An exploratory study of the effect of technology in quantitative business courses.

Bahhouth, Victor ; Maysami, Rami ; Bahhouth, Jocelyne 等

INTRODUCTION

The advent of the Internet and the widespread adoption of advanced technology are among the major forces that helped the evolution of online education and are changing the whole learning process. The present challenge is to identify the right tools and methods that should be used in the learning process.

Nowadays, as Internet-based tools are taking more critical role at the center the learning process not only in online classes but also in traditional classes, it is important to identify the right technique to integrate in the right place. Faculty members need to identify new methods that might not be basically part of the existing traditional classes, such as the use of new technological innovations, teaching pedagogy, as well as new ways of interaction between members of the class. Faculty members vary at identifying the proper tools and the approach to adopt when deciding on which ones to follow to satisfy students' needs (Fang, 2007).

Many studies have highlighted the significant impact of e-learning on shaping the future education not only of online schools, but also of traditional ones. In traditional classrooms, the instructor controls the material and pace of learning. Cuban (1993) argued that instruction in traditional classes is directed to the whole class, the pace of learning is controlled by the teacher, and the curriculum is guided by the textbook. However, with more e-tools that are used in online and face-to-face class formats, students are becoming more the center of the learning process and taking more a proactive role. In defining the frame of the E-learning systems, Papachristos et. al. (2010) argued that the students must be at the center of their own learning and that these systems must be designed to facilitate their learning process.

This study assesses the role of e-learning tools in quantitative business classes. It tests the significance of their effectiveness. The study includes the following sections: 1- a theoretical background section that highlights the most relevant research in the field 2- a testing hypotheses section that defines research problems; 3- a research methodology section which describes the research tools, data collection, data analysis, limitation and implication of the study; and finally 5-a conclusions and recommendations section that summarizes the research output.

THEORETICAL BACKGROUND

Technology is gradually changing the main features of education in the 21 century. It is making it more digital and more accessible to all comers. With e-learning, adults of all ages, nationalities, wealth and races have more learning opportunities due to flexibility in time and space (Vrasidas & Glass, 2002; Selvanathan & Cybinski, 2005). Students are satisfied with online courses when lecture notes, bulletin boards, online assessment and other tools are available (Lange et al., 2003). Although they may prefer traditional classes, students seek online education for their practicality (O'Malley, 1999; Illeris, 2004; Iryna & Concha, 2007). Therefore, to ensure effectiveness, online courses should be well structured to encourage students to engage and interact to create a motivating environment (Lange, Swardy, & Mavondo, 2003).

With the advancement of the massive open online courses (MOOC), and more companies, both for profit and non-profit organizations, are working very closely with big universities to launch their courses on the Net (Griffin, 2006; Kalogiannakis et al, 2009). Udacity is paying professors from such schools as Rutgers and the University of Virginia to give open courses on the Net. Coursera, headed by two professors from Stanford's computer science department, Daphne Koller and Andrew Ng, is working with Stanford, Princeton, Penn, and the University of Michigan. More than 200 classes in fields ranging from statistics to sociology are being offered. MIT and Harvard, on the other hand, have joined forced and formed edX. What distinguishes edX from others is incorporating virtual laboratories where students can carry out simulated experiments. MOOC software will provide students with individual paths to follow. Both online and traditional seated classes can adopt the MOOC. Students taking seated classes will be more engaged and teaching will be more effective when the classroom is flipped and students listen to lectures and do their reading outside the classroom, while they engage in meaningful learning experiences in the classroom (Carr, 2012).

Although technology can be used exclusively in online classes, it can be used to various degrees in flipped classrooms, hybrid classes, web-assisted classes or simply any seated classes (Donavant, 2009; Block et al, 2008; Cantoni, Cellario, & Porta, 2004). Seated classes that incorporate an online component gain success when the best qualities of both modes of delivery are combined: integrating technology into the classroom, while maintaining the effective role of the instructors (Garnham & Kaletta, 2002). Singh (2003) also advocated choosing from different delivery methods in order to achieve best results.

