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COMPARISON OF STUDENT PERFORMANCE UNDER TWO TEACHING METHODS: FACE TO FACE AND ONLINE.

INTRODUCTION

In college, students learn in both online and traditional classrooms; however, online classes are rapidly expanding. Online classes reach more students because of their convenience. Online works around the schedules of not only college students who live on campus, but also those untraditional students who have to arrange classes around a busy schedule. On-line courses have gained rapid popularity among students and teachers because of the unique characteristics and delivery mode. Researchers (e.g. McConnel & Lamphear , 1969; Park, & Kerr, 1993; Dutton, Dutton, & Perry, 2002) from various branches of higher education have conducted many studies comparing the advantages and disadvantages of both online and face-to-face teaching methods. However, most of the research papers published were open to dispute. Therefore, there is a need for more research emphasizing various aspects of both teaching modes.

Over the last ten years, the number of online courses offered by universities have increased and improved quite noticeably. Universities offer not only undergraduate degree programs but also graduate degree programs online. Online courses are carefully designed and competently delivered. However, online degrees are highly questioned by the academic community regarding quality, effectiveness and student performance.

Online and face-to-face teaching methods have their own advantages and disadvantages. With the more traditional teaching method of face-to-face, students are more likely to pay attention during class and have a consistent line of communication not only with the instructor but also with their peers. This means that students have personal connections with teachers, which makes it easier for them to learn and at the same time interact with other students thus reinforcing learning (Harrington, 1999.). On the other hand, online learning, gives students a lot of flexibility with demanding schedules. Other benefit that online learning offers is the wider audience that is involved due to the spread of western technology that can adjust to diverse students. Even though more people can be taught, it limits the one-on-one interactions that students can make with their instructors to further grasp the content.

In this study, student performance was compared regarding students' grades in online and face-to-face environment to examine the effectiveness of the two teaching methods. Student performance of Operations Management (MGMT 3106) class offered at Albany State University (ASU) was used as the study group for this research. A statistical analysis was done to identify any statistically significant difference between student performances for these two teaching modes.

LITERATURE REVIEW

A significant number of Authors have done research on face-to-face vs online Teaching Methods as it relates to student performance and came up with ambiguous results. McConnel and Lamphear (1969) found no significant difference in performance of students with and without class-room attendance. Contrary to that, Romer (1993) found that attendance prominently contributed to the academic performance of students in a macroeconomics course taught by him in the autumn of 1990. Prior to that, Schmidt (1983) in a macroeconomics course and Park and Kerr (1990) in a money and banking course found similar results. Later, Durden and Ellis (1995) also reported the same results for Principle of Economics course (both macro and micro).

Brown and Liedholm (2002) found that USA campus students (face-to-face teaching mode) tend to perform better compared to online students. Coates et al. (2004) reported that campus students tended to perform better compared with online students. In another study on 345 computer science students, Dutton et al. (2002) found online students perform significantly better compared to their peers who take the campus version of the same course.

Wilson and Allen (2014) analysis followed a detailed three-step approach regarding characteristics and choices of college students and the impacts they have on academic performance and course delivery preferences: descriptive information, analysis of variance, and multiple regression analysis. The notion that face-to-face student performance was superior to online students was not supported by the data analyzed. Moreover, withdrawal and failure rates did not differ from traditional to online course.

Research performed by Lundberg et al. (2008) was methodical and based on one question: If students attend an online-based course, and Interactive Computer Technology is used, was their performance better or worse than those students who chose a more traditional approach? They argued that only looking at test scores does not answer the question and there is no evidence that supports online students performing better than their face-to-face peers.

Aragon et al. (2002) study showed that online learning could be as effective as face-to-face learning in many respects, even though students have different learning style preferences. The implications of the findings of Harrington (1999) showed that students could learn successfully in a programmed instruction/distance learning course, especially those who have done well academically in the past. Second, students who had not done well academically in the past did much worse in the programmed instruction/distance-learning course than in the regular classroom course. Unfortunately, the study could not determine whether this effect is due to the distance-learning component, the programmed instruction component, or the combination of the two used for the course.

Stack (2015) work found that the academic performance of online students was the same as that of traditional students. In his study he used data on 64 students enrolled in criminology classes at a Carnegie research extensive university and controlled the selection of class (either online or face-to-face) and the proctoring of exams. Carey (2001) discovered that in a management information systems course, the online students were gaining knowledge comparable to the face-to-face students and that the online students were as satisfied on most dimensions as the face-to-face students. In addition, in that study Carey used Kolb's Learning Style instrument to decide if one learning style lends itself better to online learning than another style.

