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How can AI be utilized to personalize learning experiences for high school students?

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How can AI be utilized to personalize learning experiences for high school students?

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"These algorithms analyse vast datasets, identifying patterns and trends that inform adaptive learning experiences. Educational platforms now leverage AI to comprehend students’ individual learning styles, strengths, and weaknesses." (E-Learning 2023)

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"These algorithms analyse vast datasets, identifying patterns and trends that inform adaptive learning experiences. Educational platforms now leverage AI to comprehend students’ individual learning styles, strengths, and weaknesses." (E-Learning 2023)

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Personalized Learning: How AI Is Shaping the Future of Education

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Explore the transformative impact of AI on education through personalized learning. Discover how AI is shaping the future of learning experiences.

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"Is essentially a student-centric approach to education that deals with customizing the learning experience for each student. Rather than using the same teaching methods and materials for everyone, it takes into account each student’s individual needs, interests, and pace of learning." (Zia 2023)

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Has been proofed that this method helped more the students?

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Has been proofed that this method helped more the students?

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"Personalized learning prioritizes a clear understanding of the needs and goals of each individual student and the tailoring of instruction to address those needs and goals. These needs and goals, and progress toward meeting them, are highly visible and easily accessible to teachers as well as students and their families, are frequently discussed among these parties, and are updated accordingly."(Rand 2017)

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Research Brief

Photo by PeopleImages/DigitalVision/Getty Images

Key Findings

1. Early evidence suggests that personalized learning (PL) can improve achievement for students, regardless of their starting level of achievement.

2. Benefits of PL may take some time to emerge. Analyses suggest that effects may be more positive after schools have experience implementing PL.

3. To date, the field lacks evidence on which practices are most effective or what policies must be in place to maximize the benefits.

Interest in schoolwide personalized learning (PL) models is growing across the nation. While PL and its focus on individualized instruction seems to be a promising concept, a critical question remains: Does PL improve student learning more than other educational approaches? RAND Education experts partnered with the Bill and Melinda Gates Foundation to begin to answer this question.

Personalized learning (PL) refers to practices that tailor the pace and focus of instruction to address the needs and goals of each student. In recent years, schools and school districts have begun to adopt schoolwide PL models. Because the PL approach is sensitive to student interests and areas of academic strength or weakness, the programs have the potential to increase student learning and engagement.

While PL is promising in theory, there are very few evaluations of students' learning outcomes in such programs. The research described in this brief begins to fill this gap by presenting an evaluation of PL schools and student achievement based on fall 2014 and spring 2015 mathematics and reading test scores. The schools received funding from the Next Generation Learning Challenges (NGLC) initiative to support highly personalized approaches to learning. The study, conducted by RAND Corporation researchers for the Bill & Melinda Gates Foundation, compares the achievement of students in PL schools with (1) matched peers attending non-PL schools and (2) national norms. The study also compares achievement effects between charter and noncharter schools implementing PL.

Personalized Learning in NGLC Schools

NGLC schools used a variety of approaches in implementing PL, which can be grouped into four broad strategies.

Learner profiles maintain a rich and up-to-date record of student strengths, needs, goals, and progress.

Personal learning paths provide appropriate and meaningful choices of material for each student to work on, with the necessary adult supports.

Competency-based progression enables these personalized paths to run their natural course by removing external constraints on what material each student works on, when, and for how long.

Flexible learning environments enable schools to allocate resources in new ways to best support these processes.

The schools in this study did not necessarily implement each of these strategies. They were free to be creative and to adopt approaches compatible with their local contexts and student populations. Overall, the schools implemented PL approaches to a varying degree, with none of the schools looking as radically different from schools nationally as theory might predict. This could be due in part to the schools trying out various combinations of strategies and features, rather than all of them; to the newness of the schools in the study; and to external constraints, such as state or district policies. This lack of substantial differences probably also reflects a growing adoption of PL practices in high schools across the United States.

See John F. Pane, Elizabeth D. Steiner, Matthew D. Baird, Laura S. Hamilton, and Joseph D. Pane, Observations and Guidance on Implementing Personalized Learning, Santa Monica, Calif.: RAND Corporation, RB-9967-BMGF, 2017

What Is Personalized Learning, and How Does It Differ from Typical Practices?

To date, there is no single definition of PL. The research team distilled the following working definition based on conversations with practitioners and experts in the field:

Personalized learning prioritizes a clear understanding of the needs and goals of each individual student and the tailoring of instruction to address those needs and goals. These needs and goals, and progress toward meeting them, are highly visible and easily accessible to teachers as well as students and their families, are frequently discussed among these parties, and are updated accordingly.

This aspiration contrasts with typical instructional approaches. In many U.S. schools, efforts to meet individual students' needs may take less priority than efforts to ensure that all students work toward grade-level standards, progress at the same pace as their grade-level peers, or prepare for grade-level tests at the end of the year. In a PL classroom, students' learning objectives, pace, and content are likely to vary to a greater extent than they would in a non-PL school. The idea behind PL is that personalized instructional approaches and strategies will improve student outcomes in the short term (e.g., stronger rates of growth in achievement) and in the long term (e.g., successful completion of a postsecondary degree or successful transition into a career).

Technology can play a role in supporting the complexity of the personalization process. When properly supported by teachers, it can help students learn independently and work at their own pace. Technology can also enable educators to take a more personalized approach in their teaching efforts and other activities they undertake to support student learning and development.

Photo by Rob/Fotolia

Personalized Learning Makes a Positive Difference in Mathematics Student Achievement

The research team analyzed mathematics and reading scores for approximately 5,500 students in 32 NGLC schools who took the Northwest Evaluation Association (NWEA) Measures of Academic Progress (MAP) mathematics and reading assessments for one academic year: fall 2014 to spring 2015. The team compared the NGLC MAP scores with the scores of students who did not attend NGLC schools, but who were otherwise similar to their NGLC peers in terms of gender, grade levels, starting test scores, and geographic locations.

