*Not Offered on March 31
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This two-hour tour is for students who plan to attend SDSU on an F-1 or J-1 student visa. The tour includes a 45 minute personalized admissions information session followed by a one hour and fifteen minute campus tour. Now–April 30
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This two-hour tour for out-of-state prospective students includes a 45 minute interactive presentation and a one hour and fifteen minute walking tour of campus including a walk through the residential halls. July 15 – August 16, offered on Mondays, Wednesdays, and Fridays only
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the calendars at the bottom of your screen to select your preferred date and time. If you need any assistance, please email [email protected], call 213-740-6605, or chat with us below. School Group and Community Based Organizations can . | |
Planning an incoming class's orientation? Think outside the box...literally. Get students excited about their new campus by exploring the local community just outside it!
What do you think of when you hear campus tour ? We can bet CollegeHumor read your mind - and the minds of the other 5 million people who watched this skit, nodded, and laughed.
The campus tour can get a bit of a bad rap, but it really doesn't have to be that way . Campus tours are essential for getting students familiar with their surroundings and settling them in, but that doesn't mean they have to be basic or uninspired.
Fostering a sense of familiarity and belonging is so important, especially during (huge!) milestones like transitioning from highschools and hometowns to a totally new campus culture. Keep your tour fresh for your freshmen (sorry, couldn't resist) and send them on a wild GooseChase to get to know not just your campus, but its surrounding community.
Use these 25 Missions to get students familiar with the local infrastructure, businesses, and culture that surround your institution and will - sooner than they think! - make it feel like home.
If you're already using GooseChase to run experiences, you can click the button below to copy this scavenger hunt to your account .
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Want to create a fun, engaging game for your community? Take a look at our How It Works page to see how easy it is to get started on GooseChase!
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GooseChase is an online platform that helps organizers create and run digital scavenger hunt experiences for team building, learning, public engagement, or a variety of other events. Sign up and try creating a free recreational game, or contact us to learn more about our enterprise solutions!
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Dr. Priscilla Magrath and MPH student Lady Dorothy Elli from the Zuckerman College of Public Health visited our partner universities in Indonesia, the University of Indonesia and University Jenderal Soedirman, to build on our existing partnerships.
In May 2024, Dr. Priscilla Magrath and MPH student Lady Dorothy Elli, both from the Zuckerman College of Public Health at the University of Arizona, visited two of our partner universities in Indonesia, the Universitas Indonesia (UI) in Jakarta and the Universitas Jenderal Soedirman (UNSOED) in Central Java, to build on our existing partnerships. Both universities already have a Memorandum of Understanding (MOU) with the Zuckerman College of Public Health.
Dr. Magrath, a Medical Anthropologist and Senior Lecturer in the college who has worked extensively on public health in Indonesia, and Lady Dorothy Elli, an MPH student in Global Health, visited the two universities, gave public health presentations, and met with faculty, staff, and students. The visit served to strengthen the existing partnerships and helped expand collaboration with the University of Arizona on public health education and research.
Dr. Magrath and Ms. Elli were invited to give guest lectures at both universities. They developed a presentation titled Understanding Health Beliefs and Values for Improved Health Communication and delivered this presentation on three occasions: twice at UNSOED with public health students and nursing students, and once at UI with medical students. The presentation, which drew on case studies from Indonesia, generated considerable interest and excitement. Each of the three highly interactive presentations were attended by roughly 100 students and faculty both on Zoom and in person.
During the visits, faculty demonstrated a high level of interest in collaborating to expand opportunities in global health through student exchanges, visiting professorships, and collaborative research from both universities.
The visit also laid the foundation for Lady Dorothy Elli’s Global Health MPH internship project “Assessing levels of stigma towards TB among Health Care Workers: A key component in managing the tuberculosis burden in Indonesia,” an assessment that she has been conducting since May 2024. This assessment will fulfil Ms. Elli’s Applied Practice Experience (APE) requirement for her MPH degree program.
Ms. Elli visited a local health department (Dinas Kesehatan), a local non-profit working on TB (Mentari Sehat Indonesia or MSI), and a village community engagement activity (Pageraji village) all located in the Banyumas region of Indonesia in Central Java.
Dr. Magrath and Lady Dorothy Elli express their heartfelt gratitude for the warm welcome they received at the universities and communities they visited, and they eagerly anticipate future collaborations. The Zuckerman College of Public Health and our Global Health Institute thank the leadership and faculty at both universities for their hospitality and continued partnership to advance our common global health goals. Together, we are building a healthier world!