Navarro (2000) discussed the matter of teacher performance in online versus face-to-face teaching modes based on interviews, formal discussions and questionnaires considering more than 100 teachers and instructors. Navarro's study found that a large majority of the teachers alleged that they performed similarly or better in the on line classes. In addition, he also found that more motivated instructors tend to use Interactive Computer Technologies (ICT) more than less motivated instructors.

Hoskins and Hooff (2005) studied the effects of the dialogue method through an online environment on student performance using 110 undergraduate psychological students. They discovered that the dialogue method has a positive effect on student performance. The study performed by Neuhauser (2002) using 62 management students found that there was no significant difference in the mean value of test scores for online versus face-to-face students.

As the above review reveals, there are pros and cons for each teaching method. In this study our goal is to identify the best mode of teaching for the MGMT 3106 course (which involves analytic materials) at ASU in terms of student performance.

METHODOLOGY

The objective of this research is to compare the performance in online and face-to-face classes regarding student grade. We compared student performance in eight MGMT 3106 (Operations Management) classes, which includes four online and four face-to-face classes taught by the same instructor from the college of business at the Albany State University. MGMT 3106 class is one of the core courses in the program and it is offered online and face-to-face. Operations management course material consists of highly analytical skills including a lot of math. In addition, students need to have software skills in Lpsolver and Excel to perform well in this class.

In this study, we considered the students' assignments, quizzes, midterm and final test grades from fall 2015 to fall 2016. Most of the quizzes consisted of essay questions that can be answered by reading the book and searching on the internet. However, assignments consisted of quantitative problems and few essay questions while the midterm and final tests consisted only of quantitative problems.

We conducted a statistical analysis with hypothesis testing to find out whether any significant difference existed between the two modes of teaching in relation to student performance. Thus, this research considers the following two hypotheses:

H0: There is no significant difference in student performance between online and face-to-face classes.

HA: Online class differs from face-to-face class in student performance

We used hypothesis testing to study whether there is a significant difference between student grades in these two teaching methods. First, we collected numerical grade of each student for quizzes, assignments, and exams. We studied data on 95 face-to-face students and 64 online students. We believe that the size of this sample is enough to make inference about the population. After collecting the data, we checked for normality of the data by applying parametric tests to compare student performance of the two teaching methods. We graphed histograms and Q-Q plots for each quiz, assignment, and test. Figure 1 below shows the histogram and Q-Q plot for Quiz 1.

According to the graphs, we can observe that the distribution of data is not normal. We found a similar result for all grades such as quizzes, assignments, and test (e.g., see Figure 2 (a) and Figure 2 (b)).

Therefore, it is not proper to apply parametric tests. Thus, we applied a non-parametric test for the comparison. We used the Mann-Whitney U test or Wilcoxon rank-sum test (Hart, 2001) which is a non-parametric alternative test to the independent sample t-test. The test is used to compare two sample means that come from the same population, and used to test whether two sample means are equal or not. In addition, the Mann-Whitney U test is used when the data is ordinal or when the assumptions of the t-test are not satisfied (http://www.statisticssolutions.com/mann-whitney-u-test/). The following assumptions need to be satisfied to apply the Mann-Whitney U test or Wilcoxon rank-sum test. (http://www.stat.purdue.edu/~tqin/system101/method/copy%20of%20method/method_wilcoxon_rank_sum_sas.htm)

* Assumption 1: Comparing two samples.

* Assumption 2: The two groups of data are independent.

* Assumption 3: The type of variable could be continuous or ordinal.

* Assumption 4: The data might not be normally distributed.

We observed that the dataset satisfied the assumptions. Therefore, using the Mann-Whitney test is applicable when comparing student performance for teaching methods. We used the SAS 9.4 software (a software that can manage and retrieve data from a variety of sources and perform statistical analysis) for our calculations and graphing. PROC NPAR1WAY, which performs tests for location and scale differences based on the Wilcoxon procedure, was used to calculate test statistics and p values. The results are illustrated in the next section.

RESULTS AND DISCUSSION

As we discussed earlier, we tested the following hypothesis.

Null Hypothesis HO: There is no significant difference in student performance between online and face-to-face classes. [[micro].sub.1 = [[micro].sub.2]

Alternative Hypothesis HA:

[[micro].sub.1 = [[micro].sub.2] < 0 (Left-tailed) Online class students perform better than face-to-face students do.

[[micro].sub.1 = [[micro].sub.2] < 0 (Right-tailed) Face-to-face students perform better than online students do.

Where [[micro].sub.1] = mean of face-to-face students' grade

[[micro].sub.12] = mean of online students' grade

According to the Wilcoxon rank-sum test, either it will be a right-tailed test or a left-tailed based on the Wilcoxon test Statistic (S). If the Wilcoxon test Statistic value is greater than the Expected value under H0, then it is a left-tailed test, and if it is less than the Expected value under H0, it is a right-tailed test. Table 1 below summarizes the Wilcoxon rank-sum test results for quizzes. The first row of the table shows the results for quiz no 1.

