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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.
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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.
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
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
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
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
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
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Former employees who return to work with the same organisation are essential assets. In talent acquisition, such employees are also termed as ‘Boomerang employees’. Former employees are valuable because they require the least training and onboarding because of their familiarity with the organization’s policies. Rehiring former employees by offering them more perks is a mark of a successful hiring process. This article will elaborate on the talent acquisition strategies for rehiring former employees, supported by a few real-life examples and best practices.
Why Should Organizations Consider Rehiring?
One of the best ways of ensuring quality hire with a low candidate turnover is to deploy employee retention programs like rehiring female professionals who wish to return to work after a career break. This gives former employees a chance to prove their expertise while ensuring them the organization’s faith in their skills and abilities. Besides, seeing former employees return to their old organizations encourages newly appointed employees to be more productive and contribute to the overall success of the organization they are working for. A few other benefits of rehiring old employees are listed below.
Reduced Hiring Costs
Hiring new talent incurs a few additional costs. For example, tasks such as sourcing resumes of potential candidates, reaching out to them, conducting interviews and screenings costs money to the HR department. Hiring former employees cuts down these costs and aids a seamless transition process for them.
Faster Onboarding
Since boomerang employees are well acquainted with the company’s onboarding process, they don’t have to undergo the entire exercise. A quick, one-day session informing them of any recent changes in the company’s work policies is sufficient to onboard them.
Retention of Knowledge
As a former employee, rehired executives have knowledge of the previous workflows and insights from working on former projects. This can be valuable in optimizing a current project. They bring immense knowledge and experience with them which can be instrumental in driving new projects to success.Starbucks is a prime example of a company that has successfully leveraged boomerang employees. Howard Schultz, the company's CEO, left in 2000 but returned in 2008 during a critical time for the firm. His leadership was instrumental in revitalizing the brand amid financial challenges.
Best Practices for Rehiring Former Employees
Implementing best practices is the safest way to go about any operation. Hiring former employees can be a daunting task especially if it involves someone who was fired previously. It is important to draft certain policies around rehiring former employees. Here are a few of them that can help you to get started.
1. Create a Clear Rehire Policy
While considering rehiring a former employee, it is essential to go through data indicating the reason why they had to leave in the first place. Any offer being offered must supersede their previous offer while marking clear boundaries to maintain work ethics. Offer a fair compensation that justifies their skills and abilities which can be major contributors to the success of the organization. A well-defined policy not only streamlines the rehiring process but also promotes fairness within the organization.
2. Conduct Thorough Exit Interviews
Exit interviews provide valuable insights into why employees leave and can help maintain relationships for potential future rehires. Key aspects to cover include:
Reasons for departure.
Conditions under which they might consider returning.
Feedback on organizational practices.
Keeping lines of communication open during these discussions can foster goodwill and encourage former employees to consider returning when the time is right.
3. Maintain Connections with Alumni
Creating and maintaining an alumni association must be an integral part of HR strategies. This exercise ensures that the HR department can find former employees in times of dire need and indicates to former employees how the organization is vested in their lives even after they have left them. This gesture fosters a feeling of goodwill and gratitude among former hires. Alumni networks and social media groups help former employees stay in touch with each other, thus improving their interpersonal communication.Research indicates that about 15% of rehired employees return because they maintained connections with their former employers.
4. Assess Current Needs Before Reaching Out
Before reaching out to former employees, assess all viable options and list out the reasons why rehiring is inevitable. Consider:
Changes in job responsibilities since their departure.
Skills or experiences gained by other team members during their absence.
It is essential to understand how the presence of a boomerang employee can be instrumental in solving professional crises before contacting them. It is also important to consider their present circumstances.
5. Initiate an Honest Conversation
When you get in touch with a former employee, it is important to understand their perspective on the job being offered. Make them feel heard and empathize with any difficult situations they may have had to face during their time in the organization. Understand why they would consider rejoining the company. These steps indicate that you truly care about them and fosters a certain level of trust between them and the organization which can motivate them to rejoin with a positive attitude.
6. Implement a Reboarding Program
When a former employee rejoins, HR departments must ensure a robust reboarding exercise is conducted to update them about any changes within the organization regarding the work policies and culture changes, training them about any new tools or systems that were deployed during their absence and allowing them time to reconnect with old team members or acquaint with new ones.
7. Make Them Feel Welcome
Creating a welcoming environment is essential for helping returning employees adjust smoothly. Consider:
Organizing team lunches or social events during their first week.
Assigning a mentor or buddy from their previous team to help them reacclimate.
Providing resources that facilitate learning about any organizational changes.
A positive onboarding experience reinforces their decision to return and fosters loyalty.
Real-Life Examples of Successful Rehiring
Several companies have successfully implemented these strategies:
IBM: The tech giant has embraced boomerang hiring by actively reaching out to former employees who possess critical skills in emerging technologies. IBM has found that these individuals often bring fresh perspectives that contribute significantly to innovation7.
Zappos: Known for its strong company culture, Zappos maintains an alumni network that keeps former employees engaged with the brand. This connection has led to numerous successful rehiring instances, enhancing both morale and productivity within teams6.
Conclusion
Rehiring former employees can provide organizations with unique advantages, including reduced costs, quicker onboarding, and retained knowledge. By implementing strategic practices—such as creating clear policies, maintaining connections, assessing current needs, and fostering welcoming environments—companies can effectively tap into this valuable talent pool.
As organizations continue navigating an ever-changing workforce landscape, embracing boomerang employees may be key to building resilient teams equipped for future challenges. By recognizing the potential benefits and following best practices outlined above, businesses can create a robust strategy for rehiring that enhances both employee satisfaction and organizational performance.
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In every Hackathon, we witness people working around the clock to develop on an idea that's unprecedented in all respects.
Hackmotion, an app developed during the Hackathon conducted by WACHacks in association with HackerEarth, is a breathtaking manifestation of the same.
Read on to know more about this app.
What is Hackmotion?
Hackmotion is an app that solves problems related to stress. It allows users to talk and have a friendly conversation with their phone. Along with this, it tracks users’ emotions and conversations in a journal format.
This app identifies the emotions that students commonly experience. It is designed to help them improve their social well-being, helping them be expressive through journaling.
Technologies/platforms/languages
Android Studio: To develop and test the app.
The Microsoft Face API: To analyze and detect faces in the picture taken
Microsoft’s Emotion API: To determine what emotion the person in the picture is feeling
Clarifai API: To process the image in the picture if there is no face
Java: For internal logic
XML: For layouts
Functionality
When the app is opened, the users are introduced to a friendly UI where they get an option to take a picture. Users can click on the camera icon to get the picture clicked. The phone processes this image using Microsoft Face API. Depending on the image, two types of APIs are then called:
If there is a face in the image, Microsoft Emotions API is called. This API uses an algorithm which analyzes the face and determines what emotion the person is feeling. Once the emotion is recognised, the phone starts talking to the user according to his or her mood.
If there isn’t any face in the image, Clarifai API is called. This API then determines what significant object is there in the picture. For example, if the user takes a picture of the leftover food, Clarifai will first recognise the food as an object and will then determine its type. For accuracy, questions related to the picture will be asked to the user.The conversation will start once the correct emotion is identified.
The app talks to the user in a way that they feel like they are talking to a real person. All the conversations and the objects detected by Clarifai get recorded in the journaling section of the app which the user can refer to in the future. Also, there is a statistics part of the app where the user can see the percentage of each emotion they have felt while downloading the app.
Challenges
Here are some of the challenges that the team faced while building this application:
Making Microsoft and Face API’s to work in harmony
Creating the algorithm that analyzes the face
Making sure that Clarifai API is called when no face is detected
Efficiency and accuracy of the app will be improved.
Frequency of each emotion will be displayed in a form of bar or line graph.
The conversational flow of the chat/journal portion will be improved by making it display the specific time and some notes that the user has.
If you love this app and are inspired by it, check out our list of hackathons for you to participate in. Register, code and create awesome solutions to real life problems and stand a chance to win awesome prizes while you’re at it!
Let's say you are given with a fruit which is yellow, sweet, and long and you have to check the class to which it belongs.Step 2: Draw the likelihood table for the features against the classes.
Name
Yellow
Sweet
Long
Total
Mango
350/800=P(Mango|Yellow)
450/850
0/400
650/1200=P(Mango)
Banana
400/800
300/850
350/400
400/1200
Others
50/800
100/850
50/400
150/1200
Total
800=P(Yellow)
850
400
1200
Step 3: Calculate the conditional probabilities for all the classes, i.e., the following in our example:
Step 4: Calculate [latex]\displaystyle\max_{i}{P(C_i|x_1, x_2,\ldots, x_n)}[/latex]. In our example, the maximum probability is for the class banana, therefore, the fruit which is long, sweet and yellow is a banana by Naive Bayes Algorithm.In a nutshell, we say that a new element will belong to the class which will have the maximum conditional probability described above.
Variations of the Naive Bayes algorithm
There are multiple variations of the Naive Bayes algorithm depending on the distribution of [latex]P(x_j|C_i)[/latex]. Three of the commonly used variations are
Gaussian: The Gaussian Naive Bayes algorithm assumes distribution of features to be Gaussian or normal, i.e., [latex]\displaystyle P(x_j|C_i)=\frac{1}{\sqrt{2\pi\sigma_{C_i}^2}}\exp{\left(-\frac{(x_j-\mu_{C_j})^2}{2\sigma_{C_i}^2}\right)}[/latex] Read more about it here.
