The hybrid model has long been recognized as a viable option to cut cost and meet the needs of students (see, e.g., Young 2002). In one study, Jones (2006) addressed its effectiveness in mitigating attrition rates. In another study, Simonson, Smaldino and Zvacek (2009) shed light on its value in enhancing learner satisfaction. While there is no widely accepted characterization of a hybrid program, it is generally expected that a reasonable proportion of the course requirement for the degree will be completed remotely, and the rest in a face-to- face format. The effective balance will depend on logistical, pedagogical, infrastructural, academic and other considerations (see, e.g., Verkroost, Meijerink, Lintsen, and Veen, 2008).
Over the last ten years, the number of applications to the Statistics MA program at Columbia University has increased from 116 in 2004 to 1434 in 2014 (Figure 1).
There are several factors that are responsible for this steady growth. First and foremost, there is a growing need for trained statisticians, especially in the finance and technology sectors, where data-driven decision making is deemed indispensable. In addition, a conscious effort was made by the department to design a program that was in conformance with the evolving needs of the job market. Further, the potential pool of applicants was expanded by reaching out to major universities in East Asia.
Incidentally, the vast majority of the applicants to the statistics graduate program at Columbia are now from China, a phenomenon shared with many other similar institutions of higher learning in the United States (United States Immigration and Customs Enforcement, 2014). Most of these applicants appear to be attracted by the opportunity that an on-campus education provides, including the chance to interact with students of diverse backgrounds, the ability to have face-to-face meetings with the world-renowned faculty in the department, and the desire to experience the unique environment of campus life in the City of New York.
For domestic students, on the other hand, the traditional modes of delivery pose numerous challenges. The cost of living in the New York City area is high, as are tuitions and fees. For prospective students who work full-time, classes offered during the daytime may not be convenient. In this regard, the advantages of distance learning are substantial, with the accompanying convenience manifested in the ability to take courses at one’s leisure, and the reduction in living expenses.
The benefits of distance learning notwithstanding, there are certain fundamental issues with the nonconventional mode of instruction that technology alone cannot completely solve. Relative to on-campus experience, the degree of communication between teacher and student may not be optimal. Students who attend classes exclusively through online modes may not have the same opportunity as those in traditional settings to share experiences with other students of diverse cultural and academic backgrounds. Furthermore, there may be resources and facilities that require physical presence on campus to take advantage of. There are, of course, other non-tangible benefits to being on-campus, which provides the necessary condition for creative and independent thinking to those at a critical stage in their intellectual development.
Figure 1. Number of Applicants to the Statistics MA Program at Columbia by Year
Source: Department of Statistics, Columbia University Features of the Statistics Hybrid Program
The hybrid online/on-campus program, which is being launched and will start offering courses beginning in the Fall 2014 semester, will include an initial cohort of thirty to forty students. The first phase of the program of study will be conducted exclusively online; and as in the regular on-campus program, it is intended to give students the proper background and training in foundational courses, including modern probability, statistics, and applied statistics in a
0 500 1000 1500 2000
2004 2006 2008 2010 2012 2014
systematic fashion, and prepare them for the more advanced elective courses offered subsequently. This will be conducted exclusively online through an innovative learning management system developed at Columbia University.
Students can choose to take the entire suite of the online core courses in one semester, or on a part-time basis (two courses per semester) over semesters 1 and 2. The part-time study option allows students to complete the program at their own pace while maintaining their existing work and study commitment.
Upon completion of the four online courses, the students will be admitted to the resident program to complete the remaining required courses.
For the online phase, live lecture sessions are delivered through a sophisticated web-conferencing tool that permits students and instructors to interact through video and audio. The platform has several desirable features that are intended to mimic in-person classroom experience, including questions and answers, as well as test and exam administration. Most notably, online students will also have private, one-on-one access to faculty, program administrators, and advisors, via the platform.
As an added feature, the online live sessions are recorded and are available to students to review at their convenience. This is particularly important to ensure students get the maximal benefit from the instruction, especially in situations where time zone differences may not be optimally opportune to some participants. Nonetheless, as with traditional on-campus requirements, class attendance will be strictly mandatory and will be considered a critical component of the online learning experience.