UNSOED Public Health students lecture > UNSOED Nursing students lecture >
Universitas Indonesia: Residents and lectures of the Community Medicine Department FMUI, who attended in-person.
UNSOED guest lecture for public health students with (in front, right to left): Members of the guest lecture committee and Dr. Siwi Wijayanti, Lady Elli, Dr. Priscilla Magrath, and Prof.Dr.sc.hum. Budi Aji, Dean of the faculty of health sciences.
UNSOED: guest lecture for nursing students: Lady Elli presenting case study of TB from Philippines.
UNSOED: guest lecture for nursing students: selected student participants.
UNSOED visit to local health department, Dinas Kesehatan Kabupaten Banyumas. Head of Communicable Disease Control and Prevention, Arif Burhanudin (4th from left), with his staff, Dr. Priscilla Magrath, and Lady Elli.
UNSOED community service visit to promote breastfeeding in Pageraji village, Cilongok, Banyumas. The photo shows participants, pregnant women, village head Sutono, SE, lecturers Siwi Pramatama, Leni Maryati, Dr. Arih Diyaning Intiasari, and Public Health students.
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March 14, 2023
Illustration: Ruby Chen
We’ve created GPT-4, the latest milestone in OpenAI’s effort in scaling up deep learning. GPT-4 is a large multimodal model (accepting image and text inputs, emitting text outputs) that, while less capable than humans in many real-world scenarios, exhibits human-level performance on various professional and academic benchmarks. For example, it passes a simulated bar exam with a score around the top 10% of test takers; in contrast, GPT-3.5’s score was around the bottom 10%. We’ve spent 6 months iteratively aligning GPT-4 using lessons from our adversarial testing program as well as ChatGPT, resulting in our best-ever results (though far from perfect) on factuality, steerability, and refusing to go outside of guardrails.
Over the past two years, we rebuilt our entire deep learning stack and, together with Azure, co-designed a supercomputer from the ground up for our workload. A year ago, we trained GPT-3.5 as a first “test run” of the system. We found and fixed some bugs and improved our theoretical foundations. As a result, our GPT-4 training run was (for us at least!) unprecedentedly stable, becoming our first large model whose training performance we were able to accurately predict ahead of time. As we continue to focus on reliable scaling, we aim to hone our methodology to help us predict and prepare for future capabilities increasingly far in advance—something we view as critical for safety.
We are releasing GPT-4’s text input capability via ChatGPT and the API (with a waitlist ). To prepare the image input capability for wider availability, we’re collaborating closely with a single partner (opens in a new window) to start. We’re also open-sourcing OpenAI Evals (opens in a new window) , our framework for automated evaluation of AI model performance, to allow anyone to report shortcomings in our models to help guide further improvements.
In a casual conversation, the distinction between GPT-3.5 and GPT-4 can be subtle. The difference comes out when the complexity of the task reaches a sufficient threshold—GPT-4 is more reliable, creative, and able to handle much more nuanced instructions than GPT-3.5.
To understand the difference between the two models, we tested on a variety of benchmarks, including simulating exams that were originally designed for humans. We proceeded by using the most recent publicly-available tests (in the case of the Olympiads and AP free response questions) or by purchasing 2022–2023 editions of practice exams. We did no specific training for these exams. A minority of the problems in the exams were seen by the model during training, but we believe the results to be representative—see our technical report (opens in a new window) for details.
internal reference 1
We also evaluated GPT-4 on traditional benchmarks designed for machine learning models. GPT-4 considerably outperforms existing large language models, alongside most state-of-the-art (SOTA) models which may include benchmark-specific crafting or additional training protocols:
Many existing ML benchmarks are written in English. To get an initial sense of capability in other languages, we translated the MMLU benchmark—a suite of 14,000 multiple-choice problems spanning 57 subjects—into a variety of languages using Azure Translate (see Appendix ). In the 24 of 26 languages tested, GPT-4 outperforms the English-language performance of GPT-3.5 and other LLMs (Chinchilla, PaLM), including for low-resource languages such as Latvian, Welsh, and Swahili:
We’ve also been using GPT-4 internally, with great impact on functions like support, sales, content moderation, and programming. We also are using it to assist humans in evaluating AI outputs, starting the second phase in our alignment strategy .
