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Dissertation Prospectus PPT II Draft Persistence of Traditional Students in Online Classes

Dissertation Prospectus PPT II Draft Persistence of Traditional Students in Online Classes. Barbara Kilthau HEOC 803 Benedictine University 29 July 2012. Methodology Behind Dissertation Prospectus PPT II Development. I am currently still determining whether to

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Dissertation Prospectus PPT II Draft Persistence of Traditional Students in Online Classes

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  1. Dissertation ProspectusPPT II DraftPersistence of Traditional Students in Online Classes Barbara Kilthau HEOC 803 Benedictine University 29 July 2012

  2. Methodology Behind Dissertation Prospectus PPT II Development I am currently still determining whether to conduct a qualitative or quantitative study. The quantitative study will be dependent on the access I will/can receive from a university in support of my study. If I cannot receive the access to an IR Department willing to help with an instrument and data collection for the quantitative study I will then conduct my study as a qualitative research study. This presentation will address both approaches as they are both appropriate in relation to my research purpose.

  3. Chapter 1:The Introduction Problem Statement: Persistence of Traditional Students in Online Classes What We Know: What We Don’t Know: With over 6.1 million students taking at least one online course during the fall 2010 term; an increase of 560,000 students over the number reported the previous year and the ten percent growth rate for online enrollments far exceeding the less than one percent growth of the overall higher education student population (Allen, 2011), it is easy to assess that students are increasingly choosing online classes as part of their educational journeys. What influences a traditional students choices to enroll in an online course? Are students who are enrolled in both face-to-face instruction and online classes equally persistent in both? Do campus interventions play a role in online student persistence in a traditional university? Thirty-one percent of all higher education students now take at least one course online (Allen, 2011). Why respondents rated programs designed specifically for adult/non-traditional students and programs designed specifically for online learners least effective among 60 items that were measured for their effectiveness in supporting student retention (Noel & Levitz, 2009)? A number of studies have shown that a higher percentage of students participating in online courses tend to drop out compared to students in traditional, face-to-face, classroom environments (Hiltz, 1997; Phipps & Merisotis, 1999). Student persistence is 20% lower in online courses than in equivalent face-to-face courses (Johnson, 2003). “With the growth of online education has come the problem of exceedingly high attrition rates” (Parker, 1999). Parker suggests that online student attrition in some institutions exceeds 40%, while others (Frankola, 2001) and (Diaz, 2002) put it at between 20-50%.

  4. Chapter 1:The Introduction Purpose Statement: Quantitative Approach: Qualitative Approach: The purpose of this study is to examine the relationship between student’s decisions to choose online classes and the factors that affect them in persisting or non persisting in online classes at a traditional university offering online classes to undergraduate students. The purpose of this study is to explore undergraduate student stories regarding their decisions to choose online classes and the factors that affect them in persisting or non persisting in online classes at a traditional university offering online classes to undergraduate students.

  5. Chapter 1:The Introduction Research Questions: Quantitative Approach: Qualitative Approach: What influences a traditional students choices to enroll in an online class? Are students who are enrolled in both face-to-face instruction and online classes equally persistent in both? What campus interventions play a role in online student persistence in a traditional university? What contributions influence a student’s decision to choose an online class? What contributions influence a student’s decision to drop out of an online class? Is there a relationship between factors that affect a learner’s choice of online learning and their decision to drop out? Do campus interventions play a role in online student persistence in a traditional university?

  6. Chapter 1:The Introduction Significance of the Study: There are several studies that explore and examine factors that affect online learner’s decisions to drop out or persist in online learning: Tinto’s student integration model (1993) Bean and Metzner’s student attrition model (1985) Kember’s longitudinal process model of dropouts (1989) Rovai’s persistence model (2003) Park’s review of studies that focused on identifying factors affecting non-traditional and non-degree online program students who drop out (2007) Hart’s Review of Literature on Factors Associated with Student Persistence in online Programs of Study (2012) Other studies focus on comparing online and traditional learning student characteristics: Hannay and Newvine’s Perceptions of Distance Learning: A comparison of online and traditional learning Katz’s Attitudes affecting college students’ preferences for distance learning Although there is much more I need to accomplish in my literature review; I have noticed a gap in literature on undergraduate students who attend traditional university’s and chose online classes to augment their traditional programs of study. Therefore my research will attempt to add to the literature in the field of education by more specifically studying online learner persistence in undergraduate classes from students who attend a traditional university and chose to enroll in online classes to augment their academic programs. This will attempt to improve the practice in identifying factors for online persistence and attempt to help university’s understand or predict (dependent on the type of study ultimately decided) success from students with dual-enrollment choices.

