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Login here. Summary: With adaptive learning, instead of a broad-based approach, students in schools and colleges can have learning modules tailored around their specific needs, ways of learning, and any learning difficulties they have.
Adaptive Learning Is The Future Of Education For schools and colleges to implement this style of learning across the curriculum and every course, it would require an educational paradigm shift that is unprecedented. Benefits Of Adaptive Learning Because every student learns in different ways, even when these are classified into broad groups e.
How To Apply Adaptive Learning In Practice Thankfully, we are now at a point whereby educational software is advanced enough that it can be more easily tailored or customized around the needs of students, educators and content creators. Adaptive Learning 3.
AI-powered adaptive solutions leverage network knowledge maps to create knowledge and behavioral nodes, forming deeper relationships between content, learning objectives, and persona types, to name a few. This powers a more efficient, effective learning experience and enables:. It is not just the learning experience that is amplified. The platforms that are fully embracing AI and machine learning are also able to provide dramatic efficiencies to learning development and content creation.
For example, AI-powered platforms can assess the performance of learning content, flag underperforming content and even hide underperforming assessments until they are revised. These AI-powered adaptive solutions are not just delivering on the promise of intelligent, instructor-like adaptive learning, they are also addressing some of the old weaknesses inherent in the Adaptive 1. For example, AI can:.
In Adaptive 1. New AI-powered adaptive systems can provide adaptive feedback to help guide learners toward accuracy and keep them moving forward, so they do not get stuck in these frustrating loops. As discussed above, some Adaptive 1. For learners who already have experience or knowledge on the topic, this can be a very frustrating and tedious process. As systems move toward more AI-enabled pathways, they will no longer have to insist that learners review the content before they can pass out of it.
Systems will be able to ascertain, in real-time, what a learner knows and adjust the context and difficulty of the content accordingly. Conversely, some adaptive 1. Think of it as a technology-powered version of flashcards. All of these three approaches can be implemented based on the adaptive adjustment of data-based teaching decisions.
Therefore, data decision making is the core hub, see Fig. By using the three levels of personalized learning as the abscissa and the two types of data decision-making as the ordinate, six parts of adaptive adjustment instruction strategies can be divided by a two-dimensional coordinate system, a spectrum of these strategies is depicted, see Fig.
The wavy line in the figure represents the forth approaches route of personalized adaptive learning which starts from the bottom left towards the upper right. The spectrum of adaptive adjustment instruction strategies based on man-machine collaborative decision-making. The route is divided into three layers. They are individual characters layer , individual performance layer , and personal development layer.
Each layer consists of two phases. The data-driven decision-making stage of this layer focus on resources recommend. Which mean machines recommend a resource list that matches the individual characteristics of the student who needs help, or a resource list learned by the successful learners who have similar individual characteristics with the student. The list is sorted according to the matching degree from high to low, and students can choose their favorite resources to learn.
The data-informed decision-making stage of this layer focuses on content design. Which means that teachers use agile design ideas to design learning content for learners whose individual characters failed to match that of others. The data-driven decision-making stage at this layer mainly focuses on activities guide.
If the pattern shows that the student has a problem and the problem is only an individual phenomenon, then it can be concluded that the problem comes from the student instead of teaching strategies. If the problem is a problem that happens to the majority, then it can be concluded that there is a high possibility that the teaching strategies need to be optimized.
This process occurs in the stage of data-informed decision-making of this layer. The data-driven decision-making stage of this layer predicts whether students can complete the learning objectives ahead of time by monitoring the learning achievements for specific instruction, please refer to measuring to assist learning mechanism of precision teaching , and the answer is yes, then more challenging expanded tasks that meet their personal vision would be recommended.
The data-informed decision-making stage of the layer focuses on the problems that students encounter when they are doing challenging expanded tasks. Which is to use a centralized tutoring method for the same problems encountered by the majority, and to provide individual tutoring methods for the problems encountered by individuals.
