Tag: elearning

24 Jun 2024
AI training

Why is AI training useful?

Artificial intelligence is changing the way the world is functioning. Today, artificial intelligence (AI) is used in every organization. Even degree-holding employees need to learn AI to do simple tasks in the company. But why do employees have to learn AI? A lot of employees, almost 50% want their companies to impart training in AI.

Companies are under a lot of duress when providing AI training to employees.

The following are the benefits of using AI in a company:

  • Do mundane tasks:

Artificial intelligence is needed to solve the complicated issues. They can provide answers on how to simplify supply chains. With the help of AI, human beings can solve many pestering problems. It’s because AI can be used to evaluate colossal amounts of data. AI can be used to assess every option in a supply chain easily and quickly. AI helps in automation. For example, robotic process automation uses bots to solve easy tasks like data entry because it contains rules-based programs. But when used with AI, it also has an enhanced capability to do tasks that require decision-making.

  • Predictive analytics:

AI can also forecast, considering historical data. Therefore, it can predict when certain actions should be taken, such as maintenance of machines, and control of inventories, meaning when the inventory is going to finish based on sales pattern and hence should be replenished.

For example, it can be used to write, which ensures that human beings can be alleviated from such burdens. They can devote their energies to more innovative tasks. AI helps in automation because it has the power to see the future more than human beings. It’s because AI can use predictive analytics to make better decisions than human beings. Hence, employees should receive AI training.

  • Natural language processing:

AI can also be used for natural language processing, a very useful attribute of this technology. Due to this characteristic, AI chatbots are now used to answer and help customers. It ensures it substitutes human beings who can’t be always available. AI can understand the questions posed by customers due to natural language processing and answer them due to certain keywords in questions.

AI is helping businesses shape their future. But it can’t understand human emotions. This quality is necessary for managing people and providing customer service. If a company continues to ignore soft skills training, then the business could suffer in terms of lack of customer and employee satisfaction. Therefore, an organization has to provide both soft and technical skills training.

How can a company implement AI?

  • Find use cases:

There are certain processes in every business unit to which a company should devote its energy. Some tasks are repetitive and can be done by machines too. But organizations must see to it that every task can’t be automated. Hence, employee feedback should be taken regarding which tasks should be automated and which not. Businesses are applying AI to make their decisions, but somewhere, this eagerness is slowed down by a lack of confidence in AI by non-tech companies.

There can be specific use cases whether AI should be used or not. This technology can be used to make very complex decisions. For example, AI can help organizations in deciding whether a certain source should be used for buying. Such decisions have to be taken by supply change managers and can be used by companies to provide time to frontline employees. For this, employees should be given AI training. However, companies that don’t train their employees have less efficiency and less employee productivity.

  • Find enthusiastic employees:

Companies require employees who know how to implement this technology.

A company, when preparing its employees for AI, should explain to them how adopting this technology will change their professional lives. There are some people in every business unit who are ready to adapt to a new technology faster than others. Such people should be recognized and asked to encourage their peers to acknowledge the automation processes. They can also be asked to detect the procedures that can be automated in their business unit on a long-term business.

How can AI be used in every industry?

  • Retail:

 A lot of tasks can be streamlined with AI. For example, stocking is an important task. AI can check the sale of products, and when a product is over, it can let employees know about it. It mitigates guesswork so that employees know which items should be kept ahead in the merchandising display. It’s because some items sell more quickly than others. When AI can streamline the merchandising tasks for the employees, they can put their attention to the clients in shops.

  • Healthcare:

AI can also help the frontline workers in the healthcare industry. This is because they handle the routine tasks, making sure employees can focus their energies on taking care of patients. They can check whether the doctors are doing their jobs correctly, i.e., whether the diagnostic jobs have been done accurately. They can also analyze large volumes of data for the doctor. If employees have a problem with it, AI can even answer that and make sure that work happens smoothly. Sometimes employees are not familiar with a foreign language, but AI can produce discharge receipts in every language to help them.

The AI can be pretty useful in the insurance industry. It can help in solving simpler claims. It’s because AI can help them store any interaction data in a database so that employees can use it when required. When the AI stores all the data, the customer service representatives find it easier to talk to the customers based on prior interactions.

  • Hospitality:

AI can help in the hospitality industry also. It can ensure that the guests have the best experience possible because it recommends rooms for them based on their previous history. It can also write responses to guest reviews on websites so that employees don’t waste time.

