Statistical Analysis of E-Learning and Interactive Video Conferences in KSA Universities - Research Paper Example

Running head: E Learning and IVC STATISTICAL ANALYSIS OF E LEARNING AND IVC IN KSA UNIVERSITIES Customer Inserts His/her Name Customer Inserts Name of Tutor Customer Inserts Grade/Course (March 03, 2016) Table of contents Introduction Statement of problem Purpose of study Importance of study Research questions Hypothesis development Sampling Data Analysis Descriptive statistics MANOVA Mann-Whitney test Spearman’s correlation test Choice and Explanation of data set Significance of the study Limitations and Delimitations of the Research Recommendations Conclusion Introduction “Online learning or e-learning is a field of education that focuses on dissemination of academic knowledge and information to different geographical locations” (Garry, 2007, p.2). As a formalised learning system specifically designed to be carried out remotely, distance learning has become a very popular learning element in all universities around the world. Facilitated through Internet and web portals, distance learning has significant trend towards repertoire of most educational institutions in the future. “It is more than ever clear that distance learning is an important element of future education as it provides comfortable, easy, fast and affordable learning environment” (Awalt, 2003, p.67). The recent and evolving way of learning in the global classroom has been realised in a quite a number of universities, particularly in developed countries. “Distance learning can be described to be made up of a range of programmes, curricula and learning/learning strategies” (Commeaux, 1990, p.232) “As a recent and evolving way of learning, distance learning is an emerging global network in the mainstream of education and training” (Awalt, 2003, p.72). With regards to leadership concerning network development and access to education, this study investigates students of KSA’ University. More specifically, this study will investigate their perception towards online learning, the problems they face in applying distance learning as well as the need and accessibility to resources available on the web. “Distance learning has a radical effect of transforming education sector in many countries and the need to upgrade knowledge and competence using new information communication technologies is needed” (Abrani, 1996, p.40). This proposal examines online learning in higher education and assesses the attitude of faculty and students of universities in KSA towards applying online learning environments (Distance learning) and Interactive Video Conferences (IVC) in higher education using a likert Scale of 1 to 5. All the four universities are listed along and represent their online learning experience. All their perceptions on online learning experience will be measured against 4 variables and a likert scale 1-5 is scrutinised to determine whether students’ attitudes towards online education is more conservative than their perception. “Different methods such as t-test, descriptive statistics, and correlation study for attitudes will be used to conclude our analysis” (Creswell, 2008, p.105). This research examines four universities in KSA using a likert Scales to measure the degree of the students’ attitudes and assess their perception towards applying online learning environments (Distance learning) and Interactive Video Conferences (IVC) in higher education. Statement of Problem The term distance learning reflects both online learning environment and interactive video conferencing (IVC). It includes greater dimensions of openness and flexibility in terms of access to information and curriculum structure. “The area of interest includes how the participant utilise technology such as Web-Based Instructions (WBI) to get reading materials” (Abdulkarim, 2007, p.3). The study also investigates distance learning opportunities applied inside prison for offenders interested in continuing their education. “It however brought us to the conclusion that distance learning opportunity was not available for individuals who had not attended school before and learning environment was limited to certain subjects” (Abrani, 1996, p.42). Therefore, this proposal attempts to explore distance learning technique in distance learning environment and opportunities and difficulties encountered by students in applying distance learning system. The utilisation of DL as education system globally, have a lot of advantages such as providing students with learning and training opportunities held outside a formal classroom. The impact is already significant in developed countries and majority of developing countries are seeking to take part in emerging global educational opportunity. Purpose of the Study The purpose of this study is to investigate the attitude of students of NB University towards applying online learning