Harbinger Systems Pvt Ltd

Covid-19 has accelerated the adoption of technology across various sectors, but the speed at which EdTech advanced is remarkable. Millions of schools switched to remote learning, almost overnight. And it looks like the changes that EdTech has enabled, will continue to influence education even as educational institutes prepare for a full return to classrooms. EdTech is here to stay. With that, let’s look at the 5 trends that will possibly guide the growth of EdTech this year.

1. Artificial Intelligence (AI)

2020 has been a roller coaster ride for all of us, and this has thrown up a lot of opportunities and imperatives. One such imperative is leveraging AI to build skills for the future. There is a lot of buzz around how workplaces of the future need new skillsets. McKinsey released a report a couple of years back, which states that by 2030, 65% of today’s grade school kids will end up at jobs that haven’t been invented yet, which means they would be entering the job market with a competency gap. AI will play a significant role in this transition. Designing study assistants to identify skill gaps and appropriately recommending the right content to fill those gaps would help. To make this possible, EdTech companies would have to look at both tech and content in new ways to design such solutions.

2. Educator Support

The pace at which educators have had to adapt to tech-driven delivery methodologies in 2020 has been unprecedented. And this is likely to continue with newer technologies coming in. Professional development for online instruction would also help educators to develop online teaching skills. It is also imperative that educators not only have a say in the EdTech product design but also get help through technology support tools. Another consideration is that these tools should be user-friendly; educators should spend more time delivering education than figuring out technology. Quillionz and Raptivity are two good examples of such useful tools. Quillionz helps educators get question ideas and reinforcement points from content (both text and videos), and Raptivity helps educators build pre-class, in-class, and post-class engagement.

3. Digital Transformation

The year 2021 would be a watershed year for this digital transformation for educational institutes. The level of digital transformation might vary from K-12 to higher-ed to skilling platforms. But there is no denying that digital transformation is much more than just providing a Zoom license to get virtual sessions delivered. In addition to using AI and providing educator support, it will also involve creating the entire end-to-end hybrid learning ecosystem to bring about this transformation.

4. Integrations

Education systems have mostly been working in silos till now. Be it student information systems, learning management systems, student engagement platforms, CRM, attendance systems, or alike, most of these systems operate in isolation. The data that resides in these various systems could help design new experiences if allowed to integrate. For example, suppose a student takes a zoom class. In that case, the attendance system marks their presence automatically, or if the student undertakes an online course, the recommendation engine suggests appropriate learning pathways based on the student’s performance. EdTech products would have to ensure how they seamlessly fit into this ecosystem without adding more administrative work for their customers.

5. Equity in Education

The pandemic has widened the already significant social disparities, and as education and training have shifted online, fears about the ‘digital divide’ have intensified. We are talking about ‘digital inclusion’ and bridging the ‘digital divide.’ Institutions need to find ways to support students as well as educators. Creating asynchronous and offline learning opportunities and not trying to replicate the whole school day online are some strategies that will surely help. Technology will undoubtedly play a significant role in providing ways to do this, but in practicality, all stakeholders need to do their bit to bring equity in education. At Harbinger, we are trying to play our part in bringing equity in education through one of our flagship products – Offline Player. This player allows students to access learning data without internet connectivity.

Team Harbinger recently hosted a ‘Point of View’ on this topic. The session captures some of the above points in detail. Click here to view the recording of the session.

Think about the brick and mortar business; the shopkeepers knew their customers so well. What has the customer purchased in the past, what are their seasonal purchases, what are the quantities in which the purchase happens, everything is known to them. Based on their knowledge, the shopkeepers would recommend a commodity to the customers, which they mostly ended up buying.  To come to think of it, the web and mobile applications do the same with something called ‘Recommendation systems’ or also known as ‘Recommendation Engines.’ Simply put, a recommender system is a system that can predict the future preference of a set of items for a user and recommend the top things. Name any application Netflix, Amazon, Google all of them use recommendation systems.

HR Tech encompasses different applications such as recruitment, payroll, insurance, and so on. Naturally, it deals with a lot of data. With such extensive data, arises the need for decision making. It would be interesting to see some of the use cases where such recommendation engines are applied to HR Tech:

1.    Recruitment Now recruitment as a domain involves a lot of decision making based on candidate and employer data. Consider the simple activity of selecting an interviewer or an interview panel. If the interview panel is not suitable for interviewing a specific position, then either the organization may hire an unsuitable candidate or fail in hiring a suitable one.

How do recommendation systems help here? •    Correlating the job profile and the skillset of the interviewer/interview panel •    Sorting the most relevant interviewers in the order of importance •    Also considering if the interviewer is absent, who could be the next-fit. There are other areas in recruitment where we can see the adoption of recommendation systems such as: o    Matching the candidate with desired job skill. o    Recommend the certifications required for a job description o    Recommending jobs to a candidate based on their profile.

2.    Learning and development (L&D) The adoption of Learning experience platforms (LXP’s) or LXP like features have been quite the talk in 2018-2019 in the LearnTech market. The video recommendation idea of Netflix and Amazon could be extended to the learning systems.

How do recommendation systems help here? The course recommendation can happen based on: •    Learning from the past choices of the user •    Segmenting the users who have taken similar courses. •    Taking into consideration the trending or popular courses.

3.    Insurance So, every organization wants to take care of their employees and one such way of doing this, is through providing insurance. Consider that the employer offers a lot of insurance options the employee such as Health insurance, life insurance, disability insurance, accident insurance, etc. So now the employee has a lot of choices to select from, which can now be a cause of confusion.

How do the recommendation systems help here? The Insurance recommendation can happen with the help of the following: •    Consider crucial parameters such as age, doctor visits, critical diseases, medication, etc. •    Considering employees answers in health surveys and questionnaires. •    Having an idea about employees past preferences.

