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How Education Tech Companies Enhance Student’s Success

In today’s educational landscape, achieving students’ success is more crucial than ever. Education Tech Companies like Datatelligent, specialize in helping universities manage their data to improve student outcomes. By using advanced technology, we enable institutions to streamline operations, boost academic performance, and make informed decisions that lead to greater student success.

The Role of Education Tech Companies in Students Success

Education tech companies like Datatelligent provide innovative solutions to improve students’ success. Let’s explore how we contribute to this goal.

What is Student Success?

Students success means helping students achieve their academic goals, personal growth, and career readiness. It involves giving students the resources and support they need to thrive in their studies and future careers.

Why is Students Success Important?

Focusing on students success benefits both students and universities. It leads to higher graduation rates, better job placements, and a positive reputation for the institution. Effective data management plays a crucial role in achieving these outcomes.

How Datatelligent Supports Students Success

Partnering with Datatelligent offers numerous advantages for universities aiming to enhance students success. Here’s how we help:

Improved Data Accuracy

Accurate data is essential for tracking student progress and identifying areas where support is needed. Our automated data management systems reduce human error, ensuring reliable data that universities can trust.

Enhanced Decision-Making

Our advanced analytics tools provide actionable insights. Universities can make informed decisions based on real-time data, whether it’s adjusting academic programs, improving student services, or identifying at-risk students.

Increased Efficiency

Universities handle numerous administrative tasks that can divert attention from student support. By automating routine tasks and streamlining processes, we help universities save time and resources. This allows staff to focus more on initiatives that directly impact students success.

Targeted Support and Interventions

Data-driven insights help universities identify at-risk students and tailor support programs to their needs. By understanding the data, universities can provide targeted interventions, ensuring that every student has the opportunity to succeed.

Datatelligent: Your Partner in Achieving Students Success

At Datatelligent, we are committed to helping universities harness the power of their data to promote students’ success. Our tailored solutions address the unique needs of each institution. Here’s what we offer:

Data Integration and Management

We integrate various data sources to create a unified, accessible data platform. This ensures all your data is in one place, easy to access and manage.

Advanced Analytics

Our predictive analytics tools provide actionable insights. By analyzing historical data and identifying trends, we help universities plan for the future and make data-driven decisions that enhance students’ success.

Data Security

Ensuring that all data is securely stored and compliant with regulatory standards is a top priority. We implement robust security measures to protect sensitive information and ensure compliance with regulations.

Tailored Solutions

Every university is unique, and so are their data needs. We work closely with each institution to understand their specific challenges and provide customized solutions. Whether it’s improving data accuracy, enhancing decision-making, or increasing efficiency, we have the expertise and technology to help.

Success Stories: How Datatelligent Has Made a Difference

University A

University A struggled with managing student records and tracking academic performance. By partnering with Datatelligent, they integrated their data sources, automated data management processes, and gained valuable insights into student performance. This led to more targeted support for students and improved academic outcomes.

University B

University B faced challenges with providing targeted support to at-risk students. Our advanced analytics tools provided the data they needed to identify at-risk students and develop intervention programs. This resulted in higher retention rates and better student outcomes.

University C

University C needed to enhance their career readiness programs. By providing robust data sets and advanced analytics tools, we helped them track student progress and align their programs with job market demands. This improved job placement rates and student satisfaction.

Conclusion

As the education sector continues to evolve, partnering with education tech companies like Datatelligent can make a significant difference in achieving student succes. By improving data accuracy, enhancing decision-making, increasing efficiency, and promoting better student outcomes, Datatelligent is your ideal partner in fostering students success.

Let us help you unlock the full potential of your university’s data to achieve greater performance and success. Contact Datatelligent today to learn more about how we can support your institution in promoting students success and navigating the complexities of data management and academic planning.

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AI Events & Webinars Higher Education Insights Student Retention Students at Risk

Demystifying AI: Practical Ways to Get Started with AI in Data Analytics

DEMYSTIFYING AI: PRACTICAL WAYS TO GET STARTED WITH AI IN DATA ANALYTICS – 7.24.24 @ 2:00 pm CT

Our recent survey shows a lot of interest in AI, but organizations have not progressed beyond the Novice level of implementing AI in data analytics. 

As with any new technology, while there is a lot of potential value, it can be difficult to know where to start. 

In this informative webinar, hear real-world examples from others who are implementing AI at their institutions and get some practical tips on how to get started and be successful with AI.

Attendees will learn about:

  • The current state of AI in Higher Education
  • Key requirements for AI success
  • Lessons from an AI POC at UAB and Cornell College
  • Examples of AI use cases
  • Five steps to getting started with AI

Event Details

Featured Speakers:

  • Scott Sorenson, AVP, Data Operations and Business Transformation, University of Alabama Birmingham
  • Jodi Schafer, Senior Director, Berry Career Institute, Cornell College

Event Title: Demystifying AI: Practical Ways to Get Started with AI in Data Analytics

Date / Time: July 24, 2024, from 2:00 to 3:00 pm CT

Location: Zoom meeting

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AI Events & Webinars Higher Education Insights Student Retention Students at Risk

Practical Insights from an AI/ML Student Retention Pilot

PRACTICAL INSIGHTS FROM AN AI/ML STUDENT RETENTION PILOT – 5.8.24 @ 12:00 pm CT

Join us to explore how Cowley College, in partnership with Datatelligent is integrating machine learning into their established Student-at-Risk/Student Retention solutions.  This session will deliver actionable insights on enhancing student retention strategies through AI.

Attendees will learn:

  • Understand how Datatelligent incorporated machine learning into the existing Student-at-Risk solution, focusing on identifying the key features driving student re-enrollment.
  • How Cowley’s staff and advisors are using ML outputs to prioritize support for students most at risk.
  • Key takeaways and initial lessons from the joint pilot, highlighting both successes and areas for improvement.

Event Details

Event Title: Practical Insights from an ML/AI Student Retention Pilot

Date / Time: May 8, 2024, from 12:00 to 1:00 pm CT

Location: Zoom meeting

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AI Blog Higher Education Industry

Caution: AI Approaching Higher Education

CAUTION: AI APPROACHING HIGHER EDUCATION

INTRODUCTION

Interest in Artificial Intelligence (AI) is growing across all industries, spurred by daily advancements that showcase its potential to enhance efficiency and predict trends. Higher education institutions, faced with declining enrollments in part due to shifting demographics, are especially interested in using AI to improve their operations around student recruitment and retention. But before colleges and universities start using AI, it is crucial to consider the responsible incorporation of AI, ensuring its use enhances existing processes while mitigating potential pitfalls.

ethical considerations

Using historical data by AI introduces the risk of perpetuating existing biases, a challenge highlighted by Amazon’s reevaluation of an AI recruitment tool biased against female candidates¹. Similarly, the application of AI in risk assessments within the legal system² has faced scrutiny for racial biases. These examples underline the urgent need for comprehensive AI governance frameworks, discussed during the March 2024 Data Analytics Alliance for Higher Education meeting, that prioritize ethical data use and rigorous oversight to combat bias.

AI “Hallucinations” and Misinformation
The phenomenon of AI “hallucinations”³ — baseless but authoritative assertions made by AI systems — has raised significant concerns regarding the use of tools like ChatGPT. Examples such as Google’s Bard AI misrepresenting facts about the James Webb Space Telescope⁴ and Microsoft’s Bing chatbot displaying unpredictable behavior and professing “love” for a New York Times columnist⁵ highlight the risk of misinformation. These incidents reinforce the importance of strong training data curation to mitigate the spread of misinformation in educational settings.
 
PRIVACY AND SECURITY

The deployment of AI in analyzing large datasets accentuates privacy and security concerns, particularly around the potential for de-anonymization. AI’s ability to infer sensitive personal information from non-sensitive data⁶ introduces new data protection challenges. Therefore, adopting AI technology requires robust privacy safeguards, including secure platform designs and ethical data handling practices.

DATA IS THE KEY
A foundational principle for effective AI utilization is the term “Garbage in, garbage out,” emphasizing the critical role of data quality. Higher education institutions often rely on data from student information systems (SIS) and learning management systems (LMS) to train AI models. However, these sources frequently contain incomplete or inaccurate data, potentially leading to unreliable AI outputs.

To navigate these challenges and lay the groundwork for effective AI implementations, the use of a Unified Data Platform (UDP) is vital. A UDP consolidates and harmonizes data from diverse systems, ensuring AI models are trained on high-quality, comprehensive datasets. Key characteristics of an effective UDP include:
 
  • Centralized Data: Aggregates data from various institutional systems and external sources, providing a complete data ecosystem for accurate AI analysis.
  • Scalability: Offers a scalable infrastructure to accommodate increasing data volumes and complex AI use cases.
  • Robust Security Measures: Incorporates advanced security features to protect sensitive data, ensuring privacy and compliance with data protection laws.
  • AI-Ready Infrastructure: Facilitates the deployment of AI by ensuring the platform and tools are primed for AI applications, supporting advanced analytics, and making data AI-ready.
HOW TO GET STARTED

In response to growing inquiries from our higher education customers interested in AI, Datatelligent recommends that customers consider its Datatelligent Platform for Higher Education, which leverages a UDP to develop standard analytic solutions that most colleges and universities need. Karl Oder, one of the Chief Architects of the platform, talked about what we are doing with the platform. “We’re busy creating several AI prototypes with our partner, Snowflake, using the AI-Ready tools they provide.”