Multinomial: The Multinomial Naive Bayes algorithm is used when the data is distributed multinomially, i.e., multiple occurrences matter a lot. You can read more here.
Bernoulli: The Bernoulli algorithm is used when the features in the data set are binary-valued. It is helpful in spam filtration and adult content detection techniques. For more details, click here.
Pros and Cons of Naive Bayes algorithm
Every coin has two sides. So does the Naive Bayes algorithm. It has advantages as well as disadvantages, and they are listed below:
Pros
It is a relatively easy algorithm to build and understand.
It is faster to predict classes using this algorithm than many other classification algorithms.
It can be easily trained using a small data set.
Cons
If a given class and a feature have 0 frequency, then the conditional probability estimate for that category will come out as 0. This problem is known as the "Zero Conditional Probability Problem." This is a problem because it wipes out all the information in other probabilities too. There are several sample correction techniques to fix this problem such as "Laplacian Correction."
Another disadvantage is the very strong assumption of independence class features that it makes. It is near to impossible to find such data sets in real life.
Naive Bayes with Python and R
Let us see how we can build the basic model using the Naive Bayes algorithm in R and in Python.
R Code
To start training a Naive Bayes classifier in R, we need to load the e1071 package.
library(e1071)
To split the data set into training and test data we will use the caTools package.
library(caTools)
The predefined function used for the implementation of Naive Bayes in R is called naiveBayes(). There are only a few parameters that are of use:
The Naive Bayes algorithm is used in multiple real-life scenarios such as
Text classification: It is used as a probabilistic learning method for text classification. The Naive Bayes classifier is one of the most successful known algorithms when it comes to the classification of text documents, i.e., whether a text document belongs to one or more categories (classes).
Spam filtration: It is an example of text classification. This has become a popular mechanism to distinguish spam email from legitimate email. Several modern email services implement Bayesian spam filtering. Many server-side email filters, such as DSPAM, SpamBayes, SpamAssassin, Bogofilter, and ASSP, use this technique.
Sentiment Analysis: It can be used to analyze the tone of tweets, comments, and reviews—whether they are negative, positive or neutral.
Recommendation System: The Naive Bayes algorithm in combination with collaborative filtering is used to build hybrid recommendation systems which help in predicting if a user would like a given resource or not.
Conclusion
This article is a simple explanation of the Naive Bayes Classification algorithm, with an easy-to-understand example and a few technicalities.Despite all the complicated math, the implementation of the Naive Bayes algorithm involves simply counting the number of objects with specific features and classes. Once these numbers are obtained, it is very simple to calculate probabilities and arrive at a conclusion.Hope you are now familiar with this machine learning concept you most like would have heard of before.
While writing a full-blown compiler for a programming language is a difficult and frustrating task, writing a smaller and more specific parser can be surprisingly easy if you know a small trick.
On the other hand, parsing problems pops up at several places in modern-day programming. So, learning this useful trick can be rewarding.
You need to know the basics of Python, more specifically, you should know the concepts of recursion and flow of control.
Objectives
After reading and understanding this post, you will be able to create simple calculators, interactive interpreters, parsers, very limited and small programming languages, etc. In general, you should be able to take input, tokenize it, perform whatever actions you want to on the tokens, and output the result of the process.At the end of this post, you will have created a simple, Lisp-like prefix calculator. Following is a demonstration of how it's going to look:> ( + 3 2 )= 5> ( / 2 0 )DivisionByZero> ( - -3 2 )= -5> -2= -2> ( + ( * 3 2 ) 5 )= 11
Step 1: Writing the Grammar
The first step to writing a parser is to write a clear grammar for its syntax. The grammar determines what is and what is not right. Once you have written the grammar, translating it to Python code is a trivial chore. The grammar will serve as our first pseudocode.For our tiny calculator, we know that the input can come in two forms: a Number (-2, .5, +8, 8.5, 9.) or a more complicated Expression begins with a (, followed by an operator, etc.).For writing a grammar, we need to identify different elements of the syntax. So far, we have Expression, Number, and Operator. The next important thing to do is to structure the elements (known as terms) into a hierarchical form. This is shown below:Expression:Number( Operator Expression Expression )Number:a floating-point number ([-+][0-9][*\.][0-9]*)Operators:
+
-
*
/
You will notice that Operator and Expression have no parent; they are independent terms.A grammar is read from the bottom up and different choices appear on distinct lines. Our grammar says that:
an Operator is one of +, -, *, /.
a Number is a floating-point number which matches the RegEx [-+][0-9]*[\.][0-9]*
an Expression is either a Number or a ( followed by an Operator, followed by two other Expressions, and finally ends in a ). Note that the definition of an Expression is recursive.
Step 2: Translating the Grammar into Pseudocode
Pseudocode is fake code resembling English which is supposed to be an intermediate code that can easily be converted into real code. Although writing pseudocode is optional, it is really helpful.The trick here is to put each term from our grammar into a separate function. Whenever we need to apply the grammar of a certain term, we only have to call the function. Following is the pseudocode implementing the grammar above:https://gist.github.com/HackerEarthBlog/f0a5a4304326936142da39b0d853f944This is our rough pseudo-code that should be good enough for our purpose. In the next step, we will write the real code.
3. Writing the Code
It is said very profoundly about Python that reading and writing Python feels like doing pseudocode. The same applies here, but there is one small caveat— Python doesn't provide any function for “unreading” or putting a character back in the input buffer.For this, I have created a small class which extends the file object to include this feature. To keep things simple, I have avoided inheritance and my class is not compatible with the file object provided by Python. Treat it like a black-box if you don't want to understand it.https://gist.github.com/HackerEarthBlog/6465f93e1ca155ded5e8b0c8294f16baHere is the buffer.py file which handles buffered input:https://gist.github.com/HackerEarthBlog/5330e5f11f96a22608b45affa61fa858
Explanation
expression():
expression() is our top-level function and maps the Expression grammar term. We first ignore all the whitespace. After that, it takes a single non-whitespace character as input and checks it against several possibilities.If the input string starts with +, -, ., or a digit, it is a number. We put the character back and input the entire number.If the input string starts with (, a complete expression is to follow. We input the operator, two more expressions which will serve as the operands, and finally the closing parenthesis. We then calculate the result and return it.
number():
The number function maps the Number grammar term and is very simple—just a wrapper around getword. We input a whole word and if it converts to a float, we return it, otherwise the function returns an error message.
operator():
The operator function inputs a single character and tests it for equality against several known operators. Like the above two functions, it also maps a grammar term, i.e., Operator. In case the given operator is not valid, an error message is returned.
calc():
The calc function is actually not necessary but makes the code substantially better. In an ideal program, each function should do only one logical task. calc removes some burden from expression.
UngetableInput
Although Python 3 supports buffered input through stdin.buffer, Python 2 has no such facility. Plus, Python 3's stdin.buffer would still require us to create some wrapper of our own.The UngetableInput class wraps Python's basic input to go through a buffer. We take input into the buffer and put a character back into the buffer when ungetc is called. Unless the buffer is empty, all input comes from the buffer.
Homework
This code works and leaves a lot of cleaning as homework for the reader. :) Following is a list of things you can do to improve and extend the rudimentary calculator:Improve buffer.py to handle input whitespace more accurately. Hint: You might want to use a string as the buffer.Implement a function to get a single character while skipping all whitespace and replace the whitespace skipping loop with it.Add the ability to create variables. Following the Lisp syntax, it should look something like the following:( define var_name 839457.892 )
What's Coming Next?
One of the most important parts of our program is the input buffer we created. Unfortunately, it's not general purpose and can break when used in something more complicated than our tiny calculator program. In the next article, we will examine a bigger module which does this chore better.
Do you know SixDegrees.com was the first social network site which allowed the user to create a profile and connect?
In a world of 7 billion people, it seems hard to believe that the Six Degrees of Separation theory contends that we are all connected to each other by six or fewer acquaintances.
For example, there are, at most, six people standing between you and Tom Cruise or President Obama (or Trump if you lean that way).
Going by the numbers, the idea looks pretty plausible.
Assume that you know 50 people or have 50 friends and these 50 friends of yours know 50 others who are not your friends, and so on.
The math says that in 6 steps you would be connected with 506, or 15.62 billion people.
Six Degrees of Separation Theory
In 1929, Hungarian author Frigyes Karinthy published a volume of short stories named Everything is Different.
In one of his stories titled Chains, he said that with growing communication and travel, the friendship network would grow irrespective of the distance between two humans.
And with a growing social network, the social distance would shrink immensely.
All the people on the planet could be connected to one another by 5 or fewer people.
This theory captivated millions of mathematicians, sociologists, and physicists and also laid the founding stone of the first online social network.
Soon several “small world” projects were conducted.
The small world experiment comprised experiments conducted by Stanley Milgram, examining the average path length for social networks of people in the United States.
These experiments suggested that humans are connected to each other through a network, connected to each other by the shortest path.
In 2005, Samy Kamkar wrote a small piece of code for his myspace account.
Whenever anyone visited Samy’s profile, it copied his picture and tag line on his home page saying “Samy is my hero” and also copied the code.
