10
Learning and development (L&D), a core function of HR, is being transformed by big data technology. Even just a quick glimpse at the digital transformation happening right now in the world of education points to how data can facilitate learning at all levels, from schools and universities to corporate learning. Nowadays, everything in education can be measured, from how well a student performs in tests, to how well they engage with and understand the pages in an online course. For example, data have been used extensively in education, even in primary schools, to give a better understanding of skills levels, thereby helping to identify those who may be struggling and need extra support. And, as we saw in Chapter 7, Facebook is even able to predict our intelligence based on what we ‘like’. Developments like these can all feed into a corporate L&D programme that is intelligently designed around the organization’s and its employees’ needs.
I start this chapter by outlining the dramatic transformation happening in the world of education (both in schools and in universities), to give an idea of how HR could benefit from data-driven learning. Then I explore some of the changes beginning to take place in corporate L&D and how big data technology can help you to identify learning gaps in your organization, deliver data-driven learning programmes, and measure how learners are doing and how your L&D programme impacts on wider company performance. I also spend time looking at some of the more cutting-edge developments in L&D, namely virtual reality (VR) and augmented reality (AR). Finally, I close the chapter with an outline of the key pitfalls to be aware of when applying big data technology to your L&D activities.
How data are positively disrupting education in schools and universities
With learning now coordinated online and often taking place via a laptop or tablet, even when the student is in a traditional classroom environment, increasingly large amounts of data are being generated about how students learn. Technological innovators working with educational establishments are learning to transform these data into insights that can identify better teaching strategies, highlight areas where students may not be learning efficiently and transform the delivery of education.
Learning that is tailored to individual students
Education has always fundamentally been about feedback loops. A teacher presents a problem and the student attempts to solve it. From that attempt, the teacher can learn what the student understands and does not understand, and can adjust their instruction accordingly. Likewise, the student understands more about the problem they attempted. When a teacher is faced with a classroom overflowing with students, data and technology help to facilitate this feedback process. With hundreds of students to monitor, in the past it may have been difficult for teachers to identify which pupils were in need of an extra helping hand, and many of these decisions may have been based on gut feeling. In the past, the first sign that a pupil was in danger of failing might have been when they scored poorly in a test. A data-based approach to ongoing analysis and assessment of individual students’ achievements means that more personalized learning can be delivered, taking each student’s individual interests, prior knowledge and level of academic ability into account.
Any teacher can walk students through a course, but to pinpoint and develop the specific problem areas of each student in a classroom of many is a tough undertaking. This is why numerous adaptive learning companies like Knewton have sprung up, offering services that analyse the progress of students, from the nursery class to university level, to create better test questions and personalized learning materials. Crucially, these data-driven courses adapt to each individual student. Technology now makes it possible to assess, in real time, whether a section is too easy, too hard, or just right for that student, and to adjust the remaining course materials accordingly. Personalized learning like this also allows students to learn at their own pace, regardless of what the other students around them are doing. Then, the teacher can receive that information and understand where any one student might be struggling, or analyse the performance of a class as a whole.
In another development, IBM recently unveiled its vision of ‘smart classrooms’: online learning systems that use machine learning to help teachers pinpoint students most at risk of dropping out of a class, as well as guiding teachers on the best interventions to stop that from happening.1 The system also identifies individual students’ learning styles and guides the teacher on what kind of content is best for which students, and how best to deliver that content. Other systems like IBM’s Watson content analytics help to organize and optimize content for learners.
The impact of AI in schools
But what does all this technology mean for the teachers themselves? The best teachers go into the profession because they are passionate about educating young people and they thrive on seeing a student’s eyes light up when they understand a subject. The idea of effectively becoming a data administrator may not appeal to most teachers. It is the classic human versus machine scenario: as artificial intelligence (AI) gets better at teaching and providing educational assistance, the question inevitably turns to whether (or when) human teachers will be replaced by computers. Students across the United States are currently enrolled in online schools that provide the benefits of a teacher and curriculum with the comfort and convenience of home-schooling and, although these schools still have human teachers ready to answer questions from students, much of the teaching is done by computer programs. Now, I do not believe cyborgs are going to take over our classrooms. Instead, teachers and AI computers will team up to provide stronger and better educational experiences for students at every level. Following are just some of the ways in which AI is positively disrupting education:
· AI can automate basic, repetitive activities like marking papers.Today’s essay grading software is not up to par with human teachers, but computer programs can accurately grade all kinds of multiple choice and fill-in-the-blank-style homework.
