MOOCs, Massive Open Online Courses, if true to their name, are defined by three elements. Their openness means that they are available to anyone who wants to use them to learn. This logically implies that they are free, removing any financial barrier for even the poorest student. Being online means they are available on the internet. In providing courses, MOOCs represent a major shift in scale beyond open learning objects. They operate at the level of a whole course (or subject) – they provide a coherent learning sequence, with integrated learning materials and formative assessment, all created and managed by outstanding teachers from the world’s top institutions. If a course is of high quality, free (open) and readily accessible (online), it follows that massive numbers of students will grab the chance to get a first rate education for free. This creates scalability challenges. There is solid understanding of how to tackle the engineering of web sites that gracefully handle huge numbers of users. The much less well understood scalability issue is for the teaching, learning and assessment models. The MOOC approach meshes with the acknowledged importance of social interaction among learners as a MOOC can call upon its large community of learners to play two key roles: supporting learning, via discussions, and assessment, based on peer review.
There is a delightful idealism and altruism in the words of many of those driving the MOOC movement. We all agree that quality education is important. We all know that there is a huge gap between the educational opportunities of the most privileged and the most disadvantaged learners. MOOCS are presented as a means to help close this gap.We can conjure up images of students from the developing world, and the most disadvantaged groups in the first world, as well as lifelong learners with changing learning needs, all slaking their thirst for knowledge, by learning at the feet of the intellectual giants of the world’s leading research institutions. This is an example of Friedman’s flat world . The wide availability of inexpensive networked computers makes it possible to cater for a large unmet need. Beyond the excitement of the learning opportunities of the actual MOOC courses, a different dimension of promise is in MOOCs as open platforms, built by a new and energetic open source community. Perhaps this will be a revolution in software for authoring and delivering high quality learning opportunities.
Emerging MOOCs have the potential to improve, by exploiting diverse results and techniques from AIE( artificial intelligence in education). MOOC platforms also create new opportunities for new AIE research. They are in particularly interesting for computer scientists working in fields such as Educational Data Mining and Learning Analytics . Not only can learning-related data from MOOC courses be truly “big” (provided the fallout rate is suitably managed), the open nature of MOOCs seems likely to provide a very heterogeneous student body signing up. These students may interact in ways that are not further structured by established social contracts and roles, making MOOCs an ideal vista for applying Social Network Analysis methods in particular.
Methods from educational data mining and learning analytics can in general be applied for knowledge creation (learning more about learning and interaction, and relevant technologies). They can also serve applied purposes: supporting students, teachers, educational institutions and systems. A rather obvious applied challenge, in light of the mentioned attrition rate, is the automatic identification of students at risk of failing. Similar techniques can be used to “nudge” students who need it, as well as for course- or cohort-based monitoring . We can expect the growth of large collections of learning data, similar to the PSLC Datashop. This can provide a new scale in test-beds for EDM researchers.We can then expect to see more innovative uses of learning data to improve teaching as in the elegant system to generate hints for students, by drawing on historic data from the paths taken by successful and unsuccessful students.
Pedagogic interface agents are one of the current hot topics in AIE. These anthropomorphic conversational characters have been shown to give real benefits for learning . While one might expect this effect to be short-lived, being of limited value once the novelty has worn off, recent results indicate that interface agents may actually help people stay the course over the long term . They seem to offer promise of a valuable role in MOOCs.
In addition to the general opportunities for research on how to support (on-line) learning with technical means, MOOCs might provide a particular fruitful arena for research on e-portfolio systems, competence management (including assessment), and technical support for lifelong learning (including open learner models). The quality, timing and form of feedback is critical to effective learning. MOOCs currently rely heavily on selfand peer review. These forms already have a recognised place in higher education . However, they are more effective if students are explicitly taught how to do it, a valuable role for AIE systems. Another key form of valuable feedback can be provided for learning contexts for high quality assessment can be automated. There are many systems already for this in domains like programming, mathematics and physics. And AIE research has produced many systems that have been able to give high quality feedback in these classes of well-defined learning domains such as mathematics, physics, and computer programming.
These classes of MOOCs can also be part of a hybrid model. For example, many developing countries have
a large unmet need for skilled IT professionals, where the learning need involved well-defined technical skills.
The most recent MOOCs already have several attractive offerings in this space. This creates the opportunity for employers to create a a learning environment where the MOOC delivers content and basic formative assessment. The employer can complement this by nurturing learning communities. They can conduct summative assessment that determines employment options, a significant motivator for students. the motivator of summative assessment conducted by the employer.
More recently, AIE has moved to ill-structured domains . Notable among these are lifelong generic, particularly the meta-cognitive skills that are a key to success in MOOCs. AIE has demonstrated success in explicit teaching of these skills. The rhetoric about MOOCs refers to personalised learning, with reference to Bloom’s classic 2-Sigma paper about one-to-one tutoring . However, current MOOCs come nowhere near trying to achieve that level of personalisation. One key to the success of AIE systems is in the nature of the personalisation, which is based on a learner model. Indeed, some have argued that very core of AIE is the role of learner model . This core notion of creating an explicit learner model could be readily integrated into MOOCs. Open learner models have been demonstrated to improve learning and they could be a fundamental means for learners to monitor their progress and plan their learning.
It is hard to conceive of MOOCs as having any lasting impact on (higher) education without concern for how
the single MOOC event (course) gets integrated into individual career planning and personal development as
well as into an comprehensive certification framework . Hence, research on how to support the integration of learning events on the individual as well as the societal level will be crucial. The excitment around MOOCs is justified, both in terms of the potential value they offer and the quality of the players who have launched them. What a great opportunity to integrate the lessons, techniques, methods and tools of AIE!
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