Learning Online or the Use of Conventional Methods of Learning - Case Study Example

Marketing Research Name: Admission No: Institution: Instructor’s name: Date: Introduction This study was conducted with the purpose of establishing whether students would prefer to have their units done online or the use of conventional methods of learning. The total number of students involved in this study was 227 and similar questions were provided to the students. The students were selected from various demographic groups such as age, gender, level of education and marital status among others. The responses from the students were recorded and analyzed in order to obtain descriptive statistics and inferential statistics. The results of the analysis were obtained using Statistical package for social scientist (SPSS). The results were explained to get an understanding of the responses of the students towards online provision of units. 1. Demographic characteristics of the sample based on descriptive statistics The main demographic characteristics that were analyzed include age, marital status, number of dependent children, number of units done fully online in the last 5 years, average time taken by the student to the university, type of degree the student was undertaking and the school in which the student was studying. The results were processed using SPSS and represented as shown below. Age of the person Frequency Percent Valid Percent Cumulative Percent Valid 17-25 188 82.8 82.8 82.8 26-34 34 15 15 97.8 35 & Above 5 2.2 2.2 100 Total 227 100 100 In terms of age, 188 respondents were aged between 17-25 years, 34 were aged between 26-34 years old and 5 were aged above 35 years old. The remaining 5 students’ were aged above 35 years old. This shows that most participants in this study were aged 17-25 which is the main age group for undergraduate students. Marital Status Frequency Percent Valid Percent Cumulative Percent Valid Single 150 66.1 66.4 66.4 In a Relationship 66 29.1 29.2 95.6 Married 10 4.4 4.4 100 Total 226 99.6 100 Missing System 1 0.4 Total 227 100 In terms of marital status, the total number of single respondents was 150, those who are in relationship were 66, and those who were married were 10. This shows that most respondents involved in this study were single. Number of depend children (under 16 years old) Frequency Percent Valid Percent Cumulative Percent Valid None 221 97.4 97.8 97.8 1 2 0.9 0.9 98.7 2 2 0.9 0.9 99.6 More than 2 1 0.4 0.4 100 Total 226 99.6 100 Missing System 1 0.4 Total 227 100 In terms of Number of dependent children, 221 which accounted for 97.4% of respondents did not have any dependent children, 2 or 0.9% of the participants had 1 dependent child each, 2 had 2 dependent children and 1 respondent had 2 dependent children. This shows that most respondents did not have any dependent child. Number of fully online unit has the person done in the last 5 years Frequency Percent Valid Percent Cumulative Percent Valid None 182 80.2 80.5 80.5 1 16 7.0 7.1 87.6 2 6 2.6 2.7 90.3 3 5 2.2 2.2 92.5 4 & Above 17 7.5 7.5 100.0 Total 226 99.6 100.0 Missing System 1 .4 Total 227 100.0 In terms of the number of fully online units in 5 years, 182 respondents reported they did not have any unit online, 16 reported they had 1 unit online, 6 reported they had 2 units online, 5 reported they had 3 units online and 17 reported they had 4 and above units online in 5 years. This shows that most students at the university’s business school did not fully do any units online. Average time travel to Uni Frequency Percent Valid Percent Cumulative Percent Valid Under 15 mins 88 38.8 38.9 38.9 16-30 mins 92 40.5 40.7 79.6 31-60 mins 39 17.2 17.3 96.9 Over 1 hour 7 3.1 3.1 100.0 Total 226 99.6 100.0 Missing System 1 .4 Total 227 100.0 In terms of time taken to travel to the university, 88 took less than 15 minutes, 92 took between 16-30 minutes, 39 took between 31-60 minutes and 7 took over 1 hour to travel to the university. This shows that most students took between 16-30 minutes to travel to the university. Type of degree the person currently pursuing Frequency Percent Valid Percent Cumulative Percent Valid Undergraduate - Year 1 16 7.0 7.1 7.1 Undergraduate - Year 2 39 17.2 17.3 24.3 Undergraduate - Year 3 69 30.4 30.5 54.9 Postgraduate - Year 1 55 24.2 24.3 79.2 Postgraduate - Year 2 47 20.7 20.8 100.0 Total 226 99.6 100.0 Missing System 1 .4 Total 227 100.0 In terms of the degree the respondents were pursuing, 16 were undergraduates in 1st year, 39 were undergraduates in 2nd year, 69 were undergraduates in