Timing: 2 hours
- Identify three people in your context, or in a context you know well, who may need to know more about learning analytics in the near future. Try to select people with different roles and different levels of expertise. For each person, make a note of areas of the websites listed below that you would recommend to them, and any resources or events that would help their understanding.
- SoLAR: The Society for Learning Analytics Research is responsible for many of the key initiatives in this field, including events, conferences and a journal. More general and good place to start for those interested in learning analytics. Holds conferences and has an open access journel. Siemens as a founding member, would this influence or promote a certain pedagogical approach?
- LACE: The Learning Analytics Community Exchange built links between researchers and practitioners across Europe and beyond. Quite a broad focus across the EU. Focuses somewhat on edm as well. Holds conferences. Supports first time commercial and open source programme development.
- IEDMS: The International Educational Data Mining Society supports collaboration and scientific development in the field of data mining. More for the technical minded people, such as database mangers and programmers interested more in the statistical and technical side of learning analytics.
Timing: 2 hours
- Read the following paper carefully. It provides an overview of the development of learning analytics, including references to frequently cited work in the field.
- Ferguson (2012), Learning analytics: drivers, developments and challenges.
- Make a list in your learning journal or blog of the main points you would share with someone in your context, or in a context that you know well, who had asked for a brief introduction to learning analytics.
Lots of very important overview material here. Very good for the TMA and general references. Future references for a PhD are also given.
Definitions in paper:
Learning Analytics: “Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs.”
Edm: “concerned with developing methods for exploring the unique types of data that come from educational settings, using those methods to better understand students, and the settings which they learn in (www.educational datamining.org).”
Brief explanation of analytics:
Learning analytics encompasses variance parallel and sub-fields of analytics, such as the more business orientated academic analytics, and educational data mining (edm). The movement is fluid, encapsulating various aspects of each, and now spreading into the more specific areas of social learning analytics (nod to connectivism). This involves, and will likely continue to involve for the near future, the role of social networks and PLE’s in expanding the usefulness and personalisation of learning analytics.
This will see learning analytics move beyond merely gathering student data for their benefits, and into personalisation of learning content. This will include personalised assessment, and possibly feed into adaptive learning (my inclusion, not paper). This information is based on both student analytics, learning sciences, the information students share in response to their learning assessments, PLEs, and personal life goals.
Timing: 2 hours
- Read the bullet-pointed list in the section titled ‘A New Era’ at the start of:
- Campbell et al. (2007), Academic analytics.
The list identifies some concerns that western nations or, more specifically, the USA were dealing with in 2007.
Extracts: Western nations are looking over their shoulders at China and India.
Economies depend on a well-educated population
Minorities are an increasing percentage of the population, as well as of the college-going population
Just being able to hold a job may not be enough
If the current educational gaps remain, U.S. per capita income is projected to decline 2 percent from 2000 to 2020
- Now look at the first five pages of:
- Norris et al. (2009), A national agenda for action analytics.
Both these papers were written before the emergence of learning analytics as a field and so they provide early definitions of related terms.
- Consider the reasons for the use of learning analytics that are given in these papers, and reflect on them in relation to the recommendations you and others made in Activity 3 and the problems that you thought learning analytics might be able to address. Make a note in your learning journal or blog.
Notes to answer the above:
- They are also about using analytics to guide and enable aggressive, proactive reimagining of academic and administrative practices.
- take form in wise judgments, decisions, choices, interventions, and fresh visions;
- shape, enhance, and refine policies, processes, procedures, practices, and, ultimately, performance;
- express themselves through innovations whose successes can be scaled across entire educational and workplace enterprises;
- continuously improve value and performance in education, training and workforce development; and
- make the route to improved performance transparent to stakeholders, including learners and their families.
