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Learning Agility: What is It And How to Measure It?

Learning Agility: What is It And How to Measure It?

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Nischal V Chadaga
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December 10, 2024
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4 min read
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In the year 2004, two academics–Miachael Lombardo and Robert Eichinger–coined the term ‘learning agility’ and described it as one of the key metrics to measure leadership potential. Since then, this term has become the cornerstone of human resource processes worldwide.

Simply put, learning agility is a blend of curiosity, self-awareness, and a growth mindset. It has often been called the secret sauce for success in fast-paced industries. This blog post will explore the concept of learning agility, its importance, and strategies to foster it within tech teams.

According to Warner Burke, Professor of Psychology and Education at Teachers College, Columbia University, half of all leaders fail in their roles. The reason? A lack of learning agility.

Source: Harver.com

To grasp the importance of learning agility in leadership, let’s first clarify what this concept entails.

What is learning agility?

Learning agility isn’t a singular skill but rather a collection of abilities that enable individuals to quickly learn, adapt, and apply knowledge in new and unfamiliar situations. It’s about taking what you’ve learned in one context and using it effectively in another.

At its core, learning agility encompasses:

  • Learning, unlearning, and relearning to stay relevant in evolving environments.
  • Adapting and innovating by applying new skills and knowledge to shifting workplace demands.
  • Using feedback effectively to grow and improve continuously.

Learning agility in action

In the workplace, learning agility helps individuals navigate uncertain situations by drawing from past and present experiences to make informed decisions. It’s the mindset of being open to change, willing to experiment with new ideas, and comfortable in challenging situations.

Agile learners actively seek feedback, reflect on their experiences, and embrace discomfort as an opportunity for growth. They are forward-thinking, constantly developing new strategies to handle future challenges with confidence and creativity.

Why learning agility matters in leadership

Organizations thrive when leaders possess learning agility because it’s a key predictor of leadership potential. In fact, studies show that 50% of leaders fail in their roles because they lack this critical skill. Agile leaders are adaptable, resilient, and innovative—traits essential for managing disruptions and driving success.

According to the Korn Ferry Institute, learning agility surpasses intelligence and education as the most reliable indicator of an executive’s success. Companies led by highly agile executives see 25% higher profit margins compared to their peers.

Source: Korn Ferry Institute

Characteristics of agile learners

In the workplace, individuals with strong learning agility tend to:

  • Make confident, high-quality decisions even with incomplete data.
  • Develop innovative solutions by unlearning outdated practices.
  • See the bigger picture, connecting seemingly unrelated information.
  • Adapt quickly to change, showing flexibility in complex situations.
  • Embrace uncertainty and tackle unfamiliar challenges with confidence.

Leaders who exhibit learning agility excel in navigating diverse business scenarios, such as adapting to remote work, managing cross-cultural teams, and taking on unfamiliar assignments without hesitation. Korn Ferry research shows that agile learners are promoted twice as fast as those with lower agility.

If we had to break down the key components of learning agility, here’s how we would rank them:

Component 1: Self-awareness

A cornerstone of learning agility, self-awareness involves understanding one’s strengths, weaknesses, and learning style. It’s about knowing what you know, what you don’t know, and how you learn best. By recognizing their strengths and weaknesses, individuals can identify areas for improvement and tailor their learning strategies accordingly.

Component 2: Curiosity and open-mindedness

Curious individuals are always eager to learn and explore new ideas. They approach challenges with a sense of wonder and a willingness to experiment. Open-mindedness allows individuals to embrace different perspectives, challenge assumptions, and consider alternative solutions. As Albert Einstein famously said, “The important thing is not to stop questioning.”

Component 3: Metacognition

Metacognition is the ability to think about one’s own thinking. By reflecting on their learning process, individuals can identify effective strategies and areas for improvement. It involves asking questions like, “What am I learning? How am I learning it? How can I learn it better?”

Component 4: Risk-taking

Learning often involves stepping outside of one’s comfort zone. Risk-taking is essential for personal and professional growth. By embracing challenges and taking calculated risks, individuals can acquire new skills and experiences.

Can learning agility be developed?

Yes. Like a muscle, learning agility can be strengthened through practice and intentional effort. It’s not about past achievements but about the potential to grow, innovate, and succeed in future challenges.

Traits like openness to experience, conscientiousness, and agreeableness often correlate with learning agility, while emotional instability and a belief in uncontrollable external factors tend to hinder it. Assessing a candidate’s personality alongside their learning agility provides a complete picture of their potential.

Exceptional leaders don’t just manage disruptions—they leverage them as opportunities to innovate and grow. Understanding the components of learning agility can help leaders (and other employees) build this muscle and train themselves to be more agile.