the 3rd year, 55 were postgraduates in the 1st year and 47 were postgraduates in the 2nd year. This shows that most students were undergraduates in 3rd year. School that the person is studying Frequency Percent Valid Percent Cumulative Percent Valid School of Accounting 60 26.4 26.5 26.5 School of Business Law & Taxation 14 6.2 6.2 32.7 School of Economics & Finance 29 12.8 12.8 45.6 School of Marketing 74 32.6 32.7 78.3 School of Management 35 15.4 15.5 93.8 School of Information System 14 6.2 6.2 100.0 Total 226 99.6 100.0 Missing System 1 .4 Total 227 100.0 In terms of school in which the respondents were studying, 60 were in the school of Accounting, 14 in the School of Business Law and Taxation, 29 in the School of Economics and Finance, 74 in the School of Marketing, 35 in the School of Management and 14 in the School of Information Systems. This shows that most respondents came from the School of Marketing. 2. The views of the surveyed students about online units based on descriptive statistics and N Minimum Maximum Mean Std. Deviation ease of access to course materials. 227 1.00 5.00 3.8502 1.01080 a self-study mode. 226 1.00 5.00 4.1372 .88631 better control in planning their study schedule. 227 1.00 5.00 3.4890 1.04907 better flexibility in their study time. 226 1.00 33.00 4.2655 2.07854 better visibility of course outline and study materials. 226 1.00 25.00 3.4912 1.73139 require strong self initiative. 226 1.00 5.00 4.3540 .88363 lack frequent communication between students and lecturer. 225 1.00 5.00 4.1333 .97742 not be easy to understand the lecturer’s requirement 226 2.00 5.00 3.9646 .85821 need to have reliable internet access. 223 1.00 5.00 4.3632 .85313 find difficulties learning 226 1.00 14.00 3.8363 1.20544 value for money. 221 1.00 5.00 2.8959 1.05012 insufficient information on the study structure 225 1.00 5.00 3.3956 .89591 not cover as much content as the face to face units 224 1.00 5.00 3.6384 1.01934 better for learning than in class discussions. 226 1.00 5.00 2.7434 1.06482 require less effort in understanding. 226 -4.00 5.00 2.7743 1.17282 unprepared by the overwhelming commitments. 227 2.00 5.00 3.6828 .81784 I am very interested to take fully online units 226 1.00 5.00 2.7522 1.27214 Valid N (listwise) 202 According to the figure above various descriptive statistics characteristics of the study variables can be observed. The variable of ease of access to material (M=3.8502 and SD=1.01080), a self-study mode (M=4.1372, SD=0.88631), better control in planning their study (M=3.4890, SD=1.04907), better flexibility in their study time (M=4.2655, SD=2.07854), better visibility of course outline materials (M=3.4912, SD=1.73139), require strong self-initiative (M=4.354, SD=0.88363). The variable of lack frequent communication between students and lecturer (M=4.1333, SD=0.97742), not easy to understand lecturer’s requirement (M=3.9646, SD=0.85821), need to have reliable internet access (M=4.3632, SD=0.85313), find difficulties learning (M=3.8363, SD=1.20544), value for money (M=2.8959, SD=1.05012), insufficient information on the study structure (M=3.3956, SD=0.89591). The variable of not cover as much content as the face to face units (M=3.6384, SD=1.01934), better for learning than in class discussion (M=2.7434, SD=1.06482), require less effort in understanding (M=2.7743, SD=1.17282), unprepared by the overwhelming commitments (M=3.6828, SD=0.81784), I am very interested to take fully online units (M=2.7522, SD=1.27214). These results showed that the students were less exposed to studying using online mode compared with face to face mode. 3. The differences in thinking of fully online units by different demographic groups based on inferential statistics Various inferential statistics were used to measure samples of the population involved in the study. This was aimed at finding sampling errors through obtaining the value of ‘significance Level’ or sig. as indicated in the SPSS outputs. 3.1. Independent samples T-test This test was used to determine the extent to which there was a difference between the means of two groups. An independent T-test was performed to determine the score of various variables with respect to the variable of ‘ease access to course materials’. The results of the T-test are shown by the Sig (2-tailed) in the figure below. According to the results above, it can be seen that only two variables have significance values less than 0.05. These are ‘require strong self initiative’, and ‘lack frequent communication between students and lecturer’ whose significance values are 0.016 and 0.01 respectively. Thus the remaining variables do not explain the relationship between them and the variable of ‘ease access too course materials’. Levene's Test for Equality of Variances t-test for Equality of Means F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference 95% Confidence Interval of the Difference Lower Upper a self-study mode. 1.139 .296 .715 25 .481 .40909 .57195 -.76886 1.58704 .607 5.153 .570 .40909 .67439 -1.30915 2.12733 better control in planning their study schedule. 