When learners interact with content in your course, they leave behind ‘digital breadcrumbs,’ so to speak, which offer clues about the learning process. We’re now able to collect and track this data through learning management systems (LMSs), social networks, and other media that measure how students interpret, consider, and arrive at conclusions about course material.
The good news is that this information–called Big Data–can do wonders for personalized instruction, especially within the e-learning industry. The not-so-good news is that the rise of Big Data brings with it many risks and ethical dilemmas, all of which need to be addressed before we move forward with this new approach.
What is Big Data?
Big Data refers to the large amount of information that flows through various channels – usually online – each second. It’s data that is too large, complex, and dynamic for any conventional tools to capture and manage. The term originated in the open source community, where specialists were trying to find faster and more scalable solutions to store and process immense amounts of data. Thanks to advancements in technology, this data can now be interpreted and analyzed, providing great benefits to the healthcare, government, retail manufacturing, e-learning, and other data-driven industries.
What makes Big Data “big” (both in size and significance) is that it allows for the analysis and prediction of behavior across a huge variety of demographics, personal backgrounds, learning styles, thinking processes, IQ levels, academic intentions, genetic predisposition, environmental factors, skills, potentials–anything you can think of measuring.
In education, these data points are now being used to help design instructional strategies, evaluate the impact of these strategies on both students and teachers, fuel an evidence-based approach to experimentation, and create personalized learning environments.
The term “Big Learning Data” encompasses three aspects of learning data: volume, velocity, and variety:
1. Volume: Big Data can yield information about thousands of learners taking the same course or having the same instructional experience. It can also shed light on multiple data points, over time, about a single learner. Because of its scalability, Big Data might someday bring together learning data from hundreds of organizations to provide a global perspective on education.
2. Velocity: Big Learning Data enables learners and organizations to have rapid access to data even in real time. Imagine a student entering a wrong answer into an assessment exam. Velocity instantly would provide her with remedial and enrichment options based on her historical learning patterns and successful strategies from thousands of other learners who also failed that question. It would also allow instructors to make adjustments to content delivery, based on rapid analysis of user experience, on a continual basis.
3. Variety: Big Learning Data connects the dots, weaving together a wider variety of information from students with different backgrounds. It allows us to see the correlations between performance and environment. Without it, we have traveling expenses and limited representation.
What are the Benefits of Big Data to the E-Learning Industry?
The 2013 World Innovation Summit for Education (WISE) began in October with a plea for radical change. Leading educators, policy makers, and governments from over 100 countries congregated in Qatar, with 84 percent claiming that the way learning happens today will not adequately prepare young people for the world of tomorrow.
But one topic which led to considerable optimism among the delegates was Big Data.
Pearson’s CEO John Fallon presented the notion that “big data, and the disruptions it can lead to, have led to one of the most creative periods in history in terms of innovation.” Time to harness that in education, he said.
Speaking later in the day at WISE, Fallon also stressed that Big Data is only as powerful as our willingness to share it with each other.
“We have to become more willing to share what’s working and not working. In return, all organisations that are trying to tackle big intractable problems in education should be more generous with each others’ ideas and evidence.”
The following are a few ways Big Data is expected to help education in the near future:
1. Feedback: Big learning data can be informative from a feedback and context perspective. Because somebody often might fail at a topic but not know why he is failing, it becomes interesting when the learner can look not just at himself, but at other people who have had the same experience. He may certainly get an insight either that would explain it so he is not frustrated or that he could use to correct it so that he could succeed again.
2. Motivation: If you implemented big data in a comprehensive way, learners potentially become invested in inputting data to the process because they see the impact of how it works.
3. Personalization: Big Data will change the way we approach e-learning design by enabling developers to personalize courses to fit their learners’ individual needs. This will allow e-learning professionals to continue to raise the standard for effective and exceptional e-learning courses.
4. Efficiency: Big Data can save us hours upon hours of time and effort when it comes to realizing our goals and the strategies we need to achieve them. Say someone wants to take job B, having done job A for a year. Big data would indicate, first of all, the number of people who did job A and who then got to job B. Of the people who got job B, what preparation did they have? It also would indicate which learning programs were most effective, and what the timing was for when they attempted to change to job B.
5. Collaboration: More often than not, specialists from multiple departments must come together to keep a Learning Management System functioning at its best. This encourages cooperation, collaboration, and interdisciplinary thought processes.
6. Tracking: Big Data can help us understand the real patterns of our learners more effectively by allowing us to track a learner’s experience in an e-learning course. In examining the digital footprints or ‘breadcrumbs’ learners leave behind, we’re able to track their journey throughout the entire learning experience.
7. Understanding the learning process: By tracking Big Data in e-learning, we can see which parts of an assignment or exam were too easy and which parts were so difficult that the student got stuck. Other parts of the journey we can now track and analyze include pages revisited often, sections recommended to peers, preferred learning styles, and the time of day when learning operates at its best.
Still, when discussing Big Learning Data, we must honestly consider the risks that it raises, which in some cases may outweigh the rewards.
In the wrong hands, Big Data can do more harm than help. Regardless of whether it’s being purposely or naively mishandled, it can undermine an entire educational system with the click of a button.
Here are some of the risks and hurdles involved in using Big Data in education:
1. Privacy: As companies like Google have extended the services they offer to include email, document storage and processing, news, Web browsing, scheduling, maps, location tracking, video and photo sharing, voice mail, shopping, social networking and whatever else might be of interest to their users, they gain access to even more personal data, which they collect, store, and cross-reference.
