Introduction

Many researchers investigated which factors impact course outcomes or course satisfaction, which in turn impacts course outcomes (Martin-Rodriguez, Fernandez-Molina, Montero-Alonso, & Gonzalez-Gomez, 2015). Scholars studied whether factors like, for instance, age (Greene, Oswald, & Pomerantz, 2015), gender (Cole, Shelley, & Swartz, 2014) and prior online learning experience (Marks, Sibley, & Arbaugh, 2005) influenced academic achievement and satisfaction. Yet, while proceeding through a course, learners may encounter barriers that hinder or even prevent them from reaching their personal learning goals (Henderikx, Kreijns, & Kalz, 2017a). This is especially the case in open learning environments like Massive Open Online Courses (MOOCs) as they are accessible to anyone regardless of schooling level, country of residence, age, gender, social status etc. (Green et al., 2015). Research on barriers in MOOCs illustrated that most learners come across barriers to a greater or lesser extent (Khalil & Ebner, 2014). It can thus be said that encountering barriers is inherent to learning and is likely to affect personal learning success. Yet, research investigating the relationship between certain factors and the likelihood of encountering barriers to MOOC-learning - or more general on barriers to (open) online learning - is sparse. The purpose of this study was to fill this gap by examining whether there is a relationship between age, gender and previous online learning experience and the likelihood of encountering barriers to MOOC-learning.

Barriers to MOOC-learning

Online learning is not without challenges and learners do not always succeed in pursuing their personal learning goals due to various reasons (Henderikx et al., 2017a). Research on barriers to online learning identified many different possible obstacles. Most mentioned barriers in literature are technical problems with the computer (Song, Singleton, Hill, & Koh, 2004), family and workplace issues (Park & Choi, 2009), lack of interaction (Khalil & Ebner, 2013a), lack of time (Belanger & Thornton, 2013) and insufficient academic background (Belanger & Thornton, 2013; Park & Choi, 2009). Also, insufficient technology background (Khalil & Ebner, 2014), computer and/or internet issues (Song et al., 2004), and lack of instructor presence (Onah, Sinclair, & Boyatt, 2014) are barriers that are frequently experienced by learners. We, therefore, expect that the top-4 barriers encountered by the survey participants at least entails three of the aforementioned barriers from literature.

Learner characteristics

The relationship between various learner characteristics and online course outcomes and satisfaction has been extensively studied. Studies by Greene et al. (2015), and Park and Choi (2009) on establishing which learner characteristics were significant predictors for success in distance education and MOOC context, found that age was not a significant predictor. Furthermore, research on gender as a predictor for online course success or satisfaction showed ambiguous results. Cole et al. (2014), Greene et al. (2015), and Park and Choi (2009) established that there was no difference between male or female learners. Yet several studies, for instance a study by Lu and Chiou (2010), found that female students were less satisfied than their male counterparts. Lastly, research on the relation between prior online learning experience and student performance showed no statistical significance for prior online learning experience as a predictor for student success (Marks et al., 2005; Thurmond, Wambach, Connors and Frey, 2002).

Based on these previous findings we hypothesise that the likelihood of MOOC-takers encountering top-4 barriers while learning is related to gender but not related to age and previous online learning experience.

Participants Participants were individuals who took part in 13 Spanish-language MOOCs offered by Educalab – an innovation centre from the Spanish Ministry of Education. The data was collected using pre- and post-questionnaires. The pre-questionnaire was completed by 4925 participants and the post-questionnaire by 1058 participants. In total 596 participants (406 women, 191 men, Mage= 43, age range: 20-72 years) completed both questionnaires. The majority of the participants, who completed both questionnaires, had the Spanish nationality (81%). A further 6.1% were participants from other European countries like Croatia, Greece, Italy, Poland, Czech Republic, Serbia and Romania. The remaining 12.9% represented participants from Mexico, Argentina, Brazil, Chile and Colombia. Most of these participants held a master (29.5%) or bachelor (48.3%) degree. 6% Of the participants had a doctorate degree, while 15.2% had an associate or secondary education degree. The remaining 1% of the participants finished middle school or below. 70,8% Of the participants were employed for wages, while 5.4% were self-employed. A further 4.4% were currently looking for work and 3.4% was not looking for work. 4.5% of the participants were students, 1.2% Was retired and 10.3% indicated that they were homemaker or other. Lastly, most participants participated in up to 5 MOOCs (85.7%). 8.1% Participated in 6 to 10 MOOCs, 4.5% between 11 and 20 MOOCs and 1.4% between 21 and 40 MOOCs. Materials The questionnaires included several general questions on gender, age, educational background, employment status. To indicate their online learning experience (OLE), participants were asked to indicate how many MOOCs they had taken in the past. Furthermore, respondents were asked to indicate what barriers they encountered during their MOOC-learning. Respondents could indicate multiple barriers. These barriers were derived from an explorative, non-exhaustive, literature review on barriers in MOOCs and online learning in general, including articles from 2004 until present. Examples of listed barriers are ‘lack of decent feedback’, ‘family issues’ and technical problems with the computer’. Procedure All the registered MOOC-takers received an invitation in the first week to participate in the pre-questionnaire. At the end of the last week of the MOOCs all the registered MOOC-takers received an invitation to participate in the post-questionnaire. Participation was voluntary and informed consent was collected from participants. Analysis Four 3-predictor logistic models were fitted to the data to test the posed hypothesis regarding the relationship between the likelihood of MOOC-takers encountering respective barriers and their age, gender and OLE.

