Everything You Need to Know about Adaptive Learning

Learning or teaching has always been approached by students and educators in a one-size- -suits-all manner. Although flawed, this style of the teaching-learning process still prevails. The world population is moving towards a whopping eight billion. However, the number of people who have left their mark in their respective fields through their remarkable contributions make up only a tiny fraction of that number. These remarkable women and men were educated under similar education systems and had the same opportunities as the rest of their peers. They were all taught the same things in a manner that their educators saw fit. These brilliant minds stood out because they knew what they wanted. They did not limit their learning experiences to those offered in their classrooms.

The learning experience is an essential factor that determines how a student approaches the process of learning. Thought processes and learning patterns vary from person to person, and each student has a set of needs that they expect to be fulfilled by their learning experience. 

This article analyses the process, significance, and advantages of adaptive learning in the educational field.

What is adaptive learning?

Adaptive learning is a style of student-oriented teaching pattern that aims at delivering learning experiences tailor-made to match the needs of each student. In a regular student-teacher interaction, even the best educators cannot fully fathom the needs of their most interactive student. The best they can do is to modulate their instructions and hints based on verbal and other cues they receive from probably a handful of their most interactive students. Educators never get a complete picture of the needs of their entire classroom concerning their desired learning outcomes. This makes the teaching-learning process less effective. 

Adaptive learning utilizes Artificial Intelligence and computer algorithms to widen teachers’ reach and improve the interaction between teachers and their students. The data collected from these interactions are utilized to deliver custom learning experiences to each student. 

How does adaptive learning help teachers?

As the name states, it helps teachers adapt to the requirements of their students. Teachers usually employ a designed adaptivity to approach the teaching process. Designed adaptivity is not computer-based adaptivity. In designed adaptivity, teachers design their teaching sequence and modulate their sequences based on how their students respond to these adaptivity factors. Designed adaptivity is based on an “If this, then that” type of approach to adapt to the way students respond to the adaptivity factors such as:

• Performance: Correctness of responses, performance over a series of tasks
• Behavior: This analyses the time that is taken to complete tasks
• Information: Students receive direct feedback on the degree of learning, understanding, or confidence in the subject matter and the designed sequence.

Educators often use the “if this, then that” type of approach and previous experiences to adapt to the learners’ adaptivity factors. Most often, the data received from this approach is incomplete or insufficient. Sometimes teachers cannot effectively utilize this data to formulate the apt response, making this approach less effective in creating and delivering suitable learning experiences.

On the other hand, adaptive learning uses  an algorithmic approach to understand the responses to the adaptivity features and analyze this data to reach two important conclusions:

• The extent of the learners’ knowledge
• The next learning experience the learners should go through

Standard algorithms such as Bayesian Knowledge Tracing (BKT) and Item Response Theory (IRT) are employed in this approach. These algorithms analyze the data obtained to create a learning experience that delivers the correct item at the right time to the students as their learning progresses. These algorithms emulate the functions of a one-on-one conversation between a teacher and their student to create tailor-made learning experiences for each student. The data from adaptivity factors are used to create Adaptable, or ways the teacher can adapt based on learners’ performance. These Adaptable carry out two main functions:

• Real-time feedback: Timely feedback and appropriate help that assist learners in clearing misconceptions or understanding their mistakes and receiving additional insight.
• Differentiated Pathways: Offering varied learning experiences to learners based on their progress or extent of understanding. Learners who need extra attention could be given the assistance to understand concepts before progressing to more advanced concepts. On the other hand, advanced learners could be allowed to fast-track their learning process or choose what to learn next.

All these factors emulate the effect of conversation between teachers and learners. The analytic features and the adaptable result in manifold increases the effectiveness of a teacher-learner conversation by suggesting teachers new ways to adapt to the requirements of their students. On the other hand, students are offered chances to experience bespoke learning experiences tailored to their requirements. 

What is the Structure of an Adaptive Learning Program?

