Seminario eMadrid 2015 05 22 (UAM) Su White - Incorporando MOOCs en la formación presencial
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Seminario eMadrid 2015 05 22 sobre MOOCs on Campus (UAM) Su White - Incorporando MOOCs en la formación presencial
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Seminario eMadrid 2015 05 22 (UAM) Su White - Incorporando MOOCs en la formación presencial
- 1. Bending MOOCs into face to face teaching Su White University of Southampton, UK 21/05/15 @suukii http://blog.soton.ac.uk/mobs/home/
- 2. Our MOOCs Oceans Shipwrecks Developing your research project Portus Web Science Digital marketing Contract Management Waterloo 21/05/15 @suukii http://blog.soton.ac.uk/mobs/home/
- 3. Learning by doing •Desire •Expertise ambition •Deadlines •Conceptualiation reality •Students •Understanding realisation •Refinements •Optimisation Reflection 21/05/15 @suukii http://blog.soton.ac.uk/mobs/home/
- 4. Teaching (& blending) Orchestrated • Preparation for – Instruction – Discussion – Structured reflection – Independent learning • Vehicle for – Enhancing motivation – Sustaining motivation Enviornmental • Practice or rehearse – Skills – Argumentation • Revise/prepare • For tests and examinations • After a break (vacation) • Contextual reminder 21/05/15 @suukii http://blog.soton.ac.uk/mobs/home/
- 5. Blending (and teaching/learning) purposeful • Flipped classroom • Structured exercise – Revise for test – Consult for extended writing – Make the basis of class discussion – Use as an exemplar of independent research environmental • Let the learners decide – Access as a resource – Available if wanted • Independent or semi-independent learning 21/05/15 @suukii http://blog.soton.ac.uk/mobs/home/
- 6. Our MOOCs Oceans Shipwrecks Developing your research project Portus Web Science Digital marketing Contract Management Waterloo 21/05/15 @suukii http://blog.soton.ac.uk/mobs/home/
- 7. Web Science • Flipped/blended – Masters – Undergraduate – Generic • Personal blending – Review of approaches • Revisions in pipeling 21/05/15 @suukii http://blog.soton.ac.uk/mobs/home/
- 8. Oceans • Resources – Outreach – Public Awareness • Classroom – Motivation • Educational Development – Exemplars 21/05/15 @suukii http://blog.soton.ac.uk/mobs/home/
- 9. Digital marketing • Courses – Masters – Undergraduate • After the event using archives of discussion • Extension of existing face to face methods • Next time – In parallel 21/05/15 @suukii http://blog.soton.ac.uk/mobs/home/
- 10. Developing your research project • Began as outreach/load balancing – Augmenting existing support – Used for recruitment • Individual Integrated – Generic – all levels • Independent – appropriated 21/05/15 @suukii http://blog.soton.ac.uk/mobs/home/
- 11. Contract management • Courses • Generic business – Illustrative • Potential - clients • Professional development 21/05/15 @suukii http://blog.soton.ac.uk/mobs/home/
- 12. Portus • Courses – Summer schools – Undergraduate – Masters • Application – Virtual fieldwork – Flipped classroom 21/05/15 @suukii http://blog.soton.ac.uk/mobs/home/
- 13. Shipwrecks • Public Awareness • Courses – Marine archeology 21/05/15 @suukii http://blog.soton.ac.uk/mobs/home/
- 14. Mini MOOC • Next off the stock • Developed by the library • Using/showcasing archive collection • Courses – Nothing planned.. But – Introduction to archives for history students at all levels • Public awareness – Same principle 21/05/15 @suukii http://blog.soton.ac.uk/mobs/home/
- 15. Learning and change • Constructive alignment – Learning in the most appropriate way – Matching activities with valued skills, knowledge and understanding – Trust the subject expert • Pace and scaffolding • Keeping the learner on task • Modelling (learning) behaviours We may change our use depending on the stage of learning But also trust the learners to decide21/05/15 @suukii http://blog.soton.ac.uk/mobs/home/