GPT-4 can accept a prompt of text and images, which—parallel to the text-only setting—lets the user specify any vision or language task. Specifically, it generates text outputs (natural language, code, etc.) given inputs consisting of interspersed text and images. Over a range of domains—including documents with text and photographs, diagrams, or screenshots—GPT-4 exhibits similar capabilities as it does on text-only inputs. Furthermore, it can be augmented with test-time techniques that were developed for text-only language models, including few-shot and chain-of-thought (opens in a new window) prompting. Image inputs are still a research preview and not publicly available.
We preview GPT-4’s performance by evaluating it on a narrow suite of standard academic vision benchmarks. However, these numbers do not fully represent the extent of its capabilities as we are constantly discovering new and exciting tasks that the model is able to tackle. We plan to release further analyses and evaluation numbers as well as thorough investigation of the effect of test-time techniques soon.
internal footnote A
We’ve been working on each aspect of the plan outlined in our post about defining the behavior of AIs , including steerability. Rather than the classic ChatGPT personality with a fixed verbosity, tone, and style, developers (and soon ChatGPT users) can now prescribe their AI’s style and task by describing those directions in the “system” message. System messages allow API users to significantly customize their users’ experience within bounds (opens in a new window) . We will keep making improvements here (and particularly know that system messages are the easiest way to “jailbreak” the current model, i.e., the adherence to the bounds is not perfect), but we encourage you to try it out and let us know what you think.
Despite its capabilities, GPT-4 has similar limitations as earlier GPT models. Most importantly, it still is not fully reliable (it “hallucinates” facts and makes reasoning errors). Great care should be taken when using language model outputs, particularly in high-stakes contexts, with the exact protocol (such as human review, grounding with additional context, or avoiding high-stakes uses altogether) matching the needs of a specific use-case.
While still a real issue, GPT-4 significantly reduces hallucinations relative to previous models (which have themselves been improving with each iteration). GPT-4 scores 40% higher than our latest GPT-3.5 on our internal adversarial factuality evaluations:
We have made progress on external benchmarks like TruthfulQA, which tests the model’s ability to separate fact from an adversarially-selected set of incorrect statements. These questions are paired with factually incorrect answers that are statistically appealing.
The GPT-4 base model is only slightly better at this task than GPT-3.5; however, after RLHF post-training (applying the same process we used with GPT-3.5 ) there is a large gap. Examining some examples below, GPT-4 resists selecting common sayings (you can’t teach an old dog new tricks), however it still can miss subtle details (Elvis Presley was not the son of an actor).
The model can have various biases in its outputs—we have made progress on these but there’s still more to do. Per our recent blog post , we aim to make AI systems we build have reasonable default behaviors that reflect a wide swathe of users’ values, allow those systems to be customized within broad bounds, and get public input on what those bounds should be.
GPT-4 generally lacks knowledge of events that have occurred after the vast majority of its data cuts off (September 2021), and does not learn from its experience. It can sometimes make simple reasoning errors which do not seem to comport with competence across so many domains, or be overly gullible in accepting obvious false statements from a user. And sometimes it can fail at hard problems the same way humans do, such as introducing security vulnerabilities into code it produces.
GPT-4 can also be confidently wrong in its predictions, not taking care to double-check work when it’s likely to make a mistake. Interestingly, the base pre-trained model is highly calibrated (its predicted confidence in an answer generally matches the probability of being correct). However, through our current post-training process, the calibration is reduced.
We’ve been iterating on GPT-4 to make it safer and more aligned from the beginning of training, with efforts including selection and filtering of the pretraining data, evaluations and expert engagement, model safety improvements, and monitoring and enforcement.
GPT-4 poses similar risks as previous models, such as generating harmful advice, buggy code, or inaccurate information. However, the additional capabilities of GPT-4 lead to new risk surfaces. To understand the extent of these risks, we engaged over 50 experts from domains such as AI alignment risks, cybersecurity, biorisk, trust and safety, and international security to adversarially test the model. Their findings specifically enabled us to test model behavior in high-risk areas which require expertise to evaluate. Feedback and data from these experts fed into our mitigations and improvements for the model; for example, we’ve collected additional data to improve GPT-4’s ability to refuse requests on how to synthesize dangerous chemicals.