As Figure 1 shows, the treatment effects were estimated to be approximately 0.09 in mathematics and 0.07 in reading. Only the mathematics estimate is statistically significant. The effect sizes translate to gains of about 3 percentile points; specifically, a student who would have performed at the median in the comparison group is estimated to have performed 3 percentile points above the median in an NGLC school in both math and reading.

Figure 1. Analyses show positive effects for NGLC schools for the 2014-15 academic year

Note: Solid bar indicates statistical significance (p < 0.05) after adjustment for multiple hypothesis tests. Percentile gains translate the treatment effect sizes into the amount of improvement experienced by the median student.

Students Started Below National Norms, but Approached Them by the End of the Year

Figure 2 compares PL students' average performance with that of their grade-level peers across the country. The figure shows that the students in the PL schools started the year significantly below national norms in both mathematics and reading, but moved closer to the norms during the school year. In mathematics, students gained about 2 percentile points but remained significantly below national norms; in reading, students also gained about 2 percentile points and were performing approximately at national norms by spring.

Figure 2. Students started below national norms and approached them by end of the academic year (Fall 2014 to Spring 2015)

Note: Solid bars indicate statistically significant differences from national norms (p < 0.05) after adjustment for multiple hypothesis tests.

Personalized Learning Students Surpassed National Norms After Two Years

There were 16 NGLC schools that had been in operation the prior year, 2013-14, and had administered the MAP assessment in both academic years. To examine growth trajectories in those schools, the research team restricted the sample to students with test scores in fall and spring of both academic years and examined their scores relative to national norms. The results are shown in Figure 3.

In both mathematics and reading, cumulative growth over the two years is evident. Students started significantly below national norms, gained ground after one academic year, and gained further ground the second academic year, placing them above (though not statistically significantly above) national norms at the end of two years. The largest gains on average appeared to occur in the second year. This suggests that PL systems may require some experience before operating at their fullest potential.

Figure 3. Percentile rank changes over two academic years
For the 16 schools in the study that started implementing PL in 2013 or earlier, restricted to the students present for all four tests.

Note: Solid bars indicate statistically significant differences from national norms (p < 0.05) after adjustment for multiple hypothesis tests.

Photo by Rob/Fotolia

Personalized Learning May Benefit Students of All Abilities

The research team also examined the fraction of NGLC students who surpassed comparison students in mathematics and reading achievement during the academic year. The students were divided into five groups based on their starting levels of achievement. Figure 4 shows that across the achievement spectrum, more than half of NGLC students surpassed their comparison students.

These results suggest that PL is benefiting students of all levels of prior achievement. For the lowest four groups, approximately 60 percent of NGLC students surpassed their comparison students in both mathematics and reading; for the highest group, the percentages are in the mid-50s.

Figure 4. Percentages of students surpassing their virtual comparison groups by quintiles of starting achievement (2014-2015)

Note: Solid bars indicate statistically significant differences from national norms (p < 0.05) after adjustment for multiple hypothesis tests.

Charter Schools Showed Bigger Gains Than Schools Operated by Districts

The average positive achievement effects are composed of widely varying estimates for individual schools. A slight majority of schools were estimated to have positive effects, but a few schools were estimated to have large negative effects on student achievement.

Eight NGLC schools in the achievement sample were operated by school districts; the remaining 24 were charter schools. The district schools were all high schools and middle schools, whereas the charter schools included schools serving all grade levels. This analysis calculated the average treatment effects for district and charter schools separately.

As shown in Figure 5, the charter schools performed similarly in both mathematics and reading, with estimated effects near 0.10.

The district schools have smaller estimates in both subjects, and the reading estimate is near zero. Due to the small sample of schools, the district estimates are particularly imprecise. This imprecision, coupled with the differences in the grade levels served by the two groups, indicates that these trends in district versus charter schools should be treated as suggestive but not conclusive.

Figure 5. Estimated effects for district-operated and charter schools (2014-2015)

Note: Solid bars indicate statistically significant differences from national norms (p < 0.05) after adjustment for multiple hypothesis tests.

Two Studies: Positive Results That Vary in Different School Contexts

The results for the 2017 study,[1] discussed above, demonstrated that PL has a slightly positive effect on student learning. Yet, a 2015 study showed larger gains.[2] Figure 6 compares the results of the two studies.

Why are the results from the two studies so different? Much of the difference is likely due to different study samples. The schools in the 2017 report were mostly secondary schools that were relatively new to implementing PL. The schools included in the 2015 report, on average, had greater experience in implementing PL and were primarily elementary schools. Because of these differences, the results of the two studies are not directly comparable.

Figure 6. Comparison of 2015 and 2017 Effect Estimates (Fall 2014 to Spring 2015)

Note: Solid bars indicate statistically significant differences from national norms (p < 0.05) after adjustment for multiple hypothesis tests.

Implications

Together, these analyses offer implications for districts and schools interested in or already implementing PL to consider as they move forward.

PL appears to be promising for improving student achievement. The early evidence presented here suggests that PL approaches can help improve achievement for a broad range of students. However, these results must be confirmed using more-rigorous experimental study designs. Moreover, the implementation of PL in schools is still in the early stages of development, and it is not clear what PL practices or combination of practices have the greatest impact.

PL effects differ across contexts. The positive results on average are composed of widely varying estimates for individual schools, including some schools that showed large negative effects. Other publications in this series describe challenges to PL implementation, which may be greater in certain contexts.

The full effects of PL may take some time to emerge. The benefits suggested by two-year analyses suggest that effects are more positive after schools have at least one year of experience implementing PL. It is not yet clear how effects will accumulate over longer durations, as schools and students gain experience with this major change to schooling. As the field matures with greater understanding of effective PL strategies and more-complete packages of curriculum materials and supports, larger and more-consistent positive effects may be possible. Removal of policy barriers that inhibit PL could also contribute to more-complete implementation.