· Educational software can adjust to meet each student exactly where they are. Educational programs can adjust the speed at which individual students go through coursework, provide additional help when a student is getting stuck or provide additional enrichment when a student is working ahead of the rest of the class.
· AI can go beyond the classroom and support students at home. As any parent knows, it can be a huge challenge when a child struggles with homework. Educational programmes that can be accessed from home can provide support at any time of the day or night, and can even provide additional tutoring to students who need it.
· AI can help the teacher provide better learning experiences. If educational software notes that a large percentage of students are missing a particular question, it can flag that question, which can provide important feedback to the human teacher that their lesson may need additional details or clarity.
· Computer systems can provide valuable feedback to parents, educators and administrators. This could reduce the need for separate standardized testing and provide a level playing field for helping to assess teacher and school performance.
This dramatic transformation of education should not come as a surprise. Computers were born in colleges and universities, and, by the 1980s, they had become common in primary and secondary schools too. You may well have had your first experience on the Internet at school or college. IT and education have always gone hand in hand. Data just add another dimension.
Real-world examples from the education sector
I have encountered many cutting-edge uses of data and analytics in education, and there are numerous examples of how technology is helping both teachers and students to get the most out of their school days. In Wisconsin’s Menomonee Falls School District, for example, data have been put to use for everything from improving classroom cleanliness to planning school bus routes, after department leaders were encouraged to attend classes themselves on how to gain insights from data and analytics.2 The following are just a few other examples from the real world.
Improving student behaviour
One US middle school found that, for some reason, the number of pupils being sent to the principal’s office for disciplinary reasons had grown by a worrying amount. On examining the data, they realized that this had coincided with a reduction in school excursions such as ice skating and sledding trips. When these were reinstated, behaviour among students improved, leading to a noticeable reduction in the number being sent to see the principal.3
Reducing cheating
Schools are also finding themselves armed with new technologies aimed at cutting down on exam cheating and plagiarism among students. The Proctortrack system aims to prevent cheating by using webcams and microphones to monitor students while they sit for online exams. By building profiles of cheating behaviour, it is able to recognize and flag suspicious activity. Proctortrack also uses facial recognition to ensure that the correct student is taking the test, monitors computer activity to make sure that unauthorized sources are not being consulted and even tracks eyeball movement during the assessment. The system can be used for tests taking place in traditional exam-room environments as well as remote learning.
Improving the education experience for students
In universities, too, big data technology is being put to use to improve the education experience. Lectures in higher education establishments, by their nature, are less interactive than school lessons, perhaps based on the flawed assumption that older, more advanced learners will need less prompting to pay attention in class. This means that lecturers often get very little feedback on the efficiency of their teaching before students either graduate or fail based on their final exams. One Michigan University professor developed the LectureTools software to combat this problem. The program allows students to follow lecture presentations on their laptops, annotating them as they go along. It also lets them ask anonymous questions while the talk is in progress, and these questions flash up on the lecturer’s screen. This makes it easier for students who may be embarrassed about speaking in public or their lack of understanding. The system also includes an ‘I’m confused’ button. Lecturers can look at usage statistics for all of these features and use them to fine-tune their delivery and engage with students when individual attention is required.
Beyond the classroom
Of course, not all education takes place in the classroom. Increasingly, thanks to the Internet, remote learning is making it possible for people of all ages whose geographical location, income level or general lack of free time make attending traditional educational establishments difficult. These massive online open courses (MOOCs), which deliver all of the learning materials and exams via a computer or tablet, are providing a wealth of insights into the ways in which people learn. Harvard University has recently developed Harvard X Insights, a tool that allows data gathered from these courses to be examined in real time.4 This means data from the millions of people around the world who take these courses (and the far smaller number that actually complete them) can be analysed to find the stumbling blocks that cause learners to fail. As we will see in this chapter, online learning and the ability to track and measure progress is having a significant impact on the world of L&D.