In addition to getting your data “AI-ready” by establishing a UDP, schools should also spend time prepping for the AI Project⁷, starting with selecting the right use case. For higher education institutions, Datatelligent has developed prototypes on our platform that can accelerate this process, including:

  • Admissions and enrollment – predictive factors that will influence admissions and student enrollment projections
  • Student success and retention – identifying student success characteristics and predicting students at risk of leaving.
  • Graduation and program success – predictive factors driving graduation rates and overall program success.
CONCLUSION

Integrating AI in higher education calls for a balanced, thoughtful approach that acknowledges AI’s transformative potential alongside its challenges. By addressing issues of bias, misinformation, privacy, and ethical governance through strategic planning, institutions can harness AI to enhance educational outcomes and operational efficiency. Central to this endeavor is establishing a Unified Data Platform, ensuring data integrity, and laying a solid foundation for the responsible use of AI technologies.

RESOURCES:
CITATIONS:
  1. Dastin, Jeffrey. “Insight – Amazon Scraps Secret AI Recruiting Tool that Showed Bias against Women.” Reuters, August 10, 2018. https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G/.
  2. Angwin, Julia , Surya Mattu, and Lauren Kirchner. “Machine Bias.” Pro Publica, May 23, 2016. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing.
  3. “What Are AI Hallucinations?” IBM.Com. February 1, 2024. https://www.ibm.com/topics/ai-hallucinations.
  4. Mihalcik, Carrie. “Google ChatGPT Rival Bard Flubs Fact About NASA’s Webb Space Telescope.” CNET, February 9, 2023. https://www.cnet.com/science/space/googles-chatgpt-rival-bard-called-out-for-nasa-webb-space-telescope-error/.
  5. McMillan, Malcolm. “Bing ChatGPT Goes off the Deep End — And the Latest Examples Are Very Disturbing.” Tom’s Guide, February 17, 2023. https://www.tomsguide.com/opinion/bing-chatgpt-goes-off-the-deep-end-and-the-latest-examples-are-very-disturbing.
  6. Ahmed, Hafiz. “Challenges of AI and Data Privacy—And How to Solve Them.” @ISACA 32, (2021). https://www.isaca.org/resources/news-and-trends/newsletters/atisaca/2021/volume-32/challenges-of-ai-and-data-privacy-and-how-to-solve-them.
  7. Sassi, Steve. “AI Project Prep for Higher Education.” Datatelligent.Ai. March 26, 2024. https://datatelligent.ai/ai-project-prep-for-higher-education/.
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AI Blog Higher Education Industry

AI Project-Prep for Higher Education

AI PROJECT-PREP FOR HIGHER EDUCATION

INTRODUCTION

After almost a year of weighing the pros and cons of AI at your institution, creating an action plan, and cross-collaborating with your peers from other institutions, your team has finally decided that AI is the future for recruiting, retaining, and ensuring your students’ success. Congratulations! Before answering one of the dozen daily emails in your inbox from AI software and services vendors, you should first take some cautious pre-project steps if you are serious about the success of your future AI project. Here are six AI project prep steps you should take before setting up any meeting with an AI software or service provider:

1 – choose and define your use cases

You probably already have the high-level Future Action Roadmap¹ if you’ve come this far, but you can’t, in project terms, “boil the ocean” with a “big bang” AI project covering everything on the map. No Higher Education institution has that kind of time or money. It’s time to pick one to three high-profile use cases where the need is most urgent. Perhaps it’s all about predicting student behavior and outcomes so you can sooner identify students at risk. Or maybe your school would like to personalize student learning and support services to help increase retention. It is very likely your marketing team has been in touch with you about how AI will help them segment and target prospective students for recruitment and deliver personalized and engaging marketing campaigns that can increase awareness, interest, and conversion rates for enrollment.