Measuring learning agility in tech teams with HackerEarth

Learning agility is a critical skill for tech professionals, enabling them to adapt to the ever-evolving tech landscape. HackerEarth, as a leading tech assessment platform, offers several tools to measure and foster learning agility within organizations.

Here’s how HackerEarth can help:

  1. Continuous skill assessments
  • By regularly assessing technical skills, HackerEarth can identify areas where individuals are growing and where they may need additional development.
  • HackerEarth’s custom assessments platform cab help in pinpointing skill gaps so that organizations can tailor training programs and development opportunities.
  • Our platform helps tech teams track how quickly individuals acquire new skills and apply them to real-world problems, and assess their learning agility.
  1. Coding challenges
  • Coding challenges can help tech leaders evaluate a candidate’s ability to think critically, solve problems, and adapt to new situations.
  • By providing detailed feedback on code quality and performance, HackerEarth can help individuals learn from their mistakes and improve their skills.
  1. Technical interviews
  • Our interview platform FaceCode can help engineering managers assess how candidates communicate technical concepts clearly and work effectively with others.
  • FaceCode is also a great tool to evaluate a candidate’s ability to adapt and collaborate through challenges like Pair Programming and code reviews etc.
  1. HackerEarth Upskilling Platform
  • Our platform can be integrated with the continuous assessments pathway and helps engineering teams curate tailored learning paths based on individual skill gaps and career goals.
  • The platform also provides practical exercises and coding challenges to reinforce hands-on learning

HackerEarth’s comprehensive suite of tools can help organizations effectively measure and foster learning agility within their teams. This will enable them to build a workforce that is adaptable, innovative, and ready to meet the challenges of the future.

Remember, learning agility is a journey, not a destination. By continuously assessing, developing, and nurturing this skill, organizations can unlock the full potential of their workforce. To know more, write to us at contact@hackerearth.com.

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Nischal V Chadaga
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December 10, 2024
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4 min read
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Vibe Coding: Shaping the Future of Software

A New Era of CodeVibe coding is a new method of using natural language prompts and AI tools to generate code. I have seen firsthand that this change makes software more accessible to everyone. In the past, being able to produce functional code was a strong advantage for developers. Today,...

A New Era of Code

Vibe coding is a new method of using natural language prompts and AI tools to generate code. I have seen firsthand that this change makes software more accessible to everyone. In the past, being able to produce functional code was a strong advantage for developers. Today, when code is produced quickly through AI, the true value lies in designing, refining, and optimizing systems. Our role now goes beyond writing code; we must also ensure that our systems remain efficient and reliable.

From Machine Language to Natural Language

I recall the early days when every line of code was written manually. We progressed from machine language to high-level programming, and now we are beginning to interact with our tools using natural language. This development does not only increase speed but also changes how we approach problem solving. Product managers can now create working demos in hours instead of weeks, and founders have a clearer way of pitching their ideas with functional prototypes. It is important for us to rethink our role as developers and focus on architecture and system design rather than simply on typing c

The Promise and the Pitfalls

I have experienced both sides of vibe coding. In cases where the goal was to build a quick prototype or a simple internal tool, AI-generated code provided impressive results. Teams have been able to test new ideas and validate concepts much faster. However, when it comes to more complex systems that require careful planning and attention to detail, the output from AI can be problematic. I have seen situations where AI produces large volumes of code that become difficult to manage without significant human intervention.

AI-powered coding tools like GitHub Copilot and AWS’s Q Developer have demonstrated significant productivity gains. For instance, at the National Australia Bank, it’s reported that half of the production code is generated by Q Developer, allowing developers to focus on higher-level problem-solving . Similarly, platforms like Lovable enable non-coders to build viable tech businesses using natural language prompts, contributing to a shift where AI-generated code reduces the need for large engineering teams. However, there are challenges. AI-generated code can sometimes be verbose or lack the architectural discipline required for complex systems. While AI can rapidly produce prototypes or simple utilities, building large-scale systems still necessitates experienced engineers to refine and optimize the code.​

The Economic Impact

The democratization of code generation is altering the economic landscape of software development. As AI tools become more prevalent, the value of average coding skills may diminish, potentially affecting salaries for entry-level positions. Conversely, developers who excel in system design, architecture, and optimization are likely to see increased demand and compensation.​
Seizing the Opportunity

Vibe coding is most beneficial in areas such as rapid prototyping and building simple applications or internal tools. It frees up valuable time that we can then invest in higher-level tasks such as system architecture, security, and user experience. When used in the right context, AI becomes a helpful partner that accelerates the development process without replacing the need for skilled engineers.

This is revolutionizing our craft, much like the shift from machine language to assembly to high-level languages did in the past. AI can churn out code at lightning speed, but remember, “Any fool can write code that a computer can understand. Good programmers write code that humans can understand.” Use AI for rapid prototyping, but it’s your expertise that transforms raw output into robust, scalable software. By honing our skills in design and architecture, we ensure our work remains impactful and enduring. Let’s continue to learn, adapt, and build software that stands the test of time.​

Ready to streamline your recruitment process? Get a free demo to explore cutting-edge solutions and resources for your hiring needs.