Every business deals with data, and today it is all about making sense of such data, especially when it can help increase the sales and revenue of the company. Think about the big players such as Amazon and Netflix. According to Mckinsey, 35% of Amazon’s revenue is derived from its recommendation system and 75% of what the viewers watch on Netflix is due to the suggestions given by the recommendation systems. From the HCM perspective, look at the success of the learning systems such as EdCast and Degreed, or the recruitment systems such as LinkedIn. Much of their user engagement is derived from the recommendation systems.

So, the previous blog on app modernization answered some of the basic questions on app modernization. Some of them were, what is app modernization, what are the reasons for modernization, and how is it done using technology. When it comes to the definition of app modernization it says, refactoring the legacy software to align it more closely to business needs. An important part of that definition is business needs. As per Gartner, the world-wide spending on applications will increase by 7.5 % and mount to $201 billion in 2019, this will be hugely driven by modernization and digital transformation.

So, clearly businesses are here to spend on modernization, and they will spend for the features that catch the interest of their customers. Let’s have a look on few of those features:

1) A seamless User Experience

Let’s consider enterprise software applications, they are designed to accommodate customization so that specific processes that companies put in place, can be perfectly represented and mirrored within the system. This leads to more complexity as the software will end up looking in a completely different way in every company it is implemented. This leads to

Old looking icons and screens

More time consumption in loading table grid structures, since the data keeps on increasing in such applications

Absence of intuitive user experience

How can modernization be done here?

Have metrics in place to measure the success of the overall user experience of the product so something like, time to load a grid should be 5 seconds and so on

Improving existing UI patterns – So modernization can help here by redesigning the existing user workflows, adding insightful and predictive visualizations, and refactoring the manual activities in the UI layout

Look if there are gaps in the technology stack – Updating the code of older UI frameworks is quite cumbersome. Adopting the new UI frameworks would not only help at code level, but also improve the functioning and response time of such applications.

2) Modernization using state-of-the-art features

Most of the legacy systems such as ERP, HCM, and Fintech deal with a lot of user interaction in the application. Such businesses do have their own dedicated loyal customers. But, one cannot ignore the fact that today personalization is more of a thing expected and desired in all major applications, and it may drive your loyal customers to other products as well. Implementing personalization not only keeps the existing customers satisfied, but it also helps increase business with new customers.

How can modernization be done here?

API integrations– Integrations are a good way to connect your application to other software and have more offerings in your products. One such example is xAPI, that helps the learning systems to speak to each other in a manner that records and tracks all types of learning experience. This helps improve the LMS offerings.

Conversational interfaces– When we are talking about personalization, chatbots and voice bots are the prime choices. Answering FAQs, making the application more understandable to the end user, giving notifications, etc., conversational interfaces are making life easier for the end users.

Recommendation engines– Consider the old LMS systems, with the traditional course structure and layouts. Adding a recommendation engine that would suggest courses would help adding a personalized path for learning as per the user. This would enhance the user interaction with the system

3) Meeting regulatory compliance

More often legacy systems have high cost for implementing any compliance. Let’s consider GDPR, being non – compliant can lead to penalties. The implementation of pseudonymization strongly encouraged by the GDPR is much harder to achieve in legacy IT environments.

How can modernization be done here?

Have a keen look at the implementation of collecting of data in the data sources for test and production data

Instead of complete redesign from scratch, prefer giving more importance to step by step continuous upgradation

Implement the highest security standards to avoid any financial penalties

Watch out for worldwide compliances and make sure to refactor the legacy application to be compliant

In my first 5 years of exposure to HR Tech, I had not heard the term Diversity and Inclusion. However, it’s unlikely that anyone working in HR Tech has not come across D&I in the last couple of years. It has emerged as a horizontal business issue and is being discussed with recruitment, performance, compensation, development, succession, and what not. The D&I concept itself is not new to organizations. In fact, in the past, organizations have put in both time and money towards D&I initiatives. These included training programs, tracking representation through gender, and race ratio in the organization. Such initiatives yielded annual reports, but, did almost nothing towards building an inclusive and diverse workplace culture. I think the reason for this is two-fold. Organizations missed the trick by seeing D&I as a compliance problem rather than a culture problem. Secondly, the ability to build technology solutions powered by AI and analytics was limited.

Today, the whole narrative around D&I has changed, and we see a resurgence of it. It won’t be an overstatement to say the D&I has reached an inflection point. Various social movements have got the attention of CXOs and have them thinking about D&I at their workplace. Organizations now realize that culture and employer brand play a critical role in hiring and retaining talent. The onus is on them to build a diverse and inclusive workplace for people at work. On the technology front, though the market is not as huge as other areas in HR, interesting solutions are being built. These solutions bring out insights, trends, behaviors hidden in the data. Let’s look at how technological solutions are helping address the D&I problem.

1. Descriptive and Diagnostic Analytics: Analytics can help leaders find answers to D&I problems across the employee lifecycle. Visualizations built on top of workforce demographics help organizations define KPI’s and metrics and track them over a period of time. For example, a CEO can quickly get to see the change in gender ratio over the last 3 years. Visualizations built on top data collected during hiring, promotion, engagement, etc. help locate areas to be fine-tuned. For example, by looking at the applicant’s data, a CEO may discover that they do not attract enough diverse talent. Analytics tools provide the ability to drill-down, slice & dice, compare, benchmark and correlate data across multiple axes. Using this, one can establish a causal relationship for D&I problems. For the problem cited in the prior example, a deep dive may track the cause to a specific area, which in this case could be the job descriptions, job boards, sourcing, individual, etc.