Notes

This research was conducted by RAND Education and funded by the Bill and Melissa Gates Foundation.

This report is part of the RAND Corporation Research brief series. RAND research briefs present policy-oriented summaries of individual published, peer-reviewed documents or of a body of published work.

This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited; linking directly to this product page is encouraged. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial purposes. For information on reprint and reuse permissions, please visit www.rand.org/pubs/permissions.

The RAND Corporation is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.

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Pane, John F., Elizabeth D. Steiner, Matthew D. Baird, Laura S. Hamilton, and Joseph D. Pane, How Does Personalized Learning Affect Student Achievement?. Santa Monica, CA: RAND Corporation, 2017. https://www.rand.org/pubs/research_briefs/RB9994.html.

Pane, John F., Elizabeth D. Steiner, Matthew D. Baird, Laura S. Hamilton, and Joseph D. Pane, How Does Personalized Learning Affect Student Achievement?, Santa Monica, Calif.: RAND Corporation, RB-9994-BMGF, 2017. As of September 02, 2022: https://www.rand.org/pubs/research_briefs/RB9994.html

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How Does Personalized Learning Affect Student Achievement?

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Research Brief Photo by PeopleImages/DigitalVision/Getty Images Early evidence suggests that personalized learning (PL) can improve achievement for students, regardless of their starting level of achievement. Benefits of PL may take some time to emerge. Analyses suggest that effects may be more positive after schools have experience implementing PL.

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"Platforms like BigFuture by College Board use AI to help high school students explore thousands of career options based on their interests and skills. By analyzing students' PSAT/SAT scores and other data, these tools provide personalized career profiles and insights, aiding students in making informed decisions about their future careers​"(Big Future 2024)

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Every student should have the resources they need to unlock opportunity after high school. There are many paths to a successful future-including two- and four-year college, apprenticeships, job or skills training, military service, or direct entry into the workforce. College Board is creating the scalable tools and resources all students need to succeed on any path.

Career Navigation Resources-Expose and guide students to discover future options

Career Exploration with BigFuture

Free online planning guide tool where students can explore 1,000 career options and find possibilities based on their interests.
Explore Career Possibilities - BigFuture | College Board
Career Insights with BigFuture

Career profiles with information on how students' skills (based on their latest PSAT-related/SAT score) align with the typical requirements for careers.
Connect Students to Career Possibilities - BigFuture
Delivery of Career Insights with SAT Suite of Assessments

A new feature on the score report to ignite future planning for all students, regardless of their path after high school.
What are Career Insights in my score report? - BigFuture | College Board
Career Preparation & Acceleration Resources-Providing career-relevant coursework and skill-building, and postsecondary connections

Career Kickstart Courses

A new program offering industry-recognized credentials and college credit courses. This expansion of the trusted AP model gives all students access to in-demand, high-wage careers.
Career Kickstart Cybersecurity Pathway Courses Pilot in 2024
Multiple Pathways to Success

Information on six different pathways students can take after high school, confirming there's more than one route to a destination.
Multiple Pathways to Success - BigFuture | College Board
Research and Surveys

Learn more about what we're hearing from students ( Morning Consult, 2023 and 2023):

4 in 5 high school students have begun exploring career paths.

80%+ of students want more career exploration in high school.

Important to students: Doing something they're passionate about (92%), salary (91%), work-life balance (90%), and work environment/setting (88%).

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Student Career Pathway Tools and Resources

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College Board and BigFuture are providing tools and resources to help students with a variety of career-focused initiatives to help them own their future.

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"Adaptive systems dynamically adjust the content delivery method (e.g., videos, quizzes, interactive simulations) based on the student’s learning preferences and performance."

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Adaptive learning platforms are changing education and training by using special technology. They make learning personalized and use data to help. These platforms change how people learn by using smart computer programs that adjust to each person. This makes learning better and more interesting. So, how do these platforms change the future of learning and getting better at things in today's digital world?

Introduction to Adaptive Learning Platforms

Definition and Concept of Adaptive Learning

Adaptive learning is like having a special way of learning just for you. It uses fancy tech stuff to make learning fit exactly what you need. Instead of everyone learning the same way, it changes based on what you're good at and what you need help with. This helps you learn better because it goes at your speed and helps you with the tricky parts.

Evolution of Adaptive Learning Technology:

Adaptive learning technology has evolved from basic adaptive assessment systems to sophisticated platforms capable of dynamic content delivery and personalized learning experiences.

Early adaptive learning systems primarily focused on adapting quiz questions or exercises based on learner responses.

Modern adaptive learning platforms integrate advanced technologies such as predictive analytics, natural language processing, and machine learning algorithms.

These advancements enable real-time adaptation of learning content, interactive simulations, personalized feedback, and adaptive assessments that cater to individual learner needs and preferences.

Importance of Adaptive Learning in Education and Training:

In education, adaptive learning platforms address the diverse learning needs of students by providing personalized learning pathways.

Adaptive learning enhances engagement and motivation among learners by offering content that is relevant, challenging, and aligned with their interests.

It promotes self-paced learning, allowing students to focus on areas where they need improvement and progress at their own speed.

Adaptive learning is particularly beneficial in professional development and corporate training settings, where it helps employees acquire new skills efficiently and stay competitive in their fields.

Benefits of Adaptive Learning Platforms

Personalized Learning Experiences

Adaptive learning platforms offer personalized learning experiences tailored to individual students' needs and preferences. Through advanced algorithms and data analytics, these platforms can analyze students' learning patterns, strengths, and weaknesses to create customized learning pathways.

This personalized approach ensures that each student receives content and activities that are relevant to their learning goals, learning style, and pace of learning.

