Aronson, Benjamin D.

Eddy, Emily T.

Grundey, Jennifer K.

Hinson, Jessica L.

Long, Brittany L.

Sobota, Kristen F.

English

Ohio Northern University: DigitalCommons@ONU

Ohio Northern University DigitalCommons@ONU Pharmacy Faculty Scholarship Pharmacy 7-2019 Can We Find Pharmaceutical Calculations Low Performers Before Class Starts?: Identifying Problem Solving Deficiencies Benjamin D. Aronson Ohio Northern University, b-aronson.1@onu.edu Emily T. Eddy Ohio Northern University Jennifer K. Grundey Ohio Northern University, j-kline.3@onu.edu Brittany L. Long Ohio Northern University, b-brock@onu.edu Jessica L. Hinson Ohio Northern University, j-hinson@onu.edu See next page for additional authors Follow this and additional works at: https://digitalcommons.onu.edu/phar_faculty  Part of the Medical Education Commons, Other Pharmacy and Pharmaceutical Sciences Commons, and the Scholarship of Teaching and Learning Commons Recommended Citation Aronson BD, Eddy ET, Grundy J, Long BL, Hinson JL, Sobota KN. Can We Find Pharmaceutical Calculations Low Performers Before Class Starts?: Identifying Problem Solving Deficiencies. Accepted poster for the 2019 American Association of Colleges of Pharmacy Annual Meeting, Chicago, Illinois. This Poster is brought to you for free and open access by the Pharmacy at DigitalCommons@ONU. It has been accepted for inclusion in Pharmacy Faculty Scholarship by an authorized administrator of DigitalCommons@ONU. For more information, please contact digitalcommons@onu.edu. Authors Benjamin D. Aronson, Emily T. Eddy, Jennifer K. Grundey, Brittany L. Long, Jessica L. Hinson, and Kristen F. Sobota This poster is available at DigitalCommons@ONU: https://digitalcommons.onu.edu/phar_faculty/84 Benjamin D. Aronson, Emily T. Eddy, Jennifer Grundey, Brittany L. Long, Jessica L. Hinson, Kristen F. SobotaCan We Find Pharmaceutical Calculations Low Performers Before Class Starts?: Identifying Problem Solving DeficienciesBACKGROUNDOBJECTIVES● Recent increases in deficient pharmaceutical calculations grades have prompted internal reflection● Our experiences suggest some current students have difficulty applying problem solving skills to simple algebra-based word problems● Previous research suggests success in calculation courses is related to undergraduate GPA and PCAT scores,1,2 as well as time since and level of previous math exposure2● Research is lacking as to what factors are related to calculations success for direct-entry students● One older study was located that linked a basic math test to success in a calculations course1, but the assessment used was not published● To determine the relationship between an algebra-based word problem pretest and pharmaceutical calculations performance to identify those at risk of low performanceRESULTS IMPLICATIONSMETHODSSample● Student pharmacists from the College of Pharmacy at Ohio Northern University, a 0-6 direct-entry programProcedure● First year students were given an 18 item pretest during spring semester● The pretest contained algebraic word problems assessing percent, proportional reasoning, and unit analysis● Prior to the pretest, students were asked to provide informed consent ● During the fall semester of their second year, those students completed a course containing pharmaceutical calculations content, containing three 50-point summative assessments● Preadmission demographic characteristics were collected from student records ● This study was deemed exempt from full IRB reviewAnalysis● Pretest scores were compared with the calculations assessments● Linear regression was used to understand the relationship between pretest and calculation assessment scores after controlling for demographic and pre-admission factors● After controlling for age, gender, earlier academic performance, and standardized test scores, an algebra-based word problem pretest was associated with performance on later pharmaceutical calculations assessments● Although the pretest is associated with calculations performance, there is no perfect cutoff using the pretest alone (i.e., sacrificing sensitivity for specificity or vice versa depending on criteria)● The next step in this line of inquiry is to determine how to reduce this deficit through deliberate supplementary content and structured problem solving activities for those in needREFERENCES1. Latif DA. 2002. The relationship among pharmacy students’ basic math scores, traditional preadmission indicators, and performance in a pharmaceutical calculations course, J Pharm Teach, 10(1):17-29.1. Conn KM, Birnie C, McCaffrey D, & Brown J. 2018. The relationship between prior experiences in mathematic and pharmacy school success, Am J Pharm Educ, 82(4): Article 6257.Preadmission Demographics● Out of 123 students completing both courses, 118 provided consent for this study● The mean age of participants was 19.69● Female was listed as gender for 62.7%● The mean ACT score was 26.53● The mean high school GPA was 3.99Pretest Performance● Figure 1 shows the distribution of pretest scores● The mean score was 15/18 (83.3%), ranging from 5 (27.8%) to 18 (100%)Calculations Assessments● Figure 2 shows the distribution of letter grades obtained from all calculations assessments, of which the mean was 115.7 / 150 (77.1%)Correlations and Linear Regression● Table 1 shows correlations between select study variables, and Table 2 shows a linear regression model for calculations sum scoresCutoff Scores● Table 3 explores parameters of various pretest cutoff to predict passing pharmaceutical calculations assessments (i.e., >70%)Table 1. Correlations between select study variables1. 2.1. Calculations sum score 12. Pretest score .413* 13. Age .009 -.0094. Gender (1 = female) -.025 -.0085. High school GPA .214* .1346. ACT math sub-score .517* .387*7. ACT science sub-score .421* .278*8. ACT English sub-score .392* .246** p < .05; listwise n = 105Table 2. Linear regression for calculations sum scoreβ pConstant .582Pretest score .241 .008Age .038 .650Gender (1 = female) -.011 .901High school GPA .016 .864ACT math sub-score .302 .013ACT science sub-score .072 .559ACT English sub-score .135 .218* p < .05; listwise n = 105; model adjusted r2 = .295Table 3. Possible cutoff criteria and respective parametersSensitivity Specificity AccuracyLess than 100% on pretest 1.00 .170 .381Less than 90% on pretest .833 .420 .525Less than 80% on pretest .633 .727 .703Less than 70% on pretest .333 .898 .754Either #3 or #7 incorrect .733 .750 .746Figure 1. Points obtained on pretestFigure 2. Letter grade obtained from sumof calculation assessments

Can We Find Pharmaceutical Calculations Low Performers Before Class Starts?: Identifying Problem Solving Deficiencies

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Can We Find Pharmaceutical Calculations Low Performers Before Class Starts?: Identifying Problem Solving Deficiencies

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