- 16. Trust • Trust humans to see insights • Academics are smart • They like to be efficient • Change needs trust to happen – Coffee room conversations – I trust my friends – Reputation can work too • Like teachers – Student understanding evolves over time – Understandings can develop – There can be understanding memes – There is value in modelling 21/05/15 @suukii http://blog.soton.ac.uk/mobs/home/
- 17. Final thoughts – Let the learners decide what is valuable – Academics can’s unlearn – All students are on different paths • Thank you • Any Questions?? 21/05/15 @suukii http://blog.soton.ac.uk/mobs/home/
- 18. FOLLOW WHAT WE DO 21/05/15 @suukii http://blog.soton.ac.uk/mobs/home/
- 19. Curation activity Mendeley Group: ● Over 300 academic sources related to MOOCs ● All tagged and classified ● Open Group ● you can join ● You can follow Scoop.it page ● Daily curation ● Grey Literature ● MOOC news and journalistic articles
- 22. Other groups FLAN • FutureLearn Academic Network • Regular meetings • Some PhD students – across the UK Collaborations • Southampton • UEA • Reading – Sharing data – Research roadmap – Bidding for funding e.g. Leverhulme, ESRC 21/05/15 @suukii http://blog.soton.ac.uk/mobs/home/
- 23. Not used – for ref 21/05/15 @suukii http://blog.soton.ac.uk/mobs/home/
- 24. Courses and Runs
- 25. Some figures: a “quiet” MOOC
- 26. Some figures: A busy MOOC Source:
- 27. Our Courses
- 28. MOOCs • Researching – Behaviours, beliefs and understandings of MOOCs • Building – a collaborative network of labs and researchers • Creating – tools to automatically and efficiently log and annotate MOOC related artefacts • Assembling – a definitive historic archive for current and future researchers 21/05/15 @suukii http://blog.soton.ac.uk/mobs/home/
- 29. Building a toolkit: tools, methods and methodologies Curated e.g. papers Data Wild e.g. twitter Manual to auto’Processing Datasets e.g Excel NVivo WorldWareMendeley Custom C-Map • Logging – the ‘What? When? Where? Why? and How?’ of MOOC activity • Charting – the growth and evolution of MOOCs • Developing – expertise in MOOC data collection and analysis; • Tracking – platforms and technologies Moving from manual towards automatic 21/05/15 @suukii http://blog.soton.ac.uk/mobs/home/
- 30. Questions Stakeholders: • What are the motivations and rewards for academics running MOOCs • How are academics integrating MOOCs with their face to face teaching • What are the models for harnessing existing MOOCs to bring additional expertise into the classroom? 21/05/15 @suukii http://blog.soton.ac.uk/mobs/home/
- 31. Questions Learners: • How can groupwork insights and learning analytics be combined to enhance the learners’ exerience of MOOCs? • What role can MOOCs play in enhancing employability of young people? • What role do MOOCs play in enhancing digtial literacies? 21/05/15 @suukii http://blog.soton.ac.uk/mobs/home/
- 32. Researching Learner experiences • learner engagement and motivations • how MOOCs are impacting recruitment on F2F courses Hosting Experience • Moderating discussions Stakeholders • Educator attitudes • Institutional motivation Potential • Blended MOOCs • Personalisation in MOOCs • Affordances 21/05/15 @suukii http://blog.soton.ac.uk/mobs/home/
- 34. Curating Mendeley • publications collection ScoopIt • Grey literature 21/05/15 @suukii http://blog.soton.ac.uk/mobs/home/
- 35. 74 MOOC Datasets FOR EACH OF OUR MOOC RUNS: ● Comments ● Enrolments ● Peer Review Assignments ● Peer Review Reviews ● Question Response (quizzes) ● Step Activity ● Total Figures MOOC Runs Datasets 2 14 + 4 surveys 3 21 + 6 surveys 2 14 + 4 surveys 2 14 + 4 surveys 1 7 + 2 surveys 1 7 + 2 surveys 24 Survey Datasets FOR EACH OF OUR MOOC RUNS: ● Entry Survey ● Exit Survey
- 36. Data Analysis (some examples) ● Learner activity patterns ● Text Mining ● Network graphs from comment data ● Comment classification ● Step Completion Timings
- 37. MOOC/Learners network Author: Graeme Earl
- 38. Learner Activity Patterns (comments per day/week) Busy Mondays! End of Course (last mentoring day)
- 39. Learner Activity Patterns (comments per user) N. of users N. of Comments Active social learners? Highly active social learners? How many of these completed the course?
