In this weeks ONL work I focused my investigation on Massive Open Online Courses (MOOCs) and challenges in Higher Education (HE). In a time when masses of people worldwide, and specially in lower income countries necessitate HE, MOOCs is one possible solution. From the way MOOCs are used, criticism includes questioning its credibility, student-teacher interaction, teacher’s referral back to students on their learning activities and results and content-quality (Lourdes et al 2016). Questioning the credibility of MOOCs are in one way related to the massive amount of course participants and thereby vulnerable to, piracy, falsification and fraud. By verifying the authenticity of course participants by using identity software’s for identification, MOCCs can increase the credibility (Coursera, EdX). According to literature presenting early statistics of MOOCs showed that they had a high rate of participants (85-95%) that never completed the attended courses (Jordan et al 2014, Fidalgo-Blanco et al 2016). This was partly explained by lack of student-teacher interaction, lack of effective methods for evaluation of learning activities and feedback from the educational organisations and teachers. In my opinion, since MOOCs can include hundreds of participants and there is a need for effective pedagogical methods to engage with participants supporting the online teaching-learning environment. David Withe explained in the video “Visitors and Residents: Credibility” the institutions online “modes of engagement” and the importance for institutions to practice a “resident” mode of engagement for credibility among learners. The massiveness of learners is highly resource demanding with a need for many teachers and facilitators to keep up the “resident” mode of engagement. One way suggested to overcome this problem was according to literature to practice online automatic evaluation systems. Systems that automatically can handle massive amounts of students learning activities, content and at the same time give students active feedback on their learning process.  A few examples of these systems used today in HE are “Open Response Assessments” (ORAs) artificial intelligence (AI) (Popenici, et al., 2017), learning analytics and data mining (KDD).  However, in my opinion the lack of teacher-student interaction cannot solely be solved by automatic tools. Also and previously discussed in topic 1, teachers and student’s digital literacies impact the mode of engagement in MOOCs. Further, a meta-analysis on key factors for effective teaching in MOOCs pointed out “preparation, attraction, participation, interaction, consolidation and post-course support” (Wong 2016). These factors were considered important for explaining how teaching in MOOCs can enhance effective student learning. However, the study did not suggest one single success teaching strategy for learning effectiveness. Instead I perceive from my investigation that MOOCs, with its participants and teachers are constantly developing. Many different types of factors can impact on effective teaching and learning for MOOCs.

By Ellinor Östensson

Atiaja, Lourdes & Guerrero-Proenza, Rey Segundo. (2016). MOOCS: PROBLEMS AND CHALLENGES IN HIGHER EDUCATION. [accessed Apr 01 2020].
Signature Track Coursera, (Accessed: 26 March 2020)
EdX, (Accessed: 26 March 2020)
Jordan, Katy (2014). Initial trends in enrolment and completion of massive open online courses. InternationalReview of Research in Open and Distance Learning, 15(1) pp. 133–160. %5BAccessed Mar 26 2020].
Fidalgo-Blanco, Á., Sein-Echaluce, M.L. & García-Peñalvo, F.J. From massive access to cooperation: lessons learned and proven results of a hybrid xMOOC/cMOOC pedagogical approach to MOOCs. Int J Educ Technol High Educ 13, 24 (2016).
David White Video “Visitors and Residents: Credibility
ORAs: [Accessed Mar 27 2020]
Learning analytics in higher education, [Accessed Mar 27 2020]
Popenici, S.A.D., Kerr, S. Exploring the impact of artificial intelligence on teaching and learning in higher education. RPTEL 12, 22 (2017).
KDD, The community for data mining, data science and analytics (KDD), available at:  [Accessed Mar 27 2020]
Wong, B. (2016), ”Factors leading to effective teaching of MOOCs”, Asian Association of Open Universities Journal, Vol. 11 No. 1, pp. 105-118.

Topic 2: Open Learning – sharing and openness