Viewing videos outside class time does not make a class successful; it is the instructors' efforts in the class that make the course effective (Tucker, 2012). Training instructors to integrate technology effectively in order to appeal to different students' learning styles and ensure student centered learning is recommended (Singh, 2013; McCray, 2000). Although technology helps students develop their cognitive skills, improve their grasp of content knowledge and build their own knowledge (Kozma, 2005; Webb & Cox, 2004), it is effective when the instruction is well-designed and well managed (Hasselbring & Glaser, 2000). Success of seated courses with an online component is dependent on the content of the course material, the interaction between both the learners and the instructor and between the learners themselves, and the construction of the learners' space and direction in their learning, as Kerres and De Witte (2003) presented in their 3C model.

Many studies showed the superiority in performance of hybrid classes to the online classes per se. Schweizer, Paechter, and Weidenmann (2003) compared students' performance in hybrid classes to their performance in online classes. They found out that students did better in hybrid courses. Sauers and Walker (2004) compared students' performance in eight business communication classes. Those who received instruction through hybrid classes did much better than those who received instruction through the traditional seated classes. The online component of the course allowed students a more effective engagement and led to an improvement in their writing skills. This study showed how adding the online component to the seated portion helped tackle a wider variety of learning styles. Nemanich, Banks, and Vera, (2009) discussed successful learning resulting from a complex interrelationship between students, instructor, content and context in classes that comprised of both the traditional and online components since students benefitted from the instructors' presence and expertise, the interaction with other students, and the course relevance. Eom, Wen and Ashille (2006) found that the instructor's expertise, interaction and feedback along with students' self-motivation, learning styles, and course design led to successful learning.

RESEARCH HYPOTHESIS

The following is the alternative hypothesis: Does the class format affect students' performance?

METHODOLOGY

Sample and Data Collection

The sample data was collected over a two-year period (i.e. Fall 2011, Spring 2012, Summer 2012, Fall 2012, and Spring 2013. It included the grades of 629 students taken from the three classes that were taught in the four different formats.

Measurement of Variables

The variables in the study are the grades of the students taken from three business classes. These classes are business statistics I, business statistics II, and business finance. The experiment was controlled in terms of instructor, material, grading, syllabus, policies, and textbook i.e. each of the three classes was taught by the same professor, using the same text book, and same syllabus. The definition of online class with assisted technologies include; (1) 24/7e-tutor that provides help to students to solve exercises; (2) e-tool that highlights the areas of weakness, suggests remedies, and provides unlimited practice exercises in the area of weakness; (3) a set of videos and youtubes that explain the course material; (4) discussion activities to promote interaction between students and the professor; (5) and traditional learning tools such as e-card, power point presentations, and spread sheet problems. The definition of online class with no assisted technological tools includes power point presentations and discussion activities only.

Data collected included information about each class and was taken from the four different class formats i.e. face-to-face with assisted technologies (FA), face-to-face with no assisted technologies (FN), online with assisted technologies (OA), and online with no assisted technologies (ON).

Research Instrument

The research instrument is made of two parts. In the first part, students' performance is assessed under four different class formats, which are (1) online with assisted technologies (OA); (2) online with no assisted technologies (ON); (3) face-to-face with assisted technologies (FA); and (4) face-to-face no assisted technologies (FN). One-Way ANOVA (Analysis of Variance) is used to test if students perform equally under the four formats.

ANOVA

Source of       df    SS       Mean Square       [F.sub.stat]
Variation

Explained       C-1   SSA   MSA = SSA / (C-1)   [F.sub.stat] =
                                                MSA / MSW

Unexplained     N-C   SSW   MSW = SSW / (N-C)

Total           N-1

In the second part, multiple comparisons of two populations among the four different class formats is applied to test if there is a significant difference between the mean of the groups; testing is done using Tukey-Kramer procedure. Critical range (CR) is computed using the following equation:

Critical Range = [Q.sub.[alpha]] [square root of MSW/2([1/[n.sub.j]] + [1.[n.sub.j]]]

The absolute difference between the two means is compared to the critical range. The two means are not equal if the absolute difference (ABS) between the two means is greater than the critical range (CR). Alpha of 5% is used in this study (Hair et. Al., 2012).

