In a few weeks, our edX course Data, Analytics, and Learning (#DALMOOC https://www.edx.org/course/utarlingtonx/utarlingtonx-link5-10x-data-analytics-2186) will start. We (Carolyn Rose, Dragan Gasevic, Ryan Baker, and I) have spent the last several months thinking through the course structure and format. This is a short overview of the innovations that we want to explore during the course. The innovations build heavily on community and network approaches that I and others (Stephen Downes, David Wiley, Alan Levine, Jim Groom, Dave Cormier) have used in previous open courses.
Since MOOCs gained popularity with top tier universities, significant effort has been put into finding new ways to present learning content. Videos, simulations, and graphics now contribute to formal MOOCs often costing several hundred thousand dollars to develop. In terms of content presentation, DALMOOC will pale in comparison to existing well-funded courses. Our focus has been on improving the social experience of learners. In particular, we are looking to solve the following problems with MOOCs:
- Students often flounder in MOOCs as there is limited social contact between learners and limited timely support.
- Learners have limited engagement in developing knowledge together. Many MOOCs reflect a structured and linear process of content presentation. There is little alignment with the architecture of knowledge in a participative age.
- Learners have a difficult time getting to know each other or finding like others as major platforms do not focus on developing learner profiles
- The connection between learning and application is hampered as MOOC resources do not always persist after a course has ended and there is limited search functionality in MOOCs.
- Courses are not adaptive and serve the same content to all learners, regardless of prior knowledge
To address these challenges, we have adopted/developed the following approaches.
Timely help resources: Through the use of a tool developed by Carolyn Rose’s team called the Quick Helper, course participants will have access to timely help resources. When a student would like to ensure their request for help is seen, they may click on the Quick Helper button, which will guide them to formulate a help request. A social recommendation algorithm will then match the help request to three potential helpers from the community. They will be presented with these three choices, and will have the option to select who will be invited to their help request thread. The Quick Helper will then send an email to each selected helper with a link to the help request thread and an invitation to participate. The intent with this approach is to provide timely help to students and to engage other learners in helping answer questions asked by peers.
Social embeddedness Social has become an abused term. Everything now has social attached. Aside from this hype, the value of social learning is clear in academic literature. In order to improve connections, we will also be using a social competency based software (ProSolo) that will give learners the opportunity to identify learning goals, connect with others around shared goals, and create a pathway for recognition of learning. A second aspect of ProSolo is the creation of learner profiles so students can find others with shared interests. DALMOOC has been designed to model a distributed information structure. As such, learners will be encouraged to participate in roughly any space they would like: blogs, facebook, twitter, edX discussion forums, etc. I have a bias for the value of learners owning their own learning spaces. A key challenge that arises as learners engage in different spaces is one of fragmentation. Learning is a coherence forming process and knowledge is a state of connecting information pieces. As such, we will be adopting an aggregation approach similar to what Stephen Downes pioneered with early MOOCs: gRSShopper. Content will be aggregated and shared in a daily email to learners. By aggregating learner content and providing persistent profiles, we anticipate higher levels of learner engagement.
Another social layer is the inclusion of group work using synchronous chat activities supported by intelligent conversational agents. This intervention builds on the work by Carolyn Rose’s group on dynamic support for collaborative learning using an architecture called Bazaar also developed by her team. Group work is difficult in MOOCs because of high drop out rates. To address this challenge, we are using a lobby tool developed by Rose’s lab that enables groups to form on the fly, on an as needed basis. When students reach a point in their trajectory through the course when they are ready to engage in discussion, they will click on a live link to enter the lobby program, which will match them with other learners who are also ready to engage in that activity. This is a benefit of MOOCs – with many learners online simultaneously, scale works for quick, weak tie, group formation.
Persistence. The content of the course will remain available for students to access post-course, particularly the summary emails and learner profiles in ProSolo. Learners will have the option to search context relevant resources in ProSolo. We hope that this will assist in creating a persistent practitioner community where learners will access resources post-course and continue to engage with each other on social media and in ProSolo.
Adaptivity. While adaptive learning is a rapidly growing area of research interest, it isn’t being done well yet. Early projects like CMU’s OLI focus on content focused courses with an emphasis on supported mutli-step problem solving. Adapting a course on learning analytics is more challenging as the problems are much less well-formed. “Right answers” are not always clear, and more importantly, ideal learning trajectories are more individualize. To compensate for this weakness, we’ve taken an idea from DS106: the assignment bank. The assignment bank focuses on adaptivity at the level of application. All learners experience the same instructional content. Each learner is able to challenge herself by selecting assignments with various gradients of complexity.
Matt Crosslin – lead designer on DALMOOC – has been blogging on the design decisions we have made throughout the course. His blog is a great resource.
There are numerous other research opportunities with MOOCs, including adaptive pathways during the course, personalized learning, self-regulated learning, alternative credentialing approaches, automated assessment, evaluating the impact of socially created artifacts on learning, alternative approaches to lectures and content presentation, and so on. Those are topics for future exploration. For DALMOOC, our focus is on timely help, social learning, persistence, and adaptivity through assignments. Even this seems like a slightly heaving set of alterations to the traditional MOOC. As with previous MOOCs that I’ve taught, the intent is to provide learners with a range of tools, technologies, and approaches and provide learners with the opportunity to sensemake and wayfind through complex information spaces. All the fun (and deep learning) happens in that process.