Within 20 hours, this code was on more than 1 million myspace user profiles. It is considered one of the fastest growing web viruses of all time.
Though mostly harmless, Samy was caught by the United States Secret Service and was prohibited from using the Internet for three years.
The point I am trying to make is that within a span of few hours, a simple XSS webworm was shared among more than 1 million users, proving that the world was getting smaller and further studies and research on small world projects need to be escalated.
The real breakthrough came with the college game of “Six degrees of Kevin Bacon” where college students linked other Hollywood co-stars to Kevin Bacon in six or fewer steps.
The huge volume of data collected in the game gave scientists and researchers immense information to process and proceed and gave them opportunities to prove the concept of six degrees of separation.
In 2011, Facebook and researchers at Cornell computed that the average separation across 721 million people using Facebook was only 3.74.
In their latest research published in February 2016, this number dropped down to 3.57, with more than 1.59 billion people active on Facebook.
On average, Facebook users are connected by an average of 2.9 to 4.2 degrees of separation. The image shows the average of each person.
Six Degrees of Separation Theory meaning analysis
In its research paper, Facebook mentions that this estimation was done using the Flajolet–Martin algorithm, which is used to find distinct elements in a stream of elements.
Suppose you assign an integer called Hash to each friend in a group (Read more about Hash Function here).
Approximately half of your friends will have even numbers or even hash, whose binary representation would be 0.
A quarter of them would have the number divisible by 4, giving the binary representation as 00. This means ½n people will have their hash or numbers ending with n zeros.
To track, you find the number with the maximum number of zeros. If there are n zeros, you can find C*2n unique numbers.
To calculate the average, you find the number with the maximum number of zeroes.
Use Bitwise OR operation on these numbers and then recursively do it for one set of friends, and then friends-of-friends, and their friends and so on to find the shortest path.
The result is amazing! It is just unbelievable how small the world is.
With a growing social network, the average separation and connection would soon reduce to possibly 2 to 3 degrees of separation.
And someday, a mail from The Prince of Somalia telling you that you have won the lottery might be actually true!
Till then, connect with best developers across the planet using first degree connections by building your profile on HackerEarth and participating in various programming challenges.
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1. We require you to send an outline for the approved topic. You can begin writing the draft once the outline is approved.
2. Submissions should be kept between 1500 – 2000 words. We use American English in our posts.
3. Formatting of the blog: The blog title should be in the Title case. All headers should be in sentence case, not title case. This means all headers should be lowercase except for the first word of the header and any proper nouns or acronyms.
Title case: A simple example would be Lord Of The Flies, where the first letter in each word is capitalized.
Sentence case: A simple example would be Lord of the flies, where only the first letter in the first word is capitalized.
4. Tone of voice: You must write in second-person, not first-person. This means the content should be written in the “you/your” voice, not the “I/me/my/mine” voice. The article must remain completely non-promotional. Additionally, we prefer an informal, conversational tone. Use contractions and list out all acronyms you write.
5. Readability: Please ensure no paragraph is longer than 4 sentences. Use short sentences and keep the sentence length to less than 20 words. Write in Active voice.
6. Please run a thorough Grammarly check and make sure the score is above 95.
7. Please provide a meta description of 140-160 characters explaining your article.8. How to email your guest post to us:
The completed post must be a Google doc
Send your posts to blog@hackerearth.com
9. The blog post/article must be your own original work that has not been published on any other website, forum, chat, or social media network.10. Plagiarism or copyright infringement is not permitted. When quoting others, please make sure to cite your source properly.11. All blog posts are reviewed and approved by HackerEarth before posting. HackerEarth reserves the right to edit blog posts where necessary.12. All content must be completely vendor-neutral. If a blog post submission is inappropriate or needs improvements, a HackerEarth representative will let you know and offer suggestions so that it may be published later.
Guidelines for SEO
13. The primary keyword should have a 1000 monthly search volume & 5 secondary keywords must have a 500 monthly search volume, which must be shared along with the outline.14. We accept 2 external links (do-follow) + 1 external link (no-follow) within the article. Links to any third-party site must be relevant to the topic and approved by HackerEarth.15. The target external link should have more than 30 DA and 40 DR.16. Anchor text for backlinks should not be related to our core business keywords.17. Links should not be in the first paragraph of the blog article.18. The target URL must be a blog article, product pages for the target URL are not allowed.19. All search engine optimization ("SEO") information, such as anchor text or alt tags, will be reviewed and subject to inclusion at the discretion of HackerEarth.
Guidelines for Author Bio
20. [IMPORTANT] Blog post writers may submit a short bio statement (no more than 50 words). Please also send in a square headshot (at least 200x200) to be used on your profile.21. Blog post writers will be allowed to have one link to their website and one link to their social profile within the author acknowledgment.
Guidelines for distribution on social media
22. We ask that you ensure to share the guest article on all relevant social media platforms like LinkedIn, Instagram, and Twitter. Post that, we will do the same on our official company social media.23. Additionally, we can discuss a newsletter swap if you have a newsletter with a sizeable number of subscribers.
Additional guidelines for the overall collaboration
24. Once the post has been submitted to HackerEarth, you may not publish it anywhere online, in part or in whole, including your own website or blog, without the consent of HackerEarth.25. HackerEarth will share and promote the blog post but does not guarantee any site or audience reach.26. If HackerEarth uses your guest post, you may promote it on your own website, Facebook, Twitter, or other social media forums. Promoting does not mean you post the entire article on these forums. You may include a link to your guest post and a short explanation of the article.27. Posts will acknowledge your authorship but will be the property of HackerEarth.28. Affiliate links shall not be included in guest post submissions. HackerEarth reserves the right to add its own affiliate links where appropriate.29. Excessive links or links that appear to be affiliated or spam-related will be removed at the discretion of HackerEarth.30. We do not pay for submissions. If you decide to submit a post to our site, you do so with the knowledge that you shall not be entitled to any compensation for writing the post or for any other compensation related to the post.31. HackerEarth reserves the right to refuse publication or remove a blog post without prior notice to the blog post writer.32. By providing a blog post to HackerEarth, you agree that you are in no way becoming a part of the website or company, nor shall you hold yourself out to be a member of the HackerEarth website or company.33. We expect to receive all submissions on time. If you miss your deadline, your post will not be published. If you need an extension to your deadline, please let us know ahead of time.
from the UC Irivine ML repository. Let's start with H2O. This data set isn't the most ideal one to work with in neural networks. However, the motive of this hands-on section is to make you familiar with model-building processes.
H2O Package
H2O package provides h2o.deeplearning function for model building. It is built on Java. Primarily, this function is useful to build multilayer feedforward neural networks. It is enabled with several features such as the following:
Multi-threaded distributed parallel computation
Adaptive learning rate (or step size) for faster convergence
Regularization options such as L1 and L2 which help prevent overfitting
Automatic missing value imputation
Hyperparameter optimization using grid/random search
There are many more!For optimization, this package uses the hogwild method instead of stochastic gradient descent. Hogwild is just parallelized version of SGD.Let's understand the parameters involved in model building with h2o. Both the packages have different nomenclatures, so make sure you don't get confused. Since most of the parameters are easy to understand by their names, I'll mention the important ones:
hidden - It specifies the number of hidden layers and number of neurons in each layer in the architechture.
epochs - It specifies the number of iterations to be done on the data set.
rate - It specifies the learning rate.
activation - It specifies the type of activation function to use. In h2o, the major activation functions are Tanh, Rectifier, and Maxout.
Let's quickly load the data and get over with sanitary data pre-processing steps:
Now, let's build a simple deep learning model. Generally, computing variable importance from a trained deep learning model is quite pain staking. But, h2o package provides an effortless function to compute variable importance from a deep learning model.
Now, let's train a deep learning model with one hidden layer comprising five neurons. This time instead of checking the cross-validation accuracy, we'll validate the model on test data.
For hyperparameter tuning, we'll perform a random grid search over all parameters and choose the model which returns highest accuracy.
MXNetR Package
The mxnet package provides an incredible interface to build feedforward NN, recurrent NN and convolutional neural networks (CNNs). CNNs are being widely used in detecting objects from images. The team that created xgboost also created this package. Currently, mxnet is being popularly used in kaggle competitions for image classification problems.
This package can be easily connected with GPUs as well. The process of building model architecture is quite intuitive. It gives greater control to configure the neural network manually.
Let's get some hands-on experience using this package.Follow the commands below to install this package in your respective OS. For Windows and Linux users, installation commands are given below. For Mac users, here's the installation procedure.
In R, mxnet accepts target variables as numeric classes and not factors. Also, it accepts data frame as a matrix. Now, we'll make the required changes:
Now, we'll train the multilayered perceptron model using the mx.mlp function.
Softmax function is used for binary and multi-classification problems. Alternatively, you can also manually craft the model structure.
We have configured the network above with one hidden layer carrying three neurons. We have chosen softmax as the output function. The network optimizes for squared loss for regression, and the network optimizes for classification accuracy for classification. Now, we'll train the network:
Similarly, we can configure a more complexed network fed with hidden layers.
Understand it carefully: After feeding the input through data, the first hidden layer consists of 10 neurons. The output of each neuron passes through a relu (rectified linear) activation function. We have used it in place of sigmoid. relu converges faster than a sigmoid function. You can read more about relu here.