Data Analysis

The first step of the analysis is done by performing the one-way ANOVA to each of the three classes to test the null hypothesis--the class format does not affect students' performance; and in the second step, multiple comparisons were performed to identify the class format with highest grades. A 5% level of significance ([alpha]) is used. Table 1 presents summary characteristics of sample data.

Table 1 presents summary descriptive measures of groups. Sample data is made of 629 students subdivided into (1) 156 business finance students, average 75; (2) 200 business statistics II student, average 76; and (3) 273 business statistics I students, average 64. It shows students performed equally in business finance and business statistics classes and underperformed in business statistics I classes. In terms of technology assisted / no technology assisted classes, the sample included 1- 217 online classes with technology assisted (OA) students, average of 74; 295 online classes with no assisted technology (ON) students, average of 60; 3- 181 face-to-face classes with assisted technology (FA), average of 83; and 4- 136 face-to-face classes with no assisted technology (FN) students, average of 69.

Table 2 presents the summary output of business finance--ANOVA. F statistic value is 19.36, which is greater than the F critical value of 2.66 (p-value 0.000); results show significant evidence to reject the null hypothesis that students performed equally under the four class formats. The model explained 27.65% (16,222.43/58,667.85) of the variations.

Table 3 reflects the multiple comparisons between the four groups. It shows that (1) there is no significant difference between online with assisted technologies class formats (OA) and face-to-face with no assisted technologies class formats (FN); (2) there is significant evidence between all other class formats; and (3) students performed best in face-to-face classes with technology assisted formats (FA).

Table 4 reflects the one-way output of business statistics II class. F statistic value is 7.42, which is greater than the F critical value of 2.65 (p-value 0.000); results show significant evidence to reject the null hypothesis that students performed equally under the four class formats. The model explained 10.20% (9,929.37 / 87,378.71) of the variations.

Table 5 reflects the multiple comparisons between the four groups; it shows that (1) there is no significant difference between online with assisted technologies class formats (OA) and face-to-face with no assisted technologies class formats (FN); (2) face-to-face with assisted technologies class format (FA) and face-to-face with no assisted technologies (FN); (3) and there is a significant difference between face-to face with assisted technologies class format (FA) and online with assisted technologies (OA).

Table 6 reflects the one-way output of business statistics I class. F statistic value is 6.31, which is greater than the F critical value of 2.64 (p-value 0.000); results show significant evidence to reject the null hypothesis that students performed equally under the four class formats. The model explained 6.58% (10,994.76 / 167,109.71) of the variations.

Table 07 reflects the multiple comparisons between the four groups; it shows that (1) there is no significant difference between online with technology assisted class formats (OA) and face-to-face with technology assisted class formats (FA); and (2) between online with assisted technologies class formats (OA) and face-to-face with no assisted technologies class formats (FN). A key finding, students performed best in face-to-face with assisted technologies class format (FA) and online with assisted technologies class formats (OA) as well.

Table 8 reflects the one-way output of all classes when put together. F statistic value is 16.11, which is greater than the F critical value of 2.62 (p-value 0.000); results show significant evidence to reject the null hypothesis that students performed equally under the four class formats. The model explained 7.18% (24,510.28 / 341,458.74) of the variations.

Table 9 reflects the multiple comparisons between the four groups; it shows that (1) there is no significant difference between online with assisted technologies class formats (OA) and face-to-face with no assisted technologies class formats (FN); and it is a key to highlight that (2) there is a significant difference between face-to-face with assisted technologies (FA) and online with assisted technologies (OA). Students performed best in face-to-face classes with technology assisted formats

IMPLICATIONS

The study provided significant evidence to support the research problem. Students' performance is not the same under the four different class formats. Students performed better in most of the classes with assisted technologies than with no assisted technology classes. In business finance, the absolute mean difference of online classes with assisted technology is 22 points (significant) higher than that of no assisted technology; face-to face classes with assisted technology is 13 (significant) points higher than that of no assisted technology.