Customized Content Delivery

One of the key benefits of adaptive learning platforms is the ability to deliver customized content to learners. Instead of a one-size-fits-all approach, these platforms can dynamically adjust the content based on the learner's progress and performance.

This ensures that students receive the right level of challenge and support, leading to a more effective learning experience. Teachers can also create and curate content that aligns with specific learning objectives and standards, further enhancing the customization aspect.

Adaptive Assessment and Feedback

Adaptive learning platforms employ adaptive assessment strategies to continuously evaluate students' knowledge and skills. These assessments adapt based on students' responses, providing targeted feedback and identifying areas where students may need additional support or enrichment. By offering immediate feedback and personalized recommendations, adaptive assessments help students track their progress, identify learning gaps, and focus on areas that require improvement.

Improved Learning Outcomes

The personalized nature of adaptive learning platforms contributes to improved learning outcomes. Students are more engaged and motivated to learn when they have access to content that is relevant and challenging yet achievable.

This engagement leads to higher retention of information, deeper understanding of concepts, and increased mastery of skills. As a result, students are better prepared to apply their learning in real-world scenarios and achieve academic success.

Enhanced Engagement and Motivation

Adaptive learning platforms leverage interactive and engaging learning materials, such as multimedia resources, simulations, and gamified activities. These elements capture students' interest and maintain their engagement throughout the learning process.

By incorporating elements of interactivity, exploration, and competition, adaptive platforms motivate students to actively participate in their learning journey and take ownership of their education.

Higher Retention and Mastery of Concepts

Adaptive learning platforms change lessons and tests to help students learn better. They focus more on what students find hard, so they remember more and understand better. Students can go back to lessons whenever they want, which helps them remember and learn more.

Scalability and Cost-Effectiveness

Adaptive learning platforms help lots of different students learn better without needing lots of resources from teachers. They can teach many students at once in a way that suits each student. Also, they do some tasks automatically, like grading, so teachers have more time to teach and less paperwork to do. This makes learning easier and cheaper.

Applications of Adaptive Learning Platforms

Education Sector

K-12 Education: Adaptive learning platforms have changed how kids learn in school. They give each student their own learning plan based on what they're good at and how fast they learn. These platforms use smart computer programs to see what students are good at and what they need help with. Then, they give them lessons and tests that fit them perfectly. Kids get to do fun activities, take tests that change as they learn, and get help when they need it. Teachers can see how students are doing and change how they teach to help each student learn better.

Higher Education and Lifelong Learning: In college and even after, there are special learning programs that change based on what each person needs. These programs are for all kinds of learners - from college students to adults learning new skills for work. They give personalized lessons, fun activities, and help people reach their goals. People can learn at their own pace, get feedback that helps them improve, and find extra resources to learn more. These programs let people take charge of their learning and do better in school and work.

Corporate Training and Professional Development

Upskilling and Reskilling Programs: Adaptive learning platforms help employees learn new things and improve at their jobs. They're important because industries and technology are always changing. Companies use these platforms to give specific training to fill skill gaps and meet their goals. These platforms test how good employees are at things and how they like to learn, so they can make learning plans just for them. Employees get to do interactive learning and practice job skills.

Employee Performance Tracking and Analytics: Adaptive learning platforms also help track how well employees are doing and analyze data about their learning. This helps bosses see how employees are progressing and if the training is working. They use this info to make decisions and improve training. These platforms also support getting certifications and planning career paths, which helps employees keep learning and growing.

Healthcare and Specialized Training

Medical Education and Simulation: Adaptive learning platforms have transformed how medical education works by providing interactive learning experiences, virtual patient simulations, and personalized clinical scenarios. They help train future healthcare professionals, improve clinical skills, and enhance diagnostic accuracy. With adaptive simulations, learners can practice decision-making and procedures in a safe environment, receiving feedback to track progress.

Adaptive Learning for Skills Enhancement: Adaptive learning platforms are also used in industries like engineering, IT, finance, and hospitality to improve employee skills. They align training with industry needs and job roles, helping employees gain relevant skills. These platforms allow learners to apply their learning in real-world scenarios, improving job performance and supporting ongoing professional development and certifications based on industry demands.

Challenges and Considerations in Adaptive Learning Platforms

Data Privacy and Security

In adaptive learning, keeping data safe is a big challenge. These platforms gather lots of sensitive info about learners, like how they're doing, what they like, and their performance. It's super important to protect this data from being seen or used by the wrong people. This helps build trust and follows privacy rules.

Safeguarding Learner Data

To keep learner data safe, it's important to use strong security measures. This means using encryption, safe storage, controlling who can access the data, and checking regularly for any problems. Teachers and platform makers need to make sure they keep learner information private and secure to avoid any data leaks or privacy issues.

Compliance with Regulatory Standards

Adaptive learning platforms must follow rules about data, education, and industry standards. It's crucial to obey these rules to stay legal and ethical in handling data. This means keeping up with changing rules, using best methods, and being clear about how data is used and getting consent.

Implementation and Integration

Implementing adaptive learning platforms can be challenging. Organizations need to invest in technical infrastructure like servers and data storage. They also need to make sure the platform works well with their existing systems and tools. This requires careful planning and testing for compatibility.

Technical Infrastructure Requirements

Adaptive learning platforms demand robust technical infrastructure to deliver personalized learning experiences effectively. To run the platform smoothly, you need fast internet, good servers, cloud storage, and data analysis tools. Make sure your organization checks if everything is ready and upgrades anything needed for the platform to work well.

Training and Support for Educators and Administrators

Using adaptive learning platforms requires thorough training and ongoing help for teachers, school staff, and IT teams. Training should include how to use the platform, handle data, design lessons, and fix problems. Regular support and learning opportunities are important to help everyone make the most of the platform and solve problems as they come up.

Case Studies and Success Stories

Adoption of Adaptive Learning in Educational Institutions

Implementation in Large Urban School District:

Integrating adaptive learning platforms led to improved student engagement and learning outcomes.