environments (eLearning or Distance learning) and Interactive Video Conferences (IVC) in higher education.This will present a general impression of the learning processes, systems and theories. The researcher will be interested in analyzing the attitudes the students towards this development. The opinion of students and other stakeholders such as learners and the administration will be sought in order to contextualize those of the students. This is in addition to the examination of the factors that lead to the attitudes, differences in attitudes held between different students among other issues. Below is a list of the major purposes of this research: An examination of the relationships between students’ attitudes according to demographics including; important of using distance learning, education, background, difference counties, major, age, gender, skills and access to technology, and training experience. An examination of the opinions and attitudes students towards eLearning (Barnard, 2006,p.87). An examination of the relationship between students’ attitudes in KSA institutions of higher learning (Al-Khalifa, 2008, p.392). Challenges faced by students in the use of e Learning in Higher Education in Saudi Arabia. Challenges faced by students in the use of IVC / ITV in Higher Education in KSA. Importance of the Study (Key Strength) Provide feedback to the higher education management responsible for online learning. “Key areas of interest were; students’ perception towards higher education, need to improvement and students’ attitudes, suitable system for university use, reasons for supporting online learning and construction requirements for IVC rooms” (Garrison, 1990, p.21) For the case of my country; KSA for example, online learning with suitable IVC rooms will bridge the gap of lower education levels. In Elango and Selvam, (2008) studies confirmed that “education programs are directly responsible for recent rise in the dropout rate and decrease in crimes because many of those who drop out of high school or fail to qualify for admission to college or universities do not possess the skills necessary to compete for jobs” (p.34). This is evident in my location where new university and colleges in small cities or towns experienced lower turn out due stringent admission rules. This creates a context in which people see limited options, and therefore, fewer people are able to make it through colleges and universities lowering graduate to much lower levels. In this regard, online learning and IVC rooms will lower educational costs for individual students and even for college building costs that experienced lower student turn outs. Almegren and his colleague (2007, p.98) also evidences that “distant learning technology coined in the brick and mortar instruction is able to recruit students in different geographical location alleviating the need for travel expenses and presents the same lecture materials different times”. Allen (2005) also adds “depending upon the technology used ‘e.g. Internet-based program’, it often allows students greater scheduling flexibility, particularly if the student has other obligations such as work or family life that would otherwise prohibit them from attending a “traditional” instructional institution” (p.247). In this regard, utilising new technology in internet-based programs will be able to create many faculties which were often ignored. Arab countries evident with gender inequalities for example, distance learning will be a viable solution to ensure both men and women experience equal education opportunities. Research Questions (RQ) The study was aimed at assessing the relationship between students’ attitude towards applying online learning education (e- learning) and Interactive Video Conferences (IVC) in higher education in KSA. Demographics in terms of their background were measured against variables of importance of using distant learning, education, background, difference counties, major, age, gender, skills and access to technology, and training experience. Study questions examined were; 1. What are students’ attitudes toward applying eLearning (Online Learning) in higher education in KSA? 2. What are students’ attitudes toward Video conferences interactive (VIC) in higher education in KSA? 3. What is the relationship between students’ attitudes according to demographic variables (such as population) and the importance of using distance learning, education, background, major, age, gender, skills and access to technology, and training experience? 4. What challenges and problems are the students facing through use of online learning at KSA Universities? 5. What do students consider to be the challenges faced in using distance learning at NBU ? 