Then, the output is fed into the second layer which is the output layer. Since our target variable has two classes, we've chosen num_hidden as 2 in the second layer. Finally, the output from second layer is made to pass though softmax output function.
As mentioned above, this trained model predicts output probability, which can be easily transformed into a label using a threshold value (say, 0.5). To make predictions on the test set, we do this:
The predicted matrix returns two rows and 16281 columns, each column carrying probability. Using the max.col function, we can extract the maximum value from each row. If you check the model's accuracy, you'll find that this network performs terribly on this data. In fact, it gives no better result than the train accuracy! On this data set, xgboost tuning gave 87% accuracy!
If you are familiar with the model building process, I'd suggest you to try working on the popular MNIST data set. You can find tons of tutorials on this data to get you going!
Summary
Deep Learning is getting increasingly popular in solving most complex problems such as image recognition, natural language processing, etc. If you are aspiring for a career in machine learning, this is the best time for you to get into this subject. The motive of this article was to introduce you to the fundamental concepts of deep learning.In this article, we learned about the basics of deep learning (perceptrons, neural networks, and multilayered neural networks). We learned deep learning as a technique is composed of several algorithms such as backpropagration and gradient descent to optimize the networks. In the end, we gained some hands-on experience in developing deep learning models.Do let me know if you have any feedback, suggestions, or thoughts on this article in the comments below!
Get to know the experts behind our content. From industry leaders to tech enthusiasts, our authors share valuable insights, trends, and expertise to keep you informed and inspired.
Artificial Intelligence (AI) has permeated virtually every industry, transforming operations and interactions. The tech recruitment sector is no exception, and AI’s influence shapes the hiring processes in revolutionary ways. From leveraging AI-powered chatbots for preliminary candidate screenings to deploying machine learning algorithms for efficient resume parsing, AI leaves an indelible mark on tech hiring practices.
Yet, amidst these promising advancements, we must acknowledge the other side of the coin: AI’s potential malpractices, including the likelihood of cheating on assessments, issues around data privacy, and the risk of bias against minority groups.
The dark side of AI in tech recruitment
The introduction of AI in recruitment, while presenting significant opportunities, also brings with it certain drawbacks and vulnerabilities. Sophisticated technologies could enable candidates to cheat on assessments, misrepresent abilities and potential hiring mistakes. This could lead to hiring candidates with falsifying skills or qualifications, which can cause a series of negative effects like:
Reduced work quality: The work output might be sub-par if a candidate doesn’t genuinely possess the abilities they claimed to have.
Team disruptions: Other team members may have to pick up the slack, leading to resentment and decreased morale.
Rehiring costs: You might have to let go of such hires, resulting in additional costs for replacement.
Data privacy is another critical concern
Your company could be left exposed to significant risks if your AI recruiting software is not robust enough to protect sensitive employee information. The implications for an organization with insufficient data security could be severe such as:
Reputational damage: Breaches of sensitive employee data can damage your company’s reputation, making it harder to attract clients and talented employees in the future.
Legal consequences: Depending on the jurisdiction, you could face legal penalties, including hefty fines, for failing to protect sensitive data adequately.
Loss of trust: A data breach could undermine employee trust in your organization, leading to decreased morale and productivity.
Financial costs: Besides potential legal penalties, companies could also face direct financial losses from a data breach, including the costs of investigation, recovery, and measures to prevent future breaches.
Operational disruption: Depending on the extent of the breach, normal business operations could be disrupted, causing additional financial losses and damage to the organization’s reputation.
Let’s talk about the potential for bias in AI recruiting software
Perhaps the most critical issue of all is the potential for unconscious bias. The potential for bias in AI recruiting software stems from the fact that these systems learn from the data they are trained on. If the training data contains biases – for example, if it reflects a history of preferentially hiring individuals of a certain age, gender, or ethnicity – the AI system can learn and replicate these biases.
Even with unbiased data, if the AI’s algorithms are not designed to account for bias, they can inadvertently create it. For instance, a hiring algorithm that prioritizes candidates with more years of experience may inadvertently discriminate against younger candidates or those who have taken career breaks, such as for child-rearing or health reasons.
This replication and possible amplification of human prejudices can result in discriminatory hiring practices. If your organization’s AI-enabled hiring system is found to be biased, you could face legal action, fines, and penalties. Diversity is proven to enhance creativity, problem-solving, and decision-making. In contrast, bias in hiring can lead to a homogenous workforce, so its absence would likely result in a less innovative and less competitive organization.
When used correctly, AI in recruitment can take your hiring to the next level
How do you evaluate the appropriateness of using AI in hiring for your organization? Here are some strategies for navigating the AI revolution in HR. These steps include building support for AI adoption, identifying HR functions that can be integrated with AI, avoiding potential pitfalls of AI use in HR, collaborating with IT leaders, and so on.
Despite certain challenges, AI can significantly enhance tech recruitment processes when used effectively. AI-based recruitment tools can automate many manual recruiting tasks, such as resume screening and interview scheduling, freeing up time for recruiters to focus on more complex tasks. Furthermore, AI can improve the candidate’s experience by providing quick responses and personalized communications. The outcome is a more efficient, candidate-friendly process, which could lead to higher-quality hires.
Let’s look at several transformational possibilities chatbots can bring to human capital management for candidates and hiring teams. This includes automation and simplifying various tasks across domains such as recruiting, onboarding, core HR, absence management, benefits, performance management, and employee self-service resulting in the following:
For recruiters:
Improved efficiency and productivity: Chatbots can handle routine tasks like responding to common inquiries or arranging interviews. Thereby, providing you with more time to concentrate on tasks of strategic importance.
Enhanced candidate experience: With their ability to provide immediate responses, chatbots can make the application process more engaging and user-friendly.
Data and insights: Chatbots can collect and analyze data from your interactions with candidates. And provide valuable insights into candidate preferences and behavior.
Improved compliance: By consistently following predefined rules and guidelines, chatbots can help ensure that hiring processes are fair and compliant with relevant laws and regulations.
Cost saving: By automating routine tasks for recruiters, chatbots can help reduce the labor costs associated with hiring.
Additionally, candidates can leverage these AI-powered chatbots in a dialog flow manner to carry out various tasks. These tasks include the following:
Personalized greetings: By using a candidate’s name and other personal information, chatbots can create a friendly, personalized experience.
Job search: They can help candidates search for jobs based on specific criteria.
Create a candidate profile: These AI-powered chatbots can guide candidates through the process of creating a profile. Thus, making it easier for them to apply for jobs.
Upload resume: Chatbots can instruct candidates on uploading their resume, eliminating potential confusion.
Apply for a job: They can streamline the application process, making it easier and faster for candidates to apply for jobs.
Check application status: Chatbots can provide real-time updates on a candidate’s application status.
Schedule interviews: They can match candidate and interviewer availability to schedule interviews, simplifying the process.
For hiring managers:
These can also be utilized by your tech hiring teams for various purposes, such as:
Create requisition: Chatbots can guide hiring managers through the process of creating a job requisition.
Create offers: They can assist in generating job offers, ensuring all necessary information is included.
Access requisition and offers: Using chatbots can provide hiring managers with easy access to job requisitions and offers.
Check on onboarding tasks: Chatbots can help track onboarding tasks, ensuring nothing is missed.
Other AI recruiting technologies can also enhance the hiring process for candidates and hiring teams in the following ways:
For candidates:
Tailor-made resumes and cover letters using generative AI:Generative AI can help candidates create custom resumes and cover letters, increasing their chances of standing out.
Simplifying the application process: AI-powered recruiting tools can simplify the application process, allowing candidates to apply for jobs with just a few clicks.
Provide similar job recommendations: AI can analyze candidates’ skills, experiences, and preferences to recommend similar jobs they might be interested in.
For recruiters:
Find the best candidate: AI algorithms can analyze large amounts of data to help you identify the candidates most likely to succeed in a given role.
Extract key skills from candidate job applications: Save a significant amount of time and effort by using AI-based recruiting software to quickly analyze job applications to identify key skills, thereby, speeding up the screening process.
Take feedback from rejected candidates & share similar job recommendations: AI can collect feedback from rejected candidates for you to improve future hiring processes and recommend other suitable roles to the candidate.
These enhancements not only streamline the hiring process but also improve the quality of hires, reduce hiring biases, and improve the experience for everyone involved. The use of AI in hiring can indeed take it to the next level.
Where is AI in recruitment headed?
AI can dramatically reshape the recruitment landscape with the following key advancements:
1. Blockchain-based background verification:
Blockchain technology, renowned for its secure, transparent, and immutable nature, can revolutionize background checks. This process which can take anywhere from between a day to several weeks today for a single recruiter to do can be completed within a few clicks resulting in:
Streamlined screening process: Blockchain can store, manage, and share candidates’ credentials and work histories. Thereby speeding up the verification and screening process. This approach eliminates the need for manual background checks. And leads to freeing up a good amount of time for you to focus on more important tasks.
Enhanced trust and transparency: With blockchain, candidates, and employers can trust the validity of the information shared due to the nature of the technology. The cryptographic protection of blockchain ensures the data is tamper-proof, and decentralization provides transparency.
Improved data accuracy and reliability: Since the blockchain ledger is immutable, it enhances the accuracy and reliability of the data stored. This can minimize the risks associated with false information on candidates’ resumes.
