Analysis of Educational Computing Research Data

CECS 6510.030 Meets Tuesday June 5, June 12, June 19*, July 10*, July 24*, & July 31*. 2, 6:30 - 9:30pm, Matthews 308. (* Focused case study analyses begin at 5:30 pm on these days.)

Instructor: G. Knezek

Intern: M. Laurent

Catalog Description:  CECS 6510 Analysis of Research in Educational Computing. Students will analyze current research in educational computing as a tool for understanding the unique characteristics of technology based research activities in educational environments. Special consideration will be given to strategies for separating influences in research designs that incorporate technology as tools and as variables in the design. Students will also identify potential dissertation research topics and prepare preliminary reports that will be critiqued in class in preparation for doing the dissertation.

This class is intended for those who might wish to explore preliminary analysis of data or develop instruments for topics that might someday become the focus of their dissertations. The timetable is specially arranged to allow student to go directly from Scaling Methods to Analysis of Research if they wish. Exercises will concentrate on practical analysis of data issues using packages such as SPSS.

Presentation of Final Projects: 
July 31, 2007 6:30-9:30pm

Final Presentations

Online Student Information Sheet

Required Readings : 5 Articles. (3 from International Handbook of Information Technology in Education).

http://www.springer.com/west/home/education/learning+&+instruction?SGWID=4-40666-22-173746210-0

Optional Readings Book:  Bracey, G. W. (2006). Reading Educational Research. Heinemann. ISBN 0-325-00858-2.

SPSS User's Guide (optional, use of SPSS required & help available online)

SPSS Student Package (optional, use of SPSS required & available in lab)

Description of the Course:
This doctoral class provides an opportunity to apply the research designs, statistical methods and analytical frameworks learned in EDER 6010 and EDER 6020 to real data… your data, based on some theory base or research questions/ perspectives. Students will receive guidance on how to analyze their data for themselves, beginning with discussions, of how to formulate questions that can be answered: In addition, classmates will share their datasets and background information with peers thus giving everyone multiple “real world” examples of data from the field. Your final paper should be the beginning of a publication-quality product.

Grading:
Online participation: 10 pts.
Inclass participation: 10 pts.
Three Assignments:  30 pts. (develop instrument, administer, code/enter data)
Final project: 50 pts. (analyze characteristics of measurement scales, suggest improvements; 10 pts presentation, 40 points written paper)

Contact Information:

Instructor: G. Knezek

Intern: M. Laurent
Email: mal7500@dcccd.edu

Mailing Address:
Technology and Cognition/UNT
P.O. Box 311335
Denton, TX  76203