Since Expected value under H0 is less than the Wilcoxon test Statistic (S), it is the left-tailed test. The p- value is less than 0.05. So we reject the null hypothesis H0. Therefore, we can conclude that online students perform better than face-to-face students for quiz 1. Based on Table 1 comparison, we observe that in most of the cases online students perform better than face-to-face students for quizzes. To study the reason for this observation, we examined the format of the quizzes. We perceived the quizzes that the online students perform better consist of essay questions only.

Table 2 summarizes the Mann-Whitney test (Wilcoxon rank-sum test) results for Assignments. Based on Table 2 comparisons, we observe that in most of the cases face-to-face students perform better than online students for assignments. Also, we observed that the assignments in which face-to-face students perform better consist of questions with analytical skills. We found similar results for midterm and final tests.

Table 3 compares the performance of mid and final tests. In addition, it compares the overall grade for each student.

Based on our above comparisons we can observe that for questions with essay assignments, online students performed better compared to face-to-face students. However, for assignments or tests consisting of analytical questions, face-to-face students performed better. This observation makes sense since online students spend more time in self-learning the class material and reading the textbook. Therefore, they can answer essay questions much better than face-to-face students. However, face-to-face students may not spend more time on studying the textbook, since they attend the class face to face. Therefore, the performance of face-to-face students can be lower in comparison to online students for essay questions.

In contrast to essay questions, face-to-face students perform much better when compared with online students with regard to analytical questions. This observation also makes sense, because face-to-face students interact with the instructor daily and they have access to their peers who have been learning the same curriculum. Also, before a test, teachers usually give reviews or go over relevant material that will be on the exam, which in return give the face-to-face student an advantage. This opinion is confirmed by the comparison of Test results. As we can see from table 3 face-to-face students performed better compared to online students for all the tests since most of the questions in the tests consist of questions that required quantitative skills.

RECOMMENDATION FOR FUTURE RESEARCH

This research work will be extended to compare the performance (grades) of students considering the non-math courses such as organization behavior and math courses such as college Algebra and calculus. Our goal is to ascertain which teaching mode is suitable for courses involving math and non-math. Conduct more statistical analysis to determine if our results are consistent.

CONCLUSION

In this study, we analyzed the performance of two types of students who followed the same course under two different teaching methods (i.e. online and face-to-face) with the view to determine which one is better. We used management of Operations Science (MGMT 3106) class at Albany State University as our study group and compared the grades obtained by the students for quizzes, assignments, and tests using the Mann-Whitney test (Wilcoxon rank-sum test) method. We can summarize the results of the analysis as follows:

1. Online students performed better with respect to essay type questions

2. Face-to-face students performed better with respect to questions which required analytical skills

3. In general face to face students performed better

REFERENCES

Aragon, S.R., Johnson, S.D., & Shaik, N. (2002). The influence of learning style preferences on student success in online versus face-to-face environments. The American Journal of Distance Education, 16 (4), 227-244.

Brown, B., & Liedholm, C. (2002). Can Web Courses Replace the Classroom in Principles of Microeconomics? The American Economic Review, 92(2), 444-448.

Carey, J. M. (2001). Effective student outcomes: A comparison of online and face-to-face delivery modes. DEOSNEWS, 11, 9.

Coates, D., Humphreys, B. R., Kane, J., & Vachris, M. A. (2004). "No significant distance" between face-to-face and online instruction: Evidence from principles of economics. Economics of Education Review,, 23(5), 533-546.

Durden, G., & Ellis, L. (1995). The Effects of Attendance on Student Learning in Principles of Economics. The American Economic Review, 85(2), 343-346.

Dutton, J., Dutton, M., & Perry, J. (2002). How do online students differ from lecture students. Journal of asynchronous learning networks,, 6(1), 1-20.

Mcconnel, C. R., & lamphear, C. (1969). Teaching Principles of Economics without Lectures. Journal of Economic Education, 1 (4), 20-32.

Harrington, D. (1999). Teaching statistics: A comparison of traditional classroom and programmed instruction/distance learning approaches. Journal of social work education, 35(3), 343-352.

Hart, A. (2001). Mann-Whitney test is not just a test of medians: differences in spread can be important. BMJ: British Medical Journal, 323(7309), 391-393

Hoskins, S. L., & Van Hooff, J. C. (2005). Motivation and ability: which students use online learning and what influence does it have on their achievement? British Journal of Educational Technology, 36, 177-192.

Lundberg, J., Castillo-Merino, D., & Dahmani, M. (2008). Do online students perform better than face-to-face students? Reflections and a short review of some empirical findings. RUSC. Universities and Knowledge Society Journal, 5(1), 35-44.