In business statistics II, the absolute mean difference of online classes with assisted technology is 09 points (significant) higher than that of no assisted technology; face-to face classes with assisted technology is 02 points (not significant) higher than that of no assisted technology. In business statistics I, the absolute mean difference of online classes with assisted technology is 09 points (significant) higher than that of no assisted technology; face-to face classes with assisted technology is 08 points (significant) higher than that of no assisted technology.

As for the highest average among the four class formats, results show that in business finance, it is face-to-face class with assisted technology with absolute mean average of 09 points (significant) higher than that of online with assisted technology; business statistics, it is face-toface class with assisted technology with absolute mean average of 02 points (not significant) higher than that of face-to-face with no assisted technology; however, both had significant absolute mean of 11 points and 09 points consecutively higher than that of online class with assisted technology; business statistics I, it is face-to-face class with assisted technology with absolute mean average of 09 points (not significant) higher than that of online with assisted technology; however, face-to-face class absolute mean average is significantly higher than the other two class formats.

LIMITATIONS

Limitations of the study are set into the following points. The study

is based a primary type data taken from three business classes. The utilized sample size is small compared to the target population. The external validity of the test was not addressed. No covariates were included in the analysis such as GPA. Students were not randomly assigned to the classes.

CONCLUSIONS AND RECOMMENDATIONS

The output of the study provides significant evidence that in both face-to-face and online classes with assisted technologies students performed better than those of face-to-face and online classes with no assisted technologies. In addition, it shows that within technology assisted classes, face-to-face class format outperformed that of the online class in most of the courses.

Results are robust. Even though there is significant evidence that technology is playing more and more a critical role in shaping the education system, students' performed best in faceto-face format. It seems that the daily contact and interaction between and among students and with their professors play significant role in the learning outcomes despite technology's integral part of the learning process. Study provides significant evidence that the most effective future learning process will be that ones that combines both the traditional approach with the use of etechnologies. It is recommended to conduct future studies to include samples taken from different universities, other courses from school of business and other schools, and time frames to address the external validity of the test; in addition, it is necessary to keep assessing the elearning efficiencies with the introduction of the new technological innovations of e-learning tools. As for designing e-learning environments and tools, the approach should be one that provides appreciation for multiple perspectives, allows embedded learning in relevant contexts and encourages the use of multiple representation modes. Furthermore, the e-learning models should be continuously monitored and improved based on students' feedback. Learning process is the most successful when it cultivates an atmosphere of cooperative learning among students and teachers, utilizes dynamic, generative learning activities that promote high level thinking processes (i.e. analysis, synthesis, problem solving, experimentation and creativity among many others), and assesses student progress in learning through realistic tasks and performances.

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Victor Bahhouth

Rami Maysami

University of North Carolina--Pembroke

Jocelyne Bahhouth

Bladen Community College--Dublin

Bahhouth Victor is a Professor of Finance at the School of Business Administration at the University of North Carolina at Pembroke. He received his Doctorate of Business Administration in Finance from Newcastle Business School, University of Newcastle Upon Tyne--United Kingdom. His research interests are in the areas of contemporary issues related to international businesses, technology, and stock markets. He authored and co-authored research papers that have been published in refereed journals and in the proceedings of national and international academic conferences. Dr. Bahhouth received research awards for a number of papers presented at scholarly conferences and chaired sessions, served as a discussant. In addition, Dr. Bahhouth is a Certified Public Accountant (CPA) and Certified Management Accountant (CMA).

Bahhouth Jocelyne is the Dean of the School of Humanities and Social Sciences at Bladen Community College, NC where she teaches English. She is also an adjunct Associate Professor at University of Maryland University College, MD. She holds a Ph.D. in ESL (English as a Second Language), an M.A. in Education, a B.A. in English Literature and a Teaching Diploma in TEFL (Teaching English as a Foreign Language). Her publications appeared in refereed journals as well as in the proceedings of international conferences. She has recently published a book entitled Spoken Lebanese, which teaches the Lebanese dialect. This book which uses English as the language of instruction is being translated into four languages.

