Improving Knowledge of Quantitative Methods

(NOTE: This research has been supported by a UMUC Faculty Research Grant)

Introduction

The University of Maryland University College (UMUC) student body is growing dramatically. As an open-access institution, UMUC accepts all qualified students. These students have a broad range of skills in terms of knowledge, experience, and practice. A small percentage of our students need additional help to enhance their ability to use with greater effectiveness the quantitative methods associated with finance, accounting, and statistical analysis. In the spring 2012 semester, we provided information, reference sources, and tools related to these methods to students in three existing classes. The purpose of the study was to test quantitatively the associated value and benefits of the interventions.

Background

In a 2008 study using data that was self-reported by students entering their first year of college, 15%22% required remedial math instruction (Planty, et al., 2008). Another study during the same time frame indicated that 65% of new students took a remedial math course, and approximately one-half passed the course (Fike Fike, 2008). Currently at UMUC, we are using anecdotal information and heuristics to estimate the number of students who require remedial work in quantitative concepts. It is conceivable that the percentages have not improved much. The latest report from the U.S. Department of Education supports that notion because it is still reporting 2008 data (Aud, et al., 2011).

To successfully complete the Technology Management program and/or the Project Management specialization requires a certain level of skill in quantitative concepts and methods for PMAN 639, PMAN 650, and TMAN 625, the three courses identified as major users of quantitative techniques covering accounting, finance, or statistical analysis. Studies have concluded that in addition to manipulation and interpretive skills, competence in the demonstration of quantitative skills also requires that students have the confidence to exercise their knowledge (Taylor Galligan, 2006). One of the solutions involves providing students with tools to perform a self-assessment of their own improvement. In a study conducted at the University of North Carolina, Asheville, the researcher postulated that math skills are more than an understanding of the concepts and highlighted the importance of student confidence (Bahls, 2009).

An important aspect to note is that the interventions utilized should be focused on building the students confidence in their ability to demonstrate quantitative skills. Pajares and Miller (1995) stated, Students confidence to solve mathematics problems was a more powerful predictor of their ability to solve those problems than was their confidence to perform math-related tasks or their confidence to earn As or Bs in math-related courses.

Approach

The team identified sources, tools, examples, tutorials, and other materials that could be posted in each of the classrooms to acquaint the students with the knowledge needed for successful completion of PMAN 639, PMAN 650, and TMAN 625. This included statistical packages, online tutorials, quizzes, and free finance and accounting software. Access to the intervention material was posted in each course section by the faculty member assigned to teach the class, directing the students to make maximum use of the material.

In the first week of the course, the students were surveyed, facilitating the creation of a benchmark of the students understanding of the required quantitative skills to be successful in the course and to document the students opinions of their own skill set and readiness. At the end of the semester, a questionnaire was sent electronically to each student, collecting information on their use of the material and from their perspectives, the benefits derived. The students also identified additional material and resources that would be useful in future classes.

Survey Sample

We invited all students enrolled in the three selected courses to participate in the questionnaire. This included 21 course sections with a total of 465 students. The result for Week 1 was an average participation response rate of 58 percent. At Week 12, the enrollment was somewhat decreased with 434 present and 192 responding for a response rate of 45 percent. This decrease in the response rate was expected since students enthusiasm and energy level tend to decline from the first day of class compared to the final week. Selected questions with finite answers and associated responses from Week 1 and Week 12 are presented below. Open-ended questions were also asked and are incorporated within the Conclusions section. The finite response data are summarized in a table by each course, by all finance and accounting courses, and then by all courses. All results were then analyzed and interpreted. The results for the Week 1 and Week 12 number and percentage of survey responses are summarized below in Table 1.

Table 1. Summary of respondents

Survey Results

Week 1 Responses

The Week 1 survey included the following questions for students to answer:

Question 1: What level of quantitative skills do you think are a necessary requirement for an adequate graduate education?

Question 2: Currently, at what level would you rate your own quantitative skills?