- Improving the performance of education and workforce development in ways that are financially sustainable is critical to America’s continued global competitiveness (esp. in relation to America’s declining education standing – reading 1 above)
- Analytics should be available to everyone from top decision makers to front-line knowledge workers, faculty, advisors, and even students, as determined by authenticated roles and levels of authorization
- As a possibility, allow for the capacity of international analytics to enable benchmarking, comparison, collaboration, and innovation across global learning and workforce enterprises and agencies
- Higher education leaders must evolve from William Bowen’s famous characterization of university leadership that “raises all the money it can, and spends all the money it raises,” to a more refined perspective that “optimizes value in an environment of resource scarcity.”
- Can assist in ongoing study while working. As maximising output and performance is key when time is limited and course completion is not possible in the same way.
- Benefits leading into MOOCs and other online courses.
- A means to analysis the data received from various education projects (i.e.Online courseware initiative; Financial aid increases; Funding for community colleges; Goals to increase college-going; Stimulus funding for states and for colleges and universities.)
Action analytics will support aggressive institutional efforts to find efficiencies, innovations, and transformations so they can rediscover post-recession financial sustainability
To gain a different perspective, next you will look at the Programme for International Student Assessment (PISA) that provides a set of international comparisons of student attainment. Specifically, the three-yearly PISA studies assess the extent to which 15-year-olds in 65 countries have acquired key knowledge and skills.
Imagine you are a politician with responsibility for education in your country (or a near neighbour if no PISA results can be accessed for your country). You are thinking of making improvements to your country’s education system but before doing so you want to compare your country’s educational performance with that of a near neighbour.
- Take a look at PISA’s headline results (OECD, 2012) for your chosen country, and compare them with those of another country. You can do this by selecting your chosen countries from the list and then clicking through to the PISA 2012 results overview.
- As you read these headline figures, note the areas that worry you and the areas you want to highlight. You can check or extend your list by searching for news stories that provide examples of how the PISA results were reported locally. These two stories give a flavour of how the results were interpreted in the UK and in Norway:
- Coughlan (2013), Pisa tests: UK stagnates as Shanghai tops league table
- The Nordic Page (2013), Norway left behind Denmark and Finland in new PISA survey.
- When you have completed your list, consider each of your areas of concern in turn and note whether learning analytics could potentially address this issue and, if so, how this might be done.
- Share your thoughts with others on the discussion forum.
Findings for improvement for Japan:
- performance differences between advantaged and disadvantaged schools have widened since 2003
- Students in Japan generally feel less confident about their ability to solve a set of pure and applied mathematics problems than the average student across OECD countries
- students reported less pleasure and interest in learning mathematics
Timing: 2 hours
- Begin by looking around the H817 Moodle environment. Note where it is personalised, displaying information that only applies to you or to people in your tutor group. Explore the different tabs and links, looking for information that has been collected about you during your time at the University. TMA information, support (tutor & student support), unread posts notification, dashboard (configurable), profile, student home, OU Live sessions linked to tutor group, forums linked to tutor group (special permissions).
- Look back across the module and note occasions when you have accessed sites that may store data about you. Numerous sites where I have used a Facebook login (i.e. MindMeister, Diigolet). Journal websites, where such a Wiley uses a ‘suggestions’ feature based on previous selections. YouTube and any other Google Site (incl. OU Google Apps).
- Share your findings in the discussion forum, building up a jointly created list of the ways in which data relating to your activity are used on the module site and beyond.
- In Week 24 you will consider ethical practices related to data collection and learning analytics. At this stage, note in your learning journal or blog whether you personally object to any of this data being collected or used, and why you feel this is acceptable or unacceptable.
- Reflect on your current teaching environment or on a teaching environment that you know well. How are student data used in this case?
- Record the data use under four headings:
- Used to benefit learner(s)
- Video recordings
- Used to benefit educator(s)
- Online management system to record students progress.
- Video recordings
- Used to benefit administrators or managers
- Online management system to record students progress. Incl. results obtained.
- Initial student details
- Video recordings
- Course signups
- Used to benefit learner(s)
In a face-to-face environment, data collection is not necessarily automatic but is likely to include attendance records, library borrowing, course registration and exam results.
Some uses of data will fall under several headings; some will prove difficult to classify.
- Identify some of the educational challenges in the environment that you are considering. They may include those identified earlier. We used a translation system to assist with evaluations given to parents. There are challenges related to giving detailed students evaluations.
- Formulate two or three recommendations that set out how data gathered in this environment could be used better to support learners or educators. Have teachers gather information during lessons. I.e. what did they find SS doing that could be useful for evaluations. Had this data to the translation system manager for him/her to add into the system to create more detailed/real world accounts.
- Set out two or three recommendations for ways in which other institutions could benefit from good practice in the environment that you are considering. Constant recording of students progress. This assists in looking back to gauge progress and formulate evaluations with more accurate information for both teachers and students (even if the end translation system can hinder the deliverance of such).
- If the challenges and recommendations you have identified are not private to your institution, or business sensitive, share them in the discussion forum. Read the recommendations that other learners share, and identify issues that arise in many contexts that could be reduced or resolved by the use of learning analytics.
Timing: 2 hours
- As a starting point for your investigation into the use of big datasets outside education, read at least two of the online stories listed below:
- (1) Now extend your reading by searching for “big data” (if you use Google, double inverted commas will show that you are looking for the phrase and not two separate words) and the name of a large company that you use regularly, such as Google, Facebook or Starbucks.
- (2) For whichever you choose, note as many reasons as you can for the use of big data. Also note who benefits from its use in each case and what the benefits are.
- (3) Write a blog post, or an entry in your learning journal, about your positive and negative reactions to the use of your data in these ways.
- Post your reactions, or a link to your blog post, in the discussion forum and read other people’s reactions.
1. Emirates Airlines
2. Forecasting demand; changing products to suit customers; targeting frequent flyers with promotions; targeting customers by adding in additional deals (i.e. hotel bundles); price modelling; expanding their market reach.
Negative: Spied upon; legal restrictions (esp. internationally); human habits are not set in stone; backlash to search engines, etc.
Positive: Saving money; convenience; company preparedness (i.e. demand surges); maximising resources (i.e. certain flight routes).
Timing: 4 hours
- Visit the Wikipedia page on learning analytics and note the definition or definitions that currently appear on the page. You may find that the introduction gives a different definition from those that appear in later sections.
- Now select the ‘View history’ tab at the top of the Wikipedia page. You will be offered the opportunity to compare the current version with any of the past versions, dating back to 2010.
- Look at three or four versions of the page over time, noting any different definitions that appear and, if possible, the origins of those definitions. If you speak a language other than English, you may also find it interesting to look at definitions that appear in the Wikipedia for that language – you can access these via the site’s list of Wikipedias.
- In your learning journal or blog – or with others in OU Live – reflect on the differences and similarities between the definitions. Which, if any, elements remain constant?
- Now read:
- Long and Siemens (2011), Penetrating the fog.
- Cooper (2012), What is analytics? Definition and essential characteristics
Pay particular attention to the definitions of learning analytics, and to the ways in which Long and Siemens differentiate learning analytics from academic analytics.
- Develop your own definition of learning analytics and share this in the discussion forum.
- Read the other definitions that are shared there and comment on one or two of them. In the light of this activity, do you feel the need to revise your definition? If so, note your revised definition of learning analytics in your learning journal or blog.
During this block we will sometimes ask you to return to the definition you have recorded at this point. If you find that your definition is no longer satisfactory when you revisit it, take the opportunity to update it.
Wikipedia: Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs
Cooper: Analytics is the process of developing actionable insights through problem definition and the application of statistical models and analysis against existing and/or simulated future data.
Mine: Learning analytics concerns the process of measuring and collecting learning data from a variety of student activity, with the aim of using said data to improve and/or alter the student learning process.
Note: My definition has been left purposefully open, in order to allow for variations in time frame of data collection, and the outcome of such collection and analysis.