Tailoring content and assessments to individual student needs resulted in higher participation and comprehension rates.

Positive impact observed across various subjects, highlighting the versatility of adaptive learning technology.

Impact on Student Performance and Engagement

Research Findings:

Studies consistently show that students using adaptive learning outperform peers in traditional classrooms.

Personalized learning pathways and real-time feedback enhance motivation and academic achievement.

Increased Student Engagement:

Interactive and adaptive content keeps students engaged, leading to higher retention and application of knowledge.

Adaptive platforms cater to diverse learning styles, ensuring every student can learn at their own pace and level.

Educator Perspectives and Feedback

Improved Efficiency in Lesson Delivery:

Educators report streamlined lesson planning and delivery with adaptive learning tools.

Data analytics provide insights into student progress, allowing for targeted interventions and personalized support.

Positive Experiences:

Interviews with educators highlight positive experiences with adaptive learning, citing increased student success rates.

Educators appreciate the ability to track student progress and tailor instruction based on real-time data.

Corporate Use Cases and ROI

Multinational Company Training Programs:

Implementation of adaptive learning led to efficiency gains and skill development outcomes among employees.

Employees reported a more engaging and tailored learning experience, resulting in improved job performance.

ROI Analysis:

Reduced training costs and increased productivity contribute to a positive return on investment for the company.

Adaptive learning platforms optimize resource allocation and ensure training efforts align with business objectives.

Future Trends and Innovations

Artificial Intelligence and Machine Learning Advancements

AI and ML are making adaptive learning platforms better. They can look at lots of data, find patterns, and make learning personal for each person. With AI, the platforms can change how they teach, test, and give feedback based on what each person is good at, what they need help with, and how they learn best. This makes learning more fun, helps people learn better, and makes learning overall a lot better.

Predictive Analytics for Personalized Learning Pathways

Predictive analytics are important in adaptive learning platforms. They use past data about how students learn and perform to guess what they might need next. This helps teachers see problems early and give students the right help to do better in their studies. This proactive approach enhances learning efficiency and fosters continuous improvement.

Real-Time Adaptation and Feedback Mechanisms

Real-time adaptation and feedback mechanisms are essential features of modern adaptive learning platforms. These systems change the learning stuff, how hard it is, and how they teach based on what the user does right then.

Users get feedback right away on how they're doing, so they can fix mistakes and get better quickly. This way of learning helps people stay involved, cuts down on frustration, and keeps them interested in learning.

Integration with Emerging Technologies

Adaptive learning platforms now work with new technologies to make learning even better. They connect with virtual and augmented reality (VR/AR) apps, giving learners immersive experiences. In VR/AR, learners can practice skills in real-life situations, making it easier to remember what they've learned.

Also, these platforms use fun elements like badges, leaderboards, and rewards to keep learners motivated and engaged, helping them remember what they've learned better.

Virtual and Augmented Reality Applications

Virtual and augmented reality applications are revolutionizing how learners interact with educational content. Adaptive learning platforms use VR/AR tech to make fun simulations, labs, and interactive stuff. Students can learn difficult things, try experiments, and work with classmates in fake worlds. This helps them understand and remember lessons better. VR/AR is also good for special training, like medical or engineering practice, because it gives real-like and exciting learning chances.

Gamification Elements for Enhanced Engagement

Gamification makes learning fun and exciting on adaptive platforms. It uses games like points, levels, challenges, and rewards to keep learners interested and motivated. This way, learners compete, feel good about their progress, and stay involved in learning. Gamification helps people enjoy learning and get better results at the same time.

Conclusion

In conclusion, adaptive learning platforms are changing education, training, and digital commerce in a big way. They make learning personal and help in better results, scalability, and saving money in different areas. But we need to solve problems like keeping data safe, making these platforms work well, and making sure everyone can access them fairly.

Case studies show how well they work, and future trends suggest they'll get even better with AI and new tech. These platforms have a big impact on businesses, people, and society, making the digital world more fair, smart, and powerful.

FAQs

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How Adaptive Learning Platforms Are Changing the Game!

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Discover how adaptive learning platforms revolutionize education and training, with insights on benefits, challenges, and future trends in 2024.

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This page is with real opinions about a page for personalized learning called Aleks, and they don't seem to like it.

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All Community Reviews of ALEKS | Common Sense Education

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See what other people say about teaching with ALEKS. Find out how you might use this tool with your students and in your teaching practice.

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What it better, a customized lesson plan or a normal in person lesson by a teacher?

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What it better, a customized lesson plan or a normal in person lesson by a teacher?

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ustomized lesson plans and traditional in-person lessons have their own strengths and can be highly effective depending on the context. A blended approach that combines the best of both methods may offer the most comprehensive educational experience, utilizing the adaptability and personalized nature of customized plans alongside the human interaction and professional judgment of traditional teaching.

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Author Contributions

Conceptualization, I.G.; methodology, I.G. and M.C.; software, I.G., A.-T.G., H.G.; validation I.G., M.C., R.O., A.-T.G., H.G. and P.T.; formal analysis, I.G., M.C. and A.-T.G.; investigation, I.G., M.C., P.T..; resources, I.G. and M.C.; data curation, I.G., M.C., R.O., A.-T.G., H.G. and P.T.; writing-original draft preparation, I.G., M.C., R.O., A.-T.G.; writing-review and editing, I.G. and M.C.; visualization, I.G., A.-T.G. and H.G.; supervision, I.G and M.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The data presented in this study are available upon request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

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Figure 1. Literature review scheme for selecting sources.

Table 1. Assessment of extracted data.

Support vector machines (SVMs)
k-Nearest neighbors (kNN)
Naïve Bayes
Random forest
Logistic regression

The goal is to predict a known outcome (personality dimensions) based on input data (educational data features); this is a supervised learning task. The system focuses on developing a classifier based on supervised learning algorithms to predict the learner's personality dimensions discreetly using educational data features in an online learning system.