6. What are students opinions towards Higher Education needs in KSA regarding the use of internet? Hypothesis Development The purpose of this paper is to determine or assess the relationship between students’ attitude towards applying online learning education (e learning) and Interactive Video Conferences (IVC) in higher education in KSA. Demographics in terms of their background and variables such as important of using e learning, education, background, difference counties, major, age, gender, skills and access to technology, and training experience. The hypotheses generated are the following: Since different methods can be used to important of using distance learning, education, background, difference counties, major, age, gender, skills and access to technology, and training experience. This may suggest that differences between the mentioned variables will indicate no relationships regarding demographic variables. H1: There will be no statistical significant differences between students towards applying or using online Learning in higher education in KSA University based on demographic characteristics such as Level of education, background, difference counties, major, age, gender, skills and access to technology, and experience. Sampling The sample consisted of universities in Saudi Arabia that offered e-learning services to their students. Independent Variables The independent variables for the study are (a) gender, (b) age, (c) education level, (e) nationality, and (d) learning experience. Dependent Variables The students and stakeholders perception towards e-learning in the KSA consisted of six subscales: perceived attitude perceptions of the students towards e-learning challenges facing Saudi universities perceived enjoyment perceived usefulness perceived system satisfaction. Data Analysis A summary of the demographic profile of the respondents for this study (n = 410). The majority of the respondents were male, n = 239 (58.3%). The ages and years of Learning experience of the respondents clustered towards the middle, at 33 to 38 years (n = 139, 33.9%), and 6 to 10 years (n = 170, 41.5%), respectively. The majority of the respondents held a Doctoral degree, (n = 234, 57.1%), were of non-Saudi nationality, (n = 274, 66.8%). The majority of the respondents mainly used classroom based learning, n = 410, 100%). Frequencies and Percentages for Demographic Variables of Respondents(n=410) Variables N % Gender Male 239 58.3 Female 171 41.7 Age < 26 24 5.9 27 – 32 103 25.1 33 – 38 139 33.9 39 – 44 109 26.6 45 or more 35 8.5 Educational level Doctoral 234 57.1 Masters 108 26.3 Bachelors 68 16.6 Nationality Non Saudi 274 66.8 Saudi 136 33.2 Learning experience 1 – 2 years 44 10.7 3 – 4 years 170 41.5 5 – 6 years 159 38.8 7+ years 37 9.0 Class(es) Currently pursuing a E-learning 24 5.9 Classroom - Based Learning 410 100.0 Table showing the means and standard deviations for students experience with computer and e-learning software. the means indicates that students perceived themselves as more adapt with general-purpose software than with software more typically associated with e-learning. This indicates that the students’ perceived their overall experience with computer and e-learning software as slightly above average. The students reported more experience with PowerPoint (M = 4.01), the Internet (M = 3.99), and word processing software (M = 3.92), than with course management (M= 2.95), conferencing (M = 2.96), and chat (M = 2.96) software. This indicates that students perceived themselves as more adapt with general-purpose software than with software more typically associated with e-learning. DESCRIPTIVE STATISTICS Means and Standard Deviations for Students Experience with Computer Mean SD Experience using operating systems (e.g., Windows XP, Windows, 7, Mac OSX, Linux) 3.49 0.52 Experience using the Internet (World Wide Web, WWW) 3.99 0.16 Experience using word processing packages(e.g. Microsoft Word, WordPerfect) 3.92 0.35 Experience using presentation software(e.g. PowerPoint) 4.01 0.09 Experience using e-learning course management system for EL(e.g., WebCT, BlackBoard or Moodle) 2.95 0.46 Experience using video conferencing tools (e.g., Skype, Adobe Connect) 2.96 0.42 Experience using online chat programs (e.g. Yahoo chat, AOL chat, Facebook chat) 2.96 0.52 I have the necessary experience to learn via e-learning 3.08 0.51 Overall Computer and E-learning Experience 3.41 0.20 Note. The scale was: 1= no experience to 5 = highly experienced. Research Question #1: What are students’ perceptions of e-learning in KSA higher education? Table 4.3 shows the means and standard deviations for the five e-learning perceptual constructs used in this study. The table indicates that the overall perception scores of the research respondents were positive of e-learning, (M = 4.17, SD = .52). Table 4.3. Means and Standard Deviations for Students’ Perception of E-Learning Questions Statements Mean SD Perceived self-efficacy 4.06 0.80 17 I feel confident that I can develop an effective e-learning course. 