Creation of a dynamic question database for pharmacokinetics

American Journal of Pharmaceutical Education,  Winter 2000  by Mehvar, Reza

• E-mail
• Print
• Link

Selected Presentations

Creation of a Dynamic Question Database for Pharmacokinetics'

PROLOGUE

Based on a model proposed by AACP Academic Affairs Committee(1), effective teaching normally involves the development of outcomes, pedagogical strategies to provide students with the opportunity to learn, and assessment tools to verify the achievement of the outcomes. Among these tasks, the development of validated and appropriate assessment tools to accurately test the achievement of specific learning outcomes is, perhaps, the most challenging and time-consuming task. In areas such as social sciences and humanities, most textbooks have an instructor's edition which includes an exam database of a large number of questions. However, most, if not all, of the available textbooks for the professional pharmacy education lack question databases. Therefore, pharmacy educators usually need to develop and validate their own assessment tools.

During recent years, pharmacy educators have used technology to facilitate student learning(2-5). Technology may also be used to facilitate the assessment process. For example, the National Association of Boards of Pharmacy currently uses a computer-adaptive method, instead of a paper format, for administration of NAPLEX. Additionally, some schools and colleges of pharmacy have started to use technology for course or curriculum assessment. For instance, Drake University College of Pharmacy (Des Moines, Iowa) and Texas Tech School of Pharmacy (Amarillo, Texas) have used web-based on-line testing programs, such as TestPilot (ClearLeaming, Naperville, Illinois), for program and classroom assessments. The present work describes the use of computers for generation of dynamic questions for pharmacokinetics. It is hoped that with collaboration among pharmacokinetics instructors at different schools or colleges of pharmacy, a validated exam database will be developed which may be used nationally.

DESCRIPTION OF THE TEACHING INNOVATION Student Audience

The innovation was introduced to second-year PharmD students as a part of a pharmacokinetics course (3 SCH) during the Fall of 1999. The course had an enrollment of 56 students. The Texas Tech School of Pharmacy requires that all students purchase a laptop computer and carry it with them to their classes. All classrooms are, therefore, equipped with internet connection ports for individual students. The class consisted of two 1.5 lecture hours (75 min) per week. The innovation was used for administration of quizzes in every class session during the last 10 min of the class time. The quizzes were related to the material covered during the same class period. Additionally, the innovation was used for administration of five progress exams, during the semester, and a final exam.

Process of Creation of Exam Database

The spreadsheet program Microsoft Excel was used for the creation of the dynamic exam database. The program has a built-in formula function that can be used for manipulation of the contents of a worksheet based on simple arithmetic and/or more sophisticated predefined functions. Some of the most important functions useful for building dynamic questions in pharmacokinetics are listed in Table I. The general structure of questions was based on the exams that the instructor had prepared and administered during 12 years of teaching basic pharmacokinetics to BS and PharmD students. Generally, two types of questions were developed, dealing with calculation and conceptual outcomes as explained below.

Calculation Questions. These questions were based on provision of numerical data from which students were asked to calculate kinetic parameters and/or dosage regimens. Two examples for this type of questions are demonstrated in Figure 1 in which the static text is in regular font and the dynamic text or data is presented in bold font. Question I (Figure 1) asks for estimation of clearance of a drug from the provided plasma concentration-- time data after a constant IV infusion of the drug. In this example, the rate of infusion is randomly selected for each student from a range of 2-120 mg/hr. Additionally, individualized plasma concentration-time data are generated from randomly selected kinetic parameters (within a reasonable range) with random errors (0-10 percent) added to each plasma concentration. To test the ability of students to recognize the correct units and convert different units to each other, the student is asked to provide his/her answer in a specific unit (mL/min in this case) which requires conversion from the provided data (L/hr). Question 2 (Figure 1) deals with the estimation of dosage regimens. In this question (Figure 1), total clearance, fraction excreted unchanged in urine, dosing rate in subjects with normal function, and the renal function in a subject with renal dysfunction are randomly selected from preset ranges. The student is then asked to determine the dosing rate in the subject with renal dysfunction.

Concept Questions. These questions were based on concept scenarios for which students were asked to select the best answer from multiple choices provided to them. Two examples of such questions are provided in Figure 2. In question one, the conversion of a drug to a metabolite takes place via a first- (or zero-) order process. Students are then asked to determine the rate of metabolism of drug (or production of the metabolite), generating four possible scenarios which are covered in the four given answers (Figure 2). In more complicated concept questions, both the provided information and the multiple choices are dynamic, increasing the number of possible scenarios to 8, 16, or 32. However, rarely, the number of multiple choices for each individual question exceeds five. In other types of concept questions, students may need to carry out some calculations before applying the results to analyzing a concept. Question 2 in Figure 2 is an example of the latter questions. In this example, students should first calculate the contribution of filtration clearance to the overall renal clearance before deciding on the mechanisms) of renal clearance of the drug.