Neural networks
Graph theory
Fuzzy sets
Classifications
Agent systems

The paper describes the research, design, and implementation of intelligent learning environments (ILEs) that leverage a combination of expert systems, adaptive training systems, and several mathematical and computational methods to provide a personalized and efficient learning experience. The emphasis is on the integration of formal and informal learning, competency formation based on training and real-world knowledge, and the active use of simulators, virtual worlds, and augmented reality. Neural networks, graphs, fuzzy sets, classifications, and agent systems are used as methods in the creation of individual learning paths.

Ant colony optimization (ACO)

The ACO algorithm is used to optimize learning paths for different groups of students. The first stage involves grouping learners based on their knowledge patterns. The second stage then uses the ACO algorithm to determine the best learning path for each of these groups, with the end goal being a concept map tailored to the needs of each group.
The use of the ACO algorithm in this context demonstrates its potential for optimizing the learning path and providing personalized learning experiences based on learners' knowledge patterns.

Decision trees
Neural networks
Naïve Bayes

The type of ML method being employed here is predictive analytics, which focuses on making predictions about future outcomes based on historical data. The objective of using predictive analytics, as mentioned, is to gain insights into student learning behaviors, cluster student learning patterns, and explain academic performance. The intention is to further adaptive learning and improve education through insights obtained from this analysis.

Long short-term memory (LSTM)
Random forest classification
Convolutional neural networks (CNNs)

The machine learning algorithms used in the article are long short-term memory (LSTM), random forest classification, and convolutional neural networks (CNNs). The method employed is the creation of a comprehensive model based on deep learning to provide a highly personalized e-learning system.
The random forest classifier is used to predict the learner level, which pertains to the difficulty of the course. The random forest classifier takes in various parameters, including the assessment details of the students, to return a prediction. CNN is used in the identification of the learner style.

Evolutionary algorithm
Ant colony optimization
Social network analysis (SNA)

Evolutionary algorithm used for determining relevant future educational objectives using an adequate learner e-assessment method.
ACO is used for generating an adaptive learning path for each learner.
SNA is used for determining the learner's motivation and social productivity to assign a specific learning rhythm to each learner.
The paper seems to focus on the combination of optimization algorithms and big data technology (MapReduce) to personalize and adapt e-learning experiences.
Evolutionary algorithm inspired by the process of natural selection. It belongs to the larger class of evolutionary algorithms and is typically used to find approximate solutions to optimization and search problems.

Chatbots

The application of ML algorithms is used to analyze each learner's personal requirements and generate a corresponding personalized learning path with tailored educational content. It suggests that ML algorithms are utilized to analyze learner characteristics and preferences in order to provide personalized resources and recommendations through the chatbot.
These chatbots could potentially address the issue of limited communication encountered in online learning systems. Integrating such chatbots may streamline the educational process, but their effectiveness hinges on the presence of high-quality content curated by real instructors.
While chatbots can serve as online assistants, it is premature to conclude that they can entirely replace human instructors. Instead, chatbots are valuable tools designed to enhance instructors' capabilities and increase course accessibility.

Support vector machines (SVMs)
K-Nearest neighbors (K-NN)
Naïve Bayes

K-means clustering

K-means clustering is used to identify and cluster learners based on their learning behavior patterns, with the aim of understanding and predicting their personalities for the purpose of designing personalized online learning environments.

Bayesian networks
Ant colony optimization

Bayesian networks are used to infer the learner's features based on some input data (probably their interactions or preferences in the e-learning system). Once these features are understood, the system uses the 0/1 knapsack problem to select the best learning objects for the learner in the given time constraint. The ACO algorithm helps to solve this knapsack problem efficiently. The learning objects are then sequenced, probably based on some logical or pedagogical order, to ensure the learner receives a coherent learning experience.

Multivariate K-means clustering

The multivariate K-means clustering algorithm is used within a semantic framework to address the pure cold-start problem in e-learning recommender systems. The algorithm is used to group learners with similar characteristics, enhancing the personalization of recommendations and improving the learning experience

Random forest
K-Nearest neighbors
Decision tree
Logistic regression
Support vector machine (SVM).

This study evaluates student performance using five algorithms: random forest, K-nearest neighbors, decision tree, logistic regression and SVM (support vector machine) with the aim of improving individual learning. It intends to enhance individual learning results by classifying a risk group and by offering a self-tutoring program.

Genetic algorithm

The genetic algorithm is used to map optimal individualized learning paths for students in online courses, optimizing the ratio of the level of knowledge at course completion to time spent on the course.

Reinforcement learning (RL)
Conditional generative adversarial networks (cGANs)

RL is used to optimize the learning path and learning objects based on implicit feedback from the learner. The idea is to reward the system when it makes decisions that enhance the learning experience and possibly penalize it when the decisions are suboptimal.
cGANs are used to adapt a model of the learner's characteristics rapidly. to simulate the learner's performance for the purpose of improving the training process.
RL is a type of machine learning where an agent learns by interacting with an environment and by receiving rewards or penalties based on the actions it takes.
CGANs are used for generating new data samples. GANs have a generator that tries to generate data, and a discriminator that tries to distinguish between real data and the generated data.

Naïve Bayes
Recommendation systems
Collaborative filtering

Multi-granularity learning preference mining based on ant colony optimization (ACO-LPM)

The ACO-LPM algorithm is applied to address the problem of mining user's learning preferences in a personalized online learning system.
The algorithm takes advantage of the hierarchical characteristics of knowledge points in the course domain and defines the equivalence relation and structure of the knowledge points quotient space. It defines functions of support, pheromone concentration, and preference on various levels to guide the learning preference mining process.
By leveraging the improved ant colony optimization approach and multi-granularity data structure, the algorithm can provide personalized recommendations based on the user's preferences.
The ACO-LPM algorithm utilizes the principles of ant colony optimization and improves upon it by handling the multi-granularity data structure of the quotient space. It tackles the challenges of a large number of learning knowledge points and limited user test data in the online personalized learning system.
The pheromone concentration in the algorithm has the characteristic of dynamic evaporation, allowing the preference patterns mined by ACO-LPM to adapt and change in real-time with the user's interest.