So, AI training today is useful for every industry.

08 Apr 2024
upskilling through competency modelling

How can upskilling be done through competency modeling?

Companies today are working hard to ensure that they can meet their productivity goals. Therefore, they are training the workers based on competency models. Those are the models that help a company determine the competencies needed for a person to be successful in his/her job. These models help a company in setting parameters for upskilling.

The concept of the competency model emerged from the job analytic process, which breaks a job into tasks depending upon the job description. When the tasks underlying a job have been defined, they are mapped onto the knowledge required to perform them.

How to build a competency model?

It’s not always easy to come up with a competency model. It can be a tough task even for skilled managers. The first step is to do a job analysis for upskilling. It helps in detecting the tasks. The first people to answer this question are the stakeholders. These stakeholders are those who perform this function regularly and know the tasks required. They can consist of HR managers and ultimately the employees at the executive level.

How to create a proficiency model?

The competency model decides how much training is required for upskilling.

Then, you can get feedback from all the managers on whether the competency model is correct. Subject-matter experts can also validate the model by checking it. The company can then segregate the required competencies into different sections, like technical and leadership competencies. When you have the proficiency levels designed, you can get the training levels. There should be proficiency levels defined for each competency to decide how much training is required by employees.

Update the competency model when required:

Once the company’s L&D team has got the necessary validation for the competency model, they can begin the training processes. This competency model needs to be changed quite often so that it stays pertinent. If it’s not relevant anymore, ask the managers where it can be improved.

Regularly review and update the competency model to ensure its relevance and effectiveness in meeting the evolving needs of the organization and its workforce. Solicit feedback from users and stakeholders to identify areas for improvement. There is also one more way to detect competencies required for a role. It’s through observing the way people do their job. This can help you detect what tasks are required to perform a role. Although interviews can be used to detect the competencies required for a job, they cannot be used for all job roles because that would require a massive effort.

An AI tool can also be used for finding the competencies needed for a job. When such a loss has been generated, it can checked by the managers and the SMEs to see whether it’s perfect.

Challenges in creating a competency model

  • The requirement of different jobs:

The competency model is not required by a company for every job, rather it is required for every strategic job. The company should do competency modeling for jobs that are highly critical for it. Such jobs would require upskilling for the needed skills so that the company continues to run in the same manner in the future.

  • Future possibilities for a competency model:

The competency model should also take the future requirements of a job for upskilling. It’s because a job does not require some skills now, but it might require some skills in the future, like AI. A stakeholder needs to check which skills will be needed in a job 5 years from now.

To do an analysis of skills required for the jobs in the future, it’s better to understand the environment. Then envision what sort of changes can happen in the future in terms of technologies and trends. There can also be disruptions like the pandemic and shifts in the economic conditions of people caused by urbanization, leading to better demand for a company’s products. These kinds of possibilities can also be envisaged by a company when it takes interviews of experts in the industry. They have much more knowledge about what can affect an industry in the future, like how artificial intelligence can take over all the work. So, this can affect the tasks required to be done for a job, changing its competencies.

Apart from competency modeling, some general competencies will be required in the future in all kinds of jobs. These competencies are digital literacy and cybersecurity. All employees need to have such competencies and should be taught tasks related to them because they will be needed in the future.

15 Mar 2024
Online learning

How is online learning affecting education?

Modern classrooms have changed so much. From chalk-and-blackboard classrooms, we are now living in an age where digital classrooms (online learning) have taken over the world. Now, the students listen to their teachers talking through a Zoom screen. The world has changed a lot significantly due to the pandemic, which has brought so much modification into our lives.

Pitfalls of a conventional classroom:

In a conventional classroom, the students don’t get all the attention from the teacher. There is practically no interaction between the teacher and the students. Some of the students are not able to understand the teacher but can’t question them at the same time while they are delivering the lecture. So, these students are unable to perform because they have not understood anything.

Often, a lack of attention is also caused due to seating arrangements. Some of the students are sitting in the front rows, others in the middle, and some are backbenchers who find it tough to maintain attention. Whatever the number of students in a classroom also determines the understanding level of students.

  • Disadvantages of large class sizes:

Studies have proved that with a smaller number of students in the class, they tend to score better, and it’s also easier for teachers to manage the class. When class size is small, teachers can talk to students individually about their problems. This is not possible with a large class size because too many students can’t be talked to at once.

