Understanding Adaptive Learning: Personalizing Education for Student Success

Adaptive Learning is a revolutionary educational approach that leverages data and technology to customize learning experiences, catering to the unique needs of each student. This method utilizes real-time data on student progress, engagement levels, and performance metrics to dynamically adjust instructional content and delivery. By doing so, adaptive learning systems aim to provide a personalized educational journey that optimizes learning outcomes for every individual.

While equal opportunity in education ensures that all students have access to the same resources, equitable education goes a step further. It acknowledges and addresses the diverse learning needs of students, providing tailored support and materials to help each learner achieve their academic goals. Adaptive learning, along with adaptive teaching and assessment strategies, plays a crucial role in fostering equity in education by ensuring that every student receives the specific instruction they require to succeed.

Adaptive Learning and Assessment: A Personalized Approach

Adaptive learning is a key component of interactive learning, moving away from the traditional “one-size-fits-all” curriculum (Kurt, 2021). Instead, it focuses on individual student needs through personalized learning pathways, effective feedback mechanisms, and supplementary resources. The advancements in educational technology have made adaptive learning more accessible and easier to implement across various educational settings. Adaptive learning can be effectively applied in three primary areas:

Adaptive Content: This focuses on providing immediate and specific feedback to student responses without altering the overall learning sequence. For instance, students might receive hints, review materials targeted at a specific skill gap, or additional scaffolding based on their answer to a question. This ensures personalized support within a structured learning path.
Adaptive Sequence: This method continuously analyzes student data to dynamically adjust the learning path in real-time. Based on a student’s performance and interactions, the system automatically modifies what content is presented next. This creates a truly personalized learning journey that adapts to the student’s evolving needs and understanding.
Adaptive Assessment: This type of assessment modifies the difficulty and nature of questions based on a student’s responses to previous questions. If a student demonstrates mastery by answering questions correctly, the assessment will present more challenging questions. Conversely, if a student struggles, the assessment will adapt by providing easier questions to support their learning and identify areas needing further attention.

Modern adaptive learning software often integrates all three of these areas to create a comprehensive and dynamic learning environment. Typically, these systems break down course material into smaller, manageable units aligned with specific learning objectives. Learners receive immediate support, resources tailored to their individual learning gaps, and timely, relevant feedback. The software continuously adjusts the content, sequence, and assessment methods based on the student’s interactive responses, which are meticulously tracked within the system. Furthermore, these systems provide instructors with valuable data-driven insights, enabling them to make informed, just-in-time instructional decisions to further personalize the course and cater to the unique needs of each student.

Designing Adaptive Learning Scenarios: A Step-by-Step Guide

When educators design adaptive learning scenarios, it is crucial to carefully consider the content, sequence, and assessment methods, keeping the chosen adaptive technology platform in mind. The following steps outline a structured process for developing effective adaptive learning experiences:

Step 1: Identify Granular Knowledge Units

The initial step involves breaking down the broader learning objectives into smaller, objective-based knowledge units or short lessons (Cavanagh et al., 2020, p. 178). These units serve as the building blocks for the adaptive learning scenario. They should be interconnected and logically sequenced to build towards the overall learning objectives of the course or module. These granular lessons provide the foundational content upon which the adaptive learning sequence and assessments will be built.

Step 2: Develop Assessments and Feedback Mechanisms

Following the creation and organization of content into knowledge units, the next critical step is to develop aligned assessments and effective feedback strategies. Similar to traditional course design, assessments within an adaptive learning environment must be carefully aligned with the learning objectives and activities. These assessments are crucial for evaluating student understanding and determining their individual learning paths within the adaptive system. Given the self-directed nature of adaptive learning, providing structured and constructive feedback is paramount. Incorporating feedback directly into the assessment process, such as providing explanations for both correct and incorrect answers, is essential for promoting student learning and enabling mastery (Cavanagh et al., 2020).

Step 3: Design the Adaptive Learning Pathways

Once the knowledge units, assessments, and feedback mechanisms are in place, the next step is to design the various learning pathways that students can navigate through the content. Based on a student’s performance in pre-assessments or initial interactions with the material, the adaptive learning software will assign them to a specific pathway, guided by the instructor’s pre-defined preferences. Typically, these pathways are structured to progress from foundational knowledge to increasingly complex concepts, ensuring a gradual build-up of student mastery in relation to the learning objectives (Cavanagh, 2020). While instructors establish the basic learning pathways, advanced AI-powered software can further personalize recommendations based on continuous assessment of student performance. For example, if a student struggles on a particular assessment, the AI system might suggest revisiting a previous unit to reinforce foundational concepts before moving forward.