In March 2024, Scott Sorenson, Executive Director, Data Privacy & Analytics from the University of Alabama at Birmingham, shared how they built a pilot using Salesforce’s AI-powered Tableau Pulse at the Data Analytics Alliance for Higher Education. They focused on use cases for the Marketing and Advancement Departments. Right away, they involved the participating departments, and the team at Tableau helped them build the business case for approval. The Results: Success. The Advancement team liked it and will include it on their IT roadmap, and the Marketing team loved it and wanted it yesterday. Some lessons learned from the UAB pilot:

  • Get the interested teams involved early and define the roles each will play.
  • Develop air-tight use cases founded on strong business reasons.
  • Even if business reasons are solid, leave plenty of time for executive iterations and approval.
  • Developing AI and Data Governance will take twice as long as you think it will.
2 – DON’T DELAY YOUR AI GOVERNANCE AND SECURITY
Lessons learned on the UAB AI pilot segue perfectly into perhaps the most crucial pre-project activity: AI Governance and Security. This should be a strong focus at the beginning of your AI journey, as it is foundational for your institution’s success. Some things to consider:
 
  • Ethical Considerations: All policy decisions should align with ethical principles and the DNA of who you are as an institution. Ensure transparency, fairness, and equity. Institutional leaders (Chancellor/President, Chief Academic Officer, Chief Information Officer) are pivotal in driving ethical AI practices.
  • Senior Management: Define roles, responsibilities, and accountability related to AI governance and ensure that senior management oversees AI initiatives.
  • Risk Assessment and Iteration: Regularly assess risks associated with AI implementation and adjust policies accordingly.
  • Data Security and Privacy: Data handling and privacy protection will help keep your student and staff data safe. Mistakes made with the mishandling of private data can have serious consequences, so It’s imperative to put guidelines and best practices in place for collecting, storing, and processing data used in AI systems.
  • Transparency and Accountability: Not surprisingly, AI has the same biases as its human counterparts. Make AI algorithms and decision-making processes transparent with regular reviews, carefully define responsibilities, and hold accountable AI system performance and outcomes. 
3 – IT’S ABOUT THE DATA

The legacy systems used by your staff for the past two decades need to be assessed to determine if they are truly ready for AI. Do you have a Unified Data Platform to collect, store, process, analyze, and share your data with data visualization tools? How reliable, relevant, complete, and diverse is your data? Work may need to be done with Data systems before choosing the AI solution. The Data sources you will need will depend heavily on the use case. Here are just some Data systems commonly used for AI:

  • Student Information Systems (SIS) – Holding admissions, enrollment, grades, and financial aid information is often critical for Student Success analysis.
  • Learning Management Systems (LMS) – Platforms like Canvas, Blackboard, or Moodle will facilitate online learning, course management, and distribution of educational content.
  • Human Resource Systems – These systems handle employee data, payroll, benefits administration, and recruitment processes.
  • Vendor-based Systems – Specialized software for recruitment, student success, assessment, space management, and more.
  • External Data Sources – Registers, databases with scientific information, and other external systems that can support enrollment, recruitment, marketing, and student success decisions.
4 – CHOOSE YOUR AI SOLUTION
These steps are in this order for a reason. Not until the first three steps have been started and the first draft has been completed can you even begin to make an informed decision about AI technology that will bring your use cases to life. The questions to ask:
 
  • What type of AI solution best suits your problem or opportunity? The areas with the biggest impact on securing your institution’s future success are typically Student Success, Enrollment, and Retention. The market is catching up quickly with AI offerings to support these initiatives.
  • Do you want to build your own solution from scratch or use an existing solution from a vendor or a partner? The old rule of buy to compete, build to differentiate still applies to AI Projects. Buying off-the-shelf (OTS) AI solutions should be where you start. It is still the lowest-cost entry to AI. Building your own should be for ambitious projects where no other OTS solution exists for an identifiable, mission-critical, market-differentiating AI use case.
5 – DEFINE THE KEY PERFORMANCE INDICATORS (KPI)

At the start of the UAB AI pilot project, after the use cases, governance, data, selected solution, and approvals were in order, Sorenson met with his Marketing and Advancement teams to define what metrics they wanted to see. From there, he determined what data was needed for the metrics. He then asked them what success looked like to them. UAB implemented a pilot, but it’s no different from deploying the actual AI Solution. In fact, it’s more critical.

Defining KPIs will determine how you build the solution. Some common metrics used in higher education include the following:

  • Number of student minutes on a website – Does it lead to a greater conversion percentage to enrollment?
  • Year over Year (YoY) percentage of resources used by at-risk students – Does it correlate to YoY percentage of Student retention?

Setting these KPIs will guide improvements toward success and ensure the Leadership Team that your investment in AI is seeing the hoped-for impact on enrollment, retention, and student success. What are the key performance indicators (KPIs) you will use to track its progress and results?

6 – AND FINALLY, PLAN YOUR IMPLEMENTATION

Before you even select a vendor to implement, how will you deploy your AI solution in your institution? You’ve determined what data from which systems are needed, but now it’s time to consider how you will integrate and unify your data into a usable format for AI Analytic consumption.

