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© 2012 Boise State University Marcia Belcheir, Ph.D. Robert Anson, Ph.D. James A. Goodman, Ph.D. Boise State University On-line Course Evaluation Implementation and Improvement of Response Rates
© 2012 Boise State University Goals of the Session Offer suggestions for implementing on-line course evaluations Report research results of how faculty can improve response rates in their courses
© 2012 Boise State University A short history on implementing on-line course evaluations at Boise State Diffused ownership – collaboration of stakeholders from all parts of campus examined viability of the move Most departments wanted to participate; some skeptical, needed to see it work on campus No university-wide question set; instead, from college, department, and a variety of academic support programs Purchased CollegeNet, “WDYT” product
© 2012 Boise State University Academic-Oriented Team Led by Institutional Research Teamed with 2 faculty—one in Center for Teaching and Learning, one teaches IT Minimized OIT involvement (only prepare data source and set up user authentication) Implementation required: –No new FTE resources (“other duties as assigned”) –Need “sensitive touch” for faculty buy-in and participation To Opt-In To encourage student involvement To trust data for annual performance evaluations
© 2012 Boise State University Coordinated many hands (and no FTE body) to run on- line evaluations –Developed upload files and checked for accuracy –Handled help questions –Prepared survey calendar each semester Recommendations –Reduce # of sessions = more sessions = more cycles to support –Don’t assume course registration data is accurate –Communication is key Implementing On-Line Evaluations in a Streamlined Environment
© 2012 Boise State University Opt-In for colleges, departments (down to course level) created challenges to selectively load course enrollment data Also opt-in for trans-departmental academic programs-- “matrix university” Solution –Developed MS Access Database to check, filter and restructure data flexibly and systematically Recommendation –Overcomes potential faculty and department resistance to how teaching is evaluated –(Optional) University-wide question set should be universally adopted early in the process Implementing On-Line Evaluations in a Voluntary Environment
© 2012 Boise State University Implementing On-Line Evaluations in a Decentralized Environment Questions controlled by colleges, departments, and academic support programs; no required university question-set Solution –Vendor (CollegeNet, WDYT) selected for flexibility to create and bind specific question sets to type of course –Centralized question review, assistance, creation in Center for Teaching and Learning Recommendations –Decentralized requires centralized editing & question support –Helps to incorporate academic support programs (Honors? On-line programs? SL?) in on-line evaluations to reduce extra survey demands
© 2012 Boise State University Part 2: Improving response rates The key to good student response rates for on-line evaluations often lie with the faculty ➢ What does the literature say? ➢ What do our faculty do? ➢ How well do different tactics work to raise response rates? ➢ Are there other factors?
© 2012 Boise State University Background and literature review Response rates for online evaluations tend to be lower than of pen/paper evaluations (~70-80%/~50- 60%) (Avery et al, 2006; Dommeyer 2002; many others) Likert-type quantitative ratings remain constant even with lower response rates (Anderson et al, 2005; Dommeyer, 2004; many others)
© 2012 Boise State University Background and literature review Qualitative responses tend to increase (Handwerk, 2000; Heath, et al, 2007) Institutions and instructors that do something tend increase response rates (Nulty, 2008) The more tactics an institution and/or instructor uses, the higher the response rate (Nulty, 2008)
© 2012 Boise State University Response Rates and Adoption of On-line Evaluations * Estimate based on literature
© 2012 Boise State University ➢ Online Evaluations –76,172 enrollments (88% of total were online evaluations) 1,134 instructors 3,213 classes ➢ Response Rates –Overall: 54.9% –Colleges High: 73% in Engineering Low: 52% in Arts & Sciences –Departments High: 82% in Construction Mgmt Low: 31% in Respiratory Care –Classes: 0% to 100% Fall 2012
© 2012 Boise State University University Measures/Tactics No iPad lotteries, web advertising, or grade hooks Sent 5 student s –Start –Reminders (3) –End Sent 5 instructor s –Pre-eval (enter custom questions) –Start –Reminder –End –Results available
© 2012 Boise State University 1. Studied Fall 2012 evaluation –Used individual response rate data –Received anonymous data set from vendor 2. Surveyed all 1,134 instructors – 678 (60%) instructors responded about tactics used in 1 or 2 classes taught that Fall –1,128 (35%) classes studied –Survey asked Tactics used to increase student response rates or otherwise obtain student feedback Basic demographic questions Research Methods: Data
© 2012 Boise State University Applied series of t-tests and ANOVAS on individual variables for statistical differences Applied regression for modeling the best set of factors for predicting response rates Research Methods: Analysis
© 2012 Boise State University Research Questions Addressed What tactics do instructors use to improve their response rates? Does it help to use more tactics? What is the impact of course and instructor characteristics? What is the best model for explaining variation in response rates? Are there interactions between tactics used and course or faculty characteristics?
