We present a new approach for the organization of schools, which we call the flexible scheduling paradigm (FSP). FSP improves student learning by dynamically redeploying teachers and other pedagogical resources to provide students with customized learning conditions over shorter time periods called “mini-terms” instead of semesters or years. By conceptualizing the school curriculum as a physical map, we can use technology from other sectors, such as project management, transportation, telecommunications networks, and the like, to customize the routing of students through curriculum using a core curriculum-targeted mastery-based approach, within current school budgetary and resource constraints. The maps are built from a unit-based curriculum, similar to current curricula, yet with the addition of more detailed unit dependency relationships. Our goal in this article is to introduce FSP and to establish a prima facie case for the new paradigm.
Get full access to this article
View all access options for this article.
References
1.
AaronsonD.BarrowL.SanderW. (2007). Teachers and student achievement in the Chicago public high schools. Journal of Labor Economics, 25(1), 95–135.
2.
AngristJ. D.LavyV. (2001). Does teacher training affect pupil learning? Evidence from matched comparisons in Jerusalem public schools. Journal of Labor Economics, 19(2), 343–369.
3.
AssoulineS.Lupkowski-ShoplikA. (2005). Developing math talent. Waco, TX: Prufrock Press, Inc.
4.
BarnesG.CroweE.SchaeferB. (2007). The cost of teacher turnover in five school districts: A pilot study. National Commission on Teaching and America's Future.
5.
BlomeyerR. (2002). Virtual schools and e-learning in K-12 environments: Emerging policy and practice. Policy issues. North Central Regional Educational Lab (NCREL), Naperville, IL. Retrieved December 15, 2010 from http://www.ncrel.org/policy/pubs/pdfs/pivol11.pdf
6.
BloomB. (1984). The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational Researcher, 13(6), 4–16.
7.
BushR. N.DeLayD. H. (1968). Making the school schedule by computer: Opening new educational alternatives. International Review of Education, Uses and Values of the Computer in Education, 14(2), 169–181.
8.
CanadyR. L.RettigM. D. (1995). Block scheduling: A catalyst for change in high schools. Princeton, NJ: Eye on Education.
9.
CavanaughC. (Ed.). (2004). Development and management of virtual schools: Issues and trends. Hershey, PA: Idea Group, Inc.
10.
Center for Mental Health in Schools at UCLA, co-directed by Adelman, H. & Taylor, L. (2006). Grade retention: What's the prevailing policy and what needs to be done?Los Angeles, CA.
11.
ClarkT. (2001). Virtual schools: Trends and issues. A study of virtual schools in the United States. Report commissioned by Distance Learning Resource Network (DLRN), a WestEd project, co-sponsored by the Center for the Application of Information Technologies (CAIT) at Western Illinois University. Retrieved December 15, 2010, from http://www.wested.org/online_pubs/virtualschools.pdf
12.
CohenP. A.KulikJ. A.KulikC. C. (1982). Educational outcomes of tutoring: A meta-analysis of findings. American Educational Research Journal, 19(2), 237–248.
13.
GoldhaberD.AnthonyE. (2007). Can teacher quality be effectively assessed? National Board certification as a signal of effective teaching. The Review of Economics and Statistics, MIT Press, 89(1), 134–150.
14.
GoldmanJ. J. (1983). Flexible modular scheduling: Results of evaluations in its second decade. Urban Education, 18(2), 191–228.
HillierF. S.LiebermanG. J. (2010). Introduction to operations research (9th ed.). New York: McGraw-Hill.
17.
KoppangA. (2004). Curriculum mapping: Building collaboration and communication. Intervention in School & Clinic, 39(3), 154–161.
18.
KulikC. C.KulikJ. A. (1982). Effects of ability grouping on secondary school students: A meta-analysis of evaluation findings. American Educational Research Journal, 19(3), 415–428.
19.
KulikC. C.KulikJ. A.Bangert-DrownsR. (1990). Effectiveness of mastery learning programs: A meta-analysis. Review of Educational Research, 60(2), 265–306.
20.
MurphyJ.SutterR. (1964). School scheduling by computer: The story of GASP. New York: Educational Facilities Labs, Inc.
21.
PinedoM. L. (2009). Planning and scheduling in manufacturing and services. New York: Springer.
22.
RivkinS. G.HanushekE. A.KainJ. F. (2005). Teachers, schools, and academic achievement. Econometrica, 73(2), 417–458.
23.
SlavinR. E.BraddockJ. H. (1993). Ability grouping: On the wrong track. College Board Review, 168, 11–17.
24.
SnyderJ. (2002). National Science Foundation Grant No. 0232833. Grant proposal and final report are unpublished. Grant proposal was titled “Highmarks Advanced Planning and Scheduling System for Schools.” Note: J. Snyder now goes by the name Y. Snyder.
25.
SnyderJ.RobertA. (2001). PCT patent application number PCT/IL01/00376 “System for Scheduling Classes and Managing Educational Resources.” Note: J. Snyder now goes by the name Y. Snyder.
26.
TrelfaD. (1999). Individual differences and the United States education system. The Educational System in the United States: Case Study Findings. Retrieved December 15, 2010, from U.S. Department of Education website: http://www.ed.gov/pubs/USCaseStudy/chapter3.html
27.
TrumpJ. L. (1958). An image of the future in improved staff utilization. NASSP Bulletin, 42(237), 324–329.
28.
U.S. Department of Education. (1999). Taking responsibility for ending social promotion: A guide for educators and state and local leaders. Retrieved December 15, 2010, from U.S. Department of Education website: http://www.ed.gov/pubs/socialpromotion/index.html
29.
ZepedaS. J.MayersR. S. (2006). An analysis of research on block scheduling. Review of Educational Research, 76(1), 137–170.