2. Inclusive Language: How you write reveals a lot about who you are. Text analytics can help organizations demonstrate unbiased and inclusive culture. Organizations produce a lot of text while writing job descriptions, performance reviews, emails, promoting employer brand, recognizing, etc. Text analytics can help detect the use of bias language based on gender, age, race, and ethnicity. It can also suggest replacements for biased words.

3. Search and Attract Diverse Candidates: To build a diverse workforce, one of the important activities for an employer is to make sure that their job postings are reaching and attracting diverse candidates. To enable this recruitment, software integrate and post jobs to dedicated job boards for women, military veterans, minority groups, etc. As a result, people from diverse backgrounds can get entry into the hiring pipeline. In addition, solutions are offering enhanced search capabilities making it easier for recruiters to search for candidates from a specific cohort.

The HR function in enterprises is in the middle of a revolution, with AI playing an increasingly important role in facilitating processes and decision-making. Many organizations now leverage AI to improve the efficiency of their recruitment, performance management and succession planning processes. AI is particularly well-suited to this role because it can serve to automate low-level decision-making (filtering CVs by comparing against a job posting, compiling language-based feedback into numeric ratings, discovering similarities between different profiles.), allowing HR professionals to better utilize their time by applying their expertise to a smaller problem set.

However, organizations that blindly adopt AI without being aware of its limitations run the risk of having a system that delivers bad or incorrect results. The impact of this can range from simply missing out on quality candidates to more serious issues like being in violation of regulatory compliance that can lead to legal trouble. Perhaps the most famous recent example of this is Amazon using an AI-powered recruitment system that was found to be discriminating against women candidates. This example perfectly illustrates some of the issues to which AI-powered systems are susceptible.

The difference between AI and traditional programming paradigms is that AI systems are expected to generate results based on rules that they themselves discover from data. Because of this, AI can be used to generate results in scenarios where humans find it difficult to create a set of rules to deliver results. For example, it would be extremely difficult for a programmer to write a set of rules to find the perfect candidate for a particular job — the number of different factors and variables makes this far too complex. On the other hand, AI systems can be designed to go over the historic hiring data and generate their own rules for identifying whether a particular candidate is fit for a job. However, because the AI is simply creating rules that most closely match the results in historical data, it has some limitations:

It does not account for changes in context or user expectation, it can only extend historical trends

It does not account for regulatory compliance/best practice considerations

It does not map output to any real-world results, which makes it difficult to understand why a decision has been taken

Organizations which set up AI systems need to create a plan to address these limitations, and ensure that their AI systems are operating in concord with the organization’s strategy. Some basic techniques can help organizations control AI system operations:

Hypothesis testing: Hypothesis testing is a process by which data scientists can investigate how the AI generates specific results. This can help identify scenarios in which the AI is non-compliant with organizational strategy. For example, hypothesis testing can be used to answer questions like ‘Does the algorithm prefer male candidates to female candidates, controlling for all other profile attributes?’

Feature engineering: Feature engineering is a technique used to engineer the dataset on which the algorithm operates. It can be used to give additional weight to attributes which are important to the organization’s strategy and attenuate the impact of attributes which are not. For example, a recruitment dataset can be tweaked to increase the importance of features like education and professional experience, while attenuating or completely eliminating the impact of features like age, gender and race on decision-making.

Bias analysis: Bias analysis is a statistical analysis method that can help data scientists explore the results generated by an algorithm. This is especially important in the HR domain because bias in the algorithm can exist even in the absence of non-compliant attributes. For example, AI recruitment systems are often found to discriminate against women even when it is not given candidate gender as a data point. This is because men and women tend to speak differently about their achievements — men tend to emphasize personal achievements while women tend to emphasize collaboration and teamwork. Bias analysis can help identify and control such instances of ‘deep’ bias.

According to the Governance Studies at Brookings report, millennials will comprise more than one of three adult Americans by 2020 and 75% of the workforce by 2025. This is the new workforce whom companies are going to target and search the best candidates from. They are more digital, more global, more diverse, and technology savvy. There are many enterprises namely Google, Microsoft, Deloitte, and T-Mobile who are successfully implementing gamification activities in the workplace to drive positive change, outcomes, and enhance employee experience. To give you an example, Google has incorporated a travel expense game that focuses on employees that travel a lot. They are encouraged to keep track of their travel expenses related to the business trip. The employees feel motivated because every penny that they save from spending that is officially allocated for the business trip is then paid back to them as an addition to their salaries. Isn’t it a great initiative to keep employees interested and performing at the peak performance using gamification in the workplace?

Here are some ways for HR to generate growth in business; to attract, develop, engage and retain such talented people by adopting digitization in the workplace for below mentioned HR functions.

Job Application and Tracking:  Earlier it was difficult to reach out the right candidates. Job postings were rolled out by announcements on a local newspaper, postings at the local employment office, etc. Also, it was also difficult to stay connected with the deserving candidates and keep them engaged. But, digitization of this entire process has helped in transforming the recruitment workflow. Organizations can start posting jobs using online recruitment marketing portals. This will help in attracting many candidates and drive them to apply for your open job positions. Organizations give candidates the liberty to submit their LinkedIn profile or ask them to fill up a short application form, etc. If organizations have an Applicant Tracking System (ATS) in place then candidates can check their application status anytime and from anywhere, this will help in improving the candidate experience. There are various other ways where the recruitment process can be digitized using a chatbot based pre-screening method, shortlisting resumes from drives, sharing job updates and status on mobile via SMS or emails, etc.

Interviews: Video interviews have improved experience for the interviewer as well as the job applicant. These have resulted in saving valuable time, resources, and streamlining the hiring process. Nowadays, many organizations are looking forward to hire remote hiring and video interviews are the best way to show off their abilities. The recruiters can always check the tech savviness of the candidate, observe their body language and nonverbal cues, etc. This video call can be recorded and its transcript can later be used in the automated resume parser which will later help in deciding the right candidate for the open job position.