Introducing the digital transformation of L&D
One survey of senior L&D officers found that most respondents expected corporate learning to change dramatically over the next few years, and more than 60 per cent planned to increase L&D spending and the number of training hours for each employee.5 It is no surprise that, like the education sector, corporate L&D is also evolving quickly thanks to data and analytics. As technologies like online learning have developed, the notion of individuals learning at their own pace at a computer screen (as opposed to being part of a group training programme delivered at a set pace) has become increasingly popular. Below I briefly look at some of the ways in which L&D is being transformed by data (see Figure 10.1). I will explore the most important of these trends later in the chapter.
FIGURE 10.1 Digital transformation of L&D
Learning that is adapted to individual employees
Thanks to online learning, data and analytics, L&D is becoming increasingly personalized to individual learners. ‘Adaptive’ learning technology allows courses, segments of courses, activities and test questions to be personalized to suit the learner’s preference, pace of learning and best way of learning. As well as allowing individuals to learn at their own pace, online learning also offers the same big advantage seen in the education sector: the ability to measure how individual participants are progressing, how well they are retaining the information and where additional guidance or information might be required. Individual, self-paced online learning is also arguably more cost-effective than pulling employees out of their job for a day or week to send them on expensive training courses. Self-directed learning like this also helps to integrate ongoing development into workers’ everyday routines. Danone’s online Danone Campus 2.0 is one example of this in action.6 The food giant has created a user-friendly online platform where employees can boost their development and share best practice and knowledge with other staff.
Micro, mobile and blended learning
Running with this idea of employees learning when it best suits them, ‘micro-learning’ has become a bit of a buzz phrase in L&D. Micro-learning involves very short bursts of learning, often delivered through short videos of just a couple of minutes. These are typically delivered as part of a wider course, and are used to help learners absorb information more quickly and easily. We all know how information is easier to absorb in small chunks at a time, rather than in one massive go. Micro-learning capitalizes on this. Mobile learning is another up-and-coming trend in L&D, as increasingly more learning and training providers support mobile devices in their programmes. Mobile access to learning content allows employees the flexibility to learn when and where it suits them, for example, when there are minimal distractions. It also fits with the increase in remote working that many companies are experiencing. Finally, ‘blended learning’ – commonly used to describe the marriage of online learning and classroom learning – is proving very popular as companies transition away from traditional L&D models. So remember, what works for your company indeed may be a blend of traditional training courses and self-directed learning.
Identifying and closing gaps in learning
Before we delve further into delivering data-driven L&D programmes, you need to be able to understand exactly what kind of content is needed. As we will see in Chapter 11, data can significantly improve a company’s ability to assess performance and pinpoint exactly where employees are performing well and where they may need some extra assistance. In this way, data help HR teams to identify gaps in learning, so that they can plug those gaps through appropriate training. Clearly, with data, analytics and automation developing at the pace they are, and with no sign of that exhausting pace letting up, one major function of L&D professionals is to help fill the digital skills gap. HR teams have a responsibility to ensure more people in the organization have the necessary skills to prepare for the data-driven transformation of the business. There is no doubt in my mind that the ability to tap into and nurture digital skills is going to be critical to the success of most businesses in the future. Yet, more than 12 million employees in the United Kingdom do not have the necessary digital skills.7 As well as attracting talent with the right digital skills, companies also need to invest in delivering accessible and effective training where needed. Without this, employees will not be properly equipped to help the business thrive in the future. Online learning is perfectly placed to help companies fill the digital skills gap, and those that invest now will reap the rewards in the future.
Delivering data-driven L&D
With 68 per cent of workers saying L&D is the most important workplace policy, and with 40 per cent of employees who receive poor training leaving the company within the first year, it is vital companies get L&D right.8 In addition, L&D has a significant impact on employee engagement, and companies that use e-learning or online learning achieve an 18 per cent increase in employee engagement.8 As we have already seen in this chapter from just a quick glimpse at recent developments in education and L&D, learning is now moving away from traditional models where participants go to a specific place for a set duration of time to learn at a pre-defined pace. Now, for workers, learning is becoming something to dip into much more frequently, but perhaps in more bite-size pieces, and at their own pace. Learning is essentially becoming a core part of the day-to-day job. In this section, I explore in more detail some of the key trends in data-driven L&D that were identified earlier in the chapter (see Figure 10.2).