The new approaches route provides personalized adaptive learning services for learners in terms of the degree of personalization, and it can be well compatible and integrated with differentiated instruction, adaptive learning, and personalized learning. Therefore, this route sheds a light on the path for technology developers to achieve personalized adaptive learning based on the original informatization achievements.
Among these four attributes, building a flexible learning environment is the basic premise of the others. In the following section, learning portrait model and paths recommendation pattern of personal learning are explored.
Learning portrait model is a learning sequence composed of learning cells, it depicts the processes of the occurrence and development of learning. Considering this issue, a new learning portrait model is proposed to solve the problem of the precisive recommendation of personal learning path from a new perspective. The cells sequence, also called the learning pattern, is a directed graph which its nodes are composed of learning cells.
The time in the node of the learning sequence represents the effective learing time spent on one learning cell and the time on the side represents the time interval in which the learner starts a new cell for the last one. The former time can reflect the degree of learning engagement. The longer this time, the bigger engagement the learner has in a certain part of the learning content. The smaller this time, the stronger the learning motivation.
Therefore, the learning cell is an important element in learning portraits. The internal structure of the learning cell is shown in Fig. The materials for learning are at the lower level, that is the remembering and understanding level. This kind of materials includes various multimedia learning resources related to the learning content, such as text, video and audio, animation, etc..
It includes problem sets and tool kits, etc. Materials for creation are used for the transfer of learning content.
Activities can be divided into three levels hierarchy: activities , actions , and operations. The learning activity can be seen as a sequence of learning behaviors. Learning effects refer to the learning outcomes after the learner completes a learning activity.
Fluency consists of correct fluency and error fluency, which is the correct or incorrect test score divided by the time it takes. By introducing the test time dimension, the efficiency of fluency can be greatly improved. For example, the learner took less time can be marked better by fluency than the one who has the same score but took more time.
Previous studies define learning path as the sequences of activities and concept selected by the learners in the learning process Kardan et al. In this study, a learning path is defined as a learning sequence composed of learning cells. In the following section, we will discuss the path recommendation process, the learning portrait matching strategies, and related algorithms.
The flow of paths recommendation is shown in Fig. This is a process of continuously iterative generation; It allows the learner to have the autonomy of learning, instead of forcibly recommending the learning content, which highlights the initiative and subjectivity of the learner. The learning path generation recommendation model has three important features: precision, personalization, and generativity. This can fully reflect the initiative and subjectivity of the learner. Generativity fits the dynamics of learning levels and helps to improve the accuracy of recommendations.
The most critical step in the learning path generative recommendation is the matching of learning portraits. Class A learner refers to a learner who has no or very little recorded learning data. Class B learner matching is for a learner with a certain amount of learning data. Recommend the most similar learning cell of a successful learner will work. Class C learner matching is for the learner with a large amount of learning profile data.
By interpreting SLE, we found that it has a large potential to effectively promote the development of personalized learning and adaptive learning, accelerating the fusion of these two learning methods Hey et al. In this paper, the concept of personalized adaptive learning is proposed and used as a new pedagogical approach enabled by SLE. Additionally, the core elements and core concepts of personalized adaptive learning were presented through the comparative analysis between personalized learning and adaptive learning, and a framework of personalized adaptive learning was thus constructed in a visualized format.
Finally, personalized adaptive learning could be constructed with the following four aspects, that is, learner profiles, competency-based progression, personal learning, and flexible learning environments.
We hope that such research could present our readers a clear understanding of personalized adaptive learning, and provide a reference for the future relevant research and practices. Adams Becker, M.
Cummins, A. Davis, A. Freeman, C. Hall Giesinger, V. Google Scholar. Poo If you would like to find out more, please Contact Us. Sunday, November 14, Learning Light. Share on Facebook. Adaptive Learning Defined You may have heard of adaptive learning, or any of its many synonyms — such as adaptive instruction or intelligent tutoring systems.
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