  7. Chapter 2:Review of the Literature Attrition Models -Tinto’s student integration model (1993) -Bean and Metzner’s student attrition model (1985) -Kember’slongitudinal process model of dropouts (1989) -Rovai’spersistence model (2003) Persistence Studies -Park’s review of studies that focused on identifying factors affecting non-traditional and non-degree online program students who -drop out (2007) -Hart’s Review of Literature on Factors Associated with Student Persistence in online Programs of Study (2012) -Frankola, K. Why online learners dropout(2001) -Park, J. & Choi, H. Factors Influencing Adult Learners’ Decision to Drop Out or Persist in Online Learning (2009) -Allen, E. & Seaman, J. Going the Distance: Online Education in the United States (2011) Comparing online and traditional learning student characteristics -Hannay and Newvine’s Perceptions of Distance Learning: A comparison of online and traditional learning -Katz’s Attitudes affecting college students’ preferences for distance learning -Park, J., & Choi, H. Differences in personal characteristics, family and organizational supports, and learner satisfaction between -dropouts and persistent learners of online programs(2007).

  8. Chapter 3:Proposed Methodology Major Research Perspective: Quantitative Approach: Qualitative Approach: The intent of a qualitative approach would be to explore the issue of persistence and non persistence of undergraduate students who are considered Traditional students; attending on-campus programs, but who also choose to enroll In online classes to augment their curriculum requirements. The intent of a quantitative approach would be to examine the relationship between the issue of persistence and non persistence of undergraduate students who are considered Traditional students; attending on-campus programs, but who also choose to enroll In online classes to augment their curriculum requirements.

  9. Chapter 3:Proposed Methodology Type of Design Qualitative Approach: Quantitative Approach: With the quantitative approach I would develop and conduct a survey using a questionnaire, then select a sample of students (undergraduate students who have enrolled in at least one online class to augment their traditional classes), have them complete the questionnaire, analyze the results, and then draw conclusions about this population in relation to my research questions. Identify the type: I will use a cross sectional design so that I can examine current attitudes, beliefs, opinions, and practices. This will allow me to capture how individuals think about the issue, but also practices that support/do not support the issue as well. Population: (100) Undergraduate students attending an on campus Program at a traditional university who choose to augment their curriculum with online classes. Sampling Procedures: The questionnaire developed will have personal, attitudinal, and behavioral questions; sensitive questions, and closed and open-ended questions. The questionnaire will be piloted to a small Number of individuals to make changes and revisions based on feedback. With the qualitative approach grounded in Positivism ,I hope to discern a causal relationship between variables that exist, can be identified, and can be further explained by conducting A Narrative study that involves the implementation of a semi-structured Interview. Population and Site: (5-7) Undergraduate students attending an on campus Program at a traditional university who choose to augment their curriculum with online classes. Data Collection: Semi structured interviews will be conducted With questions that are developed to guide the conversation but with enough generality to allow respondents latitude to discuss Openly issues relevant to the topics discussed. Data will be collected from the interview guide, coded with the Identification of themes between the respondents, literal coding Will be used to generally identify the themes and then focused Coding will be used to begin generating theoretical constructs.

  10. References: Allen, E. & Seaman, J. (2011). Going the Distance: Online Education in the United States, 2011. Babson Survey Research Group. November, 2011. Bean, J. P., & Metzner, B. S. (1985). A conceptual model of nontraditional undergraduate student attrition. Review of Educational Research, 55(4), 485-540. Diaz, D. P. (2002). Online drop rates revisited. The Technology Source, May/June. Retrieved June 26, 2012, from http://technologvsource.org/article/online drop_rates revisited/ Frankola, K. (2001). Why online learners dropout. Workforce. October 10. 53-63 Hannay, M. & Newvine, T. (2011). Perceptions of Distance Learning: A Comparison of Online and Traditional Learning. Journal of Online Learning and Teaching. Retrieved on 25 May from http://jolt.merlot.org/05011.htm Hiltz, S. R. (1997). Impacts of college-level courses via asynchronous learning networks: Some preliminary results. Journal of Asynchronous Learning Networks, 1(2), 1-19. Kember, D. (1989). A longitudinal-process model of drop-out from distance education The Journal of Higher Education, 60(3), 278-301. Park, J. (2007). Factors related to learner dropout in online learning. In Nafukho, F. M., Chermack, T. H., & Graham, C. M. (Eds.)Proceedings of the 2007 Academy of Human Resource Development Annual Conference (pp. 25-1-25-8). Indianapolis, IN:AHRD.

