“Improved Photography”continued from page 23
Table Two AVERAGE NUMBER CORRECTLY IDENTIFIED STRUCTURES UNIT
Overview
Skeletal System Nervous System Special Senses Capstone (Cat)
2008 - 09 22 35 15 9
67
2009 - 10 33 41 22 11 77
GRANT RESOURCES USED:
Improvement 11 6 7 2
11
Table Three AVERAGE NUMBER ANALYSIS POINTS PER LAB 2008 - 09 10 21 17 7
UNIT Overview
Skeletal System Nervous System Special Senses Capstone (Cat)
IMPLICATIONS
With minor exceptions in all three measures considered, student achievement improved. The greatest gains appeared in average test scores on the final dissection where students scores improved by 7 %; and in the average number of structures identified in labs on both the overview and cat dissections (+11 structures). The exceptions were the scores on the Special Senses Test (down 2.49%) and the Nervous System Analysis (down 1 point) as compared to the previous year averages.
These preliminary results would support the contention that higher resolution dissection pictures promote better learning in the study of anatomy and improvement in the composition of quality written lab reports.
Anecdotally, students often remarked that the pictures taken with the grant provided cameras were far better than their cameras could take. Similarly, it was the observation of the teacher, that structures and organs photographed more distinctly resulting in more accuracy and greater detail in the student lab reports.
“Data Driven Decision Making” continued from page 22
AdvancED Web Site – For Michigan’s “One Plan, One Voice” school improvement materials and resources,
www.advanc-ed.org/mde
TechPlan.org – Michigan’s technology planning and resource site,
www.techplan.org ISTE – International Society for Technology in Education (NETS Standards),
www.iste.org/standards.aspx Available from Eye on Education
www.eyeoneducation.com
Using Data to Improve Student Learning in School Districts, Victoria Bernhardt. Using Data to Improve Student Learning in High Schools, Victoria Bernhardt. Using Data to Improve Student Learning in Middle Schools, Victoria Bernhardt. Using Data to Improve Student Learning in Elementary Schools, Victoria Bernardt. Translating Data into Information to Improve Teaching & Learning, Victoria Bernhardt. Data-Driven Decision Making and Dynamic Planning: A School Leaders’ Guide, Paul Preuss.
MACULJOURNAL | REFERENCES
Bernhardt,
Victoria.Translating Data into Information to Improve Teaching and Learning, Larchmont, NY; Eye on Education (2007, ISBN 978-1-59667- 061-7).
Preuss, Paul. Data-Driven Decision Making and Dynamic Planning, Larchmont ,NY; Eye on Education (2007 ISBN 978-1-59667-070-9).
Left to Right: Deborah Dunbar, Director of Instructional Services, Bay-Arenac ISD, Elizabeth Soggs, Technology Manager/REMC 6, Bay-Arenac ISD, Lisa Zettle, Data Specialist, Bay-Arenac ISD
36
2009 - 10 12 24 16 10 40
Nikon Coolpix P90 Cameras, digital card readers, memory cards and rechargeable batteries.
REFERENCES:
Baker, E.L., Gearhart, M., & Herman, J.L. (1994). Evaluating the apple classrooms of tomorrow. In E. L. Baker, and H.G. O’Neil, Jr. (Eds.). Technology assessment in education and training. Hillsdale, NJ: Lawrence Erlbaum
Improvement 2 3
-1 3 4
Downie, R., & Meadows, J. (1995). Experience with a dissection opt-out scheme in university level biology. Journal of Biological Education29 (3).
Hitlin, P. & Rainie, L. (2005, August). Teens, technology, and school. Data memo. Washington, D.C.: Pew Internet & American Life Project.
Marykay Marks, Ed.D., the grateful MACUL grant recipient, teaches Anatomy & Physiology at Walled Lake Central High School in Walled Lake. She can be reached via e-mail at:
marykaymarks@wlcsd.org.
Spring/Summer 2011
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