Diversity, Equity, Inclusion, and Accessibility should be part of every organization. As it begins to become part of more conversations, consider a few diversity statistics for the work force.

• Fifty-seven percent of employees think their companies should be more diverse.
• Forty-one percent of managers say they are too busy to implement diversity initiatives.
• The most gender-diverse companies are 21% more likely to experience above-average profitability.
• Although 90% of companies claim to prioritize diversity, only 4% consider disability in those initiatives.
• Although 90% of companies claim to prioritize diversity, only 4% consider disability in those initiatives, according to a report from the Return On Disability Group. And only a small subset truly serves customers with disabilities.
• Sixty-five percent of the LGBT community believes diversity and inclusion are essential to supportive company culture.

DEIA & the Workforce

In November 2021, the Government-Wide Strategic Plan to Advance Diversity, Equity, Inclusion, and Accessibility in the Federal Workforce was released in response to President Biden’s signing of Executive Order 14035, Diversity, Equity, Inclusion, and Accessibility in the Federal Workforce in June. This strategic plan not only provides a guide for the Federal agencies but also a good guide for the public. Thinking back to the statistics from the beginning, this strategic plan assists with understanding where organizations should focus or prioritize resources. To begin, there are five operating principles:

1. use data and evidence-based decision making,
2. focus on continuous improvement,
3. adopt a collaborative whole-of-agency mandate with partnership engagement,
4. prioritize accountability and sustainability, and
5. understand the perspectives of the workforce and the customers.

Accomplishing these is not easy, but these are a good starting point. The priorities include safe workplaces, data collection, establishing a Chief Diversity Officer, DEIA training and learning, and others. Finally, an important section is the roadmap or implementation section. This provides direction for successful implementation of a DEIA plan including multiple roadmaps that align with each piece of DEIA.

Talent Analytics

Take note that this strategic plan recognizes with the first and second principles a need for data. Organizations must determine the current DEIA status to make the decisions and goals. Leadership will need to track any changes based on the goals to determine how the impact of the DEIA decisions. Understanding where the current situation is key to establishing goals for improvement.

To assist with this understanding, Quality Analytics Associates has partnered to provide a talent analytics solution that is driven by Artificial Intelligence (AI) with human support which ensures a comprehensive approach while reducing the risk of bias to the results. The solution consists of five areas focused on specific data collection – culture analytics, DEIA analytics, planning analytics, continuous analytics, and modernization analytics.

Culture analytics allows for the review of historical and current workforce culture data. DEIA analytics provides detailed historical and current data for DEIA. To assist with strategic planning, the solution offers planning analytics, continuous analytics, and modernization analytics. By using planning analytics, our solution will determine gaps in the data and develop surveys or focus group questions. With continuous analytics, the focus will be on determining trends for organization updates and changes. Finally, modernization analytics will analyze new data streams, integrate these with existing while classifying using AI our solution and link all data for interactive dashboard display.

Our Talent Analytics solution provides a comprehensive, view of an organizations historical and current DEIA state which will lead to more informed strategic planning. Contact us to learn more about our AI Supported Talent Analytics Solution.

A little more than decade ago, I did research as part of my final project for the educational specialist degree. It was titled, “Do Employee Attitudes about Online Training Correlate with Safety Records in a Large Manufacturing Corporation?” and was presented as a roundtable discussion at the 2012 AECT Conference. As part of the research, I reviewed safety training completion records for multiple sites for one year and compared that to the results of a survey distributed to participants.

The focus was on “if there is a correlation between employee attitudes about online training and the number of workplace safety incidents. The correlation between the types of worksite (high versus low safety sites) and the number of online courses completed … After exploring the types of worksites for correlations, we studied the number of incidents that took place at the sites each employee surveyed and how they correlated with their attitude about online training.”  

