Proctorio is an automated, proctorless software that provides a scalable, cost-effective solution to validating student identities and activity during online exams.

Proctorio

Artificial Intelligence (AI) has been making waves in various industries, transforming the way we work, communicate, and solve problems. In education, AI tools are poised to revolutionize how educators evaluate student work. Traditional methods of grading, while effective, are time-consuming and subject to human bias. AI offers the promise of faster, more objective, and data-driven assessments. In this article, we will explore the potential impact of AI tools on evaluating student work, discussing their benefits, and challenges, and providing examples of AI-driven evaluation systems. The Current Landscape of Student Evaluation Before delving into the potential changes AI tools can bring, it's crucial to understand the current landscape of student evaluation. Traditionally, educators have relied on manual grading processes, which involve reviewing assignments, tests, and projects, assigning scores based on predefined criteria, and providing feedback. While this method has served its purpose for centuries, it has some inherent limitations. : Human graders may exhibit biases based on various factors such as gender, race, or even handwriting, leading to unequal evaluation. Subjectivity : Grading a large number of assignments can be time-consuming, diverting educators from other important tasks like lesson planning and student support. Time-Consuming : Different educators may interpret grading criteria differently, leading to inconsistencies in evaluations. Inconsistency : Manual grading does not efficiently capture data that can be used for personalized learning or to identify patterns and trends in student performance. Limited Data Utilization AI Tools in Education The integration of AI tools into the education sector presents several advantages that can significantly enhance the evaluation of student work. These advantages include: AI systems can automate the grading process, allowing educators to focus on more meaningful aspects of teaching. Efficiency: AI tools evaluate work based on predefined criteria, reducing the risk of bias in assessment. Objectivity: AI ensures that grading criteria are consistently applied to all students' work, ensuring fairness. Consistency: AI can collect and analyze data on student performance, helping educators tailor their teaching strategies to individual needs. Data Utilization: Examples of AI-Driven Evaluation Systems To better understand the impact of AI on student evaluation, let's explore some examples of AI-driven evaluation systems. Auto-Scoring for Multiple-Choice Questions: AI systems can efficiently score multiple-choice questions. For instance, platforms like Proctorio and Turnitin use AI to detect plagiarism and evaluate the originality of written assignments. These tools not only save educators time but also ensure the integrity of student work. Automated Essay Scoring: Tools like ETS's e-Rater and Pearson's Intelligent Essay Assessor employ AI algorithms to assess written essays. They analyze factors such as grammar, structure, vocabulary, and content relevance to assign scores. While they may not replace human grading entirely, they can speed up the process and provide an initial assessment for educators to review. Code Review and Programming Assignments: In computer science and programming courses, AI tools like GitHub's Copilot can assist in code reviews and grading. They analyze code quality, adherence to coding standards, and functionality, helping educators provide more precise feedback to students. Language Learning Apps: AI-powered language learning applications like Duolingo and Rosetta Stone track user progress and adapt the difficulty of exercises based on individual performance. These apps use AI to evaluate pronunciation, vocabulary retention, and grammar skills, providing personalized feedback. Adaptive Learning Platforms: Adaptive learning systems, such as Knewton and DreamBox, use AI to adjust the difficulty of learning materials based on a student's performance. They continuously collect data on how students interact with content and adapt the learning experience to optimize comprehension and retention. Challenges and Considerations While AI tools hold immense potential in transforming student evaluation, there are challenges and considerations that educators and institutions must address: AI relies on data, and the quality of that data is critical. Biased or incomplete data can lead to inaccurate evaluations and reinforce existing biases. Quality of Data: AI tools are better suited for assessing quantitative and objective aspects of student work. They may struggle with evaluating creativity, critical thinking, or complex problem-solving skills, which are often essential in education. Lack of Creativity and Critical Thinking Assessment: The use of AI in student evaluation raises ethical questions regarding privacy, data security, and the potential for algorithmic bias. Institutions must develop policies to address these concerns. Ethical Concerns: Educators need training to effectively integrate AI tools into their teaching practices and to understand the results generated by these systems. Educator Training: Implementing AI tools can be expensive, and not all educational institutions have the resources to adopt them. This could exacerbate educational inequalities. Cost and Access: Conclusion AI tools can potentially change how educators evaluate student work fundamentally. By automating and enhancing the grading process, AI can save time, reduce bias, and provide data-driven insights into student performance. However, the implementation of AI in education should be approached carefully, considering ethical concerns, , and the need for educator training. data quality As AI continues to advance, educators and institutions must strike a balance between the efficiency and objectivity AI offers and the nuanced aspects of education that require human judgment, such as creativity and critical thinking. By leveraging AI as a complementary tool in the educational ecosystem, educators can create a more personalized and efficient learning experience for students, ultimately preparing them better for the challenges of the 21st century. Do you have any questions related to this article? Leave a comment and ask your question, and I will do my best to answer it. Thanks for reading! Would you like to take a stab at answering some of these questions? The link for the template is , just start writing! Interested in what others had to say in their answers? Click . Interested in reading the content from all of our writing prompts? Click . HERE HERE HERE

Will AI Tools Change the Way Educators Evaluate Student Work?