1.003 .326 -1.222 25 .233 -.61818 .50595 -1.66021 .42384 -1.001 5.027 .363 -.61818 .61783 -2.20377 .96741 better flexibility in their study time. .001 .977 .339 25 .737 .18182 .53610 -.92230 1.28594 .360 6.396 .730 .18182 .50468 -1.03478 1.39842 better visibility of course outline and study materials. .436 .515 -.338 25 .738 -.74545 2.20733 -5.29154 3.80063 -.653 24.893 .520 -.74545 1.14182 -3.09759 1.60668 require strong self initiative. 7.288 .012 1.234 25 .229 .63636 .51574 -.42581 1.69854 2.628 21.000 .016 .63636 .24215 .13278 1.13995 lack frequent communication between students and lecturer. 7.515 .011 1.330 25 .196 .68182 .51270 -.37410 1.73774 2.832 21.000 .010 .68182 .24073 .18120 1.18244 3.2. One-Way Anova A one-way ANOVA test was used to compare the differences between the variable of “ease of access to course materials’ and the variables of ‘ a self study mode’, better control in planning study schedule’, ‘better flexibility in their study time’, ‘better visibility of course outline and study materials’ and ‘require strong self-initiative’. It was found that the significance values for all the variables were less than 0.05 which does not show a strong difference from the variable of ‘ease of access to learning materials. However, the significance value of the variable of ‘better visibility of course outline and study materials’ was greater than 0.05 thus showing a strong difference from the variable of ‘ease of access to course materials’. ANOVA Sum of Squares df Mean Square F Sig. a self-study mode. Between Groups 15.513 4 3.878 5.316 .000 Within Groups 161.235 221 .730 Total 176.748 225 better control in planning their study schedule. Between Groups 39.497 4 9.874 10.477 .000 Within Groups 209.225 222 .942 Total 248.722 226 better flexibility in their study time. Between Groups 40.878 4 10.219 2.425 .049 Within Groups 931.193 221 4.214 Total 972.071 225 better visibility of course outline and study materials. Between Groups 23.186 4 5.796 1.967 .101 Within Groups 651.297 221 2.947 Total 674.482 225 require strong self initiative. Between Groups 8.088 4 2.022 2.666 .033 Within Groups 167.594 221 .758 Total 175.681 225 4. The purpose of factor analysis and insights gained from factor analysis on section 2 of the questionnaire The purpose of factor analysis is to enable identification of redundant variables so that the number of research questions can be reduced to the most significant questions that can be answered with little time expenditure. It is also aimed at to confirm variables or model factor structure. It is also important in modeling latent factors that cause the scores on the observed variables. The following Rotated factor Matrix represents factor analysis for the 2nd part of the questionnaire. Rotated Factor Matrix(a) Factor 1 2 3 Interesting learning mode. .759 .021 .152 Motivational learning environment. .712 .180 .189 Expertise of lecturer .632 .212 .041 Two-way communication learning environment. .557 .078 .186 Good support by lecturers. .550 .433 .069 To get sufficient attention from the lecturer .438 .226 .353 24/7 student services support .062 .767 .356 24/7 IT technical support .103 .698 .282 Fast download of course materials .362 .552 .309 Easy access to course materials .387 .535 .040 Guest speakers via broadcast .146 .081 .771 Video recording of industry related seminars .082 .240 .594 Good networking system for students .212 .310 .564 User friendly website .363 .290 .396 Extraction Method: Principal Axis Factoring. Rotation Method: Varimax with Kaiser Normalization. a Rotation converged in 6 iterations. From the above rotated factor matrix, it can be observed that factor 1 contains variables whose factor values have been highlighted with yellow color. These are ‘Interesting learning mode’, ‘Motivational learning environment’, ‘Two-way communication learning environment’,’ Expertise of lecturer’, ‘Two-way communication learning environment’, ‘Good support by lecturers’ and ‘To get sufficient attention from the lecturer’. The values of these variables are 