Onboarding: There is a strong need to set the tone right from the start when you are hiring an employee. You can start by automation of compliance related work such as online paperwork, finishing up the formalities, sharing videos about the company culture and employee engagement, etc. Organizations are having a common platform where new hires and current employees can talk, share, and discuss new topics. This gives the new hires to interact with other employees and share their ideas and they can quickly feel as a part of the workplace. Organizations can also have an onboarding app that will provide all the information over mobile. This will give access to new hires on mobile and they can read documents, watch videos, etc. on the go.

Learning & Development – Gamification technique is being adopted in corporate training. It’s been observed gamification of training courses has increased the engagement of the learner with the learning material. After getting this satisfying experience, gamification encourages learners to apply their learning on the job which helps the organization in achieving business and also keeping employees engaged. There are Learning Experience Platforms that incorporates personalized learning, virtual reality, and AI-based chatbots as well. LMS vendors are now moving towards adopting LXP like features to provide the learners modern, data-driven, self-directed learning which is unique for each individual.

Rewards and Recognition (R&R) – Offering rewards and recognitions drives continuous improvement and operational excellence within the organization. Organizations can give R&R in terms of spot recognition, personalized awards, and social recognition. Spot recognition will benefit the employees at the moment and give them monetary benefits. Personalized awards can comprise of digital gift cards that can be used at different online portals. Social recognition is where you create an organization’s Facebook page or LinkedIn page where you can announce the achievements of the employees.

What is the current market situation?

Majority of the current players in HR tech offer customizable products. It means, the product can be configured to meet requirements of organizations of any size and type. When a product is customizable, the focus is on configuration flexibility, and reusable components. While supporting this feature, most of the products lose focus on user-centricity, it impacts the efficiency with which end users use the application. Different types of users have varied expectations from the interface, but, as customization is in focus, they receive an interface which doesn’t show important information on screen which helps them complete their daily tasks. They have to search a lot for the information on the interface, that is a time consuming task. Overall, such a product reduces efficiency as time spent on the application increases. Products can also hamper an employee’s interest in completing routine work with a bad user-experience.

What solutions are there in the market?

Most of the HR tech solution providers are cognizant of the fact that they need to ensure good user-experience to stay ahead of their competition. They focus on changing the interfaces with latest designs, which improves visual appearance, but is not a complete solution. This hampers the end-users’ efficiency, as it does not bridge the gap between their expectations and what the products offer.

HR tech solution providers are facing challenges to revamp customizable features, so they are gradually enhancing visual interface using latest trends. This solution is not suitable in the long run, when end customers are facing challenges like talent retention, which is significant rather than doing routine work.

Harbinger’s solution

Having a good amount of experience in the HR tech domain, Harbinger can surely help product companies. Keeping the customizable feature as it is, it can make sure that the interface provides the best experience to the user and fulfills user expectations.

This can be done by combining two things:

– Role based research and user-centric design, reducing the time spent on the interface by different roles. Same time can be utilized for doing other tasks that are human-centric and not done by machines.

– Application can collect behavioral data and help users to work smartly

Why Harbinger?

In recent years, HR Tech market has been disrupted in many ways. Tech giants like Google and Microsoft have entered the arena with Google Hire and Dynamic 365 Talent. The year 2018, saw investments worth more than $4B across 266 HR Tech deals. The market is swamped with innovative solutions built using latest technologies like AI, the blockchain, analytics etc. These solutions are innovative from business points of as well because they solve newer challenges CHRO’s face today. Few examples of such problem are operating multi-generational workforce, cultural fitment of new hires, reskilling workforce etc. All these indicators suggest HR Tech is evolving and is evolving fast. The year 2018 brought out some compelling themes which caught everyone’s attention. Let’s talk about those themes now.

1. Diversity and Inclusion (D&I)

Organizations face the challenge of building a diverse workforce and an inclusive culture. They must ensure fairness and transparency in their practice of hiring, promotion, pay etc. Today technology has created solutions to do this in many ways. Organizations can measure D&I metrics across teams, departments, role, location, levels and then benchmark them against industry figures. Example of such a metric is demographic diversity in the organization. AI-based tools such as text analyzer and writing assistant help employees to detect and prevent bias from creeping into the processes. Such tool flags inappropriate words and suggests replacements too. Organizations are also using technology to deliver small nuggets of coaching to foster an inclusive culture.

2. On Demand Pay

There is enough evidence from multiple pieces of research to show that most employees in the U.S are living paycheck to paycheck. In fact, they do not have enough funds to cover an emergency. To solve this problem, a new breed of solutions has emerged that puts employees in control of when they are paid. Employees get access to their earned wages at any time during the pay cycle. Any funds accessed before the payday are deducted from the next paycheck. This provides financial freedom and peace of mind to employees. Walmart has already rolled this out to its employees. The on-demand pay solutions are built as per mobile-first strategy so that employee can quickly access earned wages. Seamless integration with a time tracking and payroll solution is critical for these solutions.

3. Employee Wellness Beyond Health

Till about 3-4 years ago, the phrase “employee wellness” encompassed only physical health of employees. Software solutions also catered to physical aspects of health through features risk assessment, biometric screening, tracking activities and so on. Next entrant into employee wellness was financial wellness. These solutions help employees track earnings expenses, offered coaching around investment, saving and providing counseling services. The latest entrant under the employee wellness umbrella is mental and emotional wellness. These solutions built for mental wellness include mental health education, identify employees at risk, mental exercise, counseling and expert advice by experts.