FIGURE 10.2 Key trends in data-driven L&D
Remote learning and the virtual classroom
One of my favourite examples of just how much learning has transformed comes from Harvard Business School (HBS). The school has created a digital classroom as a template of the classroom of the future.9 In this virtual classroom, the lecturer teaches in a specially designed broadcast studio. Cameras follow the lecturer as they address a huge screen made up of faces: live-feed videos (with audio) of all the students participating in the lecture, connected to the class simply by their standard computers or laptops. Even though these students could be anywhere in the world, it is as though they are in the same room. This virtual classroom, which HBS has called HBX Live, took three years of planning and development, and is designed to integrate seamlessly with HBS’s online teaching programmes.
Inspired by TV
Much of the HBX Live experience was apparently inspired by visits to NBC Sports in New York, and it is clearly a big production in the same vein as a TV show. Four production staff are required to assist the teacher, operating the camera, managing the live feed, cueing up slides and videos etc. Speaking to Fortune about his experience of teaching in HBX Live, Harvard professor Bharat Anand said: ‘You can see someone who is up at 3 am in the Philippines, someone in Seattle and another in Mumbai. This feels like you are literally in the classroom, and the feedback we’re getting is that this is every bit as engaging as being in the classroom – but more intense’.9 Students participating in the virtual classes have noted how it encourages participation because no one can hide in the back row of the class.
Recreating an authentic classroom experience
One thing that is really striking about this virtual classroom is the extent to which it feels like a real classroom experience to the lecturer and students participating. The system was designed so that all microphones would be on all the time, including the students’, and no one was muted. This makes for a much more collaborative, authentic learning experience, where students can laugh along if the professor makes a joke and agree verbally when someone makes a good point. When a student wants to interject with a question or point, rather than raising their hands, they simply click a button on their computer and their nameplates on the screen turn red, letting the lecturer know that they have something to say. Students can also type comments via a chat bar, and the comments then scroll along the bottom of the huge video screen like a news ticker. And, even more impressively, up to 60 students can participate in these virtual classes at a time, which is quite a feat when you consider that is 60 separate video feeds being managed in real time without any delay whatsoever. The system also allows for up to 1,000 additional students to observe the virtual lesson with a short time delay. HBS began testing the virtual classroom back in 2014 and launched it officially in 2015. It is now also being offered as part of HBS’s custom solutions for corporate clients. For example, the school, fittingly, offers corporate clients access to an HBX course by disruption innovation expert Clay Christensen. For me, this example points to the future of education and gives us a hint as to where corporate L&D may be going in the future, either by companies developing their own version of virtual learning or tapping into services provided by forward-thinking schools like Harvard.
The use of AI in personalized learning
Online learning allows for much greater measurement, because learners leave behind digital traces of everything they do within the parameters of the course they are taking. These traces include how quickly the learner moved through a particular element of the course, where they paused, where they got a test question wrong, which material they revisited and even potentially what time of day the learner best assimilated information. Learning management systems allow providers to track these data and use the insights gained to tailor courses to individuals’ needs, thereby making them much more engaging. AI is critical in this ability to provide adaptive, personalized learning. It is AI, and particularly machine learning, technology that allows providers to identify where a learner might be struggling and what areas need extra emphasis for that individual. Analytics company Zoomi, for example, uses AI capabilities to analyse each learner’s behaviour, performance, engagement and comprehension to improve learning content and create a uniquely individual learning experience. Zoomi claims its solution can shorten training hours by up to 60 per cent.10
The evidence for an AI or machine learning-based approach is clear. A 2016 paper published by Pearson and UCL set out how AI creates learning programmes that are more flexible, efficient and inclusive.11 In particular, the authors cite how AI effectively allows personalized, one-to-one learning to be provided on a large scale, which is especially beneficial for larger companies with a diverse network of employees all with different training needs. Naturally, this works both ways: not only can personalized learning track how individuals are progressing, it also allows learners to provide instant feedback on course content and features.