With large class sizes, the use of technology is limited. This is due to the ability of students not able to see the projector. This is not beneficial for students because they can’t hone their digital abilities. They have no technical skills, which can prove to be detrimental in the later stages of their lives.

Using technology in class proves to be useful for students because it is required in corporate careers these days. When students are taught through technology like tablets, they become more adept at using them than those who are not.

Use of technology in education:

This transition from textbooks to modern-day gadgets happened because of the invention of the internet. Now instead of cumbersome books, education is available at the fingertips of every human being due to the internet. Due to this, students found it easy to access information and understand it. So, teachers started teaching students through virtual classrooms. But with technology, Learning Management Systems (LMSs) were invented where students could easily be transferred lessons, and they could study from them. They were handed over the entire lessons online, where they could study the material and try the quizzes also.

Technology has also changed a lot in recent years. With smartphones, students can browse through information at any time.

Students benefit from the flexibility offered by online learning.

When the material is available to them 24/7, they can browse through it at night or in the morning. They don’t have to follow a packed schedule like in the case of traditional classrooms where they have to be in the class at a certain time.

  • Use of LMSs:

LMS is getting used in virtual classrooms, due to which content availability is omnipresent. The LMS also provides analytics that are not present in a conventional classroom. The students might not understand something, but the teacher does not know about it. It’s because the student does not finish a lesson if they are unable to understand it. So, their LMS analytics show a lesser time spent on a lesson that they didn’t understand. The teachers can make sure that the students can have personal online learning paths, due to which they can spend more time on lessons incomprehensible to them. It’s not like they are supposed to spend a fixed time on a lesson.

  • Use of AR:

The technology is advancing like never before. Now, AR (Augmented Reality) is also being used in teaching students via online learning. With objects drawn along with real-life objects, students can see the latter from a better perspective. This is very useful in medical and manufacturing industries where everything can’t be explained with examples. So, a patient’s vitals can be shown and what is to be done in the situation through AR-based objects and headsets. Traditional classroom-based learning will not stay any longer, because students can’t focus when anything lifelike is not shown to them, only possible through technology. When they are transferred to different situations in online learning through this technology, they tend to understand information better.

07 Mar 2024
learning analytics

How learning analytics must be collected and analyzed? 

E-learning data analysis can have several problems. The creator has to see that these mistakes are avoided.

Learning analytics is the process of collecting data about the learners so that changes can be made to the learning process. This helps in a better learning process for the organization. When learning analytics are collected, the performance of the employees certainly improves. It’s because whatever the eLearning lacks is analyzed and corrected. When the right information is collected, the L&D activities are improved.

Different kinds of data can be collected for learning analytics. The following is the information:

  • How much time do employees expend on the program and each module?
  • Employee satisfaction levels with the program can be gauged through interviews
  • Different grades or score levels in the test

How can biases occur in analyzing data?

The sample is chosen for learning analytics from a population of a certain size. If a minimal sample, is selected, it does not lead to clear results. The results can be biased or skewed in favor of those who have been included.

The biggest bias can occur when samples have not been drawn carefully. This kind of bias is called the selection bias. In this case, the sample does not reflect all the groups that are part of the population. Selection bias can cause the subjects from some groups not to be included in the sample, or people from some groups to be included in excessive quantities.

Survivor bias can also be a part of the conclusion. In this, only those subjects are chosen who have cleared the tests, but those who have failed the tests are ignored. Hence, it does not lead to a clear conclusion being drawn.

There can also be problems with conclusions drawn from the data. This is called confirmation bias. In this, it can happen that we have already drawn conclusions in the mind and are looking for information to prove it. This happens when the L&D professional has a prejudice and believes in it. However, it’s important to look at the analytical result of the data rather than confirming such bias.

Also, sometimes companies just rely on historical conclusions drawn from the data in the past.

When L&D professionals suffer from this bias, they tend to ignore information that does not conform to their beliefs. There can be an error in the conclusion due to historical bias. These are further problems that can occur in collecting and analyzing data:

  • Lack of employee views:

In learning analytics, sometimes data is not the only answer before the researchers.

The learning data may not represent the whole story. It can be incomplete and not true. Apart from data, human judgment about the matter is also important. The organization must have a balance between the data and the human intuition to get the reality of the picture. It gives a proper answer as to why the learning process is not yielding results as it should.