GPT-4 incorporates an additional safety reward signal during RLHF training to reduce harmful outputs (as defined by our usage guidelines (opens in a new window) ) by training the model to refuse requests for such content. The reward is provided by a GPT-4 zero-shot classifier judging safety boundaries and completion style on safety-related prompts. To prevent the model from refusing valid requests, we collect a diverse dataset from various sources (e.g., labeled production data, human red-teaming, model-generated prompts) and apply the safety reward signal (with a positive or negative value) on both allowed and disallowed categories.
Our mitigations have significantly improved many of GPT-4’s safety properties compared to GPT-3.5. We’ve decreased the model’s tendency to respond to requests for disallowed content by 82% compared to GPT-3.5, and GPT-4 responds to sensitive requests (e.g., medical advice and self-harm) in accordance with our policies 29% more often.
Overall, our model-level interventions increase the difficulty of eliciting bad behavior but doing so is still possible. Additionally, there still exist “jailbreaks” to generate content which violate our usage guidelines . As the “risk per token” of AI systems increases, it will become critical to achieve extremely high degrees of reliability in these interventions; for now it’s important to complement these limitations with deployment-time safety techniques like monitoring for abuse.
GPT-4 and successor models have the potential to significantly influence society in both beneficial and harmful ways. We are collaborating with external researchers to improve how we understand and assess potential impacts, as well as to build evaluations for dangerous capabilities that may emerge in future systems. We will soon share more of our thinking on the potential social and economic impacts of GPT-4 and other AI systems.
Like previous GPT models, the GPT-4 base model was trained to predict the next word in a document, and was trained using publicly available data (such as internet data) as well as data we’ve licensed. The data is a web-scale corpus of data including correct and incorrect solutions to math problems, weak and strong reasoning, self-contradictory and consistent statements, and representing a great variety of ideologies and ideas.
So when prompted with a question, the base model can respond in a wide variety of ways that might be far from a user’s intent. To align it with the user’s intent within guardrails, we fine-tune the model’s behavior using reinforcement learning with human feedback ( RLHF ).
Note that the model’s capabilities seem to come primarily from the pre-training process—RLHF does not improve exam performance (without active effort, it actually degrades it). But steering of the model comes from the post-training process—the base model requires prompt engineering to even know that it should answer the questions.
A large focus of the GPT-4 project has been building a deep learning stack that scales predictably. The primary reason is that, for very large training runs like GPT-4, it is not feasible to do extensive model-specific tuning. We developed infrastructure and optimization that have very predictable behavior across multiple scales. To verify this scalability, we accurately predicted in advance GPT-4’s final loss on our internal codebase (not part of the training set) by extrapolating from models trained using the same methodology but using 10,000x less compute:
Now that we can accurately predict the metric we optimize during training (loss), we’re starting to develop methodology to predict more interpretable metrics. For example, we successfully predicted the pass rate on a subset of the HumanEval (opens in a new window) dataset, extrapolating from models with 1,000x less compute:
Some capabilities are still hard to predict. For example, the Inverse Scaling Prize was a competition to find a metric that gets worse as model compute increases, and hindsight neglect (opens in a new window) was one of the winners. Just like with another recent result, (opens in a new window) GPT-4 reverses the trend:
We believe that accurately predicting future machine learning capabilities is an important part of safety that doesn’t get nearly enough attention relative to its potential impact (though we’ve been encouraged by efforts across several institutions). We are scaling up our efforts to develop methods that provide society with better guidance about what to expect from future systems, and we hope this becomes a common goal in the field.
We’re open-sourcing OpenAI Evals (opens in a new window) , our software framework for creating and running benchmarks for evaluating models like GPT-4, while inspecting their performance sample by sample. We use Evals to guide development of our models (both identifying shortcomings and preventing regressions), and our users can apply it for tracking performance across model versions (which will now be coming out regularly) and evolving product integrations. For example, Stripe has used Evals to complement their human evaluations to measure the accuracy of their GPT-powered documentation tool.
Because the code is all open-source, Evals supports writing new classes to implement custom evaluation logic (opens in a new window) . In our own experience, however, many benchmarks follow one of a few “templates,” so we have also included the templates (opens in a new window) that have been most useful internally (including a template for “model-graded evals”—we’ve found that GPT-4 is surprisingly capable of checking its own work). Generally the most effective way to build a new eval (opens in a new window) will be to instantiate one of these templates along with providing data. We’re excited to see what others can build with these templates and with Evals more generally.