Logistic regression
Support vector machines (SVMs)
Time series forecasting (ARIMA)
Deep neural networks
Recurrent neural networks (RNNs)

Describes the use of both machine learning and deep learning algorithms, including logistic regression, SVM, ARIMA for time series forecasting, deep neural networks, and RNN, to improve and customize the online learning environment.
ARIMA stands for autoregressive integrated moving average. It is a forecasting algorithm based on the idea that the information in the past values of a time series can alone be used to predict the future values.
RNNs are a class of neural networks where connections between units form a cycle. This creates a "memory" about the previous inputs, which is especially useful for sequence prediction problems.

Collaborative filtering (CF)
Deep learning (DL)

It is used as a CF algorithm, and the method employed is the construction of a personalized learning platform based on CF. DL is used in an intelligent classroom based on AI to accurately analyze each student's learning situation and knowledge, push relevant learning materials and videos in a targeted manner, and push all materials in a differentiated, personalized, and intelligent manner, so that students of various levels can achieve the desired results.
Collaborative filtering is a method of recommender systems, which are a subset of supervised learning techniques. Collaborative filtering, in particular, recommends items by finding patterns in user-item interactions.
The algorithm principle of DL is to input the known data whose data model is not easy to find into the input layer and obtain the output data from the output layer through the function mapping of multiple hidden layers, so as to find the real relationship between variables.

Multilayer perceptron (MLP)

The purpose of using the MLP in this research is to perform a data mining task, specifically classification, within the context of a knowledge discovery in databases (KDD) process for an adaptive e-learning architecture. The classification problems analyzed in the paper were based on the classical datasets Iris (plant classification problem), Wine (wine classification problem), and Conc (stimuli arranged in a concentric way).
Additionally, the article proposes an adaptive architecture that can be applied in adaptive e-learning systems. The MySQL database management system is used to store the neural networks as binary large objects (BLOBs).
The modified internal functioning of the MLP involves reinitializing the internal weights of the network with random values if the MLP becomes stuck in a local minimum after a certain number of training epochs. This modification aims to prevent the MLP from converging to suboptimal solutions and helps improve its adaptability.

K-means clustering
Linear regression
Q-learning

K-means is likely used to identify patterns or groups of learners with similar learning needs or characteristics, contributing to creating a deep learner profile.
Linear regression might be used to predict certain aspects of the learner's performance or needs based on their profile data.
Q-learning is employed for recommending adaptive learning paths based on the deep learner profile.
A combination of unsupervised, supervised, and reinforcement learning techniques is used to create a deep learner profile and provide personalized learning path recommendations based on that profile.
Q-learning is a type of reinforcement learning algorithm. It is used to find the best action to take given the current state, in order to maximize the expected cumulative reward

Decision tree
Naïve Bayesian classifier
k-Nearest neighbor

The machine learning algorithms are used to analyze user activity data within the e-learning system. This analysis aims to identify behavioral and activity patterns of system users during the learning process. These patterns are then used to determine learners' needs and provide them with personalized learning content and a tailored learning path.
The machine learning techniques employed in this context are utilized for predictive modeling, simulation, and forecasting, enabling the learning management system to offer unique personalized learning experiences to users. The k-nearest neighbor classifier has the best accuracy, followed by the decision tree, and the naïve Bayesian classifier has the lowest accuracy.

Wide and deep learning (WDL)
Collaborative filtering
ResNet (residual network)

Here, we find an improved recommendation algorithm for online education platforms. It is based on the wide and deep learning model by incorporating collaborative filtering techniques and using ResNet ideas in its deep part for better prediction and generalization. The approach aims to provide more accurate and personalized online course recommendations.
WDL is a deep learning recommendation algorithm primarily designed for click-through rate (CTR) prediction. It combines the strengths of memorization from linear models (wide) with generalization from deep learning models (deep).
Collaborative filtering is a recommendation algorithm that makes predictions about the interest of a user by collecting preferences from many users (collaborating). Here, collaborative filtering is used to replace linear methods in the wide part of WDL.
ResNet is a deep learning architecture that uses residual blocks, which allows for the training of deeper neural networks by creating shortcuts or skip connections. Here, it is employed to improve the deep neural network (DNN) in the deep part of the WDL model, aiming to mitigate the overfitting problem.

Collaborative filtering
Deep learning

Collaborative filtering is utilized to provide personalized recommendations to users based on the behavior and preferences of other users with similar characteristics.
Deep learning is applied to process and analyze the distributed network data collected from various learning platforms. The deep learning component likely plays a role in extracting learner attribute information, learning behavior, and learning results from the large amount of data, thereby contributing to the personalized recommendation process.
The hybrid intelligent recommendation engine is integrated into this structure, utilizing both collaborative filtering and deep learning techniques to achieve user-visualized personalized recommendation and customized services for learners.
Collaborative filtering is a popular recommendation technique that uses user behavior data (e.g., user-item interactions, ratings, etc.) to identify patterns and make recommendations.
Deep learning is a subset of machine learning that uses neural networks to learn and extract patterns from large amounts of data.

Clustering
Association rules
Genetic algorithms
Deep learning

The study applies a mix of unsupervised machine learning techniques (clustering and association rules), optimization techniques (genetic algorithms), and deep learning to develop an innovative content organization system for an English online learning platform.
Association rule learning finds interesting relations between variables in large databases.