Students who successfully complete the online phase will then be permitted to enroll in the on-campus phase of the program. The latter is an integral component of the regular program, and the incoming students will have all the benefits of those who are already enrolled in the resident program, including housing and access to other university resources.
International students will be issued with the necessary documentation to get student visas to complete the program on-campus. However, international students who are in the United States on a student visa will not be eligible to enroll in the online portion of the program.
The admission criteria and the degree certification requirements are identical for both the regular and hybrid programs. In addition, there are no differences in the syllabi of the core courses that are offered to the online and regular on-campus students. This is intended to maintain the quality and rigor of the two programs at the same level, while ensuring the flexibility provided by the online component.
Instructors of courses in the hybrid program are selected from among the teaching faculty in the department who also have assignments to teach in the regular program. Further, the academic calendars for the hybrid and regular programs are seamlessly synchronized, and online students will get similar administrative communications as those studying on campus.
Anticipated Benefits of the Blended Program
By all measures, the program is expected to be beneficial both to the students and the institution. Early experience with the program will help design an expanded version that would eventually allay the stress on the teaching infrastructure. The experience with the statistics program may also help to develop and implement similar projects in other departments that contend with analogous problems of class sizes and growing enrollment.
One major aspect of the regular M.A. program in statistics at Columbia has been the relative homogeneity of the student body, consisting mainly of students from East Asia. Local and other domestic students who are unable to attend classes due to cost and logistical reasons, may now have the opportunity to enroll in the program. As a byproduct, the new model is expected to complement and enrich the educational experience through enhanced student diversity.
Relative to the online-only programs, the hybrid program may appeal to prospective students who value the increased face-to-face communication with teachers and other students that is possible in the on-campus phase. The latter also gives students relatively greater access to university resources that are not available through the on-line component, including courses from other departments, high-speed computing environment, on-campus placement and career services, internships, and all the other advantageous aspects of life on campus.
Conclusion
As the technology and the needs of the job market continue to evolve, there is considerable opportunity to evaluate the format and content of the new hybrid program. It is expected that the effectiveness of the new approach will periodically be appraised through proactive solicitation of input from students, employers and instructors. While it is too early to tell how effective the project will turn out to be, based on the level of interest expressed by prospective applicants, there seem to be a segment of the student population to whom the medium may be a viable option. Indeed, the number of applicants to the program has been sizeable, despite the limited effort made hitherto to publicize its imminent launch in the Fall l2014 semester.
Incontrovertibly, the new program is likely to present both challenges and opportunities. As is invariably the case with the launch of a new educational project, the hybrid program will encounter unanticipated and unforeseen challenges that will require meticulous handling to ensure uninterrupted delivery of the educational material. The effectiveness of the delivery is in part a function of how smoothly the technology works, especially in areas where the infrastructure may not be highly developed. Time zone differences may introduce a level of difficulty to simultaneously conduct live sessions for all students living in different parts of the world. For international students, the processing of entry visa in a timely manner to move to the United States for the on-campus portion may introduce additional administrative hurdles complexity. Nonetheless, the experience gained from the first few years will not only help improve the delivery of education in the new format, but will also help in the planning and implementation of the next phase of enhancement of the graduate program in statistics and related disciplines at Columbia and elsewhere.
References
1. Berge, Z. L., Muilenburg L.Y., and Haneghan, J. (2002). Barriers to distance education and training. Distance Learning Administration, 3 (4), 409-419.
2. Carr, S. (2000). As Distance Education Comes of Age, the Challenge Is Keeping the Students. Chronicle of Higher Education, 46 (23), A39
3. Sinn, J. (2004). Electronic course delivery in higher education: Promise and challenge. The Journal of Technology Studies, 30 (1), 39-45.
4. Holmberg, B. (1995). Theory and Practice of Distance Education. London:
Routtedge.
5. Jones, N. (2006). E-college Wales, a case study of blended learning. In C. Bonk & C.
Graham (Eds.), The Handbook of Blended Learning (pp. 182-194). San Francisco, CA: John Wiley & Sons
6. Simonson, M., Smaldino, S., Albright, M., and Zvacek, S. (2009). Teaching and learning at a distance: Foundations of distance education (4th ed.). Boston:
Pearson.