Navarro, p. (2000). Economics in the Cyber Classroom. Journal of Economic Perspectives 14(2), 119-132.

Neuhauser, C. (2002). Learning Style and Effectiveness of Online and Face-to-face Instruction. American Journal of Distance Education, 16 (2), 99-113.

Park, K. H., & kerr, P. M. (1990). Determinants of Academic Performance: A Multinomial Logit Approach. Journal of Economic Education, 21(2), 101-111.

Romer, D. (Summer 1993). Do Students Go to Class? Should They? Journal of Economic Perspectives, 7 (3), 167-175.

Schmidt, R. M. (May 1983). Who Maximizes What? A Study in Student Time Allocation. American Economic Review Papers and Proceedings, 23-28.

Sosin, K., Blecha, B., Agarwal, R., Bartlett, R., & Daniel, J. (2004). Efficiency in the Use of Technology in Economic Education: Some Preliminary Results. The American Economic Review, 94(2), 253-258.

Stack, S. Dr. (2015). Learning Outcomes in an online vs traditional course. International Journal for the Scholarship of Teaching and Learning, 9, 1.

Wilson, D., & Allen, D. (2014). Success rates of online versus traditional college students. Research in Higher Education, 14, 1-8

Damitha Bandara Albany State University

Danush Kanchana Wijekularathna Troy University

About the Authors:

Damitha Bandara holds a position as an Assistant Professor at the college of business of Albany State University, GA, USA. He is the Director of the MBA program in the college and he received a B.S. degree in mathematics from the University of Peradeniya in Sri Lanka, M.S and Ph. D. in Industrial Engineering from Clemson University, SC, USA. His research interests and areas of expertise are: Logistic and Supply Chain Management, Optimally allocating resources in Emergency Medical Service systems, Simulation, Dynamic Programming, Queuing theory, Mathematical programming, Mathematical modeling, Application of lean Six Sigma.

Danush Kanchana Wijekularathna is an Assistant professor at the math department of Troy University. He received a B.S. degree in mathematics from the University of Peradeniya in Sri Lanka, an M.S. in Statistics from the Sam Houston State University, and a Ph.D. from Texas Tech University. His research interests include Nonparametric Statistical Inference, Biostatistics, Experimental Design, Linear Models, Generalized Linear Models, Repeated Measures, Mixed Effects Models and any statistical applications. He currently teaches Statistics and Mathematics courses.
Table 1

Comparison of Quizzes

Quiz  Expected  Wilcoxon       Alternative   P-value  Conclusion
No    Under H0  Statistics(S)  Hypothesis

1     5120.00   5967.50        Left tailed   <.0001   Reject H0
                               better
2     4218.50   4503.00        Left tailed   0.0223   Reject H0
                               better
3     4526.00   4727.50        Left tailed   0.1522   Do not reject H0
4     3168.00   3493.50        Left tailed   0.0371   Reject H0
                               better
5     3008.00   2975.50        Right tailed  0.4124   Do not reject H0
6     2984.50   2782.00        Right tailed  0.0333   Reject H0
                               better
7     2904.00   2976.00        Left tailed   0.3367   Do not reject H0

Quiz  Decision
No

1     Online is

2     Online is

3     No difference
4     Online is

5     No difference
6     F to F is

7     No difference

Table 2

Comparison of Assignments

Assig  Expected  Wilcoxon       Alternative   P-value  Conclusion
nment  Under H0  Statistics(S)  Hypothesis
No

1      4544.50   4916.50        Left tailed   0.0366   Reject H0
2      4857.50   5095.50        Left tailed   0.1087   Do not Reject H0
3      2684.00   2414.50        Right tailed  0.0496   Reject H0
4      3075.00   2747.00        Right tailed  0.0454   Reject H0
5      3471.50   3231.50        Right tailed  0.0231   Reject H0
6      2310.00   2280.50        Right tailed  0.4267   Do not Reject H0

Assig  Decision
nment
No

1      Online is better
2      No difference
3      F to F is better
4      F to F is better
5      F to F is better
6      No difference

Table 3
Comparisons of Tests

Test     Expected  Wilcoxon       Alternative   P-value  Conclusion
         Under H0  Statistics(S)  Hypothesis

Mid      7307.50   6119.50        Right tailed  0.0004   Reject H0
Final    2200.00   1888.00        Right tailed  0.0146   Reject H0
Overall  3000.00   2580.50        Right tailed  0.0125   Reject H0

Test       Decision

Mid        F to F is better
Final      F to F is better
Overall    F to F is better
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Author:Bandara, Damitha; Wijekularathna, Danush Kanchana
Publication:International Journal of Education Research (IJER)
Article Type:Report
Date:Sep 22, 2017
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