We are hoping Evals becomes a vehicle to share and crowdsource benchmarks, representing a maximally wide set of failure modes and difficult tasks. As an example to follow, we’ve created a logic puzzles (opens in a new window) eval which contains ten prompts where GPT-4 fails. Evals is also compatible with implementing existing benchmarks; we’ve included several notebooks (opens in a new window) implementing academic benchmarks and a few variations of integrating (small subsets of) CoQA (opens in a new window) as an example.
We invite everyone to use Evals to test our models and submit the most interesting examples. We believe that Evals will be an integral part of the process for using and building on top of our models, and we welcome direct contributions, questions, and feedback (opens in a new window) .
ChatGPT Plus subscribers will get GPT-4 access on chatgpt.com (opens in a new window) with a usage cap. We will adjust the exact usage cap depending on demand and system performance in practice, but we expect to be severely capacity constrained (though we will scale up and optimize over upcoming months).
Depending on the traffic patterns we see, we may introduce a new subscription level for higher-volume GPT-4 usage; we also hope at some point to offer some amount of free GPT-4 queries so those without a subscription can try it too.
To get access to the GPT-4 API (which uses the same ChatCompletions API (opens in a new window) as gpt-3.5-turbo), please sign up for our waitlist . We will start inviting some developers today, and scale up gradually to balance capacity with demand. If you are a researcher studying the societal impact of AI or AI alignment issues, you can also apply for subsidized access via our Researcher Access Program .
Once you have access, you can make text-only requests to the gpt-4 model (image inputs are still in limited alpha), which we will automatically update to our recommended stable model as we make new versions over time (you can pin the current version by calling gpt-4-0314, which we’ll support until June 14). Pricing is $0.03 per 1k prompt tokens and $0.06 per 1k completion tokens. Default rate limits are 40k tokens per minute and 200 requests per minute.
gpt-4 has a context length of 8,192 tokens. We are also providing limited access to our 32,768–context (about 50 pages of text) version, gpt-4-32k, which will also be updated automatically over time (current version gpt-4-32k-0314, also supported until June 14). Pricing is $0.06 per 1K prompt tokens and $0.12 per 1k completion tokens. We are still improving model quality for long context and would love feedback on how it performs for your use-case. We are processing requests for the 8K and 32K engines at different rates based on capacity, so you may receive access to them at different times.
We look forward to GPT-4 becoming a valuable tool in improving people’s lives by powering many applications. There’s still a lot of work to do, and we look forward to improving this model through the collective efforts of the community building on top of, exploring, and contributing to the model.
For more: Read paper (opens in a new window) / View system card (opens in a new window) / Try on ChatGPT Plus (opens in a new window) / Try in Playground (opens in a new window) / Rewatch demo livestream (opens in a new window) / Contribute to OpenAI Evals (opens in a new window)
Example of MMLU questions, translated into other languages. Note, we use consistent choice tokens (A–D):
We evaluate this benchmark using Chain-Of-Thought prompting with 4 examples from the training set in-context. The specific prompt was tuned on the validation set.
P. Arredondo (Casetext/Stanford CodeX), D. Katz (Stanford CodeX), M. Bommarito (Stanford CodeX), S. Gao (Casetext). Further analysis is available in the paper (opens in a new window) .
View GPT-4 contributions
Remember: Less is more.
A strong presentation is so much more than information pasted onto a series of slides with fancy backgrounds. Whether you’re pitching an idea, reporting market research, or sharing something else, a great presentation can give you a competitive advantage, and be a powerful tool when aiming to persuade, educate, or inspire others. Here are some unique elements that make a presentation stand out.
As an intern or early career professional, chances are that you’ll be tasked with making or giving a presentation in the near future. Whether you’re pitching an idea, reporting market research, or sharing something else, a great presentation can give you a competitive advantage, and be a powerful tool when aiming to persuade, educate, or inspire others.
Computer science for everyone — benefitting all..
At Khoury College of Computer Sciences, we believe computer science is for everyone — with the power to make the world a better place . That’s why we design flexible programs, grounded in rigor, opportunity, and experience. It’s why we collaborate with industry and community partners on the technologies that shape our collective future. And it’s why we seek curious thinkers with diverse backgrounds, because multiple perspectives lead to life-changing discoveries. Ready to call Khoury home?
We embrace and empower curious problem solvers from diverse backgrounds — because multiple perspectives lead to the innovative solutions we need for society’s biggest challenges.
We’re known for flexible, innovative programs designed to help every student reach their unique goals — through industry co-ops, interdisciplinary programs, and research opportunities..