Adaptive learning programs could have different structures. All these structures are based on a framework of three concepts. The constituents of this framework are based on the adaptivity parameters and form the adaptable. These are Adaptive content, Adaptive model, and Adaptive assessment. These three parts of the framework divide the course content, the analytics, and the assessment sections into three distinctive groups. However, there is no fixed order in which these groups operate. Some are continuously operating, while others are active only at specific periods in the duration of the program.

• Adaptive Content: A simple example of this can be seen in the traditional manner of test evaluation. Usually, when learners submit an incorrect answer, their responses are simply marked wrong. The educator offers no further developments on this answer. And unless the learner or student is inquisitive, they do not know what their mistake meant and what the correct response should have been. Adaptive content, on the other hand, provides real-time feedback based on each response. This does not apply to incorrect answers only. Correct answers could deliver advanced concepts or hints or suggest advanced references or examples. A vital factor to be considered here is that these suggestions do not disrupt the overall sequence in which learners acquire skills or complete their learning goals. Adaptive content only provides additional insight to students into the responses they made.
• Adaptive Sequencing: Adaptive sequencing is active at all stages of adaptive learning. Adaptive sequencing is always at work, collecting student response data or student data at every point of the learning process. The adaptive sequencing system analyses these responses in real-time and automatically controls the next learning experience that each learner goes through. This system can change the overall sequence of the course based on the response data collected. These changes could involve changes to the order in which a student learns a skill and the type and sequence of content or learning experience they have to go through.
• Adaptive Assessment: Adaptive assessment systems are active only during the assessment phases of the adaptive learning program. The adaptive assessment system monitors the test responses given by students in real-time and alters the complexity and/or the type of questions they progress into. If a student struggles to answer a question, the following questions could be simpler, whereas students who perform well on a question would have a complex question following. This is also an effective tool to attach a metric to the extent of understanding a student has achieved at a particular time in the duration of an adaptive learning program.

What are some examples of adaptive learning?

Some mainstream examples of adaptive learning are as follows:

1. Learning Management Systems (LMS): Learning Management Systems for small businesses and education are an essential part of e-learning programs as well as courses offered at universities. Learning Management System refers to an administrative software used by providers of e-learning courses or universities to track students in terms of progress in their courses, document scores, and deliver course material related to their degrees or special training and development programs. The features of the LMS platform allow educators to assess the extent of knowledge absorption by the learners and adapt accordingly. As for the students, they can obtain real-time feedback about their performance and access course material. LMS development solutions provide customization options to tailor the platform according to the specific needs and requirements of small businesses and educational institutions.
2. Distance learning programs: Many universities offer distance learning programs to learners who cannot physically attend the program at the university. This is a situation where there is no physical interaction between students and educators. In this situation, an adaptive learning system that could emulate the teacher-student interaction could be a handy tool. The absence of a space for physical interaction could prove difficult for both the educator as well as the learners due to the lack of a system to interact and exchange feedback in real-time. An adaptive learning system could bridge this gap as well as provide real-time interaction and feedback systems.
3. Development Tools: Adaptive learning is often a feature of many development tools. Although the degree of adaptivity could vary dramatically between examples, these learning tools are aimed at new users with a low skill level to adapt to the tool’s features. One example is SelfCAD, a browser-based as well as program that runs on both Windows and Mac. SelfCAD features an interactive tutorial that helps new learners to learn 3D modeling easily. The tutorial program features design lessons that are progressively simple and teaches the function of all the key tools. There is a free version of SelfCAD that users can use to test the software to see if it’s the best tool to use. Advanced features of the software can be accessed if you are subscribed to either of the two subscription models that follow: 

• SelfCAD Pro - $14.99 per month or $149.99 per per year
• SelfCAD Perpetual License - Lifetime access to all features for a one-time payment of $599.00

Head over to the SelfCAD website and experience the power of an adaptive learning program on 3D Design.

Conclusion

Adaptive learning is a promising approach to teaching that can address and improve the quality of education. The implementation of this innovative learning remains challenging. However, The successful execution of adaptive learning will be highly beneficial for the students, teachers, and the institutions.

---

Need to learn 3D modeling? Get started with interactive tutorials.