Yesterday’s Analysis

How does my study from ten years ago relate to today’s analytics and learning? At the time, I was only able to study with a small group (less than 1000) of employees for one year. While this provided good data, the data should have been reflected in multiple years. Unfortunately, I did not have the resources (time, people, etc.) to complete the study at that level. 

We know that in research the more data provided tends to lead to more valid and reliable results. Unfortunately, this is often a time-consuming and sometimes biased process for researchers. Understanding these potential issues, how do we as researchers and learning professionals move forward? 

Today’s Learning Analytics

With today’s artificial intelligence (AI) capabilities, I would have had the potential to gather data from multiple years to assist with adding validity and reliability to the results. Further, I could have gathered more data from the other sources such as the (learning management system, safety records, safety site ratings, surveys, etc.). With AI the data can be analyzed, classified, and reviewed for correlations. 

Leading us to a Learning Analytics AI solution that allows us to gather data from all those sources and more depending on clients’ needs. To complete the data picture, we provide dashboards that display the information at diverse levels for viewing specific trends and themes allowing organizations to make strategic decisions based on reliable, valid data. 

As a demonstration, we have created multiple dashboards based on fictionalized learning data. These dashboards provide an opportunity to view the various types of themes and potential outcomes for representing the results.  

Contact us to learn more about our AI Supported Learning Analytics Solution. 

I recently had a conversation with friends regarding some of the ways we use AI in training.  

Their questions were very pointed. My friends wanted to know what the ROI would be if they decided to use AI. I tried in the simplest terms to come up with some examples of how to describe the ROI and help others understand how we use it. 

I think the best analogy would be forecasting. In terms of safety training, the data has always shown that the longer a worker was on the job the higher the risk of accidents. When training was added, the data showed a drop in the frequency of accidents. All of those things have been proven over the years.  

Using AI, we will be able to collect efficacy data, i.e., did a specific training decrease the probability by a large percentage or by just a small percentage. How effective was the training in abating accidents? How severe were the accidents if any occurred? Would additional training lower or lessen the risks? Not only will AI track these things, but it will also be used for analyzing xAPI data from the client LMS to determine if the training was effective in achieving the client’s goals.

In doing so, we will be able to forecast safety issues before they became a problem for the client. In terms of the human cost, we will be saving individuals who could experience life altering or life ending accidents. This is just one of the examples of how we use AI in training. Using surveys, xAPI data, and observation, we can determine if the money spent on training helped the organizations reach their goals.  

It completes all four levels of Kirkpatrick’s evaluation model. Kirkpatrick might be a little too deep for a simple blog post. Hopefully, I have time to dive off into the topic at a later time. 

To answer this question, we must first understand the basics of big data and its relationship with AI. According to the dictionary, big data is “extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions”. These data sets are larger and more complex than those that can be analyzed by teams of personnel in a timely, efficient manner. AI has a mutually beneficial, cooperative relationship. 

Big data is the source of information or fuel that drives the AI ‘machine’. In order for AI to grow and learn, it needs vast amounts of big data to provide the reliable analytics results that lead to strategic decisions.

Reflecting back on previous posts, we have discovered a  2018  blog about Using Training Analysis to Avoid Common Training Pitfalls. Why would this be relevant now? Due to COVID-19, employment and education locations shifted. At this time, 90% of corporations use eLearning for training. Knowing this, what did we consider the falls in 2018 and how could AI be applied to them now?

2020 has been a year of adjustments, flexibility and change for everyone. We have discovered new talents and gifts within ourselves, friends and family. We have learned new depths of strengths we never knew within ourselves. And, hopefully found who to support us in our weaknesses. 

As we personally adjusted to the changes happening around us, how are companies adjusting? What made this change occur more easily?

“Teaching is hard.” Everyone believes this…it has been proven more so with the recent pandemic. The National Center for Education Statistics (NCES) reports that teaching has an annual turnover of 16% due to two factors: 8% leave early and 8% switch school districts. What does this mean for schools? The average school will lose 3 of every 20 teachers each year. 