Every day, our leading researchers and talented students collaborate in world-class facilities to make new discoveries and create equitable opportunities across industry and society.
With locations in technology hubs around the world, including our main campus in the heart of Boston, our students and faculty work across a global network — living and learning where the action is.
We partner with more than 800 industry and community leaders around the world — empowering our students and faculty to make meaningful contributions, solve complex problems, and learn through hands-on experience.
Our world-class faculty, leading researchers, experienced staff, and talented students are the heart of Khoury. Together, they are preparing the problem solvers of today and the thought leaders of tomorrow to tackle the challenges of our times.
48 undergraduate combined majors, 9 master’s programs, 805 co-op partners, 9 campus locations with 190 faculty, khoury stories, meet andrea grimes parker.
From her earliest undergraduate days through her joint faculty appointment years later, Andrea Grimes Parker embraced Khoury College’s interdisciplinary approach. In the process, she built a robust research resume in her chosen field of human-computer interaction.
Marvin Sagastume found himself drawn to Northeastern by Khoury College’s combined majors, giving him the opportunity to blend his computing passion with something wider. Now at Outcomes4Me, he’s doing just that by helping to develop a user-friendly information platform for cancer patients.
For Becky Mashaido, the draw of Align was straightforward — a master’s program in computing that offered access to San Francisco’s tech pioneers, but did not require an undergraduate CS degree. And after finding a unique and uplifting sense of community there, she made it her mission to uplift others.
When Glen Coppersmith graduated from Northeastern in 2004, he did so with a CS–psychology double degree that was highly unusual at the time. Two decades later, he’s carved out a career by strengthening the quantity, quality, and utility of data for mental health treatment.
For a time, Katie Lowen was touring internationally as a contemporary dancer. But when the pandemic closed down theaters, she sought another avenue to develop herself. Now, earning her master’s in computer science through the Align program in Vancouver, Katie finds herself immersed in a career path she’d never imagined.
When he came from India to the United States for graduate school with only enough money for a one-way flight, Mani Sundaram landed at Northeastern purely by accident. But everything that has happened since then — from his startup work to his internet security leadership to his drive to give back — has been very much intentional.
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And you can plan your own informal visit to a college campus. Take these important steps first: Visit the college's admissions website to get details about arranging for an in-person visit. Check with your school counselor to see if any campus tours are scheduled. Set aside time to be on your own. Walk around the public area.
Before a campus visit, students should do basic research on the school. Students and their families have various opportunities to ask questions. No question is dumb. College visits, whether in ...
Gather Basic Information: Start your preparation by researching the college's size, academic programs, student-to-faculty ratio, campus setting, and overall culture. This foundational knowledge will help you focus your visit. Understand Admission Requirements: Familiarize yourself with the admissions process, deadlines, and required documents. Knowing these details will allow you to ask ...
Info Sessions: This is something that is traditionally done before on-campus tours, but colleges have transitioned these online as well. This can be attached to a virtual tour or solo. Typically, this is a 15-30 minute overview of a college - everything from academics, admissions, student life, and more. Virtual & Live Tours: Many colleges ...
Attend a concert or presentation - many schools host events that are free and open to the public; check the calendar in advance to see if anyone interesting is coming to town. Stroll the local neighborhood - whether you're surrounded mostly by trees or a quaint downtown scene, it's helpful to get a feel for what your college ...
The College Board recommends spring of junior year as a good time to visit campuses for students who have already done the research on those colleges. College visits are a good use of downtime ...
Ask about the best times to visit. 3. Take Your Own Tour. Wandering around the campus on your own or with friends can be the best way to get a feel for the college. 4. Explore the College's Facilities. Talk to students to find out the best places to eat and have lunch there. Visit the library.
Virtual campus tours will typically allow you to click on notable buildings and either read more information or hear a pre-recorded tour guide speak about them. It's easy to skip through these if you're speeding through the tour, but take the time to slow down. The context and history behind buildings can be fascinating, and it's also ...
First-Time Freshmen who have completed (1) will be admitted to the college when meeting one of the following requirements: A student having a minimum high school cumulative GPA of 2.75 out of a 4.0 GPA. A student having a minimum ACT composite score of 20 out of 36. A student having a minimum SAT composite score of 1040 out of 1600.
Take the free CollegeScore™ Quick Assessment to find out! College campus visits can help you and your family decide where you will invest your time, money, and efforts over the next four years of your life. Here are 25 tips for how to have a great college visit that is smooth, informative, and even fun.