- 40. Discussion generation analysis Zeemap: place yourself in the world map Most Popular task Reflection step Steps (x) by number of comments that they generate (y)
- 41. The ZeeMap
- 42. Twitter MOOC Data Analysis ‘dalmooc’ Twitter Seach NodeXL Graph/Tim O’Riordan ©2014/cc-by-sa 3.0 ● Our MOOCs also generate data in other social media. ● This example is from our Digital Marketing MOOC. ● Learners were interacting with each other outside the Futurelearn Platfrom (Twitter in this case) ● We also have this dataset in our observatory.
- 43. Text mining of Portus MOOC comments ● Undertaking primary research about development and communication of archaeological knowledge (see next slide) ● Using concordance (AntConc), topic maps and other approaches to mine comments ● e.g. undertaking specific research such as examining the multisensory nature of creative writing on the course through co-occurrence of words (in this case “smell”) Author: Graeme Earl
- 44. Training ● NVivo ● Facilitation/Demonstration ● Statistical Analysis (with R) ● Visualisation methods ● Text Mining
Hinweis der Redaktion
- Blending MOOCs into face to face teaching Universities are investing increasing amounts of time in the development of online courses in the form of MOOCs. At the same time, there appears to be an enormous appetite amongst learners for this more flexible and informal approach to learning. Seeking to maximise returns on investments in MOOCs, individual academics are looking to see how they can integrate their quote "free for all" online learning resources, with more formal face-to-face educational offerings. Furthermore, teachers are seeing potential gains to be made by incorporating M00Cs that have been created outside their own institution into their teaching programmes. This presentation will briefly present some observations of the experience of blending M00Cs into formal teaching. It will discuss the strengths and possible weaknesses of such an approach and propose a framework for designing and implementing this type of blended learning.
- We have a Mendeley group in which the MOOC observatory group records all the academic articles to which we come across. We also have a Soop.it site in which we curate MOOC related content in magazines, blogs, newspapers, and other online publications.
- Just in case the audience wants the links to the Mendeley group and the Scoop.it page
- This is the blog, in which the Southampton MOOC Observatory group will be posting regularly its activity. Also with a QR code.
- The University of Southampton has run 7 MOOCs in topics such as Oceanography, Web Science, Roman Archaeology, Maritime Archaeology, Digital Marketing, and Language Learning. Many of them have already run more than once. All of them are hosted in the Futurelearn MOOC Platform Through Futurelearn, each MOOC run automatically generates a set of datasets: A full comments log (similar to a twitter feed with anonymised user ID, comment ID, parent ID if it is a reply, timestamp, number of likes, and comment). Enrolment data: A dataset that records user enrolments and un-enrolments by date (user IDs anonymised) Peer Review assignments: A dataset that records learner assignments for being peer reviewed Peer Review Reviews: A dataset that records the reviews that learners make to their colleagues’ assignments Question Response: A dataset that records how well learners have done in the assessment quizzes (to be reminded that learner IDs are anonymised, but are unique and can be cross-analysed across different datasets) Step Activity: A dataset that records when a learner has first visited a “step” (a piece of content) and when they have completed it. Total figures of enrolments, comments, and types of learners (social, active, lurkers…), both weekly and over the whole MOOC term. Futurelearn also provides a voluntary survey completed by learners in which they share data, among other aspects about: Age Education level Aims, motivations, and expectations. Learners complete an entry survey and an exit survey.
