Efficacy of Using Grade Point Average to Predict Students’ Cognitive Ability

James Warnock

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Conference: 2015 ASEE Annual Conference & Exposition
Location: Seattle, Washington
Publication Date: June 14, 2015
Start Date: June 14, 2015
End Date: June 17, 2015
ISBN: 978-0-692-50180-1
ISSN: 2153-5965
Conference Session: Pipeline and Performance in BME Education
Tagged Division: Biomedical
Page Count: 12
Page Numbers: 26.589.1 - 26.589.12
DOI: 10.18260/p.23927
Permanent URL: https://peer.asee.org/23927
Download Count: 458

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biography

James Warnock Mississippi State University

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James Warnock is the Interim Associate Dean for Undergraduate Studies in the Bagley College of Engineering at Mississippi State University. His background is in biomedical engineering and he has been a big proponent of self-directed learning and active learning in his classes and was the first person to introduce problem-based learning in the department of agricultural and biological engineering at MSU. James is also the Adjunct Director for training and instruction in the professional services department at ABET. In this role, Warnock oversees the development, planning, production and implementation of the ABET Program Assessment Workshops, IDEAL and the assessment webinar series. He also directs activities related to the workshop facilitator training and professional development.

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Abstract

Efficacy of Using Grade Point Average to Predict Students’ Cognitive AbilityIn a typical engineering course, student knowledge is assessed by periodic examination, usuallyadministered as a mid-term exam or final exam. While this provides the instructor with someindication of what students know, it doesn’t provide students an opportunity to learn the thingsthey don’t know. For courses that serve as prerequisites, students can progress to the next “level”with only having to know 60-70% of the course content. In contrast, in the video gaming world,the player has to achieve a perfect “score” in order to advance to the next level. If they do notachieve a perfect score they get another chance and so progression is often achieved throughrepeated attempts, especially at the higher, more difficult levels. The gaming, iterative approachwas applied to a junior level biomaterials course, where progression was based on cognitiveability.The course was divided into three separate modules; at the end of each module students wereasked to complete three tests. The first test for each module consisted of 15 multiple-choicequestions. These questions related to the understanding cognitive domain as defined by bloom’staxonomy. Students had to make 100% to progress to the next test, and they were allowed torepeat the test until they made 100%. The second test for each module was comprised of shortanswer problems that required students to calculate answers. These questions were designed totest the students’ ability to apply their knowledge. Students that scored >90% were permitted totake the third test. Again, if they made less than 90% the test could be repeated. The third testconsisted of poorly defined questions, where students were required to analyze raw data,interpret their results, apply them to the problem and provide a justification. This assessedanalyzing and evaluating cognitive abilities.The structure of this course prompted the following research questions to be asked: (i) Doesstudent GPA correlate with the number of attempts a students needs to achieve 100% on eachtest? (ii) Do students with a lower GPA (i.e.<3.0) have the ability to master higher cognitivelevels?Data collected over two semesters did not show any correlation between student GPA andnumber of attempts to get 100% on tests. This finding was consistent across all differentcognitive levels. Student GPA was also not a good predictor of cognitive ability, as students withlower GPAs were equally able to master application of knowledge as those with higher GPAs.Very few students were able to master evaluation of data and several students with high GPAsfailed to make 100% on this test.In conclusion, GPA is not a good indicator of cognitive ability and even students with a lowGPA have the potential to learn fundamental knowledge and apply their knowledge to solvestructured problems. A high GPA does not indicate an ability to function at the analytical orevaluation cognitive level.

Citation
Format

Warnock, J. (2015, June), Efficacy of Using Grade Point Average to Predict Students’ Cognitive Ability Paper presented at 2015 ASEE Annual Conference & Exposition, Seattle, Washington. 10.18260/p.23927

TY - CPAPER
AB - Efficacy of Using Grade Point Average to Predict Students’ Cognitive AbilityIn a typical engineering course, student knowledge is assessed by periodic examination, usuallyadministered as a mid-term exam or final exam. While this provides the instructor with someindication of what students know, it doesn’t provide students an opportunity to learn the thingsthey don’t know. For courses that serve as prerequisites, students can progress to the next “level”with only having to know 60-70% of the course content. In contrast, in the video gaming world,the player has to achieve a perfect “score” in order to advance to the next level. If they do notachieve a perfect score they get another chance and so progression is often achieved throughrepeated attempts, especially at the higher, more difficult levels. The gaming, iterative approachwas applied to a junior level biomaterials course, where progression was based on cognitiveability.The course was divided into three separate modules; at the end of each module students wereasked to complete three tests. The first test for each module consisted of 15 multiple-choicequestions. These questions related to the understanding cognitive domain as defined by bloom’staxonomy. Students had to make 100% to progress to the next test, and they were allowed torepeat the test until they made 100%. The second test for each module was comprised of shortanswer problems that required students to calculate answers. These questions were designed totest the students’ ability to apply their knowledge. Students that scored &gt;90% were permitted totake the third test. Again, if they made less than 90% the test could be repeated. The third testconsisted of poorly defined questions, where students were required to analyze raw data,interpret their results, apply them to the problem and provide a justification. This assessedanalyzing and evaluating cognitive abilities.The structure of this course prompted the following research questions to be asked: (i) Doesstudent GPA correlate with the number of attempts a students needs to achieve 100% on eachtest? (ii) Do students with a lower GPA (i.e.&lt;3.0) have the ability to master higher cognitivelevels?Data collected over two semesters did not show any correlation between student GPA andnumber of attempts to get 100% on tests. This finding was consistent across all differentcognitive levels. Student GPA was also not a good predictor of cognitive ability, as students withlower GPAs were equally able to master application of knowledge as those with higher GPAs.Very few students were able to master evaluation of data and several students with high GPAsfailed to make 100% on this test.In conclusion, GPA is not a good indicator of cognitive ability and even students with a lowGPA have the potential to learn fundamental knowledge and apply their knowledge to solvestructured problems. A high GPA does not indicate an ability to function at the analytical orevaluation cognitive level.
AU - James Warnock
CY - Seattle, Washington
DA - 2015/06/14
PB - ASEE Conferences
TI - Efficacy of Using Grade Point Average to Predict Students’ Cognitive Ability
UR - https://peer.asee.org/23927
DO - 10.18260/p.23927
ER -