MOOC’s as an Alternative/Augmentation to Higher Education

The MOOC, which means “Massively Open Online Course,” is a method of delivering content to students on an open platform, providing learning opportunities virtually free to anyone who enrolls throughout the world.  MOOC’s enable students to self-direct their learning.   Higher education professionals may use MOOC’s to teach as a augmentation to existing content provided in an LMS.  However, the effectiveness of learning and the pedagogies used when a MOOC is involved may require further research.  Some examples of MOOC’s include Coursera, edX, Eliademy, Khan Academy, Lynda.com, OpenClassrooms, Stanford Online, Udacity and Udemy.  Each of these can be reached via their websites and have varying scales, audiences and licensing.

MOOC’s have been evolving from MOOC 1.0, in which a 1-to-Many relationship existed with a professor lecturing to a global audience, to 2.0 which added 1-to-1 lecture plus individual or small-group exercises, to 3.0, in which many-to-many model of massively decentralized peer-to-peer teaching to the current version, 4.0 in which there is a many-to-1 relationship where collective reflection and deep listening occurs to provide future possibilities to the learner (Scharmer, 2015).

There are many questions about how MOOC’s can be effective for learning or not.  For example, the lack the personal interaction in a college class, with a professor who can establish expectations and can directly penalize students for late, incomplete, sloppy or incorrect work.  So, simply having highly accessible content may not be enough for a student to learn.  Even though the MOOC offers vast volumes of content, it may lack the motivational aspects of structured college courses.  Perhaps smaller subgroups that access the content can be a solution for the impersonal nature of MOOC’s.  The aggregate nature and openness of MOOC’s enable interactions, discussions, and reflections from hugely diverse participants.  In addition, the multitude of participates can co-create, remix and repurpose knowledge (Mackness, 2013).

MOOC’s involve a community of learners who learn in a myriad of different depths and rates.  So, a social construction of epistemology may be emerging from these collective learning experiences.  Since a MOOC is a complex system with open, self-referencing and free flow of information coming from the participants, the organization of the MOOC takes on a life of it’s own, becoming self-organizing, and can change dynamically in response to participant interactions.  The MOOC environment can take on a fluid and flexible manifestation that deviates from its original purpose, exhibiting connectivist characteristics (De Waard, 2011).

Some learning styles may not translate well to utilization of educational technology tools like MOOC’s for learning.  Students may still prefer hardcopy books to the eBooks simply because of the portability, not needing to plug it in, and the tactile feel of the pages which many have been accustomed to as they were growing up.  Also, the nature of the MOOC as an impersonal animal, may require that actual educational support people be available to interact when the MOOC does not provide direct assistance in certain situations.

Since learning online requires basic e-Learning skill sets, as well as a context of knowledge to absorb and learn new and more advanced but related knowledge, support and resources should be available to assist learners.  For example, tutors may be employed to assist learners, to help address gaps, and to provide strategies for locating and accessing information.  Tutors or teaching assistants can be available to not just help students with the automated content, but also administering, organizing the information comparing and evaluating information.  In addition, in order to accomplish higher order Bloom’s Taxonomy activities, students may need assistance organizing and synthesizing information from the MOOC.  The social construction of knowledge requires the student to be intimate with the content (private interaction) to enable cognitive restructuring and to also be socially interactive with other learners, negotiating new knowledge (McPherson, 2004).

There are also socioeconomic factors for those who take advantage of a MOOC.  For example, the success rate is much higher among white collar, well-educated course takers than those in the lower strata of the socioeconomic hierarchy.  The “educational rich just get richer” because of the disparity between those with a lot of education early in their lives gives them an advantage when using technology for educational purposes.  The educational rich, when buying into technology, have to spend a much lower percentage of their income on technology.  So, in order for a disadvantaged socioeconomic individual to gain access to technology, they may have to use public provisions such as libraries, public school subsidized computing resources, or just utilize older technology which may not be enabled, or poorly equipped for modern technological instruments like video streaming which requires larger bandwidth over the Internet, and such things as large storage requirements (Toyama, 2015).

The bottom line is that until the socioeconomic inequities can be solved for all, that educational technology may just be a pipe dream.  Certainly, we see new modalities of delivering content through MOOC’s and other online courses involving highly collaborative environments, the use of mobile technology, which have the effect of enriching already good systems which hold content for delivery.  However, without educators behind the scene providing the personal factor that students get in face-to-face and hybrid courses, which are key motivators to learning, the delivery of courses through MOOC’s is no better than antiquated correspondence courses from years ago.

References

De Waard, I., Abajian, S., Gallagher, M. S., Hogue, R., Keskin, N., Koutropoulos, A., & Rodriguez, O. C. (2011). Using mLearning and MOOCs to understand chaos, emergence, and complexity in education. The International Review of Research in Open and Distributed Learning12(7), 94-115.

Mackness, J., Waite, M., Roberts, G., & Lovegrove, E. (2013). Learning in a small, task–oriented, connectivist MOOC: Pedagogical issues and implications for higher education. The international review of research in open and distributed learning14(4).

McPherson, M., & Nunes, M. B. (2004). The role of tutors as a integral part of online learning support. European Journal of Open, Distance and E-learning7(1).

Scharmer, Otto. “MOOC 4.0: The Next Revolution in Learning & Leadership.” The Huffington Post. TheHuffingtonPost.com, 04 May 2015.

Toyama, K. (2015, May 15). Why Technology Will Never Fix Education. Retrieved March 24, 2017, from http://chronicle.com/article/Why-Technology-Will-Never-Fix/230185/

 

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Big Data and Analytics for Education

Large data sets are generated from educational settings, for such purposes as assessment, evaluation, accreditation, regulation, etc.  How should they be analyzed?  Is data-driven analysis the way to go?  We can use traditional database queries to extract subsets of data, but a more powerful tool is emerging to address the myriad of data, and to help analyze it in ways we haven’t been able to before.

Besides the data that gets generated from classrooms, teachers, schools, students, administrators, governments, bureaucrats and the general public, collectively the stakeholders in education, we also can find that machine data is being generated in the form of # of hits to websites, frequency of logins, downloads, search criteria, messaging interactions, social media likes, various feeds from Twitter and other social media, use of electronic devices generating location information, etc.

The challenge is how do we get a big picture of the situations we want to learn about to make decisions when there are numerous forms of data.  We may choose one data set and find it was not the most appropriate for our analysis.  Being able to integrate multiple sources of data to generate valuable information is what we need.

Enter Big Data and Analytics.  With various tools that are available for businesses to analyze their customers, competitors, products, sales and other market conditions, educators can also find valuable information to help answer the pressing questions they face about student learning, effectiveness of pedagogies and instruction, where money is best spent to achieve the greatest return, how populations can be better served through public or private education, etc.  We also see Analytics being used in sports to find the best scenarios and resources to achieve winning results (see the book/movie Moneyball, for example).  Once data is collected, stored, and made available through software tools, it can be mined to find answers to these important questions.