4. Be Standing. Even though your audience cannot see you, stand when you present. This allows you to stay focused and use good presentation delivery skills such as belly breathing, vocal variety, and pausing. 5. Be Prepared. Practice delivering your presentation with your technology in advance of your talk.
Spirituality. News and Events. As of October 31, 2023, Presentation College has ceased educational operations at the Aberdeen, SD campus. St. Ambrose University (SAU) is the contact for all student information. Please reference these web-pages for additional information. If you would like to request a transcript, you may contact the.
Here are some things you might want to consider before (and during) your campus visits. Take a campus tour. Schedule an interview with an admissions officer. Sit in on a class that interests you. Have lunch in the dining hall (Most admissions officers can give you a voucher to enjoy a free lunch on campus)
Most high schools allow for, at least, a few absences in the junior and senior year for college visits. 3. Make a plan. If you have nearby schools on the list, take day trips. Use longer breaks for schools that are farther away and, obviously, group nearby schools together. If you're planning to drive to school visits and your child has a ...
At the beginning of your college search, a virtual tour can be a beneficial tool to explore the campus before your visit. When you do visit the campus, be sure to talk to current students about their experiences. A student perspective is a helpful way to gauge your future experience when attending Presentation College.
While writing out a script can help you prepare, you shouldn't follow it word for word. Use images and text in your slides to remind you of key points you want to mention. You can also use note cards to prompt you along the way. Check. Make Eye Contact. Try to connect with your audience, not just your slides or notes.
College Visit Checklist. Using a prepared college visit worksheet helps your student pay attention during the visit and focus on what's important. It also helps her ask specific questions if topics important to her are not covered in the presentation. There's also room to add information that we didn't anticipate (which was a lot!).
Step 1: Decide Which Schools to Visit. Although they're helpful, college tours can be expensive and time consuming. You might want to narrow down your options if you have a lot of schools on your list of options. Do some research and try to figure out which colleges have the greatest potential of meeting your needs.
This two-hour program includes a 30-minute interactive admissions presentation as well as a walking tour of campus. It is designed for prospective undergraduate students who are future applicants, and their families. Continue to accessible Prospective Student Tour with Admissions Presentation (In‑Person) list. PrevNext.
Freshman admissions virtual sessions for high school students are on Thursdays at 3pm. Current college students looking to transfer to UC Berkeley should select a session on Tuesdays at 3pm. In-person presentations are available each weekday except Wednesdays. Please read the title, description, and time when signing up. If the date you are ...
Use the calendars at the bottom of your screen to select your preferred date and time.If you need any assistance, please email [email protected], call 213-740-6605, or chat with us below.
Schedule a Visit at Pitt, and find opportunities to do college your way. With in-person and virtual program options to answer why Pitt is a good fit. ... The presentation will be given by a Pitt Admissions Counselor and the tour following will be led by one of our Pitt Pathfinders, our incredible student tour guides who are a wealth of ...
Mia David. Integrated Marketing Manager. Get students excited about their new campus by exploring the local community and businesses just outside it. Here are 25 fun ideas to get them started.
The visit served to strengthen the existing partnerships and helped expand collaboration with the University of Arizona on public health education and research. Public Health Presentation. Dr. Magrath and Ms. Elli were invited to give guest lectures at both universities.
High school visits are when college counselors come directly to your school to meet with you. These visits are perhaps the most individualized face-to-face time you'll get with a counselor prior to applying because the visit volume is smaller. Often, the visits are just the college counselor and a group of 5-8 students.
We've created GPT-4, the latest milestone in OpenAI's effort in scaling up deep learning. GPT-4 is a large multimodal model (accepting image and text inputs, emitting text outputs) that, while less capable than humans in many real-world scenarios, exhibits human-level performance on various professional and academic benchmarks.
A visit to Midhurst Rother College for society. ... After the presentation, Mr Edwards took us on a tour of the school. For information about Midhurst Society talks and events visit our website.
A strong presentation is so much more than information pasted onto a series of slides with fancy backgrounds. Whether you're pitching an idea, reporting market research, or sharing something ...
At Khoury College of Computer Sciences, we believe computer science is for everyone — with the power to make the world a better place. That's why we design flexible programs, grounded in rigor, opportunity, and experience. It's why we collaborate with industry and community partners on the technologies that shape our collective future.