4. Benefits for Contingent Workers

Employees are covered for different risks through insurance and benefits provided by their employer. But the same is not true for contingent workers. As a result, they are either not covered or are under covered for risks. On the other hand, the number of people opting for alternative work is increasing and by 2027 the majority of workers are expected to be freelancing. This status quo around this needs to be changed. In May 2018, the bill Portable Benefits for Independent Workers Pilot Program Act was introduced. If approved, it would incentivize states to experiment with portable benefits. Startups are using technologies to build solutions to offer benefits and insurance coverage for the contingent workforce.

5. Improved Continuous Performance Management

In recent year’s organizations have adopted continuous performance process and implemented software solutions to shift to this model. These solutions provide features like goal tracking, one-on-one meetings, peer feedback and so on. There’s no doubt that continuous performance gives a better result than annual reviews. But it is not built into our day of work. It is still one more thing to do. We work every hour, every day but goal tracking or one-on-one are at best snapshot of performance a week apart. We work with internal teams and external partners, vendors and customer but current continuous performance is built around internal teams. We are seeing solutions that integrate with tools like MS Team and Slack and enable people to recognize and appreciate each other in real-time. These data points when aggregated gives a more reliable view of employee performance.

About a decade and a half back, I used to pick up the newspaper or get a call from friends or neighbours informing me about any ongoing offers and discounts available at nearby stores. Those were the good days. Fast forward to 2019, the good days are gone and better days are here. Now I wake up to mobile notifications informing me of the upcoming sale at H&M or the fabulous on-going offers at Staples. A little further in the day, when I am driving down to work, I get to hear a radio advertisement about the exciting discounts at Walmart. And if I have a particular store saved as a favorite, Alexa will be the first to inform me of any new offers. This is how we get notified in our day-to-day life these days. Small bursts of information are delivered at lightning speed 24×7.  

The trend for everything short is now all pervasive. Everyone is short on time and prefers accessing smaller information nuggets. Needless to say, it is also impacting corporate training. Employees find it cumbersome and boring to sit through long training sessions. The millennials and Gen Z employees are in a state of constant distraction. Modern-day learners expect learning to be delivered in short bursts and that too whenever they need it. This kind of learning requires smart training platforms that provide Netflix like experiences and support bite-sized, searchable content. Micro-learning has become the new norm, an essential feature of today’s learning systems.

The situation wasn’t the same a couple of years back. Product managers and training departments were still wondering if it was just a fad or really the future of corporate training. But time has proved that micro-learning is not just a buzzword. It is here to stay. In fact, it has already started changing the way corporate training is designed. By definition, micro-learning is about delivering learning in smallest possible content size, just enough to suit the current need of the learner. But when it comes to implementation, it is a lot more than reducing the size of the content. Implementation can take many forms.

For instance, take the case of a learning platform that provides cognitive searchability and allows users to discover relevant content. User types in a specific keyword(s) and the platform skims through the entire database to extract relevant content nuggets in the form of articles, podcasts, video, images, or any other information format. This is micro-learning delivered just-in-time, i.e. the moment an employee needs it.

When an employee is reading an article and some terms are not clear to them, a chatbot residing on that system can explain those terms. This is an example of contextual-learning.

Another case-in-point is when an employee decides to take a quiz on a specific topic.  Based on the quiz results and performance gaps, the platform can recommend content or nudge specific learning nuggets. This is an example of spaced-learning.

Here is one more case to consider. Say, an executive gets promoted as a team lead. Based on this profile change, the learning system starts recommending leadership content as daily morning tips.  

You may have noticed that all of the above examples that characterize the modern learning systems, aptly called Learning Experience Platforms (LXP), have micro-learning nuggets as the basic elements. It also implies that the responsibility of micro-learning design and delivery does not lie just with instructional designers and content publishers. The learning platform too needs to be equipped with the right technology. And it is here, that a partner like Harbinger can help you. We have built intelligent micro-learning solutions for our customers. These range from chatbots for daily quizzes, xAPI based data measurements and learning analytics for insights, recommendation engines using artificial intelligence and machine learning, and custom connectors to integrate multiple subsystems within the entire learning ecosystem. We have also enabled optimum utilization of legacy content by modernizing it and automating serving smaller content bites at runtime based on search terms and content tagging.

As the saying goes, first impressions last. A new employee’s first weeks in the organization are crucial for acquiring the skills, knowledge, behavior and culture which will help them to become effective and engaged team members. Good onboarding processes allow enterprises to properly integrate new hires in their teams, reduce the ramp-up time to productivity, and encourage long-term retention.

One of the problems that many HR departments face is how to personalize the onboarding process to make it most effective for new hires. People can come from very diverse backgrounds, have a wide range of career paths, and may be looking for different kinds of support from the HR department. A one-size-fits-all onboarding process can be a bad fit for many employees and can actually backfire, leaving new hires with insufficient knowledge or a bad first impression of company processes that will impact their engagement. However, trying to personalize the onboarding process for each new hire can be a complex and tedious task that takes up too much of HR managers’ time.

Using AI to automate and personalize the onboarding process can provide a balance between these two extremes. AI-powered technologies can allow an HR department to customize the onboarding process to each individual employee. They can also make the onboarding process more self-service, allowing new hires to obtain only the information they require, without being overwhelmed. Finally, they can give the new hire the tools to socialize with the right people and build important connections with peers and team members which will positively impact their productivity and engagement.

According to SHRM , there are 4 components of a successful onboarding process

1. Compliance: Rules and regulations

2. Clarification: Role-related expectations and responsibilities

3. Culture: Organization values and norms

4. Connection: Relationships with existing staff, supervisors, and mentors

Evidently, there is a lot of ground to be covered and not all of it can be covered by simply making new hires read a few PDFs. On the other hand, line managers should not be spending too much of their time showing new hires the ropes, because this can impact their own as well as the whole team’s productivity. AI-powered technologies allow enterprises to find innovative solutions to thread the needle and give new employees a timely, sufficient, effective and personalized onboarding experience. Here are some of the ways AI can help with onboarding:

Generating documents for signing: Using natural language processing (NLP), organizations can automatically generate offer letters, contracts and other documents like NDAs with the employees. While these documents can be generated from templates, it is also possible to create a more advanced system that uses unstructured text from email exchanges or IM applications like Slack. While this process cannot be completely automated (A human needs to validate the output and ensure that it is signed properly), it can reduce the amount of time HR managers spend on clerical tasks and thus increase productivity.