Faster onboarding: A swift and reliable verification process means candidates can be onboarded more quickly. Thereby, improving the candidate experience and reducing the time-to-hire.
Expanded talent pool: With blockchain, it’s easier and quicker to verify the credentials of candidates globally, thereby widening the potential talent pool.
2. Immersive experiences using virtual reality (VR):
VR can provide immersive experiences that enhance various aspects of the tech recruitment process:
Interactive job previews: VR can allow potential candidates to virtually “experience” a day i.e., life at your company. This provides a more accurate and engaging job preview than traditional job descriptions.
Virtual interviews and assessments: You can use VR to conduct virtual interviews or assessments. You can also evaluate candidates in a more interactive and immersive setting. This can be particularly useful for roles that require specific spatial or technical skills.
Virtual onboarding programs: New hires can take a virtual tour of the office, meet their colleagues, and get acquainted with their tasks, all before their first day. This can significantly enhance the onboarding experience and help new hires feel more prepared.
Immersive learning experiences: VR can provide realistic, immersive learning experiences for job-specific training or to enhance soft skills. These could be used during the recruitment process or for ongoing employee development.
To summarize, AI in recruitment is a double-edged sword, carrying both promise and potential problems. The key lies in how recruiters use this technology, leveraging its benefits while vigilantly managing its risks. AI isn’t likely to replace recruiters or HR teams in the near future. Instead, you should leverage this tool to positively impact the entire hiring lifecycle.
With the right balance and careful management, AI can streamline hiring processes. It can create better candidate experiences, and ultimately lead to better recruitment decisions. Recruiters should continually experiment with and explore generative AI. To devise creative solutions, resulting in more successful hiring and the perfect fit for every open role.
“Every hire is an investment for a company. A good hire will give you a higher ROI; if it is a bad hire, it will cost you a lot of time and money.”
Especially in tech hiring!
An effective tech recruitment process helps you attract the best talents, reduce hiring costs, and enhance company culture and reputation.
Businesses increasingly depend on technical knowledge to compete in today’s fast-paced, technologically driven world. Online platforms that provide technical recruiting solutions have popped up to assist companies in finding and employing top talent in response to this demand.
The two most well-known platforms in this field are HackerEarth and Mettl. To help businesses make wise choices for their technical employment requirements, we will compare these two platforms’ features, benefits, and limitations in this article.
This comparison of Mettl alternative, HackerEarth and Mettl itself, will offer helpful information to help you make the best decision, whether you’re a small company trying to expand your tech staff or a massive organization needing a simplified recruiting process.
HackerEarth
HackerEarth is based in San Francisco, USA, and offers enterprise software to aid companies with technical recruitment. Its services include remote video interviewing and technical skill assessments that are commonly used by organizations.
HackerEarth also provides a platform for developers to participate in coding challenges and hackathons. In addition, it provides tools for technical hiring such as coding tests, online interviews, and applicant management features. The hiring solutions provided by HackerEarth aid companies assess potential employees’ technical aptitude and select the best applicants for their specialized positions.
Mettl
Mettl, on the other hand, offers a range of assessment solutions for various industries, including IT, banking, healthcare, and retail. It provides online tests for coding, linguistic ability, and cognitive skills. The tests offered by Mettl assist employers find the best applicants for open positions and make data-driven recruiting choices. Additionally, Mettl provides solutions for personnel management and staff training and development.
Why should you go for HackerEarth over Mercer Mettl?
Because HackerEarth makes technical recruiting easy and fast, you must consider HackerEarth for technical competence evaluations and remote video interviews. It goes above and beyond to provide you with a full range of functions and guarantee the effectiveness of the questions in the database. Moreover, it is user-friendly and offers fantastic testing opportunities.
The coding assessments by HackerEarth guarantee the lowest time consumption and maximum efficiency. It provides a question bank of more than 17,000 coding-related questions and automated test development so that you can choose test questions as per the job role.
As a tech recruiter, you may need a clear understanding of a candidate’s skills. With HackerEarth’s code replay capability and insight-rich reporting on a developer’s performance, you can hire the right resource for your company.
Additionally, HackerEarth provides a more in-depth examination of your recruiting process so you can continuously enhance your coding exams and develop a hiring procedure that leads the industry.
HackerEarth and Mercer Mettl are the two well-known online tech assessment platforms that provide tools for managing and performing online examinations. We will examine the major areas where HackerEarth outperforms Mettl, thereby proving to be a great alternative to Mettl, in this comparison.
HackerEarth believes in upgrading itself and providing the most effortless navigation and solutions to recruiters and candidates.
HackerEarth provides various tools and capabilities to create and administer online tests, such as programming tests, multiple-choice questions, coding challenges, and more. The software also has remote proctoring, automatic evaluation, and plagiarism detection tools (like detecting the use of ChatGPT in coding assessments). On the other side, Mettl offers comparable functionality but has restricted capabilities for coding challenges and evaluations.
Test creation and administration
HackerEarth: It has a user-friendly interface that is simple to use and navigate. It makes it easy for recruiters to handle evaluations without zero technical know-how. The HackerEarth coding platform is also quite flexible and offers a variety of pre-built exams, including coding tests, aptitude tests, and domain-specific examinations. It has a rich library of 17,000+ questions across 900+ skills, which is fully accessible by the hiring team. Additionally, it allows you to create custom questions yourself or use the available question libraries.
Mettl: It can be challenging for a hiring manager to use Mettl efficiently since Mettl provides limited assessment and question libraries. Also, their team creates the test for them rather than giving access to hiring managers. This results in a higher turnaround time and reduces test customization possibilities since the request has to go back to the team, they have to make the changes, and so forth.
Reporting and analytics
HackerEarth: You may assess applicant performance and pinpoint areas for improvement with the help of HackerEarth’s full reporting and analytics tools. Its personalized dashboards, visualizations, and data exports simplify evaluating assessment results and real-time insights.
Most importantly, HackerEarth includes code quality scores in candidate performance reports, which lets you get a deeper insight into a candidate’s capabilities and make the correct hiring decision. Additionally, HackerEarth provides a health score index for each question in the library to help you add more accuracy to your assessments. The health score is based on parameters like degree of difficulty, choice of the programming language used, number of attempts over the past year, and so on.
Mettl: Mettl online assessment tool provides reporting and analytics. However, there may be only a few customization choices available. Also, Mettle does not provide code quality assurance which means hiring managers have to check the whole code manually. There is no option to leverage question-based analytics and Mettl does not include a health score index for its question library.
Adopting this platform may be challenging if you want highly customized reporting and analytics solutions.
HackerEarth: The security and privacy of user data are top priorities at HackerEarth. The platform protects data in transit and at rest using industry-standard encryption. Additionally, all user data is kept in secure, constantly monitored data centers with stringent access controls.
Along with these security measures, HackerEarth also provides IP limitations, role-based access controls, and multi-factor authentication. These features ensure that all activity is recorded and audited and that only authorized users can access sensitive data.
HackerEarth complies with several data privacy laws, such as GDPR and CCPA. The protection of candidate data is ensured by this compliance, which also enables businesses to fulfill their legal and regulatory responsibilities.
Mettl: The security and data privacy features of Mettl might not be as strong as those of HackerEarth. The platform does not provide the same selection of security measures, such as IP limitations or multi-factor authentication. Although the business asserts that it complies with GDPR and other laws, it cannot offer the same amount of accountability and transparency as other platforms.
Even though both HackerEarth and Mettl include security and data privacy measures, the Mettle alternative, HackerEarth’s platform is made to be more thorough, open, and legal. By doing this, businesses can better guarantee candidate data’s security and ability to fulfill legal and regulatory requirements.
Pricing and support
HackerEarth: To meet the demands of businesses of all sizes, HackerEarth offers a variety of customizable pricing options. The platform provides yearly and multi-year contracts in addition to a pay-as-you-go basis. You can select the price plan that best suits their demands regarding employment and budget.
HackerEarth offers chat customer support around the clock. The platform also provides a thorough knowledge base and documentation to assist users in getting started and troubleshooting problems.
Mettl: The lack of price information on Mettl’s website might make it challenging for businesses to decide whether the platform fits their budget. The organization also does not have a pay-as-you-go option, which might be problematic.
Mettl offers phone and emails customer assistance. However, the business website lacks information on support availability or response times. This lack of transparency may be an issue if you need prompt and efficient help.
User experience
HackerEarth: The interface on HackerEarth is designed to be simple for both recruiters and job seekers. As a result of the platform’s numerous adjustable choices for test creation and administration, you may design exams specifically suited to a job role. Additionally, the platform provides a selection of question types and test templates, making it simple to build and take exams effectively.
In terms of the candidate experience, HackerEarth provides a user-friendly interface that makes navigating the testing procedure straightforward and intuitive for applicants. As a result of the platform’s real-time feedback and scoring, applicants may feel more motivated and engaged during the testing process. The platform also provides several customization choices, like branding and message, which may assist recruiters in giving prospects a more exciting and tailored experience.
Mettl: The platform is intended to have a steeper learning curve than others and be more technical. It makes it challenging to rapidly and effectively construct exams and can be difficult for applicants unfamiliar with the platform due to its complex interface.
Additionally, Mettl does not provide real-time feedback or scoring, which might deter applicants from participating and being motivated by the testing process.
According to G2, HackerEarth and Mettl have 4.4 reviews out of 5. Users have also applauded HackerEarth’s customer service. Many agree that the staff members are friendly and quick to respond to any problems or queries. Overall, customer evaluations and feedback for HackerEarth point to the platform as simple to use. Both recruiters and applicants find it efficient.
Mettl has received mixed reviews from users, with some praising the platform for its features and functionality and others expressing frustration with its complex and technical interface.
May the best “brand” win!
Recruiting and selecting the ideal candidate demands a significant investment of time, attention, and effort.
This is where tech recruiting platforms like HackerEarth and Mettl have got you covered. They help streamline the whole process.Both HackerEarth and Mettl provide a wide variety of advanced features and capabilities for tech hiring.
We think HackerEarth is the superior choice. Especially, when contrasting the two platforms in terms of their salient characteristics and functioning. But, we may be biased!
So don’t take our word for it. Sign up for a free trial and check out HackerEarth’s offerings for yourself!
Let’s face it—cheating on tests is quite common. While technology has made a lot of things easier in tech recruiting, it
has also left the field wide open to malpractice. A 2020 report by ICAI shows
that 32% of undergraduate students have cheated in some form on an online test.
It’s human nature to want to bend
the rules a little bit. Which begs the question, how do you stay on top of cheating, plagiarism, and other forms of
malpractice during the assessment process?
How do you ensure that take-home assessments and remote interviews
stay authentic and credible? By relying on enhanced virtual supervision, of course!
HackerEarth Assessments has
always been one step ahead when it comes to remote proctoring which is able to capture the nuances of candidate
plagiarism. The recent advancements in technology (think generative AI) needed
more robust proctoring features, so we went ahead and built The HackerEarth Smart
Browser to ensure our assessments remain as foolproof as ever.
Presenting to you, the
latest HackerEarth proctoring fix - The Smart Browser
Our Smart Browser is the chocolatey version of a plain
donut when compared to a regular web browser. It is extra effective and comes packed with additional remote proctoring capabilities to
increase the quality of your screening assessments.
The chances of a candidate cheating on a HackerEarth technical assessment are virtually zero with
the latest features! Spilling all our secrets to show you why -
1. Sealed-off testing environment makes proctoring
simpler
To get started with
using the Smart Browser, enable the Smart Browser setting as shown above. This setting is available under the test
proctoring section on the test overview page.
As you can see, several other proctoring settings such as disabling
copy-paste, restricting candidates to full-screen mode, and logout on leaving the test interface are selected
automatically.Now, every candidate you invite to take the assessment will only be able to do so through the Smart
Browser. Candidates are prompted to download the Smart Browser from the link shared in the test invite mail.When the
candidate needs to click on the ‘start test’ button on the launch test screen, it opens in the Smart Browser. The
browser also prompts the candidate to switch to full-screen mode. Now, all candidates need to do is sign in and attempt
the test, as usual.
Also read: 6
Ways Candidates Try To Outsmart A Remote Proctored Assessment
2. Eagle-eyed online test monitoring leaves no room for error
Our AI-enabled Smart Browser takes frequent snapshots via the webcam, throughout the assessment.
Consequently, it is impossible to copy-paste code or impersonate a candidate.The browser prevents the following
candidate actions and facilitates thorough monitoring of the assessment:
Any candidate actions attempting to switch tabs with the intent to copy-paste or use a generative AI like ChatGPT
are shown a warning and captured in the candidate report.HackerEarth’s latest proctoring fixes
bulletproof our assessment platform, making it one of the most reliable and accurate sources of candidate hiring in the
market today.
Also read: 4 Ways
HackerEarth Flags The Use Of ChatGPT In Tech Hiring Assessments
Experience reliable assessments with the Smart Browser!
There you have it - our newest offering that preserves
the integrity of coding assessments and enables skill-first hiring, all in one go. Recruiters and hiring managers, this
is one feature that you can easily rely on and can be sure that every candidate’s test score is a result of their
ability alone.Curious to try out the Smart Browser? Well, don’t take our word for it. Head over here to check it out for
yourself!
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In today’s fast-paced world, recruiting talent has become increasingly complicated. Technological advancements, high workforce expectations and a highly competitive market have pushed recruitment agencies to adopt innovative strategies for recruiting various types of talent. This article aims to explore one such recruitment strategy – headhunting.
What is Headhunting in recruitment?
In headhunting, companies or recruitment agencies identify, engage and hire highly skilled professionals to fill top positions in the respective companies. It is different from the traditional process in which candidates looking for job opportunities approach companies or recruitment agencies. In headhunting, executive headhunters, as recruiters are referred to, approach prospective candidates with the hiring company’s requirements and wait for them to respond. Executive headhunters generally look for passive candidates, those who work at crucial positions and are not on the lookout for new work opportunities. Besides, executive headhunters focus on filling critical, senior-level positions indispensable to companies. Depending on the nature of the operation, headhunting has three types. They are described later in this article. Before we move on to understand the types of headhunting, here is how the traditional recruitment process and headhunting are different.
How do headhunting and traditional recruitment differ from each other?
Headhunting is a type of recruitment process in which top-level managers and executives in similar positions are hired. Since these professionals are not on the lookout for jobs, headhunters have to thoroughly understand the hiring companies’ requirements and study the work profiles of potential candidates before creating a list.
In the traditional approach, there is a long list of candidates applying for jobs online and offline. Candidates approach recruiters for jobs. Apart from this primary difference, there are other factors that define the difference between these two schools of recruitment.
AspectHeadhuntingTraditional RecruitmentCandidate TypePrimarily passive candidateActive job seekersApproachFocused on specific high-level rolesBroader; includes various levelsScopeproactive outreachReactive: candidates applyCostGenerally more expensive due to expertise requiredTypically lower costsControlManaged by headhuntersManaged internally by HR teams
All the above parameters will help you to understand how headhunting differs from traditional recruitment methods, better.
Types of headhunting in recruitment
Direct headhunting: In direct recruitment, hiring teams reach out to potential candidates through personal communication. Companies conduct direct headhunting in-house, without outsourcing the process to hiring recruitment agencies. Very few businesses conduct this type of recruitment for top jobs as it involves extensive screening across networks outside the company’s expanse.
Indirect headhunting: This method involves recruiters getting in touch with their prospective candidates through indirect modes of communication such as email and phone calls. Indirect headhunting is less intrusive and allows candidates to respond at their convenience.Third-party recruitment: Companies approach external recruitment agencies or executive headhunters to recruit highly skilled professionals for top positions. This method often leverages the company’s extensive contact network and expertise in niche industries.
How does headhunting work?
Finding highly skilled professionals to fill critical positions can be tricky if there is no system for it. Expert executive headhunters employ recruitment software to conduct headhunting efficiently as it facilitates a seamless recruitment process for executive headhunters. Most software is AI-powered and expedites processes like candidate sourcing, interactions with prospective professionals and upkeep of communication history. This makes the process of executive search in recruitment a little bit easier. Apart from using software to recruit executives, here are the various stages of finding high-calibre executives through headhunting.
Identifying the role
Once there is a vacancy for a top job, one of the top executives like a CEO, director or the head of the company, reach out to the concerned personnel with their requirements. Depending on how large a company is, they may choose to headhunt with the help of an external recruiting agency or conduct it in-house. Generally, the task is assigned to external recruitment agencies specializing in headhunting. Executive headhunters possess a database of highly qualified professionals who work in crucial positions in some of the best companies. This makes them the top choice of conglomerates looking to hire some of the best talents in the industry.
Defining the job
Once an executive headhunter or a recruiting agency is finalized, companies conduct meetings to discuss the nature of the role, how the company works, the management hierarchy among other important aspects of the job. Headhunters are expected to understand these points thoroughly and establish a clear understanding of their expectations and goals.
Candidate identification and sourcing
Headhunters analyse and understand the requirements of their clients and begin creating a pool of suitable candidates from their database. The professionals are shortlisted after conducting extensive research of job profiles, number of years of industry experience, professional networks and online platforms.
Approaching candidates
Once the potential candidates have been identified and shortlisted, headhunters move on to get in touch with them discreetly through various communication channels. As such candidates are already working at top level positions at other companies, executive headhunters have to be low-key while doing so.
Assessment and Evaluation
In this next step, extensive screening and evaluation of candidates is conducted to determine their suitability for the advertised position.
Interviews and negotiations
Compensation is a major topic of discussion among recruiters and prospective candidates. A lot of deliberation and negotiation goes on between the hiring organization and the selected executives which is facilitated by the headhunters.
Finalizing the hire
Things come to a close once the suitable candidates accept the job offer. On accepting the offer letter, headhunters help finalize the hiring process to ensure a smooth transition.
The steps listed above form the blueprint for a typical headhunting process. Headhunting has been crucial in helping companies hire the right people for crucial positions that come with great responsibility. However, all systems have a set of challenges no matter how perfect their working algorithm is. Here are a few challenges that talent acquisition agencies face while headhunting.
Common challenges in headhunting
Despite its advantages, headhunting also presents certain challenges:
Cost Implications: Engaging headhunters can be more expensive than traditional recruitment methods due to their specialized skills and services.
Time-Consuming Process: While headhunting can be efficient, finding the right candidate for senior positions may still take time due to thorough evaluation processes.
Market Competition: The competition for top talent is fierce; organizations must present compelling offers to attract passive candidates away from their current roles.