When Kurt Wilson, a computer science student at the University of Central Florida, heard that his university was using a controversial online proctoring tool called Honorlock, he immediately wanted to learn more. The company, whose business has boomed during the pandemic, promises to ensure that remote students don’t cheat on exams through AI-powered software used by students that “monitors each student’s exam session and alerts a live, US-based test proctor if it detects any potential problems.” scan students’ faces to verify their identity, track specific phrases that their computer microphone captures, and even promises to search for and remove test questions that leak online. The software can One feature from Honorlock especially piqued Wilson’s interest. The company, according to its materials, provides a way to track cheating students through what Honorlock calls “seed sites” or others call “honeypots”—fake websites that remotely tattle on students who visit them during exams. Wilson pored over a patent for the software to learn more, finding example sites listed. By looking for common code and the same test questions over the past year, Wilson eventually turned up about a dozen honeypots apparently linked to Honorlock, five of which . are still operating “It kind of became an obsession at one point,” said Wilson, who hasn’t tracked the honeypots in some months but was at one point checking for them daily. The sites Wilson found are bare bones. They have names like “ ” and “ .” They’re largely a catalog of thousands of apparent test questions that are sometimes bizarrely specific. “In which part of the digestive system does chemical digestion begin?” . gradepack.com quizlookup.com one post asks requests using “VSEPR theory to predict the molecular geometry around the carbon atom in formaldehyde, H2CO.” A multiple-choice question Click on the “show answer” button below any of the questions and you won’t get help but will be rewarded with a digital chiming noise and no answer. But visitors to the sites are having detailed information about their mouse movements and even typing transmitted to an Honorlock server. In the patent, , the company explains that its sites can track visitor information like IP addresses as evidence that a student was looking up answers on a secondary device. recently flagged, along with an Honorlock honeypot site, by student media at Arizona State University When the pandemic led to shuttered schools, demand for services like Honorlock skyrocketed as educators worried about whether students would be able to easily find answers online using devices that instructors didn’t know about. In for its software, Honorlock says, “[S]tudents have access to more and more electronic devices and it’s becoming harder for instructors to preserve academic integrity.” its online materials But some experts in the ethics of education worry techniques like honeypot websites simply go too far. “I can sum up this activity in one word,” said Sarah Eaton, an associate professor at the University of Calgary who studies academic integrity. “Entrapment.” Capturing Students’ Data While several companies offer services that tap into students’ webcams to track them, setting up fake sites to catch potential cheaters appears to be an innovation—one that crosses an ethical line for some experts. Before, students searching online for answers may simply have turned up nothing, while now, a potentially incriminating website will be there to tempt them. Ceceilia Parnther, an associate professor at St. John’s University who has studied remote proctoring, said the situation is ironic: Students “are being set up” through honeypots, she said, in an attempt to detect academic integrity violations, a practice that’s itself ethically questionable. “The face-to-face comparison is a teacher walking around with the answer key and putting it on the corner of each desk and then penalizing students if they look over at it,” she said. Honorlock is not directly mentioned on the sites Wilson found. But the sites list the same test questions as each other and include terms of service pages that mention the email address “privacy@hlprivacy.com.” The network activity of the pages, reviewed by The Markup, also shows data being sent to an Honorlock.com server. To understand precisely what data the honeypot sites might be tracking, The Markup inspected the page’s source code and network activity using Firefox’s developer tools for each of the still-operating websites noted by Wilson. We found that the sites captured data on a visitor’s type of device, where that visitor’s mouse was on the page, what they entered into a search bar, and details on what they clicked. Honorlock didn’t respond to a request for comment on the sites or to answer other questions about its privacy practices. It’s not clear how many students have been caught by the company’s honeypots. Honorlock with colleges like the University of Florida and University of Maryland, among several others. The company that, fueled by pandemic-era demand, it had raised $25 million in venture funding, building on round of $11.5 million. touts partnerships announced last year a previous Universities around the United States have now signed lucrative contracts with Honorlock, paying for the service. hundreds of thousands of dollars per year Some educators, meanwhile, are using similar tactics on their own, spreading false or traceable test answers online to catch students who look up answers. In one case, reported on , a professor acknowledged that a mathematics TA there had attempted to catch students cheating by adding a “unique marker” to a wrong solution posted online. by student media at Princeton The solution included “a reference to a Theorem” that was irrelevant to the answer. The Princetonian reported that several students of cheating based on their response to the question. were accused Michael Hotchkiss, a spokesperson for Princeton, declined to comment. Rethinking Exams Apart from honeypots, proctoring software’s rapid ascent has also given rise to other privacy and ethics concerns. Some students and educators argue the software leads to an , and others have raised technical concerns, pointing to face-detection software that fails to recognize the faces . anxiety-inducing testing environment of darker-skinned students The fear of an all-seeing proctor can have brutal side effects. One woman while taking a remote bar exam but continued with the test, afraid of being flagged for cheating. went into labor For educators, the draw of software that promises to automatically track down cheaters is clear. But some experts argue that it isn’t a problem that can or should be fixed by advances in technology to surveil students but by a rethinking of how those students are tested in the first place. Pedagogy ethicists like Parnther say this kind of software is backfiring by creating an environment where students are, by default, under suspicion. That mindset itself facilitates cheating, she says, by subtly suggesting to students that they might as well cheat because teachers expect them to anyway. “Students see that there’s an environment where it’s automatically assumed that they are not to be trusted,” Parnther said. Eaton proposes that educators should consider a more radical rethinking of testing, one that doesn’t rely on surveilling students. Punishing students for using their devices fundamentally goes against how learning works in the age of the internet, Eaton says, and the cat-and-mouse game of sussing out possible cheaters isn’t working. Moving to a better system might mean shifting to more oral or open-book exams, for example, which still demonstrate proficiency without the specter of simply Googling answers. There will always be some level of cheating on exams, Parnther argues, but the costs of cracking down on students is now coming at the expense of their education. “While we can’t ensure that no students will ever cheat, we can make it a norm that most students are just trying to do their best work,” she said. Written by: Colin Lecher Additional reporting by Surya Mattu. Feature Photo by on Sonja Langford Unsplash Also published here

Are Students Being Entrapped by Fake Test Answer Sites?

¿Están siendo atrapados los estudiantes por sitios de respuestas de exámenes falsos?

Les étudiants sont-ils piégés par de faux sites de réponses aux tests ?

क्या फर्जी टेस्ट आंसर साइट्स के जाल में फंस रहे हैं छात्र?

学生は偽のテスト回答サイトに罠にかけられていますか?

Os alunos estão sendo enganados por sites falsos de respostas para testes?

Học sinh có bị lôi kéo bởi các trang web trả lời bài kiểm tra giả mạo không?

学生是否被假考试答案网站所困？

David Copperfield: Chapter 33 -  Blissful

Computer vision applications have become ubiquitous nowadays. It’s hard to think of a domain where the ability of computers to “see” what’s going on around them has not yet been leveraged. Delivering process automation and accuracy, computer vision technology is expected to have even greater momentum due to the COVID-19 pandemic as organizations have rushed to adopt automation on a larger scale. More nuanced use cases of computer vision in different industries are also predicted to emerge with the next evolutionary leaps in ; a field computer vision is a part of. Let’s look at the inroads already made by computer vision, its vast applications in different industries, and its benefits. artificial intelligence development What is computer vision, and how has it evolved? Computer vision (CV) is a that enables systems to interpret information from digital images and react to it with action or recommendations. The goal of computer vision technology is to emulate human vision for performing monotonous or complex visual tasks faster and even more efficiently. subset of AI , it all started with a lot of manual coding until machine learning progressed enough to allow developers to program smaller computer vision applications and apply statistical learning algorithms for specific CV-related tasks such as pattern recognition. With AI making major strides, ML algorithms have been largely replaced by deep learning or hybrid models that rely on neural networks to transform patterns into mathematical equations for information classification. After the launch of the first commercial software back in the 1970s, computer vision applications have evolved from enabling reading devices for the blind to transforming entire industries. Historically Some systems powered by computer vision have achieved 99% accuracy and can even surpass human performance (for instance, in ). diagnostic radiology The key drivers behind the surge in computer vision applications are: Spreading mobile technology that allows us to create and share billions of images a day Increasingly affordable computing power to process this massive amount of visual data New hardware A burst of deep learning algorithms What can computer vision do? — Major computer vision techniques With the growth in visual data and advances in computing power to process it, present-day computer vision applications rely on the following technology capabilities: to assign objects in a photograph or video to predefined classes. With binary classification, the system answers a simple question whether a particular object belongs to, say, a class of apples, tourist attractions, or cats. Object classification to locate an object in an image by enclosing it into a bounding box. Object localization to do both of the above to many objects in an image, assigning labels and localizing objects by drawing bounding boxes around them. Object detection to understand every pixel of an image and associate it with a class label (a car, a person, etc.) by creating object masks, with objects of the same class presented as a single entity. Semantic segmentation to do semantic segmentation and additionally identify different instances of the same class so that you don’t get just a one-color mask for three parked cars in a street view photo but label the vehicles with three different colors, identifying their boundaries. Instance segmentation Computer vision applications — Benefits and real-world examples Applications for computer vision have been expanding at a rapid pace over the last decade to reach a frantic level with the onset of the COVID-19 pandemic. Organizations now invest heavily in AI-driven solutions, from and to advanced manufacturing and government systems. Many are even concerned that AI technology is moving too fast for them, according to a retail software healthcare platforms survey of business leaders conducted by KPMG. While there is still an overall lack of AI regulation that comes with and bias risk, computer vision technology is a safer playing field due to its maturity. Given the investment speed and immediate benefits delivered by computer vision implementation, the worldwide market of computer vision solutions is to $19.1 billion from 2020 to 2027. AI explainability challenges projected to grow at a CAGR of 7.6% We’ve compiled a list of industries and use case examples to demonstrate how companies leverage advanced computer vision techniques to boost their results: Retail & Ecommerce Education Healthcare Fitness & Sports Precision Agriculture Manufacturing & Mining Cross-Industry Applications Computer vision applications in retail and eCommerce Benefits : Minimizing human interactions in stores to boost safety, deliver and lower labor costs digital shopping experience, Personalizing customer experience for increased engagement and more effective upselling and cross-selling strategies Leveraging next-gen in-store analytics to prevent stockouts, enhance store layout designs, and optimize staff scheduling Use cases: combine computer vision techniques with shelf sensors and deep learning to recognize shoppers, detect items they place into their carts, and automatically charge them on their accounts for items bought when they leave the store. Apart from the famous example, automated stores using sophisticated computer vision technology have also been launched by Chinese giants , , and . In the startup space, AI provider self-scanning scale solution can automatically identify fresh produce, allowing customers to check out without the need for barcodes or packaging. Automated checkouts and cashierless stores Amazon Go Lenovo JD Alibaba Tiliter’s powered by computer vision algorithms, can find the most efficient route to products on a customer’s in-app shopping list and recalculate the route if the person decides to look at other items. Currently, home improvement chain tests such an indoor mapping app developed with Google. Combining computer vision and the app reportedly locates users in an aisle more accurately than Wi-Fi or mobile phone systems while also providing access to product reviews. In-store navigation systems, Lowe’s augmented reality software, feature mobile barcode scanning software based on computer vision that recognizes objects for shoppers to get a personalized offer or information on the product in the store. Product information display apps Scandit’s that leverage deep learning algorithms make it more convenient for online shoppers to discover products, returning visually similar results. was among the first to offer its customers this engaging computer vision experience. Visual search solutions eBay are enabled by an array of computer vision-based systems. Among them are virtual mirrors that use augmented reality, allowing shoppers to try on various clothes virtually (like on site) or experiment with makeup products (like via app or the ). In-store virtual dressing room technology, embedded in Findmine’s solution, offers shoppers a touchscreen display with a CV-powered camera that recognizes items they are wearing to create an outfit based on images from the retailer’s catalog. Ecommerce sites are eyeing computer vision-enabled body scanning technology like the service to provide size or clothing recommendations. The skin care brand Neutrogena invites their customers to try the app that measures their skin health by assessing facial attributes and skin pixels and recommends a skin care routine. candy store uses facial recognition to identify regular shoppers for offering them personalized product recommendations or loyalty discounts. Personal recommendations Amazon’s Sephora’s Bourjois Magic Mirror Complete the Look Bodygram Skin360 The Lolli & Pops based on computer vision methods help retailers improve inventory management. Examples range from the technology that uses shelf-mounted computer vision cameras to alert staff about out-of-stock or incorrectly displayed items to the mobile robot that not only notifies shop assistants of gaps on shelves but also can detect damaged packaging and inaccurate pricing. Inspection systems Shelfie Tally use computer vision-powered cameras to analyze customer journeys and get product-related insights. Among them are famous pop-up store infused with capabilities to analyze dwell time, demographics, and other customer data, and Serbian fashion retailer store built in collaboration with Deloitte to gauge customer movement heatmaps. Connected stores Samsung’s Legend World Wide’s Computer vision applications in education Benefits: Understanding students’ learning behaviors to drive personalization and improve learning experiences Automating classroom monitoring to deter cheating in tests Assessing students’ papers to reduce the burden on educators Use cases: With computer vision-powered platforms, educators can measure students’ mood and behavior to capture signs of engagement or distraction both online and in a classroom. Solutions in this space include that uses facial recognition to analyze students’ emotional response to online content and the learning app that also reads facial expressions to detect frustration or boredom and adapt learning content. In addition, computer vision-driven insights help teachers regroup students into a more comfortable environment to improve learning. Student engagement detection and personalization. Emotuit Little Dragon has been made easy with computer vision-powered webcams that are used to identify students and flag cheating behaviors by tracking their postures or eye movements. Examples include with multi-factor authentication launched by the proctoring service and automated proctoring solutions like and . Attendance monitoring and automated online proctoring UAuto ProctorU Respondus Monitor Mettl is an area where computer vision is also expected to shine, as advanced algorithms are able to not only recognize responses written by students but also assist with their automatic evaluation. Handwritten character recognition Computer vision applications in healthcare Benefits: Improving patient identification to prevent wrong person procedures Delivering more accurate diagnosis through medical imaging analysis Providing assistance in surgery training and real-world surgeries for better outcomes Delivering rehabilitation assistance to patients Use cases: use computer vision-based cameras that help improve facial authentication of patients from check-in to discharge to prevent wrong person procedures. Patient identification systems assisted by computer vision is transforming radiology, helping practitioners interpret X-ray, CT scans, MRIs, and even microscopic images of cellular structures more accurately when diagnosing breast, brain, lung, or skin cancer. Computer vision applications in medical imaging also feature solutions to estimate a human pose in analyzing symptoms of neurological and neuropsychiatric disorders, monitor blood loss to optimize blood transfusions, diagnose eye conditions, and even detect COVID-19 (a deep convolutional neural network called has showed 90% accuracy in diagnosing COVID-19 based on chest X-ray images). Medical image analysis COVID-Net leverages computer vision technology to increase surgical precision. Apart from assistant surgical robot systems, there are solutions like that combine computer vision with ML and VR to create 3D visualizations for surgeons in the operating room. In the surgical training space, is a famous mobile simulator that provides a detailed guide to surgical procedures. Surgical simulation and assistance Proprio Vision Touch Surgery feature computer vision systems that are being developed to supervise exercise routines as part of at-home rehabilitation for rheumatoid arthritis, sports injury, brain injury, or stroke. Rehabilitation applications Computer vision applications in fitness and sports Benefits: Capturing performance data to aid coaches in training sessions and athletes in self-training Introducing advanced player or ball tracking methods to improve viewing experience or help referees in decision-making Collecting performance statistics for scouts, sports betting sites, and other industry professionals Use cases: powered by computer vision-enabled cameras detect and track moving players or balls in an array of games such as soccer, tennis, baseball or golf. Top examples include , designed by Sentio for soccer player tracking and analysis, and the optical tracking technology that gives football coaches a holistic view of matches. Computer vision-based systems are also used for improving shooting accuracy in basketball training ( ), help swimmers improve their techniques by collecting data from stroke rates to real-time velocity and turn times ( ), and even can take over in part the job of the umpire in professional tennis matches (the ball tracking solution). Tracking systems SentioScope SportVU 2.0 Noah System FINIS LaneVision Hawk-Eye that are based on computer vision techniques like pose estimation for motion analysis can help users improve their self-training, with , for instance, claiming that its fitness app powered by computer vision and ML can recognize yoga asanas with 95% accuracy. Such fitness solutions can also use markers as the intelligent fitness mirror does, unlocking data on user performance with the help of weight sensors to offer intelligent suggestions. Self-training solutions Zenia Carbon analyze the actions of players on the ice or field, producing meaningful insights either for building better game strategies or making smart decisions on players in the scouting market, or engaging viewers. Among famous examples are the system that collects raw data from video feeds to produce game models and the solution that can analyze any recorded basketball game for performance improvement and scouting insights. In-depth data analytics platforms Sportlogiq AutoSTATS Computer vision applications in precision agriculture Benefits: Identifying pests and weeds with greater accuracy to optimize the application of chemicals Monitoring crop development and the environment to maximize yields and produce better quality according to rising customer expectations Automating livestock management to prevent flock and herd losses and reduce the need for feet in the field Use cases: feature Blue River Technology’s y solution equipped with intelligent cameras that can distinguish between crops and weeds to apply herbicides to the right plants. Computer vision methods are also used for automatic identification and counting of flying insects and even for identifying apple diseases early. Pest and weed detection systems See & Spra are developed to detect the ripeness of fruit, including through color ratings of cherries in an outdoor environment or pick vegetables in a greenhouse using robots. Computer vision-aided monitoring solutions include , a 100% autonomous solar-powered drone system from XSun, that provides farmers with HD images capturing crop and soil conditions. AI solutions from Brazil’s can gauge information about the color, shape, and texture of crops. High-definition cameras from are equipped with sensors to help monitor soil moisture for yield prediction. Observation, harvesting, and prediction systems SolarXOne Cromai SWIR Vision Systems using drone technology can perform automatic counts, detect sick or injured animals, find strays, spot grazed areas, and even move cattle. A fair example is autonomous drone technology developed by Israeli firm to herd cattle. Livestock management systems BeeFree Agro Computer vision applications in manufacturing and mining Benefits: Implementing automated quality control to increase manufacturing accuracy, improve productivity, and produce better quality Deploying monitoring solutions to cut inspection time, minimize safety risks, improve operator productivity, and increase cost-efficiency Reduce human involvement to protect workers from hazardous environments Use cases: can be enabled by intelligent computer vision-powered cameras directed at a manufacturing line. Examples include machinery for the pharmaceutical industry to automatically count tablets or capsules on production lines and , a surface inspection system that identifies defects in items, stores images, and collects image-related metadata to classify errors by type and grade. Сomputer vision methods are also used to guide assembly operations, like assembly verification solutions from that measure product components versus manufacturing specifications, check caps and fill levels, and verify packaging components. Next-level quality control Pharma Packaging Systems’ WebSPECTOR Acquire Automation guided by machine vision automatically load or unload boxes and items to and from pallets. Robot palletizing systems assisted by computer vision technology and sensors have made it much easier to track the condition of critical infrastructure and determine when maintenance is needed. For example, FANUC’s takes photos and collects metadata to uncover any potential problems in the machine. Oil and gas giants such as Shell, ExxonMobil, and BP are using computer vision-powered predictive maintenance to anticipate failures in their equipment. Predictive maintenance systems Zero Down Time including drone-assisted systems, allow companies to conduct remote inspections of their sites and assets. This application of computer vision is especially important in mining, an unsafe industry for workers, where operators need to collect visual data in difficult areas. Visual inspections of well sites using the system, for instance, have enabled operators to reduce routine site visits by half. Intelligent monitoring solutions, Osprey Reach Computer vision in cross-industry applications systems using computer vision technology reduce inventory times from hours to minutes, delivering huge savings on operational costs. Such systems feature the computer vision-based platform that uses drones and connects to IoT devices for scanning and counting inventory. Another example is Amazon rolling out the robot technology at its sorting centers, claiming that the robot can improve sorting accuracy by 50%. Warehouse management Gather AI Pegasus enables a contactless delivery process for retail, logistics, post, and parcel businesses, transforming smartphones and other smart devices into computer vision-enabled barcode scanners like solutions from . The frictionless way to pick items from warehouses, distribution centers, or retail rooms is growing in popularity in the age of COVID-19. In retail, barcode scanning software is used for order fulfillment while allowing shoppers to safely collect items with one contactless mobile scan. Mobile computer vision Scandit using computer vision help keep public spaces safe during the pandemic by detecting ill employees or students as well as monitoring social distancing or exposure times. At construction sites and on the manufacturing floor, computer vision systems like monitor behavior-based safety and can protect people working around hazardous zones by alerting machine operators of dangerous events or incidents. Safety monitoring solutions IRIS What's next? It might be hard to believe, but we can uncover more computer vision applications and benefits with the advancement of technology such as edge computing, emotion AI, mixed reality, and embedded vision. And they can be quite incredible as artificial intelligence will get as sophisticated as we humans are. if you want to explore the benefits that computer vision can bring to your organization. Their AI experts will be happy to help you with complex or straightforward computer vision initiatives to address your specific business needs. Drop ITRex a line Previously published on https://itrexgroup.com/blog/computer-vision-applications-in-different-industries/

Computer Vision Applications are Everywhere: Top Use cases in 2021

Online learning has been there for years. Yet today, eLearning technology poses a real challenge to the traditional education models with more and more schools and universities turning to . Still, many educational institutions feel skeptical about adopting online learning tools. “A lack of in-person interaction sucks all the joy out of education” and “innovations in online education are too complex to handle” are the most commonly cited barriers halting the future of online and blended learning. artificial intelligence solutions for education The good news is that artificial intelligence can help overcome these barriers and transform eLearning as we know it. To prove that, we’ve summarized key online education trends giving a look over the applications of AI for eLearning and offering a blueprint for a successful AI adoption. So, let’s waste no time and dive right in. AI and eLearning: Top 4 trends in online education 1. Personalized learning With artificial intelligence in eLearning systems, educators can create personalized learning pathways and provide students with tailored content following their academic performance and learning goals. An AI engine may be integrated into a school’s LMS system, turning a simple data repository into a real-life version of the Hogwarts’ sorting hat. The only difference is that it puts students into learning buckets, like visual and aural learners or loners and team players. So, students who don’t easily succeed in a standard one-size-fits-all paradigm can, for example, take a survey that would identify their learning styles, preferred educational resources, and other performance-influencing factors. And the AI engine powering the survey would draft recommendations on how to adjust the curriculum and the instructions to make the student engage. Take, for example, the that is currently implemented across schools in Flanders, Belgium. Flemish educators leverage AI and online education to embrace personalized learning. They’ve partnered with a tech vendor to create a portal for deploying various EdTech apps for different domains, topics, proficiency levels, and learning profiles. The portal is to be rolled out in at least 10% of the Flemish schools by September 2022. i-Learn program 2. Skills-oriented learning Educators often think that online education lacks practice. But tapping in eLearning trends, schools can implement interactive online learning with a focus on building up vital skills. AI-powered chatbots, interactive mobile apps, and virtual assistants can help students master hard and soft skills, like speaking a foreign language, diagnosing a patient, or leveling up their communication and problem-solving skills. Students who are embarrassed to speak a foregin language, for example, can tame their anxiety conversing with AI-powered chatbots. The chatbots — now accessible through popular language learning apps like Duolingo — allow learners to go through life-like communicative scenarios and prepare for offline conversations. In turn, online assistants like (Social Agent Mediated Interaction) can help address a lack of social contact and emotional engagement for online students, informing them about interests and backgrounds they share with classmates. custom-made SAMI One of the hottest online education trends, Coursera-like online educational platforms are often used to enable skills-based learning. Colleges and universities, too, are and switching to fully online degrees based on similar eLearning platforms. In such cases, AI steps in to improve student retention and keep online learners engaged. AI technology in online education can monitor how consistently a student attends online lectures and submits assignments. And if the algorithms predict a possible drop-out, they give the student a push to go forward with learning. The experience of edX, for example, proves that a timely nudge accounts for a 30% higher student retention. increasingly adopting tech 3. Automated administrative tasks AI technology used in online education spares tutors and instructors the trouble of manually reviewing and grading assignments and monitoring students during online tests so instructors can have more time to develop quality eLearning content and interact with students. AI-powered reviewing and grading software can independently “read” and grade assignments building on the grades given by human instructors. After training, such software can bulk-check student papers with the precision of a human. Grading software has already in science, technology, engineering, and math classes, unbiasedly grading two times more assignments than a human professor during the same time. However, grading systems are more challenging to apply in social sciences and humanities. At least 21 states in the US to evaluate essays, from middle school to college level, yet AI algorithms powering the adopted software remain biased. For example, using advanced words tricks algorithms into thinking an essay is well-written when in fact, it doesn’t have any substance. The algorithms have also turned out to be biased against students from different nationalities and cultures, deeming their essays as poorly written. proved its effectiveness use technology When it comes to preventing cheating at online exams, AI-powered remote proctoring software can help. Educators may rely on proctoring solutions to automatically detect suspicious behaviors by recording students during tests via their computer’s camera or preventing students from switching browser tabs. An Educause poll that 54% of institutions are already using eLearning tech for proctoring, and another 23% are considering using it. However, some educational organizations failed to weigh out the ethics of remote proctoring, which caused severe resistance from students and parents. Still, there are institutions whose approach to remote proctoring can be named a best practice. Florida State University, for example, encrypts all of the data collected by proctoring software, and it is professors, not the AI algorithms, that make the final decision on whether a student cheated or not. suggests 4. Eliminating barriers International students or students with special needs benefit from artificial intelligence eLearning tools as well. Translation tools, for instance, can accurately translate a lecture into a student’s native language. Voice recognition and speech-to-text software can transcribe lectures. And referencing tools can help students grasp complex content, say, breaking down a lengthy reading assignment into a set of simple, easily digestible, and structured pieces. Many education institutions worldwide have already adopted assistive learning technology, including the University of Massachusetts and Sheffield University. Both schools use similar speech-to-text software that transcribes live speech and makes it easier for online students to digest educational content. Incorporating AI into eLearning systems: 4 steps towards success If you’re planning to enrich your eLearning software with AI capabilities, but don’t know where to start, here’s our high-level AI adoption plan you may rely on. Study what technology tools and software you already have to estimate the scope of work you’ll have to carry out when adopting AI for online education. Some eLearning solutions, for example, have built-in AI capabilities, which only need to be configured, and some require relatively simple customization. Other software solutions, in turn, can be enhanced via third-party or custom plugins. But occasionally, you might have to go for custom , giving your IT infrastructure a complete overhaul. 1. Conduct an inventory of your eLearning technology tools and systems. eLearning software development The key to AI’s successful performance is thorough training of AI algorithms on the large volumes of quality data. So, you’ll have to put effort into aggregating your educational data, including the information from your eLearning systems, a corporate website, social media, student surveys, and other sources. However, mind that additional data restoration and cleaning may be required to eliminate bias in many cases. 2. Collect your data. Identify how you can leverage AI to improve your online education process without causing critical disruption to your current operations while guaranteeing economic efficiency. Then, put down a specific use case, like task automation or curriculum personalization, mapping it to your objectives and thinking out general compliance and security factors. And if you think you lack the experience to do so, it might be better to turn to a consultant. 3. Elaborate on an AI use case. Create a high-level AI implementation strategy, putting down desired outcomes and KPIs, amending current operations to include AI, and choosing tools that will help you monitor both adoption progress and AI performance. 4. Design an AI adoption strategy. Final thoughts Artificial intelligence will continue to transform online learning, making it more enjoyable and results-driven. It could also help educators transition to new, more effective educational models with an emphasis on competencies, not grades. But to maximize the benefits of AI, both educators and EdTech providers will need to balance the AI’s impact and ethics, eliminating bias and constantly improving algorithm accuracy. If you think about tapping into the future of online learning and expanding your eLearning software with AI capabilities, drop us a line! Previously published at https://itrexgroup.com/blog/online-education-trends-will-ai-become-future-of-elearning/

4 Ways AI Can Improve Online Education

EC-Council’s Certified Ethical Hacker (CEH) is one of the most established and recognized offensive security certifications. The certification is ANSI-accredited and DoDD 8140-approved because of which the certification is highly valued by security professionals from both the public and private sectors. Since CEH is the gold standard for validating offensive security skills, the is around $1,199 and the cost to retake the exam is $450. This article will cover the various processes you have to go through to earn this certification as well as the associated costs. CEH certification cost CEH Training Costs During the application process, it is verified that you either took approved training or have at least two years of security experience. 1. CEH including Training: Average cost is $4,000 Base costs for CEH with EC-Council-approved training: CEH training: $850 to $2,999 CEH exam fee: $1,199 CEH remote proctoring: $100 The total cost varies from $2,149 to $4,298 2. CEH with Self-Paced Training: Average cost is $2,000 Base costs for the certification with self-paced training options and CEH assessments are as follows: CEH self-paced training (Duration: 6 months): $250 to $600 CEH exam fee: $1,199 CEH remote proctoring: $100 CEH application fee: $100 The total cost varies from $1,699 to $2,049 3. CEH Study Material Cost CEH study material can range from free YouTube videos to dedicated CEH certification boot camps, and their costs vary accordingly. While choosing a suitable study material, it is essential to look at the cost of CEH training in terms of value rather than price. The cheapest training is one which prepares you to pass the CEH on the first try. Additional EC-Council Training Tools - $149 For further training and guidance, you can purchase the EC-Council prep assessment for $149. This will help you evaluate your skills and focus on your weakness. Self-paced CEH training - $250 to $600 Self-paced training is an excellent option for experienced security professionals preparing for this certification. It will give you a quick brush up on the basics while allowing you to learn the more technical aspects on your own broadly. 3.Online CEH bootcamps - $850 to $2,999 This is the most comprehensive option while preparing for CEH. CEH boot camps emulate a classroom experience online, including trainer-led instruction, evaluation, supplemental material, and several hands-on lab activities. 4. Free Training Tools - $0 EC council certifications provide specific free training tools and resources as well as a free online assessment tool. This would involve answering fifty multiple-choice questions that are based on the material you’ll see on the 125-question CEH exam. Study material is the highest variable cost while preparing for this exam since the appropriate study material is completely based on your needs. The CEH application cost is $100. Ensure that you are eligible for applying to the exam per EC-Council’s prerequisites. Enrolling for CEH prep course is the most preferred method as it will walk you through the sections and modules that CEH evaluates. In case you decide to pursue any other CEH training, you are required to apply for a waiver that allows you to sit for the exam without meeting EC Council’s prerequisites. CEH Exam Cost The costs of the CEH exam vary between $950 and $1,119 depending on where and how you take it. Depending on where you choose to test online, CEH exam vouchers are divided at an authorized testing centre or at a Pearson VUE testing centre. EC-Council Testing online or at an EC-Council authorized testing centre, costs $950 whereas providing your own proctor will cost an additional $100. The test at a Pearson VUE testing centre will cost you $1,199. The most expensive scenario is if you fail the exam and need to retake it. A CEH retest voucher costs $499. For individuals who are completely prepared to take the exam, the exam can be attempted with the help of free study material and an EC-Council-approved proctor. This way, you will only need to pay for the self-study waiver and the exam, which would cost you $1,050.

How Much Does the CEH Exam Cost?

Ever since the use of papyrus in ancient times, the way people write has been a fascinating curiosity known as graphology or the study of physical characteristics and patterns of handwriting. The first book on handwriting analysis appeared in 1575 and was written by the renowned scholar Juan Huarte de San Juan. In modern times, how much does one reveal when typing on a keyboard or device? Quite a lot, it turns out. The fist of the Sender – ally or enemy? During World War II, military intelligence successfully identified the rhythm of to distinguish allies from enemies. Even in the mid-1800s, based on the typestyle, telegraph operators' identities could be revealed. morse code How we interact with devices divulges a series of unique patterns, which, when analyzed, can reveal our age, gender, personality, state of mind, IQ, and, in a nutshell, who we are—our identity. The technology that captures and analyzes typing patterns is known as typing biometrics. Over the last few years, typing biometrics has seen rapid growth and is quickly gaining momentum. The use cases and industries they apply to are endless. The popularity of this technology could be guaranteed by a faulty technology that might soon be obsolete, illegal, and replaced for good: facial recognition. Facial recognition technology will be obsolete soon Most smartphones today incorporate facial recognition, which is a great alternative to other biometrics such as fingerprints. Still, more and more people today see facial recognition as a faulty method to authenticate individuals and a very easy to use tool for mass surveillance. Facial recognition was first introduced to devices in 2005 by OMRON Corporation. The breakthrough method of authentication easily spread from personal devices to private and public organizations using it for various applications. One successful use case was proctoring online courses and exams. It didn't take long to notice the racial bias in facial recognition technology, with several studies conducted by governmental research or debunking the myth of secure facial recognition. A reliable, unbiased alternative was needed. universities In the worst case, the failure rate on darker female faces is over one in three for a task with a 50 percent chance of being correct. In the best case, one classifier achieves flawless performance on lighter males: 0 percent error rate. - The Gender Shades Project Another alarming toll of this technology is individuals' privacy concerns. More and more people around the globe criticize the technology because it can be used to infringe human rights or civil liberties and for mass surveillance. In June 2019, law enforcement agencies held the most substantial chunk of the facial recognition technology on the market. And according to a new bill—the Facial Recognition and Biometric Technology Moratorium Act, which was introduced to ban the use of facial recognition technology by federal law enforcement agencies—this will change. Also, as of June 2020, tech giants like Microsoft, Amazon, and IBM have decided to stop the research, use, and selling of facial recognition technology at least until stronger laws have been enacted to ensure safe deployment. The alternative: Typing biometrics In addition to physiological biometrics (e.g., fingerprint, facial identification, and retina scans), there are other metrics that measure human behavior characteristics called behavioral biometrics. Broadly, behavioral biometrics represent uniquely identifying and measurable patterns in individual interaction with devices. These behaviors are virtually impossible to imitate due to the innate nature of their characteristics. Powered by machine learning algorithms, typing biometrics technology is able to learn about a variety of pattern elements that are unique to the individual. The prevalence of typing biometrics in the future is also highlighted by the shift in modes of human interaction. According to research by the American Psychological Association, people favor typing over talking. , much of oral communication has transitioned to text-based communication, and now 75% of individuals under 30 prefer written communication over phone calls. Also, keyboards are widely available in any household and are present in most technologies today. Since 2014 Shifting from authentication to other uses cases Since facial recognition opens a playing ground for other biometrics in authentication, typing biometrics can do more and thus are intriguing due to their versatility and broad use cases. Mental health and well-being As previously noted, the technology can be used to analyze the way people are. The complexity of the human mind and body is yet to be understood. Still, the traces we leave when typing is quite revealing with respect to physical and mental traits. This leads to a more complex analysis of more than just who we are but also how we behave. Just just like our DNA can tell a lot about our ancestry, reveal health conditions, and identify personality indicators, our typing similarly says a lot about us, including gender or IQ. To illustrate, a new product called , which is currently in the research phase can analyze data based on how people type on their keyboards and reveal information about their state of mind. A fitness tracker for the mind, Focus provides statistics about when users are tired, focused, or stressed, and that data can then be analyzed to enhance productivity. TypingDNA Focus Focus aims to help users better understand how and when they focus and gain visibility and control over the stressful periods during a day. Additionally, users get insights based on their typing behavior and see weekly trends, a breakdown of daily activity, a window into when they’re most engaged, focus levels, immediate mood analysis, and more. This use case of typing biometrics is a natural response to the current state of individuals experiencing burnout, high anxiety, stress levels, and the inability to focus on tasks and get productive. According to the , stress over a long period leads to anxiety and depression, disorders associated with severe chronic diseases. Anxiety and Depression Association of America The fin-de-siècle neurasthenic, in whom exhaustion and innervation converge, uncannily anticipates the burnout of today. They have in common an overloaded and overstimulated nervous system. - Josh Cohen, psychoanalyst, and Professor of Modern Literary Theory at the Goldsmiths University of London. Reports from the World Health Organization (WHO) indicate depression will become a primary reason for disability worldwide by 2030, while more than 16% of the global population is already affected by it today. Medical disorders prevention and diagnostics Due to its innate nature, typing biometrics are virtually impossible to imitate, revealing each individual's unique typing pattern. Therefore, behavioral biometrics technology is suitable for medical research to address disease prevention. Recent medical advancements focus on analyzing any changes that occur in someone's typing pattern over time, which can sometimes indicate neurological system disorders, damage, or other transformations. Abnormal locomotions, movement patterns, or synergies have been described after CNS disorders, such as stroke or traumatic brain injury. The aim here is to help patients suffering from different diseases or disabilities to rehabilitate and, in the future, even detect various anomalies that can indicate a specific disorder, assisting medical professionals in diagnosing their patients more effectively. An alternative to privacy-intrusive surveillance technologies Over the past decade, data protection and privacy-enhancing online and offline tools have gained momentum, and various public institutions are addressing the ban of intrusive surveillance technologies. This approach may seem like a win for privacy and civil rights advocates who speak out against the use of facial recognition for mass surveillance. This sends an alarming global signal on the effects of the lack of regulation in facial biometrics and addresses the need for less privacy-invasive substitute technologies. Given this context, typing biometrics represents a viable authentication solution for mobile and desktop without putting civil liberties at risk. E-learning and student verification Suffice it to say that 2020 has ushered in challenges around innovation, digitalization, and adaptability. Governments have been required to take an integrated cross-sectoral approach to prevent and minimize the impact of COVID-19. According to UNESCO, the changing education imperative comes after 1.38 billion learners have been impacted by national school closures worldwide due to the coronavirus. The ability to verify a student's identity is a crucial part of an equitable and effective learning experience and process. Since most of the curriculum is currently online at all education stages, typing biometrics is a seamless, non-intrusive way to verify the course and exam participants' identities, allowing students to focus on learning—not the proctoring technology. Looking ahead, typing biometrics will be present in our daily lives And it will also be found in complex use cases developed today that will shape the future for good, influencing our lives and while helping people explore the unknown areas of who we are .

The Way you Type Tells A Lot About You and Your IQ

During the pandemic, e-learning technologies have been placed under the spotlight. The reason is that the EdTech industry became the key enabler of remote and distance learning. Thanks to applying technology in e-learning, thousands of students around the globe successfully completed the last semester. Chances are, they will continue studying remotely in autumn as well. Today, the public’s interest in EdTech, e-learning, and educational software is still high. Educators and instructors get acquainted with modern and debated notions, like , LMS, online course development, proctoring and assessment software. e-learning authoring tools In fact, Google searches for online courses skyrocketed in April 2020, then slightly dropped and remained at through July 2020. One of the most - online plagiarism checkers, such as Unicheck, see a rise in their popularity too. Read on to find out how using an online plagiarism detector can help educators during the fall semester. 80% vital e-learning technologies The Pros of Using an Online Plagiarism Checker in E-Learning Do you know why educators favor online plagiarism checker software? The reason is that it has so much to offer. Take a look at what you can do with these advanced tools. Cross-check papers through open-access and private databases We’re used to thinking that an online plagiarism checker for research papers isn’t very accurate. Well, that’s only true for tools that check works against websites alone. The best tool is the one that searches for similarities in the text in a wide range of sources. For instance, Unicheck has impeccable accuracy. Its real-time search covers different data sources. You can check documents for similarity against both the Internet and Unicheck institutional library - the tasks submitted by other students earlier. Also, you can check a paper against a university’s database if your local FTP server is connected to it. Altogether, Unicheck has access to more than 100 billion online web pages through open access and educational databases, as well as the web index. Integrate an online plagiarism checker into an LMS Remarkably, most online plagiarism checker tools support integration with popular LMSs (Learning Management Systems), such as Canvas, Moodle, Google Classroom, and others. Paired with any LMS, our software makes the teaching process a lot easier. Just imagine how convenient it is to automatically check for plagiarism right after each paper’s submission, and all within the LMS. What’s more, having completed the software setup, a teacher can choose different useful options within the LMS. Depending on the LMS, Unicheck allows educators to leave comments and feedback within the similarity report, and even check the students’ papers against each other. Enhance the learning experience Some may think that the main purpose of an online plagiarism checker for teachers is to penalize students. But that’s not true. One of the main goals is to help students learn from their mistakes. By exposing academic dishonesty, whether intentionally or not, teachers can encourage students to think for themselves and create original works. Also, you may gain insight into what some students have difficulties referencing. Knowing that, the instructor can teach the students how to reference, cite quotes, and use the required resources in assignments properly. On top of that, using an will help teachers show the meaning of respecting content created by others. It’s a must-have skill for students’ successful professional careers. online plagiarism detector As for Unicheck, it has remarkable features that can enhance students’ learning experience. - if a student's current paper contains parts from the previous papers within the same course, the system will exclude such self-plagiarism from the similarity score. It still notifies an instructor about which parts of the submission coincide, but won’t consider them plagiarized. Avoiding self-plagiarism - students can check the drafts of their papers before the final submission if an instructor enables the student pre-check option. Pre-check of drafts before final submission - the system will show all the in-text citations and references that need improvement. Indicating referencing and formatting mistakes Raise academic integrity Academic dishonesty has been one of the main problems of traditional learning for a long time. An International Centre for Academic Integrity survey showed that of surveyed high school students admitted to cheating in some way or form (copying homework answers, plagiarism, or cheating on a test). 95% With distance learning, maintaining academic integrity has become even harder. Also, during the quarantine, some academic institutions decided to adopt the . This makes a teacher consider the impact of the pandemic and stress on students while assessing their works. , an academic manager for student integrity at Coventry College, says that scholars use “no-detriment” as an excuse for cheating. “no-detriment” policy Dr. Irene Glendinning “My fear is that some students may have interpreted this as “anything goes, I’m not going to be penalized whatever I do so it is OK to cheat”. I’m hearing some messages from different universities that this might have happened.” An online plagiarism checker for students can help to expose academic dishonesty. Modern tools constantly improve their search algorithms and engines to make sure no form of cheating will go unnoticed. For example, uses sophisticated digital text modification algorithms and an AI algorithm that verifies authorship. Both technologies offer what many simple plagiarism checkers can’t - finding the inexact matches in the text and the discrepancies across several student’s works. The latter can help the teacher identify instances of contract cheating. Unicheck How to Use an Online Plagiarism Checker Effectively Now, take a look at how teachers can make the most out of plagiarism detection software. Remind students of the institution’s anti-plagiarism policy. Warn them about all the possible penalties and negative consequences. Explain the importance of academic integrity frequently, not just at the beginning of the course. In addition, you can even place the information about cheating in the course syllabus. Inform students. Fostering students’ originality can help you reduce the number of plagiarized papers. A way to do that is by developing assignments oriented towards critical thinking. Since those are extremely hard to plagiarize, students won’t risk trying. Encourage students to speak their own minds. Before using a plagiarism detection tool, take a moment to get acquainted with all its features. You can only use it effectively if you know the features and functions the checker offers. Also, take a look at how to interpret the similarity reports. Know your online plagiarism checker’s abilities. For those educators who developed an online course, running its content through plagiarism detection is a good idea. This way you’ll avoid any issues, such as copyright infringement. Also, you’ll become a paragon of academic integrity for your students. Check your e-learning course content for plagiarism. Conclusion All in all, an online plagiarism checker, such as Unicheck, is a powerful tool in times of e-learning. It helps teachers detect various popular types of cheating and academic dishonesty. Combined with an LMS it saves educators’ time. Most importantly, a thorough search and advanced algorithms make online plagiarism checker software a must-have for fighting academic dishonesty. So, when you get ready for the fall semester, don’t forget to add to your professional toolkit. an online plagiarism checker

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