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Online Courses. Research Problem and Analysis

Problem statement and purpose of the study

Upon introduction of online courses, instructors and analysts believed that there would be a revolution in the field of education (Kling & Hara, 2001). Institutions realized that they could save on seating and teaching resources; they could reach a wider student base and would also have more time to deliver course materials (Dutton et al, 2002). On the other hand, students could benefit by studying in desired institutions even when they were miles away i.e. it facilitated distance learning and also provided students with the ability to meet their personal obligations while still attaining a degree/diploma/certificate. However, studies have shown that online learning has not delivered on these expectations as attrition rates are ten to twenty percent higher in these programs. The purpose of this study is to analyze why poor retention rates are being reported and what can be done to improve them.

References/ literature review

Angelino et al (2007) assert that four major strategies exist for reducing attrition rates for online students. They cite increased student engagement and integration through orientation, online chats and orientation in order to facilitate interactions. They also suggest learner centered approaches by posting introductions and encouraging discussions. Learning communities through group projects and online student services have also been mentioned. On the other hand, Sanders (2005) asserts that the application of online learning in teaching languages should not be completely condemned even after he found that students in online classes had lower language proficiency than in traditional classes. He asserted that the best method for improving success rates in these institutions is by upgrading one’s learning outcomes and also by improving instructional delivery. Booker (2005) wanted to find out the factors that cause low retention rates in e learning and found that student’s perception of technology had an effect on retention, students’ perception of online flexibility had an effect on retention, however, their perception of online course value did not affect their retention rates, their demographics had an influence on their choice to complete an online course, level of material media richness affected retention rates and consistency and availability of technology had a positive effect on retention rates.

Research questions

The major research question in this study is: “What factors influence retention rates and enrollment rates of 2nd and 3rd semester online students?” 2nd and 2rd semester students were chosen for this analysis because they are more familiar with the online system and they have also demonstrated commitment to the course by passing the 1st semester. Since this is a broad question,. The following will be the hypotheses to be affirmed or rejected in the study: 1)2nd and 3rd semester online students’ interactions with one another increases retention and enrollment rates, 2) The nature of course design (use and flexibility of technology and materials) for 2nd and 3rd semester online students increases retention and enrollment rates, 3) An instructor’s qualities (approachability, flexibility, clarity and communication) increases 2nd and 3rd semester online retention and enrollment rates. The first research hypothesis focuses on learner to learner interactions, the second and third on learner to course interactions and learner-instructor interactions respectively.

Research design

The starting point in the research design will be establishing a theory. In this case, it has been asserted that there are lower retention rates in online learning amongst 2nd and 3rd semester students and that the causative factors need to be ascertained. After coming up with a theory, one should make deductions on the theory so as to make proposals on possible causative factors (Peters, 2009) It has been established that the nature of interactions between the learner and other learners, the learner and instructors and the learner and course content has an effect on retention rates. This has been illustrated through the specific hypotheses of the research. Thereafter, the researcher will attempt to develop measures for the variables under consideration and sample selection will be done here. For this study, the dependent variable is retention rates for 2nd and 3rd semester online learners while the independent variables include interactions between learners, course design and instructor’s qualities. After the latter phase, a data collection phase will follow on the latter issues and an analysis done to establish correlations between the dependent and independent variables (Emerson, 2001). At this point, the earlier hypotheses will either be confirmed and the initial theory upheld or they will be rejected and a new theory will be created.

Data collection methods

There will be two major approaches to be combined in this research and they include the use of secondary sources and a survey. Secondary sources will be critical in determining the sub factors that make up or contribute towards the independent variables stated in the research (Corbin & Strauss, 1990). They will also be critical in backing up findings by placing them in a wider online education context. On the other hand, primary data collection will be done through surveys which will seek to test the hypotheses by asking standardized questions. In order to provide some structure and direction in the survey, questions will be written down in the form of questionnaires and participants asked to answer them; their responses will then be recorded or keyed in on the researcher’s laptop.

Data analysis

Although graphs are considered as a relatively standard and simple way of carrying out data analysis, one cannot ignore the level of clarity that they can provide for research results (Seidel & Kelle, 1995). This study will employ the use of line graphs that have been used to correlate all independent variables and dependent ones on the x and y axes respectively. As such, there will be three major line graphs illustrating the correlation between retention rates and course content, retention rates and learners’ interactions and retention rates and instructors’ qualities. If the lines will have a positive slope, then this is a general indication that there is a relationship. However, regression analysis of the data will also be done to confirm these reports and ensure that there is indeed a positive relationship between the variables. Here, if the regression value will be more than one then the hypothesis will be accepted and if less than one or negative then it will be rejected.

Validity techniques

Validity is defined as an indication of how well a certain research has managed to measure what it had actually set out to measure in terms of its variables. In any research there is always ‘noise’ that an analyst needs to learn how to work outside of. One way of ensuring high validity for this study will be through the use of a large sample size from different geographical regions. This will ensure that interferences from demographic or other factors is eliminated since a thorough mix of subjects will have been chosen for the analysis (Jorgensen, 1999). On top of the latter, a pilot study will first be carried out so as to identify any potential interferences or small effects that can come in the way of an accurate outcome. Also, a detailed list of sub factors will be made of all the potential variables that constitute ‘instructor’s qualities’, ‘course content’ and ‘student interactions’ in order to affirm that everything to measured is actually completed.


Angelino, L. (2007). Strategies to engage online students and reduce attrition rates. Journal of Educators online 4(2), 1-14

Sanders, R. (2005). Redesigning introductory Spanish: increased enrollment, online management, cost reduction and effects on student learning. Language annals 38(4), 515-531

Booker, Q. (2005). E-student retention: factors affecting customer loyalty for online program success. Journal of Issues in information systems 6(1), 183-190

Seidel, J. & Kelle, K. (1995). Functions of coding in data analysis. CA: Sage

Corbin, J. & Strauss, A. (1990). Basics of quantitative research. Newbury CA: sage

Peters, M. (2009). What is research design? NY: Routledge

Kling, R. & Hara, N. (2001). Student distress in web based distance education. Edu-cause quarterly 3(4), 68

Dutton, M, Perry, J & Dutton, J. (2002). How online students differ from lecture students. Asynchronous learning networks journal 6(1), 45

Emerson, R. (2001). Contemporary field research. Boston: Little brown publishers

Jorgensen, (1999). Participant observation. Philadelphia: Temple press


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