  • Lack of data:

The lack of data sources is a reason why the conclusion might not be right. It’s because sometimes, the companies look no further than the test scores. Such data can be easily provided by the LMS admin. They don’t ask employees at all about their viewpoints regarding the LMS. Also, sometimes, the number of employees who have failed the tests needs to be taken into consideration. Then only you can get a real conclusion whether the e-learning needs improvement or not. You can only get an answer to whether there has been any improvement after e-learning through several data sources. 

Importance of learning analytics:

Learning analytics are very important for any organization that has implemented an eLearning program. It’s because they help them detect those areas where the learners are facing problems. The problems can also be caused because the students are leaving the course in between due to the issues from the course. When such analytics are evaluated, the L&D head can see what the difficulties are. They can work on the course itself or even ensure that there is blended learning in which learners attend classes to sort out their difficulties.

Challenges of learning analytics:

Learning analytics provide data about the employees in an organization. But this data has to be protected. The company should be extremely careful about the data getting leaked. It’s because it can affect an employee’s career when his learning progress is in the hands of nefarious sources. Hence, all kinds of unauthorized access must be regulated.

Most often, the L&D professionals are happy in their own space and don’t want to bring any change. But they have to focus on the impact of their learning activities too. L&D Professionals need to learn too because that can improve their performance.

23 Nov 2023
software training

What are the best ways to design software training?

The eLearning has made its mark in the world today. However, knowledge retention should be the main aim of eLearning because if that does not happen, there is no use. Why does knowledge retention not happen in eLearning?

What is wrong software training?

Miller has specified how much knowledge can be there in a person’s brain at a single time. But not all those seven things created in the mind are equally remembered. Some things are remembered more easily.

For that, the eLearning content creation companies must use videos and infographics. Such companies mustn’t use text to explain every matter but rely on interactive material to better retain certain things.

The most typical example of wrong learning is trying to teach software to your employees. The workers can’t mug up all the steps of how to carry out a certain process in software.

However, companies have to understand that although software is an integral part of every company process, the employees can’t be expected to learn every step. It’s not only impossible, but it also has no major effects on the company when the employees can Google software steps and carry on with daily tasks.

In fact, the employees should be taught soft skills that are more important. For example, employees who are in sales should be taught how to canvass the customers to buy more products or related products.

Effects of wrong software training:

When a company has not properly imparted software training, it can lead to many negative effects. There won’t be an expected ROI and all the software costs would be wasted. It’s because the employees don’t understand the software and can’t use it fully.

How to properly teach the software?

The company must know the software flows for all the employees. This helps them understand which users will use which features of the software. It’s better to design the training accordingly for such users.

  • Remove their fears

So, how can companies make software training easier for employees? First of all, the trainees must be encouraged to participate in training. Their fears regarding the new software must be eradicated. Those kinds of people in the company are not at all happy with change. They are just complacent with the current situation. They hate to ask questions when they have doubts about learning software. So, it’s better that such resistant people are asked questions about their fear of change and then pacified to adopt the new software.

  • Training should be role based

Since explaining each and every software feature to the user is cumbersome and results in cognitive overload, it’s better to refrain from it. Instead, only those features that are complicated in nature and can’t be figured out on their own should be explained to the user. The user might find these features tough to use and commit errors, leading to wrong operations.

Also, some roles require the user to only know certain features that are a part of their daily work and hence should be focused on. Extra features a certain employee will never use should not be focused.

  • Explain the software through articles:

Also, software features can be discussed in articles or documentation for those users who like to go into depth. Such articles save those users who are not interested in knowing in depth about all the features.

  • Use screencasts instead of videos:

The best way for software training is to use images instead of videos.

Videos are tough to edit when there is a change in the software processes. But images are not so hard to edit. You have to screen capture a certain image that has changed. When the user wants to revise his knowledge of the software, he finds it easy to come back to a certain set of images rather than rewinding and watching the video.

These are some steps for screencast videos;

  • Include a table of contents: Firstly, there should be a table of contents so that the users know which page to check for which information.
  • Use less text: The text on the screenshot should be less than the length, as much as equal to 10 words or fewer. A box should be used to highlight the correct portion of the screenshot. 
  • Include all screenshots: Don’t skip any screenshots because the users might get confused.

The eLearning vendors have to work hard to get the process right. They must ensure that in software training, the process is indicated in the software itself so that users can check it carefully. They can be given screenshots of the software processes in the help row itself.