- •logging the growth and evolution of MOOCs; •assembling a definitive historic archive for current and future re- searchers; •developing the tools to automatic- ally and efficiently log and annotate MOOC related artefacts; •identifying and establishing a col- laborative network of labs and re- searchers; Logging the ‘What? When? Where? Why? and How?’ of MOOC activity Charting the growth and evolution of MOOCs Developing expertise in MOOC data collection and analysis; Tracking platforms and technologies
- RESEARCHING • on learner engagement and motivations: 2 surveys conducted, one with potential MOOC learners, the other with actual MOOC learners. • on educator attitudes • on marketing: how MOOCs are impacting recruitment on F2F courses • on customised automated recommender systems in MOOCs • Research on perspectives from other stakeholders within HEIs • on the role of social media in MOOCs • on MOOCs impact on the openness agenda CURATING • Mendeley group (Around 250 tagged academic sources) • 2 Scoopit blogs LOGGING PROGRESS • MOOC Observatory Blog • Building a MOOC timeline HOSTING • Seminars • Research discussions • Summer School (future plans) • Doctoral consortia (future plans)
- RESEARCHING • on learner engagement and motivations: 2 surveys conducted, one with potential MOOC learners, the other with actual MOOC learners. • on educator attitudes • on marketing: how MOOCs are impacting recruitment on F2F courses • on customised automated recommender systems in MOOCs • Research on perspectives from other stakeholders within HEIs • on the role of social media in MOOCs • on MOOCs impact on the openness agenda CURATING • Mendeley group (Around 250 tagged academic sources) • 2 Scoopit blogs LOGGING PROGRESS • MOOC Observatory Blog • Building a MOOC timeline HOSTING • Seminars • Research discussions • Summer School (future plans) • Doctoral consortia (future plans)
- The University of Southampton has run 7 MOOCs in topics such as Oceanography, Web Science, Roman Archaeology, Maritime Archaeology, Digital Marketing, and Language Learning. Many of them have already run more than once. All of them are hosted in the Futurelearn MOOC Platform Through Futurelearn, each MOOC run automatically generates a set of datasets: A full comments log (similar to a twitter feed with anonymised user ID, comment ID, parent ID if it is a reply, timestamp, number of likes, and comment). Enrolment data: A dataset that records user enrolments and un-enrolments by date (user IDs anonymised) Peer Review assignments: A dataset that records learner assignments for being peer reviewed Peer Review Reviews: A dataset that records the reviews that learners make to their colleagues’ assignments Question Response: A dataset that records how well learners have done in the assessment quizzes (to be reminded that learner IDs are anonymised, but are unique and can be cross-analysed across different datasets) Step Activity: A dataset that records when a learner has first visited a “step” (a piece of content) and when they have completed it. Total figures of enrolments, comments, and types of learners (social, active, lurkers…), both weekly and over the whole MOOC term. Futurelearn also provides a voluntary survey completed by learners in which they share data, among other aspects about: Age Education level Aims, motivations, and expectations. Learners complete an entry survey and an exit survey.
- This network represents the % of learners who made comments that are shared between pairs of UoS MOOCs vs the maximum possible authors that could be shared between the courses (the lower of the two counts of authors). The thicker and darker the edges are between two vertices, the higher the proportion was of learners made comments in both vertices.
- For providing the best MOOC delivery, we need to know our learners’ habits. This example represents the comments per day made by learners in one of our MOOCs. We have identified a pattern in which the activity increases in the extremes of the week, and decreases by the middle of the week. This can help devise more effective and efficient strategies for communicating with our learners and facilitate their learning process.
- We can also identify which learners (and how many) are most active, and compare it with their levels of success in other course activities. In this chart, we see how many learners (y axis) make how many comments (x axis)
- For individual datasets, we can determine how much discussion a piece of content generates. In this example, the “zeemap” step is what generated most comments (see next slide)
- In our Understanding Language MOOC, there was a step that received more than 10000 comments. Learners would post where they come from and that was reflected in this map. We can identify popular steps with our analytics tools.
- Having our data together in the MOOC observatory (within the Web Observatory architecture) allows us to join datasets and run aggregate analyses, as well as make comparisons between different courses, universities, and platforms. For example, we can analyse how similarly or differently learners behave in twitter, compared to their behaviour in the Futurelearn platform.
- The MOBS also aims to provide training to both its members and external parties in the form of seminars, summer schools, webinars, workshops, and consortia. For example, I (Manuel) am going to run a workshop in the Futurelearn forum in London next week about facilitation and mentoring, and the will run the same workshop in the JTEL summer school in Ischia this July. All this under the umbrella of the MOOC Observatory.