Maysami, Ramin Cooper is Professor of Economics and Finance , and Dean of School of Business at the University of North Carolina at Pembroke. His areas of research are regulation of financial institutions, interest-free banking and finance, entrepreneurship, and most recently online learning. His publications have appeared in academically refereed journal as well as professional/practitioners journals. Dr. Maysami's regular teaching schedule includes courses in Personal Finance, Entrepreneurship and Entrepreneurship Finance, Financial Institutions, and Microeconomics.

Table 1

Summary Descriptive Measures of Groups

Class     Bus Finance        Bus Stat II

Groups    Count    Average   Count   Average

OA           46        80      78        76
FA           41        89      29        87
ON           46        58      63        67
FN           23        76      30        85
Overall     156        75     200        76

Class     Bus Stat I         Overall

Groups    Count    Average   Count   Average

OA           93        65     217        74
FA           25        75      95        83
ON           72        56     181        60
FN           83        67     136        69
Overall     273        64     629        70

Table 2

Business Finance - One-way ANOVA

ANOVA - Business Finance

Source of     SS          df    MS
Variation

Explained     16,222.43    3    5,407.48
Unexplained   42,445.42   152    279.25
Total         58,667.85   155

Source of     F       P-value   F crit
Variation

Explained     19.36    0.00      2.66
Unexplained
Total

Table 3

Business Finance - Multiple Comparisons

Groups      Group Grades       ABS        CR     Results
                           (mean diff)

OA - FA       80 - 89          09        8.40    Significant
OA - ON       80 - 58          22        8.16    Significant
OA- FN        80 - 76          04        9.99    Not Significant
FA- ON        89 - 58          31        8.40    Significant
FA - FN       89 - 76          13        10.19   Significant
ON - FN       58 - 76          18        9.99    Significant

Table 04

Business StatisticsII--One-way ANOVA

Source of Variation       SS                MS

Explained              9,929.37     3    3,309.79
Unexplained            87,378.71   196    445.81
Total                  97,308.08   199

Source of Variation     F     P-value   F crit

Explained              7.42    0.00      2.65
Unexplained
Total

Table 5

Business Statistics II - Multiple Comparisons

Groups        Group        ABS          CR    Results
             Grades    (mean diff)

OA - FA      76 - 87       11        10.75    Significant
OA - ON      76 - 67       09         8.37    Significant
OA- FN       76 - 85       09        10.62    Not Significant
FA- ON       87 - 67       20        11.09    Significant
FA - FN      87 - 85       02        12.87    Not Significant
ON - FN      67 - 85       18        10.96    Significant

Table 6

Business Statistics I - One-way ANOVA

Source of         SS       df       MS
Variation

Explained     10,994.76     3    3,664.92
Unexplained   156,114.95   269    580.35
Total         167,109.71   272

Source of      F     P-value   F crit
Variation

Explained     6.31    0.00      2.64
Unexplained
Total

Table 7

Business Statistics I - Multiple Comparisons

Groups        Group    ABS (mean      CR    Results
             Grades      diff)

OA - FA      65 - 75      10       12.70    Not Significant
OA - ON      65 - 56      09       08.85    Significant
OA- FN       65 - 67      02       08.51    Not Significant
FA- ON       75 - 56      19       13.09    Significant
FA - FN      75 - 67      08       12.86    Not Significant
ON - FN      56 - 67      11       09.08    Significant

Table 8

All Classes - Qne-way ANOVA

Source of          SS       df       MS
Variation

Explained      24,510.28     3    8,170.09
Unexplained    316,948.46   625    507.12
Total          341,458.74   628

Source of        F     P-value   F crit
Variation

Explained      16.11    0.00      2.62
Unexplained
Total

Table 9

All Classes - Multiple Comparisons

Groups        Group        ABS         CR     Results
              Grade    (mean diff)

OA - FA      74 - 83       09        06.48    Significant
OA - ON      74 - 60       14        05.31    Significant
OA- FN       74 - 69       05        05.76    Not Significant
FA- ON       83 - 60       23        06.68    Significant
FA - FN      83 - 69       14        07.05    Significant
ON - FN      60 - 69       09        095.98   Significant