Table 2 below shows that for Question 1, clearly a majority of the students felt that they were well or very well prepared for the class in question. According to the responses to Question 2 in Table 2, on average, students believed that their quantitative skills were good. Students taking the finance and accounting courses (TMAN 625 and PMAN 650) felt that their quantitative skills were better than those taking a course that required the use of standard statistical tests (PMAN 639). TMAN 625 is part of the core degree program and is taken among the first courses. PMAN 650 is part of the specialization and is taken later in the students academic career. For most students, it is the second course in finance and accounting.

More than 59% of the students had already taken a course in accounting, finance, or statistics, mostly in undergraduate school, while 21% had taken such a course in graduate school. That left 18% with no preparation in finance, accounting, or statistics. Another question addressed the knowledge the students acquired and the grades they secured in taking a prior course in accounting, finance, or statistics. As many as 84% of the students who took such a course reported excellent or passing grades.

After reviewing the syllabus, 95% of the students felt they would pass the class based upon their current knowledge, and 60% indicated that they would not consider taking a preparatory course if offered.

Table 2. Week 1 survey results, in which students rated the level of quantitative skills needed and a self-assessment of their own quantitative skills

Week 12 Responses

At the completion of the course, the students concluded that they did very well (35%) or reasonably well (57%). Of all the resources made available to the students, the generic information (examples, definitions, cases) was the most utilized, followed by tutorials and software packages.

The final questionnaire included these two questions:

Question 4: If you utilized the resources and information posted in the course content section, did it improve your confidence in your ability to do the quantitative assignments?

Question 5: If you utilized the resources and information posted in the course content section, did you find it helpful in your completing the actual quantitative assignments?

According to the responses in Table 3, students reported a dramatic improvement in their confidence and ability to perform the quantitative assignments. It should be noted that improvement in confidence is probably as valuable as improvements in skills and performance. The students who took advantage of the posted material reported that it was useful. This result encourages us to look for more varied, extensive, and easier-to-use resources for the future.

Table 3. Week 12 survey results, in which students rated their confidence levels and the helpfulness of the interventions

Conclusions

From the students perspective, the identified interventions provided benefit and value to those who took advantage of the resources. Given this benefit, students wanted more examples from the professor and from the textbooks. In the open-ended survey questions, some students stated that an in-class mentor would be beneficial. The conclusion derived from this information and other open-ended comments is that even when students feel confident in their ability to perform the calculations, they often need help in determining when to use specific formulas. Although the math itself was an issue at times, knowing when to use the formulas and which formula to use for solving different problems was of greater concern. Tutorials supplemented by videos showing step-by-step usage of the formulas may be of great benefit. The videos should not be stand-alone but be put into context via incorporation into real-life scenarios.

One potential follow-up to this research could be to correlate the student results to actual performance outcomes or grades. This would provide an indication of what effect the expression of student confidence has on performance. In the absence of this correlation, it was concluded that given the low cost and scalability, this solution was practical and effective based upon student confidence and their self-assessments.

References

Aud S., Hussar W., Kena G., Bianco, K., Frohlich, L., Kemp, J., Tahan, K. (2011). The condition of education 2011 (NCES 2011-033). Washington, DC: U.S. Department of Education, National Center for Education Statistics.

Bahls, P., (2009). Math and metaphor: Using poetry to teach college mathematics. The WAC Journal. 75-90.

Fike, D.S., R. Fike. (2008). Predictors of first-year student retention in the community college. Community College Review 36(2), 68-88.

Taylor, J., Galligan, L., (2006). Mathematics for maths anxious tertiary students: Integrating the cognitive and affective domains using interactive multimedia. Literacy Numeracy Studies, 15(1), 23-43.

Pajares, F., Miller., M.D. (1995). Mathematics self-efficacy and mathematics performances: The need for specificity of assessment. Journal of Counseling Psychology, 42(2), 190-198

Planty, M., Hussar W., Snyder, T., Provasnik, S., Kena, G., Dinkes, R., KewalRamani, A., Kemp, J. (2008). The Condition of Education 2008 (NCES 2008-031). Washington, DC: U.S. Department of Education, National Center for Education Statistics.