Recent studies have shown that 41.3% of new teachers leave within the first five years. Of the remaining teachers, 36.4% are likely to quit at any time. I am part of the former statistic as a former teacher. It is difficult to remain in a profession with changing standards, varying levels of support and dissatisfaction with career and working conditions. Why?

Well…think about these facts for teachers.

1. Bathroom breaks only happen when students can be supervised by another adult
2. Break times are used for grading, bathroom, lunch, copying, etc.
3. ‘Summer break’ is half-filled with professional development
4. Most teachers take home stacks of papers each night and weekend for grading and planning

Read More

Let’s be honest, everyone wants to be a rock star or wants that recognition for succeeding. But, how do training and learning managers/leadership demonstrate real return on investment? Our teams are busy designing, developing and implementing training/learning programs. It takes time to provide real quantifiable data to prove results.

The Data

Think about the past two decades, how has the system for gathering and analyzing training or learning data changed? Or, should we ask, has it really changed? Is your organization still relying on surveys and learning management system data for the bulk of the knowledge gained for influencing and prioritizing how instruction or instructors should adjust or make changes to meet learners’ needs and expectations? 

Surveys are wonderful tools. We receive them in email, get asked to answer a few questions when handed our receipts and on and on. These simple tools provide details about wants, needs and performance. However, analysis of these simple tools has proven to be time-consuming and biased. Consider for a moment this hypothetical situation. A training director, who is preparing for an annual presentation, is reviewing surveys and notes a repeated complaint that there is no online training for a particular topic. However, the director is aware that the new online topic will be released next week. The director decides to omit this complaint because it brings the overall performance rating of the training department down.

Despite the learner’s age, occupation (if applicable), location or learning mode, the technique used did not have an impact on the results. Similarly, as technology evolved to allow less bias these factors seem to have a less impact on the data gathering and analyzing techniques today. Data collection began and continues today at times today with paper-based data collection. This can be time consuming and lead to missed data or misinterpretation. We admit that AI can be biased if the programming is not observed or verified for inconsistencies. However, our team of analysts review the AI program repeatedly and consistently to ensure unbiased results.

AI – The Evolving Solution

A solution that is being used outside of the training and learning industry is Artificial Intelligence. Although you may be using it in some ways, such as tagging data, it is most likely not being used to its full potential. To reduce training team resource strain while providing quantifiable ROI data, AI can be used to gather data from multiple sources. These sources include surveys, LMS, open source (such as employee sites or student sites), financial reports, HR reports, etc.). With the data imported from the multiple sources, our team of analysts determines what themes are appropriate based on parameters and conversations with our clients based on their needs.

As a result, AI combines with learning analytics and existing facts leading to more data to be potentially collected and analyzed at a more expedient, efficient and effortless rate than previously for your training team. Perhaps more importantly, your team will know the metrics presented for the training ROI is data driven.

You Succeed How?

While these details sound interesting, how does it help you succeed? Well, again your team is free to do what they do best – design, develop and implement training programs. You are given a customized interactive dashboard that allows you to drill down from the organization to the employee/learner level. This not only allows you to provide ROI for your organization senior leadership and prove the training value. It allows you to set achievable priorities and goals based on the identified needs in the data. More importantly to the organizations senior leadership, you will be able to align these with the strategic action plans. 

Interacting with the Dashboard

While this may sound interesting, we are sure it would probably be more interesting if you could actually interact with an example dashboard and see the results. Well, now you can. 

We have an example dashboard, Learner Analytics – Professor Comparison, available for exploration on the open Tableau site. Although not all our capabilities and customization possibilities are displayed, it will provide you with an opportunity to explore.

This dashboard was created based on a fictional university with two professors. We considered open source (ratemyprofessors.com and university survey data) along with grades, demographics, etc. in designing this. As you explore, consider how this could relate to your learners and/or employees. 