First, data analysis showed that lack of time, family issues, workplace issues and insufficient technology background were the 4 most encountered barriers. Next, gender and OLE showed a significant relationship with ‘lack of time.’ The odds ratio for gender is 1.435 (95% CI .997 to 1.013, B = .361, p=.049). Thus, females are 43.5% more likely to encounter this barrier than men. The odds ratio for OLE is .961 (95% CI .927 to .997, B = -.040, p=.034), indicating that for each additional MOOC taken the chance of encountering this barrier decreases with 3.9%. In addition, age, gender and OLE showed a significant relationship with ‘insufficient technology background’. The odds ratio for age is 1.036 (95% CI 1.006 to 1.066, B = .035, p = .017). With the increase of one year of age participants are 3.6 % more likely to lack in technology background. The odds ratio for gender is 2.687 (95% CI 1.363 to 5.294, B = .988, p = .004). Thus, females are 168.7 % more likely to encounter this barrier than men. The odds ratio for OLE is .917 (95% CI .844 to .997, B = -.087, p = .041), which indicates that for each additional online course taken the chance of encountering this barrier decreases with 8.3%. All other tested relationships were not significant. Conclusion The main conclusion is that gender is related to two of the four barriers, which partly confirms the hypothesis. Being female makes a considerable difference when it comes to ‘lack of time’ and ‘technology background’. Second, in contrast to the research findings by Marks et al. (2005) and Thurmond et al., (2002), OLE was found to be a significant predictor for both ‘lack of time’ and ‘insufficient technology background’, which is not consistent with our hypothesis.

Belanger, Y., & Thornton, J. (2013). Bioelectricity : A quantitative approach. Durham, NC. Retrieved from http://dukespace.lib.duke.edu/dspace/bitstream/handle/ 10161/6216/Duke_Bioelectricity_MOOC_Fall2012.pdf?sequence=1 Cole, M., Shelley, D., & Swartz, L. (2014). Online instruction, e-learning, and student satisfaction: A three-year study. The International Review of Research in Open and Distance Learning, 15(6), 112–113. Greene, J. A., Oswald, C. A., & Pomerantz, J. (2015). Predictors of retention and achievement in a massive open online course. American Educational Research Journal, 52(5), 925–955. Henderikx, M., Kreijns, K., & Kalz, M. (2017a). Refining success and dropout in MOOCs based on the intention-behavior gap. Distance Education, 38, 353-368. Khalil, H. & Ebner, M. (2013a). Interaction Possibilities in MOOCs – How Do They Actually Happen?. International Conference on Higher Education Development (pp. 1-24). Mansoura University, Egypt. Khalil, H., & Ebner, M. (2014). MOOCs Completion Rates and Possible Methods to Improve Rentention - A Literature Review. In World Conference on Educational Multimedia, Hypermedia and Telecommunications (pp. 1236–1244). Chesapeake, VA: AACE Levy, Y. (2007). Comparing dropouts and persistence in e-learning courses. Computers & Education, 48, 185–204. Lu, H. P., & Chiou, M. J. (2010). The impact of individual differences on e-learning system satisfaction: A contingency approach. British Journal of Educational Technology, 41(2), 307–323. Marks, R. B., Sibley, S. D., & Arbaugh, J. B. (2005). A structural equation model of predictors for effective online learning. Journal of Management Education, 29, 531–563. Martín-Rodríguez, O., Fernández-Molina, J. C., Montero-Alonso, M. A., & González-Gómez, F. (2015). The main components of satisfaction with e-learning. Technology, Pedagogy and Education, 24(2), 267–277. Onah, D. F. O., Sinclair, J. E., & Boyatt, R. (2014). Dropout rates of massive open online courses: behavioural patterns. In 6th International conference on Education and New Learning Technologies (pp. 5825–5834). Barcelona, Spain: EDULEARN14 Park, J. H., & Choi, H. J. (2009). Factors influencing adult learners’ decision to drop out or persist in online learning. Educational Technology and Society, 12(4), 207–217. Song, L., Singleton, E. S., Hill, J. R., & Koh, M. H. (2004). Improving online learning: Student perceptions of useful and challenging characteristics. The internet and higher education, 7(1), 59-70. Thurmond, V. A., Wambach, K., Connors, H. R., & Frey, B. B. (2002). Evaluation of student satisfaction: Determining the impact of a Web-based environment by controlling for student characteristics. The American Journal of Distance Education, 16, 169–189.