4.17 0.69 18 I feel confident that I can learn a successful e-learning course. 4.09 0.86 19 I feel confident using e-learning course-management tools (e.g., BlackBoard, WebCT) 3.92 1.19 Perceived enjoyment 4.22 0.71 20 I enjoy using computers in my learning. 4.42 0.64 21 I enjoy learning e-learning courses. 4.25 0.92 22 I enjoy developing e-learning courses. 4.00 0.91 Perceived usefulness 4.40 0.65 23 E-learning is an effective medium for learning. 4.42 0.76 24 I can learn effectively through e-learning. 4.33 0.75 25 I can communicate efficiently through e-learning 4.43 0.80 Behavioral intention to use e-learning 4.37 0.66 26 I intend to learn e-learning courses when I am given the opportunity. 4.45 0.70 27 I intend to use the Internet in my future learning assignments. 4.42 0.79 28 I intend to use e-learning tools in my future learning assignments. 4.22 0.80 Data Analysis Descriptive statistics of the responses from the students consisted of frequencies, means, medians and comparison of means and medians for describing the demographic variables, technology and computer experience, as well as the students perceptions toward challenges and obstructions facing students in the implementation of e-learning at Saudi universities. Multivariate analyses of variance One-way multivariate analyses of variance (MANOVA) statistic was used for question 2 and also tested whether a significant difference existed based upon the demographic characteristics (gender, age, education level, and learning experience), and corresponding univariate ANOVA results for each of the demographic variables (Al-Sarrani, 2010; Gay et al., 2006; Hongbo, 2006). In addition, Post hoc comparisons using Tukey HSD test was used when more than two levels of an independent variable were analyzed SPEARMAN’S CORRELATION TEST Descriptive statistics indicates that the variables are not normally distributed. This therefore means that the data is not parametric. Spearman’s correlation test therefore tests the in inter-dependency or relationship between two sets of data to determine their degree of relationship (Warner, 2008). In the case study, the test can be used to test the relationship between age, education level and nationality among other variables. The degree of interdependency between gender and the independent variables (perceived enjoyment, perceived usefulness and perceived system satisfaction male female M SD F SD Enjoyment 1.34 0.75 1.25 0.59 usefulness 1.43 0.23 1.33 0.45 System satisfaction 2.84 0.99 2.73 0.94 The strength of correlation is based on the distance +1or -1 from the two involved variables. A strong relationship is evidenced by closeness of the values (Warner, 2008). After running the data in SPSS software the following correlation table was obtained. enjoyment usefulness satisfaction Perceived Social Support Variables V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 From people From pets Subjective Quality of Life V1 1.00 V2 -.26* 1.00 V3 .24* -.17 1.00 V4 -.12 .22* .36** 1.00 V5 .27* -.16 .46** .23 1.00 V6 .12 .22* .47** .47** .40** 1.00 V7 .48** -.20 .25* -.03 .21* .28* 1.00 V8 -.13 .18 .32* .68** .12 .45** .06 1.00 V9 -.73** .40* -.24* .15 -.36** -.04 -.33* .16 1.00 V10 -.53** .37** -.11 .37** -.11 .06 -.39** .26* .56** 1.00 V11 -.09 .46** -.12 .28* -.10 .46** .04 .18 .29* .27* 1.00 V12 .13 .01 -.18 -.06 -.01 -.05 .02 -.13 -.04 -.04 .08 1.00 V13 -.50** .27* -.15 .02 -.19 -.01 -.29* .06 .60** .50** .38** .05 1.00 Correlation matrices were generated to explore relationships among the variables for each group. These matrices are shown in the table above. Spearman Rho’s coefficient was calculated among the genders. The correlation coefficient showed a significant relation between the independent and the dependent variables and therefore was acceptable for this study. In this study, a correlation matrix for six subscales of perception was used. Ranges for the highest and lowest correlations in each subscale are the following: self-efficacy, (.671 to .923), enjoyment (.702 to .896), perceived usefulness (.617 to .991), behavioral intention ( .597 to .977), system satisfaction (.651 to .815). From these data, significant statically a positive relationship between subscales items /questions is observed. MANN WHITNEY TEST The Mann-Whitney test compares the differences between two independent samples to determine if they differ in location (Warner, 2008). The null hypothesis is that there is no significant difference and the alternative hypothesis is that there is a significant difference Mann-Whitney Test Challenges in implementing e-learning Mean SD 35 Students have time for e-learning training. 3.41 1.45 36 Administrative support for e-learning is strong at my college/department 3.66 1.44 37 My college/department has adequate financial resources to develop technology-based initiatives. 3.58 1.32 38 Students must spend extra time to create e-learning courses. 