Even information that is accessible to the public, when assembled from different sources into a comprehensive dossier, can create a revealing picture of a person. A simple Google search can turn up an enormous amount of information about an individual, though the accuracy of much of it is questionable. As one researcher put it, “while the quantity of publicly available information about individuals to be found online is vast, it is riddled with inaccuracies.”
Certain young children may be judged “at risk” because of the personal profiles the school or the state has developed on them, and placed in school accordingly. That designation–whether accurate or not–could then follow those children through school, denying them the chance to develop normally with their peer group.
2. Dehumanization: Apart from the obvious potential for error and prejudice, this use of profiling is objectionable because it dehumanizes those being judged, as well as those making the judgments. It substitutes calculation for human judgment on what should be very sensitive human issues, and thus treats those profiled as objects, as collections of facts, rather than as persons.
3. Deception by Numbers: Cyril Burt of the University of London was the man responsible for the introduction of the standardised 11+ exam in the UK. Burt was subsequently discredited for publishing largely in a journal that he himself edited, falsifying not only the data upon which he based his work, but also co-workers on the research. The correlation coefficients on IQs in Burt’s twin studies were the same to three decimal places, across articles, despite the fact that new data had been added twice to the sample of twins. Leslie Hearnshaw, Burt’s friend and official biographer, claimed that most of Burt’s data after World War II were fraudulent or unreliable.
This is just one of many standardised tests that have become common in education but many believe that tests of this type serve little useful purpose and are unnecessary, even socially divisive. Many argue that standard tests have led to a culture of constant summative testing, which has become a destructive force in education, demotivating and acting as an end-point and filter, rather than a useful mark of success. Narrow academic assessment has become almost an obsession in some countries.
4. Correlation vs. Causation: Have you ever heard the phrase, “Correlation does not prove causation”? If you’re a good scientist, all of your efforts will be based in recognizing the difference between these two terms. If you’re an effective user of Big Data, you will be careful not to jump to conclusions–or, worse, act on your impulses–when you see a pattern.
5. Claims Beyond the Data: Take university rankings, for example. University rankings are used by politicians, universities, parents, and students alike. But oftentimes, where they claim to ‘rank’ universities, they tell you very little about about teaching. What may be labeled as ‘measures’ on teaching is actually data drawn from proxies, such as employment and research activity–offering no direct evidence of teaching quality itself.
“The problem with learning data, historically, is that we’ve always gone for the low-hanging fruit,” says Elliott Masie for the American Society for Training and Development. “Learning professionals have collected inexpensive, easily acquired data from people while they are in our domain, usually the classroom or program. In a big learning data world, we will need to rethink our data sources.”
Since big learning data is just evolving, it is difficult to be prescriptive about such issues. Part of the innovation process is an active and open dialogue, along with collaboration on these risks. However, to add to this discussion, here are a few approaches that you might consider to better align big learning data with these concerns.
1. Transparency. Learners have the right to know how learning data will be used, shared, stored, or leveraged. We should develop a clearly stated system so that there are no surprises.
2. Privacy. Who gets to see the aggregated data of 1,000 learners? Who gets to see a single learner’s data? Levels of privacy, as well as designated access to them, should be carefully considered.
3. Value to the learner. Big learning data can provide great value back to the learner. What have other learners who have taken the same program found most difficult? What are the types of questions that learners most often get wrong? What remedial actions have been most successful for other learners who failed that question or program?
4. Depth of measurement. We have looked at whether learners passed an exam, but more valuable data might include the answer, as well as characteristics of how learners answer the question. For example, how long it took them to answer and whether their mouse hovered over a wrong answer for a while.
7. Expense. Some data that we will use in big learning data will be more expensive to get than what we have traditionally used. But what we easily collect tends to be superficial or inaccurate. Collecting data through interviews with managers of learners, says Masie, costs more but yields much more data.
8. Many factors influence learning. We need to have an anthropological view of the learning process to understand that there are many factors that may influence learning. We need to realize that learning may influence or may support or destroy the impact of learning, thus broadening our view of potentially relevant data.
9. Presenting data. We need to adopt a strategic approach to presenting data. How do we display data so that it brings meaning to people? If you are given this data, what do you do with it strategically and how do you handle it?
10. Readiness. This refers to the extent to which individuals making decisions are ready to operate with a massively enhanced set of data.
12. Infrastructure. Institutions will need to upgrade, alter, or change learning systems to prepare for big data use.
13. Openness. We need to understand where, how, and in what way it’s appropriate to share and use that data, simply because it can yield such powerful results.
Technology is revolutionizing the way learning and development practitioners do their work. Leveraging big data is the next logical step in this evolution. We now have access to volumes of data, but we must understand what it can tell us, what is does tell us, and as importantly what it can’t and doesn’t tell us. Going forward, we need to recognize the potential and risks. We also need to respect the views of our fellow colleagues, whether they are fearful, low risk, or deeply correct in their concerns.
Being open to all perspectives is the only way to safely handle this evolving approach.
image credit emc.com & informedED, themojocompany. previously posted on informED
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Saga has built her writing and editing career at Tin House Books, Night Owls Press, and Dancing Moon Press. Along the way, writing education and education reform have become two of her primary interests. She earned a B.A. in Creative Writing from Oberlin College and lives in Portland, OR.