FAQ chatbots: Most new hires will have questions about basic processes like applying for PTO, connecting to the office WiFi, or setting up their email account. In addition to giving employees packets which contain this information, a chatbot is a good way to answer FAQs. Chatbots allow new hires to ask for new information when they need it instead of being overwhelmed by everything at once. They help reduce the time it takes for new employees to become productive.

Relationship-building aids: Building relationships with peers and team members is crucial to allow new hires to integrate into the organization, increase productivity and become engaged employees. Using organizational network analysis (ONA), organizations can understand which relationships new employees must cultivate to be productive and introduce new hires to critical points of contact in their team and in the organization.

Feedback analysis: Getting feedback from employees is important for HR professionals to continuously improve the employee experience. AI can provide HR professionals with the tools to understand direct and indirect feedback. Using NLP, HR managers can extract quality insights from large quanitites of textual feedback. This can allow HR managers to understand the major feedback themes, general employee sentiment, and the effectiveness and ease-of-use of HR processes.

The points above are just a snapshot of the kinds of advanced capabilities organizations can unlock using AI. To discover opportunities for leveraging AI in their processes, HR departments must first start by conducting a thorough audit of their processes to discover pain points and bottlenecks. These action points can then be analyzed by data scientists/ML engineers to see if AI/ML technologies can be leveraged to provide a solution.

The learning industry has undergone various changes in the last few years. Today the learners want their learning experience to be motivating, millennial oriented, engaging, in the reach of their finger-tips, with a better user interface and intuitive suggestions. The traditional LMS falls short in delivering such experience. According to INC42, traditional LMS is not suitable for 21st-century learning. That explains the shift of the learning market towards the Learning Experience Platforms (LXP/LEP), which deliver personalized learning experience to the learner.

With engaging features like better user experience and analytical abilities, notepad or note-taking skills, LXP has created a good response in the corporate learning segment. According to HR Tech Central, the LMS market accounts to $5 billion in size and grows by 3-4% per year and the LXP market is much smaller (around $200-250M) , but it is growing at 200%. LXPs have different features, but the ones that are gaining more importance in the learning sector are:

Netflix style user experience design

AI -based recommendations for courses

Gamification with points and badges.

Microlearning- oriented course structure

Predictive analytics to gain insights on employee learning behavior.

Integrations with collaboration tools, file sharing systems, and document creation platforms.

To stay competitive in the learning market the LMS vendors have begun to adopt LXP like features. Various LMS vendors such as Docebo, Saba, CornerstoneOnDemand have added LXP features such as the engaging user experience, machine learning based search functionality, personalized micro-learning experience and so on. Most of the LMS systems that were restricted to the desktop-only approach, have shifted to the multi-platform approach. Most of them also include the course rating and feedback mechanism.

When it comes to having a wide growth in the LearnTech market the learning experience platforms are leaving no stones unturned. Be it acquisitions, mergers or fund-raising the LXPs are doing every bit of it. Start-ups such as Curatr are gaining good response for their innovative offerings. The Valamis LXP has created some buzz as NextGen learning system. It has analytical capabilities that help in understanding and establishing a connection between corporate learning and business goals. Learning experience platform Degreed has acquired Pathgather. This acquisition brings 200 clients, over 4 million licensed users and nearly $100 million in funding. Another LXP, Fuse Universal raised 20 million dollars in May 2018 to expand its learning system with machine learning and AR. To be in the forefront, the LXP EdCast acquired two companies Sociative, a machine-learning company and, Seattle-based Sales University. Also, to add to the growth, EdCast introduced GuideMe, a digital enablement tool for online performance support systems. Every major LXP is now looking, how it can utilize technology improve learning capabilities and grow in LearnTech.

Today the needs of the learners are a major priority. Since LMS has been here long enough, the questions are primarily for LXP. The debate over here is whether LXP is just a hype, a blooming buzzword or simply an extension over the LMS. To add to the heat, there is the introduction of terms such as NextGen learning systems. Some say LXPs are the NextGen Learning Systems and for some, to be the NextGen systems you need to be more than the LXPs. Right now, it is really interesting to see how some LXP-like features and the complete LXP suite have had their separate adoptions.

So, you have zeroed in on an ideal candidate for the job. You have been through all the stages like sourcing, screening and interviewing. Then what is holding you back from giving the offer letter to the candidate?  A very crucial step is still pending, and that is background check of the candidate.  If we exclude this step, it could result in the hiring of an unsuitable candidate for the organization.

A survey conducted by HireRight in 2017 found that 85% of HR professionals found fake information on candidate resumes.

Some of the critical challenges in the background check process are:

1. Costly and time-consuming.

Nearly 45 % of the employers surveyed in Background Screening Trends & Best Practices Report 2017 said their biggest challenge was reducing the time to hire while 41% said it was improving the background screening process efficiently.

2. Difficulties in checks with past employers

Turnaround time may be extended depending on the availability of the last employer. Complexities may increase if the past employer organization cease to exist.

3. Traceability of data with third-party verification systems.

Since employers connect with various third-party systems and verification agencies, with so many entities managing the candidate data, it can get difficult to track the candidate data and get a quick response on the verification.

Blockchain, a distributed ledger technology can help overcome the above challenges. To understand how blockchain can contribute, let’s have a look at the below diagram.