Although the above mentioned factors can pose challenges in the headhunting process, there are more upsides than there are downsides to it. Here is how headhunting has helped revolutionize the recruitment of high-profile candidates.
Advantages of Headhunting
Headhunting offers several advantages over traditional recruitment methods:
Access to Passive Candidates: By targeting individuals who are not actively seeking new employment, organisations can access a broader pool of highly skilled professionals.
Confidentiality: The discreet nature of headhunting protects both candidates’ current employment situations and the hiring organisation’s strategic interests.
Customized Search: Headhunters tailor their search based on the specific needs of the organization, ensuring a better fit between candidates and company culture.
Industry Expertise: Many headhunters specialise in particular sectors, providing valuable insights into market dynamics and candidate qualifications.
Conclusion
Although headhunting can be costly and time-consuming, it is one of the most effective ways of finding good candidates for top jobs. Executive headhunters face several challenges maintaining the g discreetness while getting in touch with prospective clients. As organizations navigate increasingly competitive markets, understanding the nuances of headhunting becomes vital for effective recruitment strategies. To keep up with the technological advancements, it is better to optimise your hiring process by employing online recruitment software like HackerEarth, which enables companies to conduct multiple interviews and evaluation tests online, thus improving candidate experience. By collaborating with skilled headhunters who possess industry expertise and insights into market trends, companies can enhance their chances of securing high-caliber professionals who drive success in their respective fields.
The job industry is not the same as it was 30 years ago. Progresses in AI and automation have created a new work culture that demands highly skilled professionals who drive innovation and work efficiently. This has led to an increase in the number of companies reaching out to external sources of recruitment for hiring talent. Over the years, we have seen several job aggregators optimise their algorithms to suit the rising demand for talent in the market and new players entering the talent acquisition industry. This article will tell you all about how external sources of recruitment help companies scout some of the best candidates in the industry, the importance of external recruitment in organizations across the globe and how it can be leveraged to find talent effectively.
Understanding external sources of recruitment
External sources refer to recruitment agencies, online job portals, job fairs, professional associations and any other organizations that facilitate seamless recruitment. When companies employ external recruitment sources, they access a wider pool of talent which helps them find the right candidates much faster than hiring people in-house. They save both time and effort in the recruitment process.
Online job portals
Online resume aggregators like LinkedIn, Naukri, Indeed, Shine, etc. contain a large database of prospective candidates. With the advent of AI, online external sources of recruitment have optimised their algorithms to show the right jobs to the right candidates. Once companies figure out how to utilise job portals for recruitment, they can expedite their hiring process efficiently.
Social Media
Ours is a generation that thrives on social media. To boost my IG presence, I have explored various strategies, from getting paid Instagram users to optimizing post timing and engaging with my audience consistently. Platforms like FB an IG have been optimized to serve job seekers and recruiters alike. The algorithms of social media platforms like Facebook and Instagram have been optimised to serve job seekers and recruiters alike. Leveraging them to post well-placed ads for job listings is another way to implement external sources of recruitment strategies.
Employee Referrals
Referrals are another great external source of recruitment for hiring teams. Encouraging employees to refer their friends and acquaintances for vacancies enables companies to access highly skilled candidates faster.
Campus Recruitment
Hiring freshers from campus allows companies to train and harness new talent. Campus recruitment drives are a great external recruitment resource where hiring managers can expedite the hiring process by conducting screening processes in short periods.
Recruitment Agencies
Companies who are looking to fill specific positions with highly skilled and experienced candidates approach external recruitment agencies or executive headhunters to do so. These agencies are well-equipped to look for suitable candidates and they also undertake the task of identifying, screening and recruiting such people.
Job Fairs
This is a win-win situation for job seekers and hiring teams. Job fairs allow potential candidates to understand how specific companies work while allowing hiring managers to scout for potential candidates and proceed with the hiring process if possible.
Importance of External Recruitment
The role of recruitment agencies in talent acquisition is of paramount importance. They possess the necessary resources to help companies find the right candidates and facilitate a seamless hiring process through their internal system. Here is how external sources of recruitment benefit companies.
Diversity of Skill Sets
External recruitment resources are a great way for companies to hire candidates with diverse professional backgrounds. They possess industry-relevant skills which can be put to good use in this highly competitive market.
Fresh Perspectives
Candidates hired through external recruitment resources come from varied backgrounds. This helps them drive innovation and run things a little differently, thus bringing in a fresh approach to any project they undertake.
Access to Specialized Talent
Companies cannot hire anyone to fill critical roles that require highly qualified executives. This task is assigned to executive headhunters who specialize in identifying and screening high-calibre candidates with the right amount of industry experience. Huge conglomerates and companies seek special talent through external recruiters who have carved a niche for themselves.
Now that you have learnt the different ways in which leveraging external sources of recruitment benefits companies, let’s take a look at some of the best practices of external recruitment to understand how to effectively use their resources.
Best Practices for Effective External Recruitment
Identifying, reaching out to and screening the right candidates requires a robust working system. Every system works efficiently if a few best practices are implemented. For example, hiring through social media platforms requires companies to provide details about their working environment, how the job is relevant to their audience and well-positioned advertisements. The same applies to the other external sources of recruitment. Here is how you can optimise the system to ensure an effective recruitment process.
Craft Clear and Compelling Job Descriptions
Detail Responsibilities: Clearly outline the key responsibilities and expectations for the role.
Highlight Company Culture: Include information about the company’s mission, values, and growth opportunities to attract candidates who align with your organizational culture.
Leverage Multiple Recruitment Channels
Diversify Sources: Use a mix of job boards, social media platforms, recruitment agencies, and networking events to maximize reach. Relying on a single source can limit your candidate pool.
Utilize Industry-Specific Platforms: In addition to general job boards, consider niche job sites that cater to specific industries or skill sets
Streamline the Application Process
Simplify Applications: Ensure that the application process is user-friendly. Lengthy or complicated forms can deter potential candidates from applying.
Mobile Optimization: Many candidates use mobile devices to apply for jobs, so ensure your application process is mobile-friendly.
Engage in Proactive Sourcing
Reach Out to Passive Candidates: Actively seek out candidates who may not be actively looking for a job but could be a great fit for your organization. Use LinkedIn and other professional networks for this purpose.
Maintain a Talent Pool: Keep a database of previous applicants and strong candidates for future openings, allowing you to reach out when new roles become available.
Utilize Social Media Effectively
Promote Job Openings: Use social media platforms like LinkedIn, Facebook, and Twitter to share job postings and engage with potential candidates. This approach can also enhance your employer brand
Conduct Background Checks: There are several ways of learning about potential candidates. Checking out candidate profiles on job boards like LinkedIn or social media platforms can give companies a better understanding of their potential candidates, thus confirming whether they are the right fit for the organization.
Implement Data-Driven Recruitment
Analyze Recruitment Metrics: Track key metrics such as time-to-hire, cost-per-hire, and source effectiveness. This data can help refine your recruitment strategies over time. Using external hiring software like HackeEarth can streamline the recruitment process, thus ensuring quality hires without having to indulge internal resources for the same.
Use Predictive Analytics: In this age of fast paced internet, everybody makes data-driven decisions. Using predictive analytics to study employee data will help companies predict future trends, thus facilitating a productive hiring process.
Conclusion
External sources of recruitment play a very important role in an organization’s talent acquisition strategy. By employing various channels of recruitment such as social media, employee referrals and campus recruitment drives, companies can effectively carry out their hiring processes. AI-based recruitment management systems also help in the process. Implementing best practices in external recruitment will enable organizations to enhance their hiring processes effectively while meeting their strategic goals.
The Progressive Pre-Employment Assessment is a crucial step in the hiring process, as it evaluates candidates through various dimensions including cognitive abilities, personality traits, and role-specific skills.
While employers and recruiters have this in the palm of their hand, candidates who master it will successfully navigate the assessment and have a higher chance of landing that dream job. But what does it entail in the first place?
Candidates can expect to undergo tests that assess verbal, numerical, and work style capabilities, as well as a personality assessment. Hence, understanding the structure and purpose of the Progressive Pre-Employment Assessment can give candidates a competitive edge. But before one tackles online tests, we must first dissect what this assessment is and what it consists of.
The evolution of pre-employment assessments
Pre-employment assessments have undergone significant changes over the decades, from rudimentary tests to sophisticated, modern evaluations. Let’s put the two side by side.
Traditional methods:
Initially, pre-employment assessments focused on basic skills and educational qualifications. These paper-based tests primarily assessed cognitive and verbal abilities, without any conclusions about the candidates’ output in very specific situations.
Modern techniques:
Today, online assessments are prevalent, evaluating a variety of dimensions, including cognitive skills, personality traits, and behavioral evaluations. These tools offer a more comprehensive view of a candidate's job performance potential, while, at the same time, saving precious time for both parties involved.
In today’s competitive job market, progressive pre-employment assessments play a crucial as they not only measure technical skills and knowledge but also provide insights into a candidate's ethical bias, cultural fit, and communication skills.
Likewise, assessment tests have evolved to include situational judgment tests and culture fit analyses, which are pivotal in assessing the suitability of a candidate for specific roles. And this isn’t just in terms of skillsets—they help in identifying candidates who align well with the company's values and working environment.
