2011). Data mining: Practical machine learning tools and techniques. Waltham, MA: Elsevier.
WuX.KumarV.QuinlanJ. R.GhoshJ.YangQ.MotodaH. (2008). Top 10 algorithms in data mining. Knowledge Information Systems, 14, 1–37. Retrieved November 9, 2012 from http://www.cs.umd.edu/∼samir/498/10Algorithms-08.pdf