To understand the concept, let’s consider the following process:

A candidate uploads university certificates and work experience letters from past employers on a server (it could be any server, e.g., Node.Js server.)

The universities and the previous employers could verify the documents.

These verified documents get stored on IPFS

Why IPFS?

IPFS stands for Interplanetary File System. It is a versioned file system which stores files and tracks versions over time. Due to the similarity in their structure (like distributed, peer-to-peer) IPFS and blockchain are a right combination. Some of the key features of IPFS are as follows:

No Duplication – Files with the same content cannot be duplicated and only stored once.

Content Addressing – Content has a unique identifier that is the cryptographic hash of the file.

Tamper Proof – Data is verified with its checksum, so if the hash changes, then IPFS will know that the data has been modified.

After the documents are stored on IPFS, the IPFS then returns the document hash.

The hash values of these documents can be stored into blockchain by using Smart Contracts

When a recruiter accesses the documents verified by past employers and universities, the recruiter is assured about their authenticity. Such blockchain based verification platforms would require that the hiring employers pay a fee to get the verified candidate details.

How did having blockchain help here?

Employers pay hefty fees to different agencies to check the degree of the candidates, and candidates will have their degrees verified each time they change jobs.

As the candidate details are secured on the blockchain based platforms, employers can access the verified data from one platform which would save the money spent in the process.

One cannot tamper the documents as their hashes are stored on blockchain; hence the process ensures more security and authenticity.

As the process is not manual, the time and efforts involved are reduced.

In an earlier blog, we discussed how integration is essential for the next generation learning systems to break the silos and be truly distributed systems. Learning Management Systems (LMS) need to embrace multiple devices, offer offline support and work across multiple learning platforms. Tracking and measuring every learning experience to maintain a detailed log of learner’s activities is thus critical. Data capture and learning analytics, therefore, play a central role in the next generation learning systems.

Experience API (xAPI), along with the Learning Record Store (LRS), provides a mechanism to track all such activities. It employs a standard format and vocabulary to maintain learning records. One can also complement it with a custom vocabulary. It thus facilitates the measuring of a variety of data that business use cases demand. To know more about adding support for xAPI in your learning system, read this blog.

Learning Analytics

Analytics offers deeper insights than reports. For example, a report could tell you the grades a learner secured in a course. Analytics would help you understand the correlation of those grades with the performance of the learner, as well as the performance of your business. To generate detailed analytics, your application would need to capture a learner’s activity to a much finer granularity. For example, how much time the learner spent on a page, which type of content is the learner skipping, and how many attempts did the learner need to pass in a course. The steps involving in extracting analytics have been covered in the following section.

Four Steps for Rich Analytics

Here are the four steps that you would generally come across while extracting analytics:

Measurement: It’s the simple act of tracking things and recording values. It doesn’t demand any complex algorithm – just the discipline of capturing data that matters. There can’t be any analytics without data.

Evaluation: The next step is extracting meaning from the data. With small sets of data, simple statistical values like the most or least popular courses across the organization can be extracted on an ad-hoc basis.

Advanced evaluation: When data sets get large, you can use advanced techniques like data mining and machine learning for statistical insights on essentially any parameter(s). This can help you extract information like region wise course popularity, courses popularity by employee age group, or course performance based on content providers.

Predictions and recommendations: The next step is to discover patterns and make predictions and recommendations. Technologies like machine learning and artificial intelligence ease this process and help to automatically identify patterns in your data and make better predictions.

Varied Learning Analytics for Different Stakeholders

Learning analytics can serve varied purposes for different stakeholders, i.e. admins, content writers, learners, managers, and business leaders.

For Learners Every learner action is logged by xAPI. For instance, it tracks learner interaction with other people through social media, task status updates, stages and milestones in games, play and pause actions over video content, and switching between different sites during quizzes. Such detailed logs of the learner’s’ activity make it possible for L&D teams to design dashboards that reveal patterns of the following kind.

For content writers Analytics extracted by xAPI can be useful for content writers to improve the effectiveness of the courses.  For example, they can get answers to the following questions.

For admins For administrators, xAPI can provide standard analytics, just like traditional learning systems. These include insights on which learners have completed a given course,  percentage of course completion for each learner, and assessment results.

Performance measurement Just knowing a learner’s progress is not enough for any organization. Businesses need to know how learning impacts employee performance. This performance is measured by a number of parameters, like industry knowledge, product knowledge, problem-solving skills, and technology skills. Weightage to each parameter varies by situation. These parameters are typically available in multiple external systems. SCORM doesn’t allow integration with multiple systems. With xAPI one can fetch information from multiple external systems and store in LRS, thus allowing learner’s performance measuring across systems.

Business Insights Analytics can reveal business insights – e.g. which content provider is working and which one is not, should the system aim for wider breadth or have more specialized content, which is the trending topics, and so on. The next level of analytics is powered by machine learning, where, based on historic patterns, the system can predict which courses will be sought after. The system can also make recommendations for course selection based on the learner profile and objectives.

Statistics of viewed v/s skipped a video

Which sites or additional help sources do learners switch between during the course of learning?

How much time is spent on each learning section?

How many learners answered a specific question correctly/incorrectly?

What was the average number of attempts for each question?

How did the demonstration video perform (if it was used)?

What kind of content is most effective – text,  audio, or video?

What kind of interactivities drives the most engagement?

Are learners interested in the depth or breadth of the content?

In today’s competitive market it is imperative for every organization and employee to be in lockstep with trending technologies. Conventional Learning Management Systems (LMS) administered by the management have proven to be too slow to keep pace with the demands of the modern workforce. More than 75% of today’s workforce comprises of millennials. Their learning style is autonomous. This calls for a paradigm shift in the way LMS deliver content. Learning platforms need to put users, and not administrators, at the center and allow them to discover and establish learning pathways. Learning Experience Platforms, LXP or LEP for short is the next generation learning systems that fulfill these demands.