© 2012 Boise State University Which Tactics Were Instructors Most Likely to Use? Reminded students during class (61%) Explained to class how I use the results to improve teaching (57%) Sent personal s to students as reminders (33%) Posted a reminder or assignment on Blackboard (32%) Provided incentives to complete the evaluation (22%)
© 2012 Boise State University What instructor tactics improved response rates? Tactics Used tactic Response rate Didn’t use Response rate Provided incentives 79%57% Provided time in class to complete 70%61% Sent personal s as reminders 66%60% Reminded students during class time 65%57% Explained to class how I use the results 65%57% Posted reminder/assignment on BlackBoard 64%61% I did nothing 50%63%
© 2012 Boise State University Incentives: Does the kind of incentive and approach affect response rates? Basis for Awarding Incentive Type of Incen- tive Individual% of classTotal Points78% (n=62) 79% (n=156) 79% (n=218) Other incentive77% (n=15) 82% (n=18) 80% (n=33) Total78% (n=77) 79% (n=174) 79% (n=251)
© 2012 Boise State University Point-based class-wide rewards –If 80% complete the eval, everyone gets 1 point added to final grade –If 90% complete eval, all students gain back all participation points Non-Point-based class-wide rewards –If 70% complete eval, instructor will bring cupcakes to final –If 90% complete eval, everyone can bring a 3 X 5 notecard to the final Class-based Incentive Examples
© 2012 Boise State University Impact of threshold on response rate Incentive thresholdN who used that threshold Average response rate 100%889% 90%+1087% 80%+2286% Sliding scale873% 70%+1268% 60%+362% Unknown974%
© 2012 Boise State University Did using more tactics improve response rates?
© 2012 Boise State University Course and instructor characteristics: Response Rate N Small courses (<20) vs larger (>40) 64% vs 59% 477 vs 154 In-person courses vs On-Line 63% vs 50% 955 vs 119 Graduate courses vs Undergraduate 65% vs 61% 153 vs 974 Tenure/tenure-track faculty vs Adjunct 64% vs 57% 748 vs 379 Higher response rates were associated with:
© 2012 Boise State University Final model (R 2 =.32): VariableStandardized estimate Provided incentives0.41 Number of tactics used0.43 Took class time to complete evaluation0.07 Small class0.09 Taught on-line-0.09 Undergraduate course0.04 Taught by adjunct-0.12 Interaction: Number of tactics & undergraduate course level -0.32
© 2012 Boise State University Response rates based on number of tactics and course level
© 2012 Boise State University Conclusions Incentives provide the biggest boost –If you use incentives, set the threshold for the class (not individual) and set it at 80% or more –If using incentives, best to set high threshold (80% or greater) for class as a whole Use multiple tactics On-line and large section classes have a built in disadvantage—use even more tactics
© 2012 Boise State University Future Changes Institutionalize small core set of university- wide questions to enable comparisons Pushing grade hook proposal Keep expanding use (now 91.5%) Explore means to impact response rates for on-line courses
© 2012 Boise State University Questions? Marcia Belcheir - Robert Anson – James A. Goodman –
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