7. United States Immigration and Customs Enforcement Agency (2014 April).
Student and Exchange Visitor Information System General Summary Quarterly Review. Retrieved June, 2014: http://www.ice.gov/doclib/sevis/pdf/by-the- numbers1.pdf
8. Verkroost, M., Meijerink, L., Lintsen, H., and Veen, W. (2008). Finding a balance in dimensions of blended learning. International Journal on ELearning, (7)3, 499-522.
9. Young, J. R. (2002, March 22). 'Hybrid' teaching seeks to end the divide between traditional and online instruction. Chronicle of Higher Education. Retrieved June, 2014: http://chronicle.com/free/v48/i28/28a03301.htm
DIFFERENT RELATIONSHIP OF GENDER, ACADEMIC ACHIEVEMENT, PERCEIVED SOCIAL SUPPORT AND EDUCATIONAL SATISFACTION IN
STUDENTS WITH DISTANCE EDUCATION EXPERIENCE Alizadehfard Susan, Psychology Department, I.R. of IRAN
ABSTRACT: Introduction: The aim of this study is determine different prediction of academic achievement by perceived social support and educational satisfaction in men and women students with distance education experience.
Method: The sample consists of 120 second year students in Payame Noor University. Participants completed self-reported education satisfaction questionnaire and multidimensional scale of perceived social support (1988).
Results: Finding discovered that educational satisfaction is a meaningful predictor of academic achievement between male and female students. Also it was determined that perceived social support predicted academic achievement in women but it isn't a meaningful predictor in male students.
Conclusion: Implications of the findings of the present study are crucial for institutions planning to offer distance education courses.
KEY WORDS: distance education, perceived social support, academic achievement, gender.
INTRODUCTION
The majority of undergraduate students and students with distance education experience at IRAN are women. Women have used distance education to try to complete courses and degrees after high school for different reason: age, interests, economic and marital status, and failed to attending in formal university with traditional education.
In one of the few studies to focus on gender as critical personal variables, May (1994) studied women pursuing distance education. Among her conclusions, she stated that the distance education experience was significantly different experience for female learners than for male. This finding was echoed by Gillis, Jackson, Braid, MacDonald, and Macquarie (2000) in another study focusing on women learners, which leads us to consider the impact of social systems on learners.
The most important function of modern education is to help the individuals to develop as a whole from the aspects of physical, social and psychological ways.
(888) because of this systematic view, there are different kind of physical, social and psychological factors that influence on academic achievement.
Two important subjects of this list are social and psychological factor that clearly are very different in men and women. In this study we focused on social support (as a social factor) and educational satisfaction (as a psychological factor). Social support can be defined as the support which is taken from family, peers, friends, neighbors and institutions which enhance the psychological dynamics, and help the individual in the aspects of affective contribution (Bahar, 2010). The individual's interaction model is different between men and women, so this question appears that what's different effecting on academic achievement?
From the other hand, recently there is a focus on student satisfaction as a customer and recent studies explore the relationships between Student Intentions, Retention and their educational satisfaction in Higher Education. So the mail purpose of this study is determine different prediction of academic achievement by perceived social support and educational satisfaction in men and women students with distance education experience.
Method
Participants were undergraduate students at Payame Noor University in Iran (Tehran), that studying with distance education system. All of them were second year psychology students which 60 were males and 60 were females. The sample was drawn object ively and systematically from available students who participate. The research instrument included 4 scales:
1- Participant demographics. 2- Distance education satisfaction survey: This scale has 10 questions that measure how much students were satisfied with distance education. Higher scores showed higher levels of satisfaction from distance education. This survey included Likert type items with response choices ranging from 1 (strongly disagree) to 5 (strongly agree). 3- University grade: University grade is individuals' total score across all subjects taught during the two year university program. The average score ranged from 0 (lowest) to 20(highest). 4- Multidimensional scale of perceived social support: Perceived socia l support was assessed through by Zimet, Dahlam, Zimet and Farley (1988). It has three different subscales: (a) Family, (b) Friends and (c) Significant Other. The test scores of the Likert type of scale scored from 1 to 5 points, and the scale’s total scores vary between 12 and 60. Being high scores means higher perceived social support.