Remember, this is only an example of a potential interactive dashboard. Our dashboard can be customized to suit your needs whether your organization is higher education, medical facility, government or industry.

More Questions or Need More Information?

After reviewing the dashboard, feel free to contact us if you have any questions, would like more information or would like to schedule a demo for your organization. 

When training employees, you work with details, procedures and facts. This provides your learners with the knowledge and capability to successfully accomplish their jobs. As you start to use AI in training and analysis, do you have “all the facts”?

In this Ted Talk, Matt Beane speaks about how surgical residents train with and without AI tools.  What lessons learned apply to all industries from his insights?

AI + Learner

Consider Mr. Beane’s Ted Talk, not everyone uses AI to teach. We train using eLearning, VR and augmented reality solutions. These methods involve similar issues discussed in the video: an inability to “see one, do one, teach one”. Training teams develop the materials, present and gather data from observation and/or assessment(s). Most of the time, your training teams gather these data sets from the learning management system (LMS) or surveys.

Moving away from the expected LMS parameters, training teams use AI within an LMS to tailor for individuals. Each learner receives course suggestions based on past performance, enrollment or job. This is similar to Google Ads, Netflix, etc.

Let’s go another step further, are there questions and answers hidden in the data? If the data or facts are limited, AI learning analytics adds a new dimension to your details. Consider what the eLearning Industry discussed as the importance of AI to L&D (training). Moving beyond the algorithms within the LMS, AI offers an ability to “explore limitless possibilities”. Of those surveyed, almost 85% believe their company will gain a competitive edge from AI. Why?

AI + Learning Analytics + Facts = Training Future

From a training survey perspective, one option often ignored are open-ended questions. Although these provide more insight than a simple likert scale, the answers are time-consuming and inefficient for training teams to decipher. However, an AI could be designed to target words and themes which would allow the training team to gain the knowledge efficiently, expediently and effortlessly. Perhaps more importantly, your team will know the metrics presented for the training return on investment (ROI) is data driven.

To learn more about an AI training solution for your organization, read our whitepaper or view the AAILAT demo. We offer a customized solution. Contact us to schedule a time for a demo and answer questions.

When was the last time you completed a puzzle? Was it a landscape scene? Did it have similar colors? This may have made it more difficult to distinguish between one piece location and another. Did you lose objectivity of the whole picture from a single piece?

The same is true as we begin to look and review data for ourselves. We may have difficulty discerning between the important pieces of information or determining how one piece of data relates to another. This can become even more complicated with additional data.

AI’s Objectivity

Due to its objective and program abilities, AI is being used in multiple industries with success. Recently, Artificial Intelligence, published an article listing the top 10 businesses using AI. These include marketing, sales, human resources, and customer experience, to cite a few examples.    

Each of these industries are using AI for data analytics and big data to gain insights and improve an overall set goal or objective. In this capacity, the industries are learning more about their products, services and/or customers.

AI’s Role in Training

In a more simple role, it can be argued that data analytics has been used for years by the training industry. Current learning management systems and survey systems have the capacity to collect high level data analytics from learners. The issue has been how to objectively relate the data from the learners. 

We know that AI can assist with this from its use in other industries. AI can move the training team a step beyond to answering the ROI question effectively. To be able to answer this question appropriately, we need to be able to analyze behavior. This would include a more complex survey response and gathering data from outside the LMS. An example of this could be, if the training team desired ROI on customer service training, the AI would incorporate data from the customer satisfaction ratings, cost of training, retraining, etc.

AI, Learning Analytics & QAA

With AI, more data can be analyzed allowing for a complete learning analytics view. This will allow for organizations to be confident in their ROI answer. With known ROI, the future of training goals and objectives will be based on insights discovered within the data. 

QAA provides a customized Advanced AI Learning Analytics Tool (AAILAT) that aggregates data from multiple sources for analysis. Read our whitepaper or view the AAILAT demo to gain a better understanding of the customization potential. Contact us to schedule a time for a demo and answer questions.