Why is LMS integration important?

LXP aim at providing a personalized learning experience based on user profiles, aligned with their business and career goals. Content need not be hosted only locally but should be sourced from anywhere on the Internet. Such a platform needs to be orchestrated with multiple technologies and varied learning paradigms – gamification, micro-learning, machine learning, artificial intelligence, and chatbots are some examples.

An LMS, however powerful, can’t do justice to the training needs if it were to rely only on local content. Integration with third-party applications and data sources is an absolute necessity for the next generation LMS to remain competitive in the marketplace. Following use cases illustrate how integration is set to disrupt the learning industry.

Integration with Catalog Products

A simple integration with external services, like YouTube channels, TED talks, MOOCs and articles would allow learners to search and choose content that aligns with their career goals. As the next step, such content can be imported and curated within the platform so that it blends well with the conventional content.

Integration with Performance and Goal management Systems

Goal setting is now an integral part of the modern performance management systems. Learning goals that align with business objectives are the norm. Such learning goals need to be tied with learning paths in the LMS. Integration of LMS and performance management systems will enable automatic goal creation and tracking.

Integration with Analytics Tools

LMS contains a wealth of data ranging from popular courses to individual preferences and performance. To leverage this information for strategic decisions is of vital importance. Following are some useful metrics that LMS integration with analytics tools can reveal.

What is the level of popularity of different courses?

How frequently, and when are the courses visited?

Which regions and units are most and least active on the LMS?

What kind of support and new content requests are learners making?

Which is the most and least popular type (medium) of courses?

Integration with Social Networking sites

Integrating LMS with social networking sites opens avenues for marketing. LMS users can share their updates about completed courses on social networking sites at the click of a button. Such posts can help promote the organization’s brand at no extra cost. E-Learning companies could share discount codes, enrollment keys on social networking sites through integration.

Integration with HRMS

Integrating an LMS with an HRMS application would facilitate an instant exchange of course details via web API.  LMS reports could be transferred into an HRMS application without the need of accessing LMS. Such tight integration would ensure that all the relevant information about employee credentials and skills is available in real time in HRMS to aid resource allocation and promotion decisions.  

The Learning Ecosystem

The overarching objective of integrating an LMS with other information systems is to create a learning ecosystem where each participating system can focus on its core competency and leverage information from others without worrying about the logistics of information exchange.

Payroll processing is one of the key functions of any organization. To run it, one needs a complete understanding of the laws around minimum wage, overtime calculation, taxes, withholdings, payment methods, and a lot more. This is to ensure correct tax filing and payout processing for employees. A payroll system that can perform these functions while delivering a streamlined experience for accessing data and reporting is the need for all organizations. An organization based out of multiple locations face steeper challenges, as the laws governing payroll and labor vary in different states, and within cities in a state. Owing to these challenges, it becomes difficult for organizations to cope up with global payroll needs.

Organizations offload (outsource) the burden of payroll processing and compliance requirements to a payroll service provider, and rely on the provider to run the payroll, and file tax documents such as Form 941. The choice of the payroll provider depends on several criteria that businesses must consider. This includes features offered by payroll system, taxation and compliance support, business needs, cost, security, and flexibility. One of the most important aspects to consider is – how easily and seamlessly the payroll software can integrate with the current HCM data.

With 2 different systems in place, there are challenges in syncing data and generating real-time reports. The connection between systems needs to be set up either manually or via external tools, and a mechanism needs to be in place to ensure the consistency of data. Organizations which have multiple categories of workers such as full-time employees, part-time employees, contingent workforce, the problem is more acute for them. Reports which need data from both payroll and HR systems demands extra efforts from the end user. In addition to these, employees want to know all the minute details related to their paycheck. This includes things like how much their benefits cost them, their time off records, etc. Such information is either directly accessed from payroll systems, or via queries to HR representatives. To overcome these challenges and deliver a better user experience, one needs to have an integrated HCM and payroll system.

An integrated Human Capital Management system with payroll services brings in significant improvement by streamlining all related functions for HR, as well as employees. There are multiple approaches to connect payroll processing systems with HCM such as – sync data using external tools, EDI files, point-to-point connectors, API endpoints, etc. Out of these approaches, in our experience integrating systems at the data level via the use of APIs and services has proved to be much effective and flexible. Use of APIs guarantee fluid and secure data transfers with the flexibility to adapt to changes easily.

With the use of cloud technology and APIs, the integration of an HCM system with payroll systems is simplified. Even the time taken for integration is also less as compared to traditional point-to-point integration approaches. Majority of payroll and HCM systems now have a pre-defined set of API endpoints defined using standard protocols like – REST, SOAP to enable seamless data sync across systems. This also enables systems to exchange data based on certain events that take place on employee data. For example, the payroll system can subscribe to any modification of employee data in HCM and trigger an action in the payroll system. These events can be enabled by making use of web hooks and APIs made available by communicating systems. Mapping of data elements on basis of entities exposed over APIs across both the systems lays the foundation for such integrations. Custom connectors leveraging these API can also be used to add an additional layer of abstraction and handle complex scenarios.

Listed below are few benefits of an integrated HR and payroll system:

For HR administrators, such integrations not only help in cost savings but also reduce administrative burden, and saves time by helping them get rid of routine activities for data sync across multiple systems

Reduces potential risk of duplicated data, human error

Cross-functional reporting ability without the need for manual consolidation of different datasets

Updates to employees’ personal information in any of the connected systems can be tracked in real time

Data transfer across systems is more secure as any data is captured via API layers running on secure communication protocols

Enhanced employee experience with HR functions by providing them with self-service capabilities as supported by payroll systems.