  11. References Cont: Park, J., & Choi, H. (2007). Differences in personal characteristics, family and organizational supports, and learner satisfaction between dropouts and persistent learners of online programs. In G. Richards (Ed.), Proceedings of World Conference on ELearningin Corporate, Government, Healthcare, and Higher Education 2007 (pp. 6444-6450). Chesapeake, VA: AACE. Park, J. & Choi, H. (2009). Factors Influencing Adult Learners’ Decision to Drop Out or Persist in Online Learning. Educational Technology & Society, 12 (4), 207-217) Phipps, R., & Merisotis, J. (1999). What's the difference? A review of contemporary research on the effectiveness of distance learning in higher education. Washington DC: Institute for Higher Educational Policy. Rovai, A. P. (2003). In search of higher persistence rates in distance education online programs. Internet and Higher Education, 6, 1-16. Tinto, V. (1982). Limits of theory and practice in student attrition. Journal of Higher Education, 53(6), 687-700. Tinto, V. (1993). Leaving college: Rethinking the causes and cures of student attrition (2nd ed.). Chicago, IL: University of Chicago Press.

  12. The Literature Matrix

  13. Back up Slides

  14. The Issue/Topic sixty-five percent of all reporting institutions said that online learning was a critical part of their long-term strategy(Allen, 2011). With over 6.1 million students taking at least one online course during the fall 2010 term; an increase of 560,000 students over the number reported the previous year and the ten percent growth rate for online enrollments far exceeding the less than one percent growth of the overall higher education student population (Allen, 2011), it is easy to assess that students are increasingly choosing online classes as part of their educational journeys. With students and institutions realizing the advantages of online learning we must also be diligent in recognizing the concerns associated with this new community in higher education.

  15. The Issue/Topic cont. A number of studies have shown that a higher percentage of students participating in online courses tend to drop out compared to students in traditional, face-to-face, classroom environments (Hiltz, 1997; Phipps & Merisotis, 1999). In spite of the growth in online learning many organizations and higher education organizations are concerned with the high dropout rates of this population of student.

  16. The Research Problem High attrition rates for online learners

  17. The Evidence Thirty-one percent of all higher education students now take at least one course online (Allen, 2011). “With the growth of online education has come the problem of exceedingly high attrition rates” (Parker, 1999). Parker suggests that online student attrition in some institutions exceeds 40%, while others (Frankola, 2001) and (Diaz, 2002) put it at between 20-50%. 31%

  18. The Evidence Tinto’s student integration model (1993) Bean and Metzner’s student attrition model (1985) Kember’slongitudinal process model of dropouts (1989) Rovai’spersistence model (2003) Park’s review of studies that focused on identifying factors affecting non-traditional and non-degree online program students who drop out (2007) Several theories and theoretical frameworks have been developed to explain why students drop out: Analyzing these studies and comparing and contrasting them against factors that identify why students choose online learning may show relationships between the two phenomenon

  19. The Purpose of the Research The purpose of this study is to examine the relationship between student’s decisions to choose online courses and the factors that affect them in dropping out of online courses. Factors Affecting Online Student Retention: A Study of the Factors of Students Decisions to Choose Online Courses and their Relationship to their Decisions to Drop Out Research Questions: What contributions influence a student's decision to choose an online course? What contributions influence a student’s decision to drop out of an online course? What is the relationship between factors that affect a learner’s choice of online learning and their decision to drop out?

  20. The Purpose of the Research cont. Based on the findings of this research: -We anticipate that we will be able to identify correlating factors that affect online learner choices to enroll and drop out of online courses -Contrast them in relationship to those factors that affect the decision to drop a course in hopes to discern those factors that specifically apply to increasing the retention of an online learner

  21. The Targeted Audience By conducting a quantitative analysis involving several studies in dropout rates and students choices for online learning, we will be able to contrast and compare these factors to better understand any relationship between choosing online learning and dropping out of online learning. With the identified relationships, administrators and advisors will be better prepared to plan interventions that will assist them in retaining online learners.

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