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5 There are challenges to this assumption. Chiefly, it is possible for an unmotivated student with a high IQ to generate higher grades than a highly motivated student with a lower IQ; after all an extensive body of literature has found a significant positive correlation between IQ scores and measures of school learning (Jensen (1998) and Geary (2005)). Given that Frey and Detterman (2004) find that SAT scores are essentially a measure of IQ, the counterexample to (3) is unlikely to occur in our sample as Elon s business students have a relatively narrow SAT range. The other Time Spent variable in Carroll s model is the Opportunity to Learn. To capture this factor, we consider the number of courses each student was enrolled in during this assessment period. The majority of the students enrolled in either one or two courses during the summer session, but a small percentage enrolled in three courses. While obviously not a perfect measure for Opportunity to Learn, the more courses a student takes within a period of time, the less time and opportunity they have for learning a given subject. Thus, we assume: (Student x has more opportunity to learn than Student y) if, and only if, (x takes fewer courses in this summer session than y) (4) We now consider the other side of the Carroll Model; there are two factors driving Time Needed, as described in equation (5). Time Needed = g 2 (Aptitude (-), Quality of Instruction (-)) (5) If the student has a high aptitude for learning then the student can learn quickly, as indicated by the first negative sign in (5)). In Carroll s model, aptitude refers to general intelligence, which can be measured by an IQ test. As mentioned above, there is plenty of evidence that this kind of aptitude positively affects student learning, all else constant. Letting MSAT stand for the math sub-score of a student s SAT results, the following assumption is made (recall the Frey and Detterman (2004) findings): (Student x has more aptitude than Student y) if, and only if (the MSAT of x > the MSAT of x) (6) The SAT math score is used in place of the total SAT score as it is more highly correlated with the assignment scores and has historically been a stronger predictor of overall student performance in the business school at Elon. Finally, the last factor is Quality of Instruction. Carroll s model assumes that instruction can vary in effectiveness such that better instruction implies less time needed; hence, the second negative sign in (5). Variation in instruction can be due to the effectiveness of teachers. Some teachers may be so effective that, all else held constant, the teacher can reduce the amount of time that the student needs to attain the requisite learning outcome. Perhaps certain methods of teaching are particularly effective; or perhaps the effectiveness of instruction varies by the subject matter being taught. For example, Sweller et al. (1998) have argued that due to its high element interactivity, mathematics is more difficult to learn than a subject like history where ideas are often only weakly connected. Following Sweller at al. (1998), we assume: (Student x has better instruction than Student y) if (y is in STAT and x is in one of the other four courses: ACC, OPS, ECON, or BLaw) (7) In (7) we assume that teachers are equally effective across the courses, but that there is a greater instructional challenge in teaching the statistics course. Further, (7) also allows for the possibility that one delivery method is better than the other is. Two More Predictions When Carroll created his model of school learning, the theoretical construct of working memory (WM) was relatively unknown; however WM is now considered a central idea in cognitive science and WM 5

10 C. DiRienzo & G. Lilly BEA Vol. 6 No alternative seems to require extra work or discipline beyond that demonstrated by our students, especially when it comes to learning the more difficult concepts. Given our theoretical model and empirical findings, we disagree. Most of our students view online and face-to-courses as equally difficult. Based on Table 4, there appears to be no difference in student motivation levels between the two delivery methods and the students in our online courses do as well on complex assignments as our face-to-face students. It should be noted; however, that our study is not without its limitations. First, while the sample used in this analysis is not small, a larger sample size would provide results that are more robust. Further, as with all studies attempting to measure variables that are largely qualitative such as intrinsic student motivation, the proxies cannot be perfect measures and thus these results should be considered with this note of caution. Finally, we conclude with avenues for future research in this area. Specifically, Marissa Mayer, the CEO of Yahoo, recently promoted the value of workplace interaction and communications by removing previous company policies that had allowed employees to telecommute. Many Yahoo employees expressed distress in the policy change as many stated the significant benefits of telecommuting; especially their increased productivity. While this study found no significant difference in student learning outcomes from the two different delivery methods, an avenue for future research would be to explore possible differences in other factors such as student and faculty relationships and the effectiveness and quality of communication and sharing of ideas. Such research could shed light on some of the more intangible and qualitative differences of teaching, learning, and working in the same physical location compared to online or telecommunication. REFERENCES Allen, E., & Seaman, J. (2011) Going the Distance: Online Education in the United States, 2011, Retrieved April 30, 2013 from the Babson Survey Research Group Web site: Brown, B., & Liedholm, C. (2002) Can Web Courses Replace the Classroom in Principles of Microeconomics? The American Economic Review, 92(2), p Carroll, J. B. (1963) A Model of School Learning, Teachers College Record, 64, p Carroll, J. B. (1989) The Carroll Model: A 25-Year Retrospective and Prospective, Educational Researcher, 18, p Coates, D., Humphreys, B., Kane, J., & Vachris, M. (2004) No significant distance between face-to-face and online instruction: evidence from principles of economics, Economics of Education Review, 23, p Conway, A. R. A., Kane, M. J., & Engle, R. W. (2003). Working Memory Capacity and its Relation to General Intelligence, Trends in Cognitive Sciences, 7, p Ericsson, K., & Kintsch, W. (1995) Long-Term Working Memory, Psychological Review,102, p Flower, L., & Hayes, J. (1981) A Cognitive Process Theory of Writing, College Composition and Communication, 32, p Frey, M., & Detterman, D. (2004) Scholastic Assessment or g? The Relationship Between the Scholastic Assessment Test and General Cognitive Ability, Psychological Science, 15, p

11 Geary, D. (2005) The Origin of Mind, American Psychological Association: Washington D.C. Hair, J.. Jr., Anderson, R., Tatham, R., & Black, W. (1992) New York. Multivariate Data Analysis, Macmillian: Herman, R. (2012) Letter from the Editor in Chief: The MOOCs are Coming, The Journal of Effective Teaching, 12(2), p Hyman, P. (2012) In the Year of Disruptive Education, Communications of the ACM, 55(12), p Howell, S., Williams, P., & Lindsay, N.. (2003) Thirty-two Trends Affecting Distance Education: And Informed Foundation for Strategic Planning, Retrieved April 15, 2013 from the Online Journal of Distance Learning Administration Volume VI(III) Web site: Jensen, A. (1998) The g factor: The science of mental ability. Westport, CT: Praeger. Nelson, J., & Hayes, R. (1988) How the Writing Context Shapes College Students Strategies for Writing from Sources, Center for the Study of Writing Technical Report No. 16. Berkeley: University of California. ERIC Document ED Sweller, J., Van Merrienboer, J, & Paas, F. (1998) Cognitive Architecture and Instructional Design, Educational Psychology Review, 10, p BIOGRAPHY Dr. Cassandra E. DiRienzo is an Associate Professor of Economics and Associate Dean at Elon University. She can be contacted at: Elon University, 2075 Campus Box, Elon, NC, Phone: Dr. Gregory Lilly is an Associate Professor of Economics at Elon University. He can be contacted at: Elon University, 2075 Campus Box, Elon, NC, Phone:

B E A Business Education & Accreditation VOLUME 6 NUMBER 1 2014 CONTENTS Online Versus Face-to-face: Does Delivery Method Matter for Undergraduate Business School Learning? 1 Cassandra DiRienzo & Gregory

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