The Benefits of Adaptive Learning in Education

Adaptive learning offers a wide range of potential advantages that can significantly enhance the educational experience and improve learning outcomes (McGuire, 2021):

Enhanced Student Success and Self-Direction: By providing students with clear insights into their mastery levels and allowing them to learn at their own pace, adaptive learning fosters self-directed learning and empowers students to take ownership of their educational journey. This personalized approach can lead to increased student success and confidence.
Improved Student Engagement: Adaptive learning systems deliver lessons and activities that are directly tailored to each student’s individual needs and learning style. This personalization significantly improves student engagement by ensuring that the learning experience is relevant, challenging, and appropriately paced.
Cost-Effective Alternative to Traditional Resources: In some contexts, adaptive learning platforms and digital resources can serve as a cost-effective alternative to expensive traditional textbooks and materials, making education more accessible and affordable.
Alignment of Course Elements and Objectives: Adaptive learning promotes a well-structured learning environment where course objectives, lessons, practice activities, and assessments are tightly aligned. This clear structure helps students understand how each element of the course contributes to the overall learning objectives. Furthermore, when students struggle with specific concepts, instructors can readily analyze the alignment between instructional elements and objectives to identify and address any potential mismatches.
Data-Driven Insights for Equity and Inclusion: Adaptive learning platforms provide valuable, timely data that faculty and administrators can use to analyze the performance of specific student subpopulations within a course. This data can be a powerful tool for identifying and addressing barriers to equity for underrepresented and marginalized student groups, including minority and low-income students.
Timely and Targeted Student Support: The data analytics capabilities of adaptive learning systems enable faculty and administrators to identify individual students or even specific sections within multi-section courses that require additional support. This allows for timely and targeted interventions, ensuring that students receive assistance when they need it most.
Continuous Improvement Through Data Analysis: By comparing data across different semesters and academic periods, faculty and administrators can leverage adaptive learning data to drive continuous improvement in course design, instructional strategies, and overall program effectiveness.
Personalized Learning at Scale and Reduced Cheating: Adaptive learning facilitates the delivery of personalized learning experiences to a large number of students simultaneously. Moreover, the personalized nature of content and assessments in adaptive learning systems can effectively reduce instances of academic dishonesty and cheating, as each student’s learning path and assessment can be unique.
Maximized Learning Outcomes and Proactive Intervention: Adaptive learning empowers instructors with a deeper understanding of areas where students are struggling and which students require additional support. This enhanced insight allows for proactive intervention before students fall behind or are at risk of course withdrawal, ultimately maximizing learning outcomes for all students.

Best Practices for Successful Adaptive Learning Implementation

While adaptive learning offers numerous benefits, its successful implementation relies on careful planning, thoughtful design, and ongoing human interaction. Here are some best practices to ensure adaptive learning is effective:

The Importance of Human Interaction: Adaptive learning is not intended to replace instructors. Human presence and guidance remain essential for successful adaptive learning. Instructors play a crucial role in helping students understand the value of the adaptive system, facilitating their transition from passive recipients of information to active collaborators and learners within the personalized learning environment.
Strategic Platform Selection: Choosing the right adaptive learning platform is critical. It is recommended to select a platform that provides instructors with sufficient flexibility to choose specific learning activities and assessments, ensuring that all content remains closely aligned with the overarching course objectives.
Proactive Planning and Support Systems: Instructors should engage in thorough planning when designing adaptive learning experiences. It is also important to identify available support resources and establish clear channels for seeking assistance when needed, both for themselves and for their students.
Instructor Training and System Understanding: Instructors need to invest time in understanding how the chosen adaptive system works, its features, and its limitations. This knowledge is crucial for effectively communicating the system’s purpose and functionality to students, setting clear expectations, and maximizing its potential.
Clear Communication of Expectations: Clearly communicating course expectations and the process of engaging with the adaptive learning materials is essential for student success. Students need to understand how the adaptive system will be used, how it will benefit them, and what is expected of them within this learning environment.
Leveraging Learning Analytics for Informed Interventions: Instructors should become proficient in utilizing the learning analytics provided by the adaptive learning tool. Effectively interpreting and applying these analytics to inform instructional interventions and implement learner-centered teaching strategies is key to maximizing the positive impact of adaptive learning.

Works Cited and Additional Resources

Cavanagh, T., Chen, B., Lahcen, R.A.M., & Paradiso, J. (2020). Constructing a design framework and pedagogical approach for adaptive learning in higher education: A practitioner’s perspective. International Review of Research in Open and Distributed Learning, 21(1), 173-197. https://doi.org/10.19173/irrodl.v21i1.4557

Kurt, S. (2021). Adaptive learning: What is it, what are its benefits and how does it work? Educational Technology. https://educationaltechnology.net/adaptive-learning-what-is-it-what-are-its-benefits-and-how-does-it-work/

McGuire, R. (2021). What is adaptive learning and how does it work to promote equity in higher education. Every Learner Everywhere. https://www.everylearnereverywhere.org/blog/what-is-adaptive-learning-and-how-does-it-work-to-promote-equity-in-higher-education/

Peng, H., Ma, S., & Spector, J.M. (2019). Personalized adaptive learning: an emerging pedagogical approach enabled by a smart learning environment. Smart Learning Environment, 6(9). https://doi.org/10.1186/s40561-019-0089-y

Redmon, M., Wyatt, S., & Stull, C. (2021). Using personalized adaptive learning to promote industry-specific language skills in support of Spanish internship students. Global Business Languages, 21, 92-112. https://doi.org/10.4079.gbl.v21.6