Making use of MOOCs
Because of their vastness (‘massive’ being the big clue in the name), MOOCs provide a unique opportunity for data. Huge amounts of data can be gathered not only on individuals but also across multiple learners to pick up broader patterns and insights. These courses allow providers to map an individual’s learning trajectory, identify trouble spots and provide targeted interventions where needed. MOOCs from the likes of Coursera and Khan Academy provide accessible learning opportunities for millions of people around the world, covering anything from vocational learning to degree-level courses. In MOOCs, learners undertake self-directed learning when it suits them, engage with bite-size micro-learning content like short videos, and participate in collaborative discussions with other learners. Now, many corporate L&D programmes are making use of MOOCs to deliver training to their employees. Companies like Microsoft are creating their own custom MOOCs for employees. For example, global steel manufacturer Tenaris has created MOOCs on various topics, including technical topics like ‘Introduction to steel’ and broader business topics like ‘International trade’.12 Tenaris is now offering the MOOCs externally to attract university students and boost its employer brand. Other organizations, such as Bank of America, are leveraging content from existing MOOCs to deliver training on core competencies. This strategy allows businesses of all sizes to curate a wide range of content that suits their needs in a simple, cost-effective way. Both strategies are innovative ways of rethinking L&D and capitalizing on the latest advances in learning technology.
Measuring how learners are doing and how L&D impacts on performance
When learners work through the content in a digital course, they leave a digital trace of all their actions. This ability to track leaners’ journeys gives training providers and L&D professionals the opportunity to understand a great deal about the learning experience. Indeed, most learning providers incorporate some sort of learning management system that tracks how learners progress and provides insights that can help both individual learners and the company-wide L&D programme.
The importance of learning analytics
Learning analytics should therefore underpin every aspect of employee learning, from developing better learning programmes, to delivering them in the most engaging way and tracking how employees interact with the programme. Data and analytics can also dramatically improve the measurement of L&D by showing how effective (or not) it is in practice. Currently, assessing the effectiveness of corporate training often comes down to little more than trainees completing an evaluation questionnaire after a course. Data allow us to go so much further, such as showing how trainees respond to each section of a course, which areas they struggle with and which trainees are ready for more advanced materials. With the in-depth insights available from data like these, L&D professionals can pinpoint exactly what is working and what is not.
Two specific data points that every company should be measuring are employee comprehension (eg are people struggling with various aspects of the content?) and employee engagement with content (eg are they taking up opportunities for learning and are they then participating in courses all the way through or are they ignoring various aspects?).
Demonstrating value by linking training to performance
Data also allow HR teams to create clear, evidence-based links between training and performance, which is helpful for improving future L&D, establishing return on investment (ROI) and securing leadership buy-in for training programmes. There is more on measuring and driving performance in Chapter 11. This detailed measurement and assessment is already commonplace in the education sector, where students, teachers and whole schools are commonly assessed according to various metrics and benchmarks. Sometimes this has proven problematic, such as the argument that standardized testing (such as statutory assessment tests or SATs) provides just a snapshot of what has really been learned. The development of learning analytics therefore has dramatically improved the ability to measure, understand and improve teaching and learning right down to an individual level.
Purdue University in the United States previously developed the Signals learning analytics programme, which uses a coloured traffic-light system to tell students whether their learning is going well or whether they may need more help.13 Not only do systems like this help measure learners’ progress, they also encourage self-reflection and monitoring in order to improve performance while undertaking a course. This benefits the individual, but also helps the company to get the utmost out of its training programmes.
The cutting edge: incorporating VR and AR into L&D
Interestingly, VR and AR are becoming more common tools in corporate L&D and, in particular, many vendors are now offering VR-enabled training programmes.
Stepping into a virtual world
VR creates an interactive environment by generating realistic images, sounds and other sensations to fully immerse the user in that environment. It is easy to imagine how this technology could be used to provide immersive training experiences in fields as diverse as medicine, the armed forces, engineering etc. This is not a new idea – just think of flight simulators, for example – but the ever-decreasing cost of VR hardware (headsets, gloves etc) has made the technology more accessible to a far wider audience. VR technology even can be used on smartphones, although that tends to be less immersive than the VR hardware.
Some current uses of VR include Medical Realities’ education platform for surgical trainees.14 Using the Oculus Rift VR system, Medical Realities’ platform provides a collection of modules, each offering immersive 360-degree videos of real operations. VR training is also available for armed forces and law enforcement personnel in the form of the VIRTISM VR system, created by US defence contractor Raytheon.15 The system, which comprises full-body motion capture, VR headsets and fake guns, provides simulations of realistic combat environments, where squads of trainees are pitted against each other.