0.759, 0.712, 0.632, 0.557, 0.550 and 0.438 respectively. This implies that it is possible to use the variable of ‘Interesting learning mode’ to represent other variables. The second factor analysis shows that factor 2 is composed of variables whose values have been highlighted with bright Green Color. These are ‘24/7 student services support’, ‘24/7 IT technical support’, ‘Fast download of course materials’, ‘Easy access to course materials’. The values of these variables are 0.767, 0.698, 0.552 and 0.535 respectively. This implies that these variables have similar attributes and it is possible to use the variable of ‘24/7 student services support’ to represent other variables under this factor. The third factor analysis shows that factor 3 is composed of the variables of ‘Guest speakers via broadcast’, ‘Video recording of industry related seminars’, ‘Good networking system for students’ and ‘User friendly website’. These are shown buy the variables highlighted using the Turquoise color. The values of these variables are 0.771, 0.594, 0.564 and 0.396 respectively. According to the results of factor 3, it is possible to use the variable of ‘Guest speaker broadcast’ to represent other variables. 5. Factor analysis for 3rd part of the questionnaire A factor analysis was conducted for the 3rd part of the questionnaire and the following Rotated Factor matrix was obtained. Rotated Factor Matrix(a) Factor 1 2 3 Blogs written by other students in the unit .707 .023 .200 Case Studies Videos .654 .122 .109 Online Discussion Forum .589 .232 .271 Online Quizzes and Tests .521 .292 .221 Web Conferencing with the lecturer .387 .162 .377 Pre-recorded Audio/Video Lectures .230 .848 -.003 Live Streaming of Lectures .434 .546 .102 Email Communication with the lecturer .070 .501 .399 PowerPoint Slides .042 .443 .132 Internal Messaging Software .125 .103 .722 Chat Rooms .328 .075 .580 E-book Versions of Recommended Text and Journals .266 .211 .360 From the above rotated factor matrix, it can be observed that factor 1 contains the variables of ‘Blogs written by other students in the unit’,’ Case Studies Videos’, ‘Online Discussion Forum’, ‘Online Quizzes and Tests’ and ‘Web Conferencing with the lecturer’. The values of these variables are 0.707, 0.654, 0.589, 0.521 and 0.387 respectively. This is show by the values highlighted using the green color. These variables have similar characteristics and can be represented by the variable of ‘Blogs written by other students in the unit’. Factor 2 contains the variables of ‘Pre-recorded Audio/Video Lectures’, ‘Live Streaming of Lectures’, ‘Email Communication with the lecturer’ and ‘PowerPoint Slides’. The values of these variables are 0.848, 0.546, 0.501 and 0.443. These are shown by the values highlighted with a grey color. This factor shows that the variables in this factor can be represented by the variable of ‘‘Pre-recorded Audio/Video Lectures’. Factor 3 contains the variables of ‘Internal Messaging Software’, ‘Chat Rooms’ and ‘E-book Versions of Recommended Text and Journals’. The values of these variables are 0.722, 0.580 and 0.360 respectively. These are shown by the values highlighted using the violet color. This factor shows that it is possible to use the variable of v to represent other variables under this factor. Conclusion This paper provides an insight into the extent to which students in the Business School of Curtin University perceive the introduction of online units. According to the results, most students are not involved online taking of units. However, most students do not object to the idea of offering units online. This shows that the management of Curtin University need to intensify provision of online units so that performance can be compared with the normal way of learning where students meet face-to-face with the lecturer. However, this study does not find any negative impact associated with fully offering units online. 6. References Horn, J. 1965. A rationale and test for the number of factors in factor analysis. Psychometrika, 30, 179 – 185. O'Rourke, N., Hatcher, L., & Stepanski, E.J. 2005. A step-by-step approach to using SAS for univariate and multivariate statistics, Second Edition. Cary, NC: SAS Institute Inc. Patil, V. H., Singh, S. N., Mishra, S., & Donavan, D. T. 2007. Parallel Analysis Engine to Aid Determining Number of Factors to Retain [Computer software]. Retrieved 08/23/2009. Zwick, W. R., & Velicer, W. F. 1986. Factors influencing five rules for determing the number of components to retain. Psychological Bulletin, 99, 432 – 442. Read More