2.18 1.15 39 Students have access to hardware essential for teaching e-learning courses. 3.08 1.32 40 Students must spend extra time responding to students’ inquiries in an E-learning course. 2.67 1.18 41 Students have access to software essential for teaching e-learning courses. 3.09 1.12 42 Students in my department/ college think that e-learning is effective. 3.83 0.90 43 Students in my department/college have time to adopt e-learning. 3.24 1.16 44 Students in my department/college believe it is easy to manage an e-learning course. 3.16 0.80 Choice and Explanation of Data Set Once the raw data has been obtained, it should be standardized to respective gical measures using the relevant statistical tool (Vankatesh and Davis, 2000). Thus, measures of central tendency: mean, mode and median and measures of dispersion; range, quartiles, percentiles, variance and standard deviation should be considered in standardizing and analysis of the sample. Not to mention the inestimable need of graphical representation and analysis of the data. Thus, a set of raw data can be expressed in score form, which is then standardized into such standard scores as Z-score, T-score, scaled scores and IQ scores, the central objective being to give an effective and meaningful description of the samples in the distribution. The- standardized scores are fixed interval scales of measurement, which give a clear view of how the data is spread (Vankatesh and Davis, 2000). Due to the complexity of data collection, various measurement errors compromise the reliability of the raw data, resulting to biased and distorted conclusions. An approximation of the error size should therefore be considered during the evaluative phase of gical testing. The key sources of measurement errors are; coverage, scoring, non-response and computational errors It is thus crucial to correct empirical outcomes to accommodate the distortions arising from inaccurate measurement. Thus, appropriate correlation coefficients should be computed to estimate the corresponding reliability coefficient. Thus the reliability coefficient would be a relative expression of the internal consistencies and temporal stabilities of the sample in question. Of another importance still is the act of relating reliability coefficient to the standard error of measurement which aids in the statistical interpretation of the computation Limitations and Delimitations of the Research This research was limited to NBU in KSA. During the research, course structure was found to have inverse relationship to impact score which indicated that instructors for online classes may also serve as instructional designers. In this regard, “adequate attention is required to be given to course structure to ensure well organised and flexible schedules are appropriated to meet learners’ needs” (Gay et al, 2008, p.179). Secondly, the relationship between students’ satisfaction and their perception in this study towards online experience with their course instructor positively correlated to their satisfaction needs. This study however evaluated students’ perception of the adequacy of their satisfaction within their online course and failed to differentiate between different courses undertaken by each student. Possible limitations could be that it was restricted in terms of students in universities and tertiary colleges alone. This leaves out other education systems, such as high school and the like. “As such, generalizations for the whole of the Saudi Arabian education system, and that of the world in extension, cannot be effectively made” (Abouchedid and Eid, 2004, p.24). Other education systems will not be studied given the limited resources of the study. Recommendations It is recommended that future research replicates the study across a range of education systems in different geographical areas like countries and this would greatly improve generalization. Additionally, researchers or investigators “can increase the models used for study and test other components that influence online learning” (Armatas et al, 2005, p. 86). Conclusion It’s evident that both online learning environment and interactive video conferencing (IVC) facilitated conducive learning environment for students of different geographical locations. Thus, the reason for this research was to generate more knowledge and improve understanding on distance learning and IVC on the concept of education. “All countries were remarkably applauded for encouraging learning through web platforms. And therefore to facilitate higher enrolment levels, investing in learning tools that will facilitate effectiveness in their courses is needed” Coventry, 2002, p.1). . Future research can be examined on larger sample sizes and target specific countries. Researchers can apply different methodologies to one hypothesis to determine whether it will produce the same result on the reconciliation attitudes and perception. Other variable items