Keep in mind, just because the letters A and I are in front of your project, it is still an IT project, and the best practices for this haven’t changed much in the past couple of decades. Bring all the lessons learned at your institution, your institution’s developed best practices, and industry PMO Best Practices² to this project, as you would any project.

RESOURCES:
CITATIONS:

1. Jenay Robert and Nicole Muscanell, 2023 Horizon Action Plan: Generative AI (Boulder, CO: EDUCAUSE, 2023) 2023 EDUCAUSE Horizon Action Plan: Generative AI

2. Abudi, G. (2011). Developing a project management best practice. Paper presented at PMI® Global Congress 2011—North America, Dallas, TX. Newtown Square, PA: Project Management Institute. https://www.pmi.org/learning/library/project-management-best-practice-organization-6167

Categories
AI Blog Higher Education Industry Snowflake

The Importance of AI-Powered Analytics in Higher Education

The Importance of AI-Powered Analytics in Higher Education

The future of AI is now

At Datatelligent, we look to the future for ways to help our customers solve decades-old Higher Education problems. We hear a lot of questions lately about AI and what it means for Institutional Research. Questions like, “How can Generative AI and Large Language Models help our analytics? Will adding AI extract the predictive insights we need to help students and help us with retention, recruitment, and funding?”

Well, it’s funny you should ask. On November 15, 2023, we are hosting a webinar on these very topics with our most AI-innovative partner, Snowflake. Elevate Education: AI Solutions for Higher Education.

Snowflake is moving fast, at Chicago-blizzard pace, embracing all that’s AI and announcing earlier this month, during Snowday, a host of new AI tools.  We will in turn innovate with our Higher Education customers and implement these tools into the Datatelligent Unified Data Platform.

The Definition of AI in Higher Education

AI-powered analytics is the use of artificial intelligence to analyze large datasets to identify patterns, trends, and insights. Here are some of the areas will innovate with AI-powered analytics with our higher education customers:

  • Student success: AI-powered analytics can be used to identify students who are at risk of dropping out or failing a course. This information can then be used to provide targeted interventions, such as tutoring or academic advising.
  • Student Recruitment and Enrollment: considered one of the holy grails of analytics, identify the best mix of students who will benefit and are succeed from the specialties offered by the institution. Closely related, AI can help identify so you can focus recruitment on the students that will help your institution win and retain their grant funding.
  • Enrollment Trends: Identifying the trends early that will impact future enrollment. Linking to all sorts of internal and external data sources, AI-powered insights helps plan for student recruitment in fast-changing demographics.
  • Faculty Planning: Recruitment doesn’t stop with students. AI can help with faculty planning, identifying the educational specialties that are in demand now and in the future. Recruitment efforts and education certifications can be planned years in advance.
  • Personalized learning: Personalized learning experiences can be created for students using insights from AI-powered data. This can be done by adapting course materials, providing individualized feedback, and recommending additional resources.
  • Administrative efficiency: Why not have that AI-bot be the helpful assistant it wants to be, automating scheduling, grading, and admissions processing? This can free up time for faculty and staff to focus on more strategic initiatives.
The Challenges of AI

Of course, AI is not the magic pill to make all our analytic and Institutional Research headaches go away. At Datatelligent, we help mitigate the challenges AI-powered analytics brings to higher education:

  • Data quality: AI-powered analytics immediately bring up Data Quality.  Institutions need to ensure the data is accurate, complete, and consistent, or your “insights” will be none of these.
  • Bias: AI algorithms can be biased, which can lead to unfair or inaccurate results. Data Analysts need to be aware of the potential for bias and make sure a human takes steps to mitigate it.
  • Ethics: The use of AI in higher education raises a number of ethical concerns, such as the potential for surveillance and discrimination. Institutions need to develop clear ethical guidelines for the use of AI in higher education. At Datatelligent, we have a well-developed AI Governance and Ethics framework.

Overall, AI-powered analytics has the potential to revolutionize higher education. As with any revolution there are always challenges, which is why it’s best to align with an ally before overthrowing any king. We’ll be talking about the AI Revolution on November 15. We hope to see you at the Datatelligent and Snowflake AI Solutions for Higher Education webinar.

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The Impact of Generative and Predictive AI on Higher Education: Revolutionizing Administration, and Student Success and Student and Faculty Satisfaction

This article highlights the profound influence of generative and predictive AI on higher education. It discusses how these technologies are streamlining administrative tasks, enhancing student success through early interventions, and improving overall satisfaction for both students and faculty. Institutions are leveraging AI for better decision-making, personalized learning experiences, and optimized course offerings, all of which contribute to a more efficient and effective educational environment.

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