The applications of this technology can reach far into the world of business too. For example, one global manufacturer has created a virtual model factory, where employees are completely immersed in a 3D experience that lets them ‘see’ and ‘feel’ equipment in the companies’ factories.5 And even softer business skills can be learned with VR technology. VirtualSpeech’s app makes use of Google’s Cardboard VR smartphone technology to allow users to practise their public speaking and interpersonal communication skills.16 Combining practical experience, online reading materials, videos that teach basic skills and instant feedback, the course aims to help people to develop quickly and build their confidence in a safe environment. It is easy to see how VR technology like this could be incorporated into an L&D programme to boost individuals’ presentation skills.
‘It’s reality, Jim, but not as we know it’
While VR plunges the user into a simulated world, AR is rooted firmly in reality, and adds an extra layer of information to the real world that the user sees in front of them. Google Glass is based on this technology. Although AR is, for now, less commonly used in training, there are certainly many potential uses for AR in L&D. For example, an engineer in training could be able to look at an aircraft engine with Google Glass and be given information on each part of the engine.
Creating digital twins
‘Digital twin’ technology is closely related to AR because it pairs the virtual and physical worlds. The digital twin concept is now so imperative to business, it was named one of Gartner’s Top 10 Strategic Technology Trends for 2017.17 Quite simply, a digital twin is a virtual model of a process, product or service that allows analysis of data and monitoring of systems to head off problems before they even occur, preventing downtime and even planning for the future by using simulations. How do digital twins work? First, smart components that use sensors to gather data about real-time status, working conditions or position are integrated with a physical item. The components are connected to a cloud-based system that receives and processes all the data that the sensors monitor. This input is then analysed and lessons are learned and opportunities are uncovered within the virtual environment that can be applied to the physical world. Digital twins are powerful tools for driving innovation and performance. In fact, IDC predicts that, by 2018, companies which invest in digital twin technology will see a 30 per cent improvement in cycle times of critical processes.18 One example of this in action comes from GE’s ‘digital wind farm’. GE uses digital twin technology to inform the configuration of each wind turbine prior to construction in order to generate efficiency gains. As Ganesh Bell, chief digital officer and general manager of Software & Analytics at GE Power & Water, told me: ‘For every physical asset in the world, we have a virtual copy running in the cloud that gets richer with every second of operational data’.19 While digital twins are largely used to drive performance and efficiency, it is not a huge leap to imagine how this technology could be used to enhance training for a wide range of employees, particularly in the field of engineering.
Looking at the downside of data-driven L&D
As with almost any application of data, there are ethical concerns concerning working with individuals’ learning data, particularly when it comes to data privacy and security. There is more on this in Chapter 6.
Key pitfalls to look out for
Data breaches are always a legitimate concern, and rightly so. In 2009, one school district in Tennessee inadvertently left the names, addresses, birth dates and full social security numbers of 18,000 students on an unsecured server for months.20 Therefore, ensuring your employees’ data are private and secure is a critical concern for any HR team. These days, it is incredibly naive to think you do not have to worry about protecting your employees’ data. Where possible, anonymizing employees’ L&D data will help. Where anonymizing data is not possible, you will need to ensure the data are kept secure.
There is also the concern, particularly in the world of schools and universities, that education providers can simply know too much about their students. In a brilliantly titled 2014 article – Blowing off Class? We know – the author, Goldie Blumenstyk, stated: ‘the stuff some colleges know right now about their students, thanks to data mining of their digital footprints, boggles the mind’.21 With the latest advances in technology, it is possible for education providers and companies to gather huge amounts of data on an individual’s performance, activities and behaviour, whether they are a student or an employee. And I can certainly see how that would be a concern for many people.
Navigating these pitfalls
Good practices of data minimization and transparency help to steer the way here. As with any use of data, there is no point gathering data for data’s sake. Therefore, if you do not intend to use L&D data to make improvements, then do not gather them – it is as simple as that. And when you do intend to gather them, make sure you are upfront with your employees about what information you are gathering and why. If it is clear these data are being analysed to help improve the delivery of learning programmes in the future, and to facilitate individuals’ development within the company, staff are much more likely to get on board.
Key takeaways
Helping employees to grow and develop is a critical part of any HR team’s function – perhaps even one of the most rewarding parts – and it is clear that data have a big role to play in this field. Following is a rundown of what we have covered in this chapter:
· With 40 per cent of employees who receive poor training leaving the company within the first year, it is vital companies get L&D right.
· Corporate L&D is undergoing a massive digital transformation, with key trends being adaptive learning, micro-learning, mobile learning and blended learning.
· Data help HR teams to identify gaps in learning, so that they can plug those gaps.
· AI is critical to providing adaptive learning. It allows companies to identify where a learner might be struggling and which areas need extra emphasis.
· Many corporate L&D programmes are making use of MOOCs. Some companies like Microsoft are creating their own custom MOOCs for employees, while others are leveraging content from existing MOOCs.
· Learning analytics should underpin every aspect of employee learning, from developing better learning programmes, through delivering them in the most engaging way, to tracking how employees interact with the programme.
· Data also allow HR teams to create clear, evidence-based links between training and wider company performance, which helps to improve future L&D, establish ROI and secure leadership buy-in.
· VR and AR are becoming more common tools in corporate L&D and these areas are definitely worth keeping an eye on.
· Take necessary steps to protect your employees’ learning data and minimize data collection wherever possible.
As we will see in the next chapter, the use of data and analytics extends even further, helping companies better measure and drive employee performance and identify where employees may need extra assistance to perform at their best, which links back to these data-driven L&D activities.
Endnotes
1 Davison, M (2016) [accessed 23 October 2017] AI and the Classroom: Machine Learning in Education [Online] http://blog.trueinteraction.com/ai-and-the-classroom-machine-learning-in-education
2 Rich, M (2015) [accessed 23 October 2017] Some Schools Embrace Demands for Education Data [Online] http://www.nytimes.com/2015/05/12/us/school-districts-embrace-business-model-of-data-collection.html?smid=tw-share&_r=1
3 Marr, B (2016) [accessed 23 October 2017] Big Data and the Evolution of Education [Online] http://data-informed.com/big-data-and-evolution-education
4 Harvard University [accessed 23 October 2017] Harvard X Insights [Online] http://harvardx.harvard.edu/harvardx-insights
5 van Dam, N and Otto, S-S (2016) [accessed 23 October 2017] Corporate Learning’s Transformation in the Digital Age [Online] http://www.clomedia.com/2016/12/05/corporate-learnings-transformation-digital-age
6 YouTube [accessed 23 October 2017] Danone Campus 2.0 [Online] https://www.youtube.com/watch?v=oBJAvsl6gRI
7 Cellan-Jones, R (2015) [accessed 23 October 2017] More Than 12 Million Fall into UK Digital Skills Gap [Online] http://www.bbc.com/news/technology-34570344
8 Olenski, S (2017) [accessed 23 October 2017] Why C-Levels Need to Think about eLearning and Artificial Intelligence [Online] https://www.forbes.com/sites/steveolenski/2017/02/06/why-c-levels-need-to-think-about-e-learning-and-artificial-intelligence/#76748552ff70
9 Byrne, J A (2015) [accessed 23 October 2017] Harvard Business School Really Has Created the Classroom of the Future [Online] http://fortune.com/2015/08/25/harvard-business-school-hbx
10 Zoomi [accessed 23 October 2017] Artificial Intelligence for Learning [Online] http://zoomiinc.com
11 UCL Institute of Education (2016) [accessed 23 October 2017] Why We Should Take Artificial Intelligence in Education More Seriously [Online] https://www.ucl.ac.uk/ioe/news-events/news-pub/april-2016/New-paper-published-by-pearson-makes-the-case-for-why-we-must-take-artificial-intelligence-in-education-more-seriously
12 Franceschin, T [accessed 23 October 2017] Case Study: How Tenaris University Built a Successful MOOC for Employee Training [Online] http://edu4.me/en/case-study-how-tenaris-university-built-a-successful-mooc-for-employee-training
13 Arnold, K (2010) [accessed 23 October 2017] Signals: Applying Academic Analytics [Online] http://er.educause.edu/articles/2010/3/signals-applying-academic-analytics
14 Medical Realities [accessed 23 October 2017] Learn Surgery in Virtual Reality [Online] http://www.medicalrealities.com
15 Lang, B (2012) [accessed 23 October 2017] VIRTSIM is the Virtual Reality Platform That Gamers Crave but Can’t Have [Online] http://www.roadtovr.com/virtsim-virtual-reality-platform
16 VirtualSpeech [accessed 23 October 2017] Communication Skills Courses with VR [Online] http://virtualspeech.com
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