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The video Gooru Q&A provides valuable insight into the structure and inner organization of OERs. The question that seems to be of most interest is where can such a project find appropriate funding and how the money question can affect the actual outcomes in the organization. In the video, Dr. Ram mentions that one of the main reasons for the prevalence of STEM courses in Gooru is financial. Therefore, one can decipher that funding plays a central role in choosing the direction of development. At the same time, the speaker does not provide any detail about the Gooru’s sources of funding.
The standard sources of financial support for OER are private and government foundations. While William and Flora Hewlett Foundation has contributed the most for developing OERs, other international, provincial, or even local research funds can help in shaping up an OER. Moreover, crowdfunding can also be a viable option for acquiring money for the purpose. In conclusion, OER funding sources are scarce and often predetermine the course of the project development.
Open Learning Analytics
In the article “Open Learning Analytics: An Integrated & Modularized Platform” Siemens et al. discuss how big data analysis should be applied to education to provide better outcomes. The authors argue that stakeholders of the education system have access to an excessive amount of information that they cannot manage. They provide an overview of their integrated learning analytics platform and establish the goals for the platform to be the most beneficial.
Siemens et al. believe that the software should develop a common language of information exchange; the analytical engine must demonstrate transparency and customizability of the research methods. Moreover, the platform should have an intuitive interface and connect to the existing research being conducted by related analytics initiatives. In short, analytics can be beneficial for education leaders to by providing relevant, concise data and its analysis.
The authors seem to base their analytical platform on the theory of Connectivism. The software can be associated with one of the “nodes” that is connected to the network of similar engines. Every time the node enters the learning environment, it shares the information and modifies its knowledge by interacting with other nodes.
Social Learning Theories
The article “Connectivism: Learning Theory of the Future or Vestige of the Past?” by Kop and Hill analyzes attempts to answer the question if Connectivism can be considered a learning theory. First, the article offers an overview of the theory by introducing the notion of “node” and interpreting its interactions with “network.” Second, the authors state that Connectivism, while inspired by the theory of distributed knowledge, cannot yet be called a fully formed learning theory. However, the theory can be efficiently applied in pedagogy and curriculum, as the way of learning remains the same, while the technology that supports learning changes. In short, a paradigm shift may be occurring in educational theory and Connectivism may be considered a first sign of “radical discontinuity.”
Connectivism discards the idea of individual learning; however, personal meditations can also be the source of knowledge. At the same time, there is no doubt that observations and experiences happen beyond the confines of the individual mind. Therefore, social and individual learning work in accord, as people gather new information from the others and with the help of the others and then meditate on the matter to formulate one’s own attitude towards the issue.
In the article “Artful: Adaptive Review Technology for Flipped Learning” Szafir and Mutlu introduce a high-tech educational innovation that is designed to improve learning outcomes through adaptive review. The authors of the article demonstrate a new gadget that monitors students’ attention during a lecture and offers topics for revision based on the gathered data.
The implications of such innovations in teaching and learning are tremendous, as the device can substitute the teacher as the monitoring party and provide better opportunities for flipped classroom activities. These changes can support the development of OER and remote education. However, gadgets similar to the one described in the articles can lead to the disclosure of personal information. Users’ privacy can be violated if the data acquired by the devices are not adequately protected. Personalized content, including the attention demonstrated by the learner, has proven to a very sensitive subject for the users. Therefore, insufficient protection of the information gathered through innovate systems may lead to distrust from all the stakeholders.
Open Education Resources
The article “Crowdsourcing and Curating Online Education Resources” by Porcello and Hsi provides a review of essential components for public education resources (OERs) focused on Science, Technology, Engineering, and Math
(STEM) to be successful. The authors state that OERs tend to balance between the two poles in their structure: crowdsources and employing professional staff to populate their catalog. The authors argue that STEM OERs should be based upon a standard set of terms and the data should be mostly acquired from crowdsourcing, but verified and administered by experts. STEM OERs should let the community leave digital footprints, such as comment and ratings. Moreover, OER platforms must be unified in their back-end design to be interoperable. Porcello and Hsi conclude that an approach that perfectly balances all the four crucial aspects will improve the quality of user experience.
Massive open online courses (MOOCs) are a form of OER, but these terms are not synonymous. OER is an online resource that may have enrollment restrictions, while MOOCs are courses open for everyone willing to apply.