Guide to Conducting Successful System Design Interviews in 2025

What is Systems Design?Systems Design is an all encompassing term which encapsulates both frontend and backend components harmonized to define the overall architecture of a product.Designing robust and scalable systems requires a deep understanding of application, architecture and their underlying components like networks, data, interfaces and modules.Systems Design, in its...

What is Systems Design?

Systems Design is an all encompassing term which encapsulates both frontend and backend components harmonized to define the overall architecture of a product.

Designing robust and scalable systems requires a deep understanding of application, architecture and their underlying components like networks, data, interfaces and modules.

Systems Design, in its essence, is a blueprint of how software and applications should work to meet specific goals. The multi-dimensional nature of this discipline makes it open-ended – as there is no single one-size-fits-all solution to a system design problem.

What is a System Design Interview?

Conducting a System Design interview requires recruiters to take an unconventional approach and look beyond right or wrong answers. Recruiters should aim for evaluating a candidate’s ‘systemic thinking’ skills across three key aspects:

How they navigate technical complexity and navigate uncertainty
How they meet expectations of scale, security and speed
How they focus on the bigger picture without losing sight of details

This assessment of the end-to-end thought process and a holistic approach to problem-solving is what the interview should focus on.

What are some common topics for a System Design Interview

System design interview questions are free-form and exploratory in nature where there is no right or best answer to a specific problem statement. Here are some common questions:

How would you approach the design of a social media app or video app?

What are some ways to design a search engine or a ticketing system?

How would you design an API for a payment gateway?

What are some trade-offs and constraints you will consider while designing systems?

What is your rationale for taking a particular approach to problem solving?

Usually, interviewers base the questions depending on the organization, its goals, key competitors and a candidate’s experience level.

For senior roles, the questions tend to focus on assessing the computational thinking, decision making and reasoning ability of a candidate. For entry level job interviews, the questions are designed to test the hard skills required for building a system architecture.

The Difference between a System Design Interview and a Coding Interview

If a coding interview is like a map that takes you from point A to Z – a systems design interview is like a compass which gives you a sense of the right direction.

Here are three key difference between the two:

Coding challenges follow a linear interviewing experience i.e. candidates are given a problem and interaction with recruiters is limited. System design interviews are more lateral and conversational, requiring active participation from interviewers.

Coding interviews or challenges focus on evaluating the technical acumen of a candidate whereas systems design interviews are oriented to assess problem solving and interpersonal skills.

Coding interviews are based on a right/wrong approach with ideal answers to problem statements while a systems design interview focuses on assessing the thought process and the ability to reason from first principles.

How to Conduct an Effective System Design Interview

One common mistake recruiters make is that they approach a system design interview with the expectations and preparation of a typical coding interview.
Here is a four step framework technical recruiters can follow to ensure a seamless and productive interview experience:

Step 1: Understand the subject at hand

  • Develop an understanding of basics of system design and architecture
  • Familiarize yourself with commonly asked systems design interview questions
  • Read about system design case studies for popular applications
  • Structure the questions and problems by increasing magnitude of difficulty

Step 2: Prepare for the interview

  • Plan the extent of the topics and scope of discussion in advance
  • Clearly define the evaluation criteria and communicate expectations
  • Quantify constraints, inputs, boundaries and assumptions
  • Establish the broader context and a detailed scope of the exercise

Step 3: Stay actively involved

  • Ask follow-up questions to challenge a solution
  • Probe candidates to gauge real-time logical reasoning skills
  • Make it a conversation and take notes of important pointers and outcomes
  • Guide candidates with hints and suggestions to steer them in the right direction

Step 4: Be a collaborator

  • Encourage candidates to explore and consider alternative solutions
  • Work with the candidate to drill the problem into smaller tasks
  • Provide context and supporting details to help candidates stay on track
  • Ask follow-up questions to learn about the candidate’s experience

Technical recruiters and hiring managers should aim for providing an environment of positive reinforcement, actionable feedback and encouragement to candidates.

Evaluation Rubric for Candidates

Facilitate Successful System Design Interview Experiences with FaceCode

FaceCode, HackerEarth’s intuitive and secure platform, empowers recruiters to conduct system design interviews in a live coding environment with HD video chat.

FaceCode comes with an interactive diagram board which makes it easier for interviewers to assess the design thinking skills and conduct communication assessments using a built-in library of diagram based questions.

With FaceCode, you can combine your feedback points with AI-powered insights to generate accurate, data-driven assessment reports in a breeze. Plus, you can access interview recordings and transcripts anytime to recall and trace back the interview experience.