Results
Descriptive statistics on the participant students are shown in Table 1. In the next step, t- test was calculated for comparing male and female scores. As it is shown in table 2, there are significant different between them. In order to answer the research question, firstly inter-correlation among all the variables was calculated (see Table 3 & 4). All correlations between the variables and the dependent variable are statistically significant and positive. Then multiple regression analysis technique has been used. The results relating to prediction of university grade are shown in Table 5. It was found that Educational satisfaction is a significant predictor of university grade in both male and female. In the other hand, perceived social support is a significant predictor of university grade only in female.
Table1. Descriptive Statistics on the Participant Students University grade perceived social
support
Educational satisfaction
mean Std. mean Std. mean Std.
Female 16.34 1.08 37.02 8.27 41.53 10.95
Male 14.87 2.66 43.18 9.85 42.20 12.27
Table2. T-test results in comparing male & female
t df sig
University grade 3.27 118 0.01
perceived social support 6.79 118 0.001 Educational satisfaction 2.95 118 0.01
Table 3. Correlations between variables in male
variables 1 2
1 University grade - -
2 perceived social support 0.09* -
3 Educational satisfaction 0.21* 0.18*
* Correlation is significant at the 0.01 level
** Correlation is significant at the 0.001 level
Table 4. Correlations between variables in female
variables 1 2
1 University grade - -
2 perceived social support 0.37** -
3 Educational satisfaction 0.24* 0.13*
* Correlation is significant at the 0.01 level ** Correlation is significant at the 0.001 level
Table 5. Multiple regression analysis results relating to prediction of university grade
constant B Std.
Error
β t Sig
Male Perceived social support
0.162 0.110 0.134 1.469 0.149
Educational satisfaction
1.452 0.156 0.682 4.390 0.000
Female Perceived social support
1.298 0.252 0.506 7.159 0.000
Educational satisfaction
0.520 0.156 0.394 3.393 0.002
Conclusion and discussion
The results of the study supports the hypotheses predicted and indicate that there is a positive relationship of academic performance with the student's experience and there is no different between men and women. Students with positive experience show satisfaction with the quality of education (Liao, 2006). This is also proved to be significant from the correlation results of student experience and satisfaction.
Also the result shown perceived social support has the maximum ability of predicting university grade and it's just seen in female student. The females are more likely to receive social support from friends and significant others, than males are. One explanation could be that females are more emotional as compared to males; thus they might be able to share their feelings more freely and readily with friends. By doing so, the females perceive having someone to talk to as having adequate social support. On the other hand, males are expected to live up to certain social expectations that have been set and that if they were to share their feelings, it would be deemed as a sign of weakness. Hence, males tend
to perceive lower social support because they are more likely to feel that they have no one to express their feelings to. Social interaction in distance education is lower than traditional system. Lower interaction could be making its satisfaction lower as well (Altermatt, 2007). So, social support is important to predict university grade especially in women.
Refrences
1. Altermatt, E. (2007). Coping with academic failure: Gender differences in students' self-reported interactions with family members and friends. The Journal of Early Adolescence, 27, 479-508. doi:10.1177/0272431607302938
2. Bahar, H. H. (2010). The effects of gender, perceived social support and sociometric status on academic success. Procedia – Social and Behavioral Sciences, 2, 3801-3805. doi: 10.1016/j.sbspro.2010.03.593
3. Gillis, A., Jackson, W., Braid, A., MacDonald P., & MacQuarrie, M. (2000). The learning needs and experiences of women using print-based and CD-ROM technology in nursing distance education. Journal of Distance Education,15,(1), 1–
20.
4. Liao, L. (2006). A flow theory perspective on learner motivation and behavior in distance education. Distance Education, 27 (1), 45-62.
5. May, S. (1994). Women’s experience as distance learners: Access and technology.
Journal of Distance Education, 9(1), 81–98.
M-LEARNING EFFECTS ON QUALITY OF LEARNING AND ENGAGEMENT