This is mainly for the tests’ ability to accurately gauge a candidate's interpersonal skills and emotional intelligence, which are essential for roles that require teamwork and client interactions.
What are progressive pre-employment assessments?
Progressive pre-employment assessments are structured evaluations designed to judge a candidate’s abilities and fit for a role at Progressive Insurance. Unlike traditional aptitude tests, these assessments encompass various elements such as cognitive abilities, situational judgments, and personality traits.
These tests typically include verbal and numerical reasoning sections, as well as work style assessments that gauge behavioral tendencies. Through this merger of multiple dimensions, Progressive seeks to understand not just the skills and knowledge of the candidate, but also their ethical perspectives and communication skills.
Components of a progressive assessment strategy
What sets progressive assessments apart? Well, as most employers just focus on the basic credentials and competencies, the comprehensive assessment strategy at Progressive includes several key components:
Cognitive evaluations: These tests measure candidates' logical reasoning and problem-solving capabilities through verbal, numerical, and abstract reasoning questions.
Personality assessments: These tests evaluate traits and tendencies to understand how a candidate might behave in various workplace scenarios. They aim to provide insight into their ethical bias and interpersonal skills.
Behavioral evaluations: These sections analyze how candidates might act in specific situations, ensuring a good cultural fit and alignment with Progressive's values.
Role-specific skills tests: These assessments focus on the specialized skills required for the position, ensuring the candidate has the necessary technical knowledge and expertise.
Implementing progressive assessments
Successful implementation of Progressive Assessments in the hiring process requires designing an effective assessment process and following best practices for administration. This ensures accuracy, better data security, and reliable decision-making. In particular, the implementation hinges on the feasibility of the original design.
Step 1 --- Designing the assessment process
Designing an effective Progressive Assessment involves understanding the specific needs of the role and the company's approach to hiring. Each test component — verbal, numerical, and work style — must align with the desired skills and personality traits for the role.
HR teams need to define clear objectives for each assessment section. This includes establishing what each part aims to evaluate, like the problem-solving or personality assessments. Incorporating legal and policy guidelines ensures the assessments are fair and non-discriminatory, which is crucial for avoiding legal issues.
Likewise, everaging online assessment tests provides flexibility and efficiency. These tests allow candidates to complete them remotely, easing logistics and scheduling concerns. Ensuring security is also essential, and implementing testing and other recruitment tools can help enhance data security and accuracy.
Step 2 --- Best practices for assessment administration
Administering assessments effectively revolves around consistency and fairness. Establish structured guidelines for the administration process to ensure each candidate undergoes the same conditions, promoting reliability. This includes standardizing the timing, environment, and instructions for all assessments.
Training HR representatives is vital. They should be well-versed in handling the assessments, from initial candidate interactions to evaluating the results. Regular training updates ensure the team remains knowledgeable about best practices and any new tools used in the assessment process.
Administering assessments also involves maintaining better data security and accuracy. This is achieved by utilizing secure online platforms and ensuring that only authorized personnel have access to sensitive data. Leveraging top API penetration testing tools is one approach to securing candidate data and preserving the integrity of the assessment process.
Implementing consistent feedback mechanisms for candidates can also improve the process. Providing insights on their performance helps candidates understand their strengths and areas for growth, which reflects positively on the company’s commitment to candidate experience.
Benefits of progressive assessments
Progressive assessments offer significant advantages in the hiring process, such as improving the accuracy of hiring decisions and enhancing the overall candidate experience. These benefits help companies find better-fitting candidates and reduce turnover rates.
1. Improved hiring accuracy
Progressive pre-employment assessments allow companies to evaluate candidates more comprehensively. By assessing personality traits, cognitive abilities, and ethical biases, employers can identify individuals who align with the company’s values and have the necessary skills for the job.
For example, personality assessments can pinpoint traits like empathy, communication, and problem-solving abilities. This helps employers select candidates who are not only qualified but also fit well within the team. Evaluating these qualities ensures that new hires can thrive in customer service roles where empathy and effective communication are crucial.
Moreover, using tools like the DDI Adaptive Reasoning Test helps to simulate real job tasks. This gives employers deeper insights into a candidate's capability to handle job-specific challenges. As a result, the company is more likely to experience lower turnover rates due to better candidate-job fit.
2. Enhanced candidate experience
A well-structured assessment process can significantly enhance the candidate experience. Clear instructions,fair testing procedures, and timely feedback create a positive impression of the company. Candidates appreciate transparency and feel valued when the process is designed with their experience in mind.
Implementing assessments that reflect actual job roles and responsibilities gives candidates a realistic preview of the job. This reduces later dissatisfaction and turnover. Additionally, personality assessments that highlight traits such as confidence and empathy provide a more engaging candidate experience.
Companies can also strengthen their employer brand by showcasing their commitment to a fair and comprehensive hiring process. Providing resources like practice tests helps candidates feel better prepared and less anxious about the assessment, leading to a more positive perception of the company.
Common pitfalls in progressive assessments
Candidates often struggle with the cognitive abilities section, which requires strong analytical skills and problem-solving capabilities. The situational judgment tests can also be tricky as they assess empathy, decision-making, and customer service scenarios. Personality assessments can pose challenges as well, especially for those unsure how to present their personality traits aligned with the job role.
A significant issue is also misinterpretation of the test's format and expectations. Many find it daunting to navigate through various sections, such as verbal, numerical, and work style assessments. Lastly, some candidates might overlook the legal nuances of personality assessments or document redaction protocols, leading to compliance issues.
Strategies to overcome challenges
To tackle cognitive abilities assessments, candidates should engage in consistent practice with sample questions and mock tests. This helps enhance their analytical and problem-solving skills. For situational judgment tests, it is essential to practice empathy and customer service scenarios to develop a better understanding of role-specific challenges.
In personality assessments, being honest while demonstrating relevant personality traits like being a team player is crucial. Seeking guidance from study materials such as Job Test Prep can provide a realistic testing environment.
Understanding legal considerations, such as those around document redaction, is important for compliance. Utilizing a document redaction SDK can ensure adherence to required policies. Familiarity with each section's format will aid in navigating the assessments confidently and effectively.
Trends and innovations in employee assessments
There is a growing emphasis on AI-powered assessments —these tools analyze vast amounts of data to predict a candidate's job performance, ensuring a more objective and efficient selection process.
Personality assessments are evolving to include metrics like empathy and communication skills, which are crucial for roles in customer service and other people-centric positions.
Additionally, gamified assessments, which make the evaluation process engaging, are gaining popularity. They not only assess problem-solving skills but also gauge how candidates perform under pressure.
Organizations can prepare for the future by integrating cutting-edge technologies into their hiring processes. Investing in training for evaluators to accurately interpret new assessment metrics is crucial. This involves
Moreover, companies should stay updated on legal requirements to maintain compliance and ensure fair assessment practices.
Encouraging candidates to focus on developing their personality traits, such as being team players and showing confidence, can also better prepare them for progressive assessments that look beyond technical skills.
The strategic value of progressive assessments
Progressive pre-employment assessments rigorously evaluate candidates on multiple fronts, including cognitive abilities, situational judgment, personality fit, and role-specific skills. This multifaceted approach not only helps in identifying the best match for specific roles but also reduces the risk of bad hires.
By investing in these assessments, companies can significantly enhance their recruitment processes. Consistent use of these tools leads to more informed decision-making, reducing turnover rates and ensuring employee retention.
Appropriate preparation and implementation of these assessments can streamline the hiring pipeline, saving time and resources. Furthermore, this approach bolsters team performance and aligns employee roles with their strengths, promoting a culture of efficiency and productivity. While Progressive is far from the only company using this approach, they’ve set a standard in terms of looking at candidates holistically and making sure they’re truly ready for the job.
Frequently Asked Questions
This section covers common inquiries related to the Progressive Pre-Employment Assessments, including differences from psychometric tests, benefits for small businesses, legal considerations, and the role of technology.
How do progressive assessments differ from psychometric testing?
Progressive assessments typically examine a candidate's ethical bias and personality traits. In contrast, psychometric tests focus on cognitive abilities and personality dimensions. The Progressive Pre-Employment Assessment includes verbal, numerical, and work style components, offering a broader evaluation spectrum.
Can small businesses benefit from implementing progressive assessment strategies?
Small businesses can gain significant advantages from adopting progressive assessment strategies. These assessments help identify candidates that align closely with the company’s values and culture, reducing turnover rates. Additionally, they provide insights into a candidate's ethical stance and work style, which are crucial for cohesive team dynamics.
What are the legal considerations when using pre-employment assessments?
Legal considerations include ensuring compliance with equal employment opportunity laws and avoiding discrimination based on race, gender, or disability. It is essential to validate the assessment tools and ensure they are scientifically proven to be fair. Companies must also maintain transparency about the purpose and usage of the assessments.
How can technology enhance the effectiveness of progressive assessments?
Technology can streamline the assessment process by allowing candidates to complete the tests remotely. Advanced analytics help in the accurate interpretation of results, ensuring a better match between the candidate and the job role. Many platforms offer practice tests that mirror the actual assessment, aiding in preparation and reducing test anxiety.
Our AI-enabled Smart Browser takes frequent snapshots via the webcam, throughout the assessment.
Consequently, it is impossible to copy-paste code or impersonate a candidate.The browser prevents the following
candidate actions and facilitates thorough monitoring of the assessment:
