In the past few years, Internet online education has solved the problem of uneven distribution of some high-quality educational resources. Most people can find the course content they want to learn through various terminal devices. With the continuous development of online education courses, the content of the courses is becoming more and more perfect. However, online education still has a distance compared with traditional offline education.
If the main problem of the traditional offline mode is the uneven distribution of teachers, then the problem of online education may be whether it can achieve the unified teaching effect through online "adaptive learning" technology and realize "teaching students according to their aptitude."
Adaptive learning is the application of adaptive technology in the field of education. There are many definitions in the current definition. The definition of "Decoding Adaptive Learning" given by Pearson Group is: "Adaptive learning is an educational technology. It passes Independently provide independent help for each student, and real-time interaction with students in reality." That is to say, the adