You are reading the article Using Genius Hour Projects To Help Students Find Meaning updated in December 2023 on the website Daihoichemgio.com. We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested January 2024 Using Genius Hour Projects To Help Students Find Meaning
Teachers who guide middle and high school students to work on passion projects see several benefits, including greater learning gains.
Genius projects are back, and they’re better than ever. Students are searching for meaning, and genius projects are the perfect vehicle to show them how much they matter.
The term “genius projects” stems from the 20 percent time Google encourages their employees to take learning things that are of interest to them.
I found this past semester that the genius projects my students created rose to new heights of achievement. Last year, I saw more interest, more time commitment, and better results from my class than in the last 10. From the three boys who built the whole school in Minecraft to the two girls who recorded their first album single, genius projects created an electric environment in my classroom this past December.
Why Genius Projects? Why Now?
With the current mental health crisis affecting our children and the need to invest in the mental health and well-being of our students in a broad, comprehensive way, sensitively run genius projects are one way to help kids find the meaning they need.
Whether they like soccer or science, video games or volleyball, cooking or cutting up and telling jokes, anyone who works with students knows that their interests are as diverse as the clothes they wear on the weekend.
Unique personalization is nothing new. The great educator Booker T. Washington warned against one-size-fits-all teaching. In his autobiography, Up From Slavery, he wrote, “The temptation often is to run each individual through a certain educational mold regardless of the subject or the end to be accomplished.”
Personalization has always been a hallmark of successful genius projects. The results I’ve noticed reflect the studies that show that people who have created meaning in their lives are happier and more satisfied. Genius projects are in the unique category of projects whose very presence can change a life, unleash a talent upon the world, and change history.
4 Steps to Creating Genius Projects That Matter
Step 1: The verbal pitch. From the moment we start genius projects, we use the empowering language of leadership. The question is not “What does my teacher want me to do to get good grades?” but “What do I want to learn?” and “How can I solve an authentic problem?”
Students pitch their ideas verbally, and I listen for wonder and excitement to help guide them. I feel like Robert Frost, who said, “I’m not a teacher, but an awakener.” Negative environments are idea killers. A sneer, chuckle, or wrong word at the wrong time can kill good ideas, so I have to be fully engaged with students in conversation.
During her verbal pitch, one student said, “I wonder if I could record a song?” Her friend piped up, “I wonder if I could produce it?”
My answer? “Yes! Here’s what you’ll need to learn and do to make that happen; let’s do it.”
Another student said, “I made a Rube Goldberg machine before; I’d like to do that again.” Since I want students to level up, we made a device that was much more extensive, as they decided to light the Christmas tree in front of a high school assembly.
Step 2: The written pitch and project plan. Next, students write a pitch using a template to define their real-world problem. Additionally, they identify the software and expertise they will need to complete the project.
We use the traffic light metaphor throughout the pitch process, much like Hollywood producers. The green light means all is good, and they may go into “production.”
The yellow light means that some things need to change to gain approval. For example, students created a parent app that was too much to do in nine days. So, they redefined the scope of work to earn their green light.
While the red light means stop, I have never had to red-light a project. For example, one student wanted to build a tennis racket stringer. He learned that it would be time-consuming and expensive and asked to join the Minecraft team instead. Students give themselves the red light, not me.
Step 3: Project implementation. As students have goal clarity, engagement skyrockets. Psychologist Kari Eddington found that the areas of the brain that produce dopamine and the “urge to do something” are highly active when someone feels very motivated and action-oriented about a goal.
My primary job, at this point, is that of facilitator. Here are three examples: I found a room for music recording, got fast administrative approval for the Christmas tree lighting machine, and helped one student adjust the length of the book she was writing. I help every student figure out the next action step and constantly share new ideas for leveling up. I keep a list on the board where students request meetings, inquire about a training class, or ask for help.
Students have three self-selected checkpoints during implementation where they reflect and take photographs and videos.
Step 4: Audience presentation and reflection. Finally, students share their creations, perform, or present their learning to the class.
This past December, we celebrated. Not only did we celebrate the holidays, but we celebrated a new single, a special-effects triumph in Adobe After Effects, some fun videos, a new app, and more.
Most of all, my students began to collaborate, communicate, and, best of all, laugh. I have no doubt that some lives were changed this past December, including mine. Nowadays, genius projects teach us the most valuable thing we could ever give students—knowledge of their own unique gifts.
I’m not here to mark papers, I’m here to make a mark on students’ lives. May we never run so fast to do all the “things” that we miss the things that really make a difference. Genius projects are one of the difference makers, especially now.
You're reading Using Genius Hour Projects To Help Students Find Meaning
Before the start of the school year, my instructional leadership team meets to review several data sets thoroughly. We then discuss practices that all teachers should implement during classroom instruction. One of our required practices this past school year was teaching students how to utilize the claim-evidence-reasoning (CER) framework in all content areas and grade levels. CER is a writing strategy that promotes analytical thinking.
Our math instructional lead teacher, Mrs. Crusoe, saw this as an opportunity to have all math teachers participate in a professional development (PD) session focused on using CER when teaching students how to solve rigorous, multistep math problems. Ultimately, we wanted to ensure that all math teachers thoroughly understood each CER component because we believe that yields mathematically proficient students.
From my experience, educators tend to use the SOLVE or CUBES strategy for problem-solving. These methods are good mnemonics; however, the CER model has several benefits beyond assisting a student’s memory.
The CER model is particularly helpful when students are solving multistep math problems that require them to explain each step meticulously. The math PD session that my school facilitated to build teacher capacity focused on teaching students how to apply each part of CER and the eight standards for mathematical practice.
Twelve math teachers worked independently to align each component of CER with the standards and wrote a short report explaining their alignment. Then the math leaders met to develop an agreed-upon alignment to implement when teaching students how to use CER.
When creating the framework, we were intentional about using math vocabulary that the students were expected to know, as well as asking the students questions that would guide their critical thinking and assist them in leveraging the standards.
Collectively, the math department agreed that it was vital for the framework to counter students’ desire to opt out of doing word problems, write the infamous acronym IDK (“I don’t know”) when answering word problems, or rush through them and come up with an illogical answer.
When students apply this strategy, they can state their final answer, answer the question thoroughly, be precise, and communicate a claim that makes sense mathematically. When identifying the claim, students are strongly encouraged to employ Mathematical Practice 1: Make sense of problems and persevere in solving them, and Mathematical Practice 6: Attend to precision.
For the evidence component, we intentionally got students into the habit of showing their work in a method that best suited their problem-solving. Students may use words, numbers, graphs, symbols, data tables, or drawings when communicating their problem-solving.
When supporting their claim, students are encouraged to employ Mathematical Practice 2: Reason abstractly and quantitatively, Mathematical Practice 4: Model with mathematics, and Mathematical Practice 5: Use appropriate tools strategically.
For the reasoning component, we wanted students to justify their problem-solving method by explicitly communicating how the information given in the problem helped them decide upon a strategy to employ, the math skill(s) they learned that helped solve the problem, and the concepts they built on to solve the problem.
When conveying reasoning, students are encouraged to employ Mathematical Practice 3: Construct viable arguments and critique the reasoning of others, Mathematical Practice 7: Look for and make use of structure, and Mathematical Practice 8: Look for and express regularity in repeated reasoning.
Our CER framework can be implemented as is or tweaked based on your staff’s input and student needs. The critical thing to remember is that students will not master this skill overnight. Teachers will experience better student success if they allocate time daily to model the CER process when solving multi-step word problems.
To ensure that the process is student-led, it is strongly recommended that teachers allow students to use CER when working independently, in groups, and as a whole class. Ultimately, the end goal is for students to understand the CER framework so that they can apply it in real-world situations.
In addition to utilizing collaborative planning to facilitate PD on effective CER implementation, my math team monitors student CER work samples every quarter via a rubric. We engage in weekly discourse about pedagogical practices and student misconceptions about problem-solving.
The admin team continuously assesses student and teacher CER implementation through quarterly learning walks. Learning walk data is shared within 72 hours, and each teacher is afforded individualized feedback and action steps to implement.
As we close out this school year, my math department recognizes that we still have a lot of work to do. However, we are celebrating the fact that we have seen student gains in benchmark assessment scores, CER work samples, and student attitudes toward math. Going into the next school year, we expect even greater gains.
Sabrina Crusoe, Sayema Tareq, Adrienne Westlake, and Michelle Richardson contributed to this article.
If there is one sentence, which summarizes the essence of learning data science, it is this:
The best way to learn data science is to apply data science.
If you are a beginner, you improve tremendously with each new project you undertake. If you are an experienced data science professional, you already know what I am talking about.
If you think that the situation above applies to you – Don’t worry! you are just at the right place. This article will provide you a list of websites / resources from which you can use data to do your own (pet) projects or even create your own products.How can you use these sources?
There is no end to how you can use these data sources. The application and usage is only limited by your creativity and application.
The simplest way to use them is to create data stories and publishing them over web. This would not only improve your data and visualization skills, but also improve your structured thinking.
On the other hand, if you are thinking / working on a data based product, these datasets could add power to your product by providing additional / new input data.
So, go ahead, work on these projects and share them with the larger world to showcase your data prowess!
I have divided these sources in various sections to help you categorize data sources based on application. We start with simple, generic and easy to handle datasets and then move to huge / industry relevant datasets. We then provide links to dataset for specific purpose – Text Mining, Image classification, Recommendation engine etc. This should provide you a holistic list of data resources.Simple & Generic datasets to get you started
chúng tôi – This is the home of the U.S. Government’s open data. The site contains more than 190,000 data points at time of publishing. These datasets vary from data about climate, education, energy, Finance and many more areas.
chúng tôi – This is the home of the Indian Government’s open data. Find data by various industries, climate, health care etc. You can check out a few visualizations for inspiration here. Depending on your country of residence, you can also follow similar websites from a few other websites – check them out.
World Bank – The open data from the World bank. The platform provides several tools like Open Data Catalog, world development indices, education indices etc.
RBI – Data available from the Reserve Bank of India. This includes several metrics on money market operations, balance of payments, use of banking and several products. A must go to site, if you come from BFSI domain in India.
Five Thirty Eight Datasets – Here is a link to datasets used by Five Thirty Eight in their stories. Each dataset includes the data, a dictionary explaining the data and the link to the story carried out by Five Thirty Eight. If you want to learn how to create data stories, it can’t get better than this.Huge Datasets – things are getting serious now!
Amazon Web Services (AWS) datasets – Amazon provides a few big datasets, which can be used on their platform or on your local computers. You can also analyze the data in the cloud using EC2 and Hadoop via EMR. Popular datasets on Amazon include full Enron email dataset, Google Books n-grams, NASA NEX datasets, Million Songs dataset and many more. More information can be found here.
A few months back, Google Research Group released YouTube labeled dataset, which consists of 8 million YouTube video IDs and associated labels from 4800 visual entities. It comes with pre-computed, state-of-the-art vision features from billions of frames.Datasets for predictive modeling & machine learning:
UCI Machine Learning Repository – UCI Machine Learning Repository is clearly the most famous data repository. It is usually the first place to go, if you are looking for datasets related to machine learning repositories. The datasets include a diverse range of datasets from popular datasets like Iris and Titanic survival to recent contributions like that of Air Quality and GPS trajectories. The repository contains more than 350 datasets with labels like domain, purpose of the problem (Classification / Regression). You can use these filters to identify good datasets for your need.
Kaggle Kaggle has come up with a platform, where people can donate datasets and other community members can vote and run Kernel / scripts on them. They have more than 350 datasets in total – with more than 200 as Featured datasets. While some of the initial datasets were usually present at other places, I have seen a few interesting datasets on the platform, not present at other places. Along with new datasets, another benefit of the interface is that you can see scripts and questions from community members on the same interface.
Analytics Vidhya You can participate and download datasets from our practice problems and hackathon problems. The problem datasets are based on real-life industry problems and are relatively smaller as they are meant for 2 – 7 days hackathons. While practice problems are available to people always, the hackathon problems become unavailable after the hackathons. So, you need to participate on the hackathon to get access to the datasets.
Quandl Quandl provides financial, economic and alternative data from various sources through their website / API or direct integration with a few tools. Their datasets are classified as Open or Premium. You can access all the open datasets for Free, but you need to pay for the premium datasets. If you search, you still get good datasets on the platform. Eg. Stock Exchange data from India is available for free.
Past KDD Cups KDD Cup is the annual Data Mining and Knowledge Discovery competition organized by ACM Special Interest Group on Knowledge Discovery and Data Mining. Archives includes datasets and instructions. Winners are available for most years.
Driven Data Driven Data finds real-world challenges where data science can be used to create a positive social impact. They then run online modeling competitions for data scientists to develop the best models to solve them. If you are interested in use of data science for social good – this is the place to be.Image classification datasets
The MNIST Database – The most popular dataset for image recognition using hand-written digits. It includes 60,000 train examples and a test set of 10,000 examples. This serves as typically the first dataset to practice image recognition.
Chars74K – Here is the next level of evolution, if you have passed hand written digits. This dataset includes character recognition in natural images. The dataset contains 74,000 images and hence the name of the dataset.
Frontal Face Images If you have worked on previous 2 projects and are able to identify digits and characters, here is the next level of challenge in Image recognition – Frontal Face images. The images were collected by CMU & MIT and are arranged in four folders.
ImageNet Time to build something generic now. Image database organised according to the WordNet hierarchy (currently only the nouns). Each node of the hierarchy is depicted by hundreds of images. Currently, the collection has an average of over five hundred images per node (and increasing).Text Classification datasets
Twitter Sentiment Analysis The Twitter Sentiment Analysis Dataset contains 1,578,627 classified tweets, each row is marked as 1 for positive sentiment and 0 for negative sentiment. The data is in turn based on a Kaggle competition and analysis by Nick Sanders.Datasets for Recommendation Engine
MovieLens MovieLens is a web site that helps people find movies to watch. It has hundreds of thousands of registered users. They conduct online field experiments in MovieLens in the areas of automated content recommendation, recommendation interfaces, tagging-based recommenders. These datasets are available for download and can be used to create your own recommender systems.
Jester Datasets about online joke recommender systemWebsites which Curate list of datasets from various sources:
KDNuggets – The dataset page on KDNuggets has long been a reference point for people looking for datasets out there. A really comprehensive list, however some of the sources no longer provide the datasets. So, you will need to apply your own prudence on the datasets and the sources.
Awesome Public Datasets A GitHub repository with a comprehensive list of datasets categorized by domain. Datasets are classified neatly in various domains, which is very helpful. However, there is no description about the datasets on the repository itself – which could have made it very useful.
Reddit Datasets Subreddit Since this is a community driven forum, it might come across a bit messy (compared to previous 2 sources). However, you can sort datasets by popularity / votes to see the most popular ones. Also, it has some interesting datasets and discussions.End Notes
Looking forward to hearing from you.If you like what you just read & want to continue your analytics learning, subscribe to our emails, follow us on twitter or like our facebook page.
macOS comes with a security feature known as Gatekeeper, which can help prevent unwanted apps from launching on your Mac without your permission. It can also prevent potentially malicious apps from launching because it can be used to limit the kinds of apps that are allowed to open on your Mac.
In lieu of the recent Sparkle updater framework vulnerability having been uncovered in a variety of popular macOS apps, now is a great time to set up your Gatekeeper settings to prevent potential issues with malware on your Mac in the future.
In this tutorial, we’ll be showing you how Gatekeeper works and how you can configure it to keep your Mac just as secure as you want it to be.What is Gatekeeper?
Gatekeeper is a security system Apple launched with OS X Mountain Lion and OS X 10.7.5 Lion that is still present in macOS today. The feature allows you to limit the types of apps that are allowed to launch on your computer, preventing unwanted apps from launching on their own and also preventing malicious apps from baiting and switching on unsuspecting users.
Gatekeeper is configured from the Mac’s System Preferences app, and from there, users can manually configure what their security options will be. As you might expect, Apple programs macOS to be as secure as possible out of the box and leaves it in the responsibility of the user if they choose to tamper with the stock settings (unless you’re on OS X Lion – then it defaults to the weakest security setting possible).In what ways does Gatekeeper protect me?
As noted in an Apple online support document, Gatekeeper can filter apps based on their origin, preventing apps that aren’t from a secure origin from ever opening on your Mac in the first place. The system comes with three different filter options:
App Store: Enabling this option means that only apps downloaded from the Mac App Store will be allowed to be opened on your Mac.
This is the most secure option and prevents any software downloaded from the internet, whether intentionally or by accident, from being launched executed on your Mac.
App Store and identified developers: Choosing this option means that apps downloaded from the Mac App Store and apps downloaded from the internet that have a signed Apple Developer ID certificate included in them will be allowed to launch, but not rogue third-party software downloaded from the internet that hasn’t been created by a developer with a signed Apple Developer ID.
This is a good medium-strength option, but then, it’s open to potential risks because any third-party app using a third-party update method could be susceptible to man-in-the-middle attacks, as we learned from Sparkle, and it’s always possible some hacker could come up with a way to spoof an Apple Developer ID certificate and include it in a malicious app.How to configure Gatekeeper
So now that you understand the gist of what Gatekeeper is about and what it can do for you, let’s get into the nitty gritty of how to configure Gatekeeper with your favorite settings so you can use your Mac the way you want to.
There are a few steps you have to take to get to the Gatekeeper settings on your Mac, so just follow along with us using the steps below:
4) If your password was entered correctly, the Gatekeeper preferences section should unlock, allowing you to pick from:
App Store and identified developersSo… what now?
Depending on how you’ve configured Gatekeeper, your Mac may or may not let you open some apps you’ve downloaded from the internet.
If you’ve chosen a more secure option, such as “App Store” apps only, then when you attempt to launch an app downloaded from the internet instead of the Mac App Store, you will be greeted with an error message, such as the one below:
The reason we’re getting this message is because Gatekeeper was set to only allow Mac App Store apps to run, and we downloaded Cyberduck from the developer’s website in our web browser. macOS knows that, and since it wasn’t downloaded from the Mac App Store, it prevented the app from executing to protect us based on our settings.
Just imagine – what if that was a malicious app instead of Cyberduck (which we know isn’t malicious)? If that were the case, Gatekeeper would have just saved us from potentially messing up our computer.Gatekeeper isn’t perfect
Gatekeeper, as easy to use as it is, isn’t perfect. In fact, Symantec, a security research company known for their Norton antivirus software, notes on their blog that it has been possible for hackers to bypass Gatekeeper before, and it’s likely not the last time that a good hacker who knows what he or she is doing will be able to accomplish this.
For this reason, we want to emphasize that all Gatekeeper does is help improve the security of your system from malware. It’s not a full antivirus replacement, and it’s not going to replace common sense in those who need to practice safe internet downloading techniques. It’s a line of defense that can help you stay safe, but it’s not an impenetrable wall; all security systems have their weaknesses.Conclusion
If you are one of them wanting to start a career in deep learning, then you must read these top deep 10 learning projects
is a domain with diverse technologies such as tablets and computers that can learn based on programming and other data. Deep learning is emerging as a futuristic concept that can meet the requirements of people. When we take a look at the speech recognition technology and virtual assistants, they are run using
deep learning technologies
. If you are one of them wanting to start a career in deep learning, then you must read this article as this article features current ideas for your upcoming deep learning project. Here is the list of the top 10 deep learning projects to know about in 2023.Chatbots
Due to their skillful handling of a profusion of customer queries and messages without any issue, Chatbots play a significant role for industries. They are designed to lessen the customer service workload by automating the hefty part of the process. Nonetheless, chatbots execute this by utilizing their promising methods supported by technologies like machine learning, artificial intelligence, and deep learning. Therefore, creating a chatbot for your final deep learning project will be a great idea.Forest Fire Prediction
Creating a forest fire prediction system is one of the best deep learning projects and it will be another considerable utilization of the abilities provided by deep learning. Forest fire is an uncontrolled fire in a forest causing a hefty amount of damage to not only nature but the animal habitat, and human property as well. To control the chaotic nature of forest fires and even predict them, you can create a deep learning project utilizing k-means massing to comprehend major fire hotspots and their intensity.Digit Recognition System
This project involves developing a digit recognition system that can classify digits based on the set tenets. The project aims to create a recognition system that can classify digits ranging from 0 to 9 using a combination of shallow network and deep neural network and by implementing logistic regression. Softmax Regression or Multinomial Logistic Regression is the ideal choice for this project. Since this technique is a generalization of logistic regression, it is apt for multi-class classification, assuming that all the classes are mutually exclusive.Image Caption Generator Project in Python
This is one of the most interesting deep learning projects. It is easy for humans to describe what is in an image but for computers, an image is just a bunch of numbers that represent the color value of each pixel. This project utilizes deep learning methods where you implement a convolutional neural network (CNN) with a Recurrent Neural Network (LSTM) to build the image caption generator.Traffic Signs Recognition
Traffic signs and rules are crucial that every driver must obey to prevent accidents. To follow the rule, one must first understand what the traffic sign looks like. In the Traffic signs recognition project, you will learn how a program can identify the type of traffic sign by taking an image as input. For a final-year engineering student, it is one of the best deep learning projects to try.Credit Card Fraud Detection
With the increase in online transactions, credit card frauds have also increased. Banks are trying to handle this issue using deep learning techniques. In this deep learning project, you can use python to create a classification problem to detect credit card fraud by analyzing the previously available data.Customer Segmentation
This is one of the most popular deep learning projects every student should try. Before running any campaign companies create different groups of customers. Customer segmentation is a popular application of unsupervised learning. Using clustering, companies identify segments of customers to target the potential user base.Movie Recommendation System
In this deep learning project, you have to utilize R to perform a movie recommendation through technologies like Machine Learning and
. A recommendation system sends out suggestions to users through a filtering process based on other users’ preferences and browsing history. If A and B like Home Alone and B likes Mean Girls, it can be suggested to A – they might like it too. This keeps customers engaged with the platform.Visual tracking system
A visual tracking system is designed to track and locate moving object(s) in a given time frame via a camera. It is a handy tool that has numerous applications such as security and surveillance, medical imaging, augmented reality, traffic control, video editing and communication, and human-computer interaction.Drowsiness detection system
The Linux find command is one of the most important and handy commands in Linux systems. It can, as the name suggests, find files on your Linux PC based on pretty much whatever conditions and variables you set. You can find files by permissions, users, groups, file type, date, size and other possible criteria using the find command. Here we show you how to find a file in Linux using the find command.
The find command is available on most Linux distro by default, so you do not have to install a package for it.Find Files by Name in Current Directories
The most obvious way of searching for files is by name. To find a file by name in the current directory, run:
-namechúng tôi you want to find a file by name that contains both capital and small letters, run:
-inamechúng tôi you want to find a file in the root directory, prefix your search with sudo, which will give you all the permissions required to do so, and also the / symbol, which tells Linux to search in the root directory. Finally, the -print expression displays the directories of your search results. If you were looking for Gzip, you’d type:
If you want to find files under a specific directory like “/home,” run:
-namechúng tôi you want to find files with the “.txt” extension under the “/home” directory, run:
To find files whose name is “test.txt” under multiple directories like “/home” and “/opt”, run:
-namechúng tôi find hidden files in the “/home” directory, run:
To find a single file called “test.txt” and remove it, run:
To find all empty files under the “/opt” directory, run:
-emptyFind Directories Using Name
If you want to find all directories whose name is “testdir” under the “/home” directory, run:
To file all empty directories under “/home,” run:
-emptyFind Files with Certain Permissions
The find command can be used to find files with a specific permission using the perm option.
To find all files whose permissions are “777” in the “/home” directory, run:
To find all the files without permission “777,” run:
To find all read-only files, run:
To find all executable files, run:
To find all the sticky bit set files whose permissions are “553,” run:
To find all SUID set files, run:
To find all files whose permissions are “777” and change their permissions to “700,” run:
}; Find Files and Directories Based on Date and Time
To find all the files under “/opt” which were modified 20 days earlier, run:
To find all the files under “/opt” which were accessed twenty days earlier, run:
To find all the files under “/opt” which were modified more than 30 days earlier and less than 50 days after:
To find all the files under “/opt” which were changed in the last two hours, run:
-120Find Files and Directories Based on Size
To find all 10MB files under the “/home” directory, run:
To find all the files under the “/home” directory which are greater than 10MB and less than 50MB, run:
To find all “.mp4” files under the “/home” directory with more than 10MB and delete them using a single command, run:
As you can see, the find command is incredibly useful for administering a system, looking through directories to find files, and generally pruning the virtual directory tree in Linux. If you enjoyed this Linux article, make sure you check out some of our other Linux content, like how to use the scp command to securely transfer files, how to use nnn as a file manager in the terminal, and how to fix broken packages.
John is a young technical professional with a passion for educating users on the best ways to use their technology. He holds technical certifications covering topics ranging from computer hardware to cybersecurity to Linux system administration.
Subscribe to our newsletter!
Our latest tutorials delivered straight to your inbox
Sign up for all newsletters.
Update the detailed information about Using Genius Hour Projects To Help Students Find Meaning on the Daihoichemgio.com website. We hope the article's content will meet your needs, and we will regularly update the information to provide you with the fastest and most accurate information. Have a great day!