AEA SFSU 2002

Efficiency in the Use of Technology in Economics Instruction: 

Efficiency in the Use of Technology in Economics Instruction Mellon CEUTT Conference Northwestern University November 18-19, 2002

Project Purpose: 

Project Purpose Assess the cost-effectiveness of integrating technology into introductory economics courses. Macro and micro principles are generally large FTES generating courses. Little reliable data on the cost-effectiveness of new technologies in teaching these courses.

Project Team: 

Project Team Created as part of the American Economic Association’s Committee for Economic Education Research Project’s Conference. Kim Sosin: University of Nebraska at Omaha Rajshree Agarwal: University of Illinois Robin Bartlett: Denison University Betty Blecha: San Francisco State University Joe Daniel: University of Delaware

Outline for Today: 

Outline for Today Research Design Timeline Data Products Data Analysis

Research Design: 

Research Design Matched pair of technology and non-technology users in the same institution over approximately 20 universities. Information on Time Costs: Captures differentials in labor input for instructors and students. Information on Service Quality: Captures differentials in performance of students. Information on Institution, Instructor and Student Characteristics: Controls for institutional differences, instructor experience and student learning styles. Analysis of Impact of Technology Assess the cost-effectiveness of technology for economics instruction. Develop “best practices” for effective use of technology.

Summer 2001: 

Summer 2001 Email all economic department chairs requesting nominations for the project. Develop and test a methodology for collecting faculty time data: weekly data record in 15 minute increments 24-7. Develop and test a methodology for student time data: two weekly sample periods in 15 minute increments 24-7. Develop pre and post student surveys for the Fall pilot study. Formulate research protocols.

Fall 2001: 

Fall 2001 Five institutions and nine faculty use project materials. Evaluate instruments and make additions or deletions. Finalize instructor groups for the two field semesters. All technology participants must be experienced users.

Study ParticipantsSpring 2002 – Fall 2002: 

Study Participants Spring 2002 – Fall 2002

Data Collection: 

Data Collection Pre and post student surveys Pre and post instructor surveys Pre and post TUCE Instructor weekly time logs Student two period time logs Institutional Cost Data MBTI (Learning Styles)

ABC, Average Cost, and Marginal Cost: 

ABC, Average Cost, and Marginal Cost AC = TC/Q Total Cost Components: Faculty Cost (w x L) Variable Expenses Fixed Expenses What is Q? FTES Quality of Service Measure (TUCE) Modeling allows us to assess the cost associated with an increment in Q for tech and nontech courses.

Faculty Time Sheets : 

Faculty Time Sheets Detailed Labor Input. Separate “Task” from “Tools and Methods” in two separate columns of time sheet. All instructors are asked to evaluate the accuracy of their time sheets at the end of the semester. By comparing the responses for the same instructor for two semesters and for different instructors across institutions, we are able to separate the fixed and variable components of various tasks.

Institutional Cost Data: 

Institutional Cost Data Interviews with instructors determine institutional inputs for course. Inputs are recorded in real terms as much as possible for institutional comparisons. In consultation with university staff, determine costs of inputs to specific course. We also determine an FTES based measure of technology spending for the campus.

Slide13: 

Modified Flashlight and Wiche categories for comparison across different campuses. Perhaps more use of NACUBO categories than other CEUTT projects to facilitate comparison of cost data across institutions. We do not compute an opportunity cost based on a community rental rate because we do not feel this is the correct opportunity cost measure for classroom space.

Other Data Analysis: 

Other Data Analysis Student data set is rich with respect to a variety of questions on how technology affects student learning. For example: Do learning styles condition the effects of technology? Does the asynchronous nature of technology condition the productivity of study time for working students?

Conclusion: 

Conclusion Project data sets allow us to address several issues related to the cost-effectiveness of technology use for principles instruction. Information is pertinent to decision makers at all levels in education. Help inform the technology investment decisions of universities. Help instructors quantify the costs and benefits of incorporating technology in courses. Help students make choices regarding enrollment in courses that vary in technology use.