Learn how FaceCode can help you conduct system design interviews and boost your hiring efficiency.

How Candidates Use Technology to Cheat in Online Technical Assessments

Impact of Online Assessments in Technical Hiring In a digitally-native hiring landscape, online assessments have proven to be both a boon and a bane for recruiters and employers. The ease and...

Impact of Online Assessments in Technical Hiring


In a digitally-native hiring landscape, online assessments have proven to be both a boon and a bane for recruiters and employers.

The ease and efficiency of virtual interviews, take home programming tests and remote coding challenges is transformative. Around 82% of companies use pre-employment assessments as reliable indicators of a candidate's skills and potential.

Online skill assessment tests have been proven to streamline technical hiring and enable recruiters to significantly reduce the time and cost to identify and hire top talent.

In the realm of online assessments, remote assessments have transformed the hiring landscape, boosting the speed and efficiency of screening and evaluating talent. On the flip side, candidates have learned how to use creative methods and AI tools to cheat in tests.

As it turns out, technology that makes hiring easier for recruiters and managers - is also their Achilles' heel.

Cheating in Online Assessments is a High Stakes Problem



With the proliferation of AI in recruitment, the conversation around cheating has come to the forefront, putting recruiters and hiring managers in a bit of a flux.



According to research, nearly 30 to 50 percent of candidates cheat in online assessments for entry level jobs. Even 10% of senior candidates have been reportedly caught cheating.

The problem becomes twofold - if finding the right talent can be a competitive advantage, the consequences of hiring the wrong one can be equally damaging and counter-productive.

As per Forbes, a wrong hire can cost a company around 30% of an employee's salary - not to mention, loss of precious productive hours and morale disruption.

The question that arises is - "Can organizations continue to leverage AI-driven tools for online assessments without compromising on the integrity of their hiring process? "

This article will discuss the common methods candidates use to outsmart online assessments. We will also dive deep into actionable steps that you can take to prevent cheating while delivering a positive candidate experience.

Common Cheating Tactics and How You Can Combat Them


  1. Using ChatGPT and other AI tools to write code

    Copy-pasting code using AI-based platforms and online code generators is one of common cheat codes in candidates' books. For tackling technical assessments, candidates conveniently use readily available tools like ChatGPT and GitHub. Using these tools, candidates can easily generate solutions to solve common programming challenges such as:
    • Debugging code
    • Optimizing existing code
    • Writing problem-specific code from scratch
    Ways to prevent it
    • Enable full-screen mode
    • Disable copy-and-paste functionality
    • Restrict tab switching outside of code editors
    • Use AI to detect code that has been copied and pasted
  2. Enlist external help to complete the assessment


    Candidates often seek out someone else to take the assessment on their behalf. In many cases, they also use screen sharing and remote collaboration tools for real-time assistance.

    In extreme cases, some candidates might have an off-camera individual present in the same environment for help.

    Ways to prevent it
    • Verify a candidate using video authentication
    • Restrict test access from specific IP addresses
    • Use online proctoring by taking snapshots of the candidate periodically
    • Use a 360 degree environment scan to ensure no unauthorized individual is present
  3. Using multiple devices at the same time


    Candidates attempting to cheat often rely on secondary devices such as a computer, tablet, notebook or a mobile phone hidden from the line of sight of their webcam.

    By using multiple devices, candidates can look up information, search for solutions or simply augment their answers.

    Ways to prevent it
    • Track mouse exit count to detect irregularities
    • Detect when a new device or peripheral is connected
    • Use network monitoring and scanning to detect any smart devices in proximity
    • Conduct a virtual whiteboard interview to monitor movements and gestures
  4. Using remote desktop software and virtual machines


    Tech-savvy candidates go to great lengths to cheat. Using virtual machines, candidates can search for answers using a secondary OS while their primary OS is being monitored.

    Remote desktop software is another cheating technique which lets candidates give access to a third-person, allowing them to control their device.

    With remote desktops, candidates can screen share the test window and use external help.

    Ways to prevent it
    • Restrict access to virtual machines
    • AI-based proctoring for identifying malicious keystrokes
    • Use smart browsers to block candidates from using VMs

Future-proof Your Online Assessments With HackerEarth

HackerEarth's AI-powered online proctoring solution is a tested and proven way to outsmart cheating and take preventive measures at the right stage. With HackerEarth's Smart Browser, recruiters can mitigate the threat of cheating and ensure their online assessments are accurate and trustworthy.
  • Secure, sealed-off testing environment
  • AI-enabled live test monitoring
  • Enterprise-grade, industry leading compliance
  • Built-in features to track, detect and flag cheating attempts
Boost your hiring efficiency and conduct reliable online assessments confidently with HackerEarth's revolutionary Smart Browser.
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