Two-stage Bayesian
Chatbots

Two-stage Bayesian is used in a supervised manner. This means the algorithm would have been trained on labeled data to make predictions or classifications based on the input data, and functions as a recommendation system. This system provides customized learning materials based on the learner's current situation, which means it recommends certain content based on the detected needs or gaps in a learner's knowledge.
This system assists in recognizing gaps in a learner's knowledge and in recommending appropriate digital materials or videos to address these gaps.
The chatbot feature further enhances interactivity, providing real-time assistance to learners based on their needs.
Bayesian algorithms are statistical methods that apply probability theory to predict the likelihood of certain events based on prior knowledge.
Chatbots are conversational agents used to address the queries and blind spots of learners during their e-learning process.

Knowledge graph
Logistic regression

The algorithm analyzes data from an online learning platform to infer whether the content assigned by teachers aligns with students' readiness to learn.
The knowledge graph uses the knowledge graph to determine the student's zone of proximal development, which refers to the skills that are appropriate for the student's current level of knowledge and readiness to learn.
Logistic regression is used to compare student mastery outcomes based on whether they were assigned ready-to-learn skills versus unready-to-learn skills.
A knowledge graph is a structured way to represent knowledge in terms of entities and their interrelationships.

Gligorea I, Cioca M, Oancea R, Gorski A-T, Gorski H, Tudorache P. Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review. Education Sciences. 2023; 13(12):1216. https://doi.org/10.3390/educsci13121216

Chicago/Turabian Style

Gligorea, Ilie, Marius Cioca, Romana Oancea, Andra-Teodora Gorski, Hortensia Gorski, and Paul Tudorache. 2023. "Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review" Education Sciences 13, no. 12: 1216. https://doi.org/10.3390/educsci13121216

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Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review

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The rapid evolution of e-learning platforms, propelled by advancements in artificial intelligence (AI) and machine learning (ML), presents a transformative potential in education. This dynamic landscape necessitates an exploration of AI/ML integration in adaptive learning systems to enhance educational outcomes.

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Is using AI to influence in the decision of choosing a major is good or bad?

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Is using AI to influence in the decision of choosing a major is good or bad?

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While AI-powered career pathway recommendations offer significant advantages in terms of personalization, data-driven insights, and scalability, they also come with challenges related to data privacy, potential biases, and the lack of human interaction.

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Want to know the impact science, technology, engineering, and mathematics (STEM) have on your daily life? Consider your morning routine. From the running water in your sink to the internet access you use to check your email, nearly every aspect of our lives has been touched by someone in a STEM profession. Just as the outcomes of STEM are vast, so are the job opportunities. For every seemingly simple product (such as a kitchen sink), there are teams of people involved in its creation. Architects, chemists, engineers, manufacturers, designers, plumbers...the list goes on. That's why learning STEM in high school can give your student a head start on a career and unlock a world of opportunities.
STEM Career Paths
How many types of STEM jobs are there? Because the field is always expanding and evolving, the number is immeasurable. Instead, to keep things simple, the U.S. Bureau of Labor and Statistics created a list of the top 100 careers in STEM. Ranging from astronomers to math teachers to zoologists, the possibilities are endless for your teen. While the job options are vast, there are a few that dominate. Seven out of ten of the largest STEM occupations are computer-related. But what about professions outside of Information Technology (IT)? Engineering is next in line, making up nearly 20 percent of all STEM jobs.

STEM career paths require sharp professional skills and strong technical skills. Learning STEM in high school can help your teen develop their analytical and creative thinking, as they complete projects and experiments. Through career readiness education, your teen can learn the technical side of STEM while mastering professional talents as well. Teamwork, leadership, and attention to detail are all strong skills to have to be competitive in STEM, but they take experience to develop. Through hands-on, project-based learning, your teen can learn these skills before launching into a full-time career.
Engineering and Manufacturing Career Paths
Through career readiness education, your teen can get a head start on a STEM path in high school. Stride Career Prep, a K12-powered career readiness high school, prepares students for in-demand occupations in STEM. Your teen can take courses to explore job types to see if STEM is the right fit for them. If it's a match, your teen can then pursue a pathway program and start developing the technical skills needed for their future career.
Engineering/Technology Pathway
Engineering is the backbone of how our society operates. From the roads you drive on to the cars you drive in, engineers ensure everything built is safe and will work properly. In the Engineering/Technology Pathway, students start with Engineering Explorations to learn the basic principles of the field and understand the variety of professions within the field. Throughout the pathway, students take courses to learn fundamental skills like technical drawing and physics. With a career-focused course load, every class (even the non-STEM classes) helps them become analytical, detail-oriented, and innovative engineers.
Heavy Machine Operations Pre-Apprenticeship Pathway
Did you know STEM jobs have a median salary of $86,980? That's over double the national average. Within those careers, lie manufacturing specialties like derrick operating, in which the median salary is over $48,000. In the Heavy Machine Operations Pre-Apprenticeship Pathway, students learn the fundamental skills needed to work in manufacturing and construction. Being a heavy equipment operator requires a lot of on-the-job training and experience, typically in the form of an apprenticeship. This pathway prioritizes learning technical skills that give students a competitive edge when applying for highly sought-after union apprenticeship programs.
Robotics for High School Students
Learning robotics in high school is an exciting and engaging way for your teen to develop their STEM knowledge. At Stride Career Prep, your teen can explore robotics through manufacturing pathways. Classes like Lean Manufacturing and Automation teach students the fundamentals of robotics and electrical systems, while developing math skills for technicians. In addition to learning in the classroom, your teen can join clubs that focus on robotics. From interscholastic competitions to hobby groups, there are a number of communities your teen can join at Stride to dive deeper into robotics.

Through career readiness education, your teen can start pursuing a STEM career while still in high school. Help them unlock their potential at Stride Career Prep!

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STEM Career Paths & Career Prep for High School Students - Stride

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Learning STEM in high school can help your teen develop their analytical and creative thinking. Find out more from Stride Career Prep & get started today!