can be conducted to decide whether they will produce the same results. Since the study is focused specifically on KSA universities, future research may consider using universities in other countries to conduct further analysis. “Distant learning has a radical effect on transforming education sector in many countries and the need to upgrade knowledge and competence in using new information communication technologies is needed” Coventry, 2002, p.1). Reference List Abdulkarim, A. (2007). Prisoners' Attitudes toward Using Distance Education Whilst in Prisons in Saudi Arabia. Informing Science and Information Technology, 4, 1-7 Abrani, P. (1996). “Computer supported collaborative learning and distance education”. The American Journal of Distance Education. 10(2):37-52. Abouchedid, K & Eid, G. (2004). eLearning Challenges in the Arab World. Quality Assurance in Education, 12 (1), pp 15 - 27. Alharbi, Y.A. (2002). A Study of the Barriers and Attitudes of Faculty and Administrators toward Implementation of Online Courses. Educational technology at University, 4, 1-9. Allen, K. (2005). Online Learning: Constructivism and Conversation as an Approach to Learning. Innovations In Education And Teaching International, 42 (3), 246 - 255. Al-Khalifa, H. (2008). “Building an Arabic Learning Object Repository with an Ad Hoc Recommendation Engine”. 10th International Conference on Information Integration and Web-Based Applications and Services Pp. 390-394. Linz, Austria. Alharbi, Y.A. (2002). “A Study of the Barriers and Attitudes of Faculty and Administrators toward Implementation of Online Courses”. Educational technology at University. Allen, K. (2005). “Online Learning: Constructivism and Conversation as an Approach to Learning”. Innovations In Education And Teaching International, 42 (3), 246 - 255. Almegren, A., Al-Yafei, A., & Hashem, A. (2007). “Pilot Nationwide E-learning Provision in the Kingdom of Saudi Arabia: Issues and Challenges”. 21st Asian Association of Open Universities Conference. Kuala Lumpur, Malaysia. Almohaisen, I. (2007). E-learning in the Kingdom of Saudi Arabia. National Centre for Distance and E-Learning Seminar. Keynote speech. Riyadh. Armatas, C, Holt, D & Rice, M (2005. “Balancing The Possibilities for Mobile technologies In Higher Education”. In proceedings of the 2005 Ascilite Conference. Brisbane Angeli, C. & Bonk, J. (2003). “Communication in a web-based conferencing system: The quality of computer-mediated interactions”. British Journal of Educational Technology. 34(1):24-45 Awalt, J. (2003). Moving from the classroom to on-line teaching: A study of change in faculty attitude. The University of Texas: Austin. Bannan-Ritland, B. (2002). “Computer mediated communication, e-learning and interactivity: A review of the research. The Quarterly Review of Distance Education. (32):160-180 Barnard, Y. (2006). “Didactical and Pedagogical Aspects of E-Learning Tools”. In: J.P. Pardillo, Proceedings Of The Conference On European Guidelines For The Application Of New Technologies For Driver Training And Education. Madrid Barron, D. (1987). Faculty and student perceptions of distance education using television. Journal of Education for Library and Information Science, 27, 257-271. Chickering, A. W., & Gamson, Z. F. (1987). Seven Principles of Good Practice in Undergraduate Education. AAHE Bulletin, 39, 3-7. Chou, S. & Liu, C. (2005). Learning Effectiveness in a Web-Based Virtual Learning Environment: A Learner Control Perspective. Journal of Computer Assisted Learning, 21 (1), 65–76 Creswell, W. (2008). Educational research: planning, conducting, and evaluation Quantitative and qualitative research (3rd ed.). Merill Prentice Hall: Upper Saddle River, NJ. Commeaux, P. (1990). Communication and collaborative in the online classroom: Example and applications. Bolton, MA: Anker. Coventry, L. (2002). “Video conferencing in higher education”. Retrieved on 11 February 2011 from http://mvc.man.ac.uk/SIMA/video3/contents.html Elango, R. Gudep, V. & Selvam, M. (2008). “Quality of e-Learning: An Analysis Based on e-Learners’ Perception of e- Learning.” The Electronic Journal of e-Learning Volume 6 Issue 1, pp. 31 – 44. Galbreath, J. (1995). “Compressed Digital Videoconferencing”. Educational Technology. 35(1):31-38. Garrison, R. (1990). An analysis and evaluation of audio teleconferencing to facilitate education at a distance. The American Journal of Distance Education. 4(3):15-23 Garry, J. (2007). Advantages and disadvantages of online learning. e-Learn Magazine, 23, 1-3 Gay, L. Mills, G. & Airasian, P. (2008). Educational research: Competencies for analysis and applications. Upper Saddle River, New Jersey: Pearson Merrill Hall Vankatesh, V., & Davis, F. D (2000). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451-481. Warner, R. M. (2008). Applied statistics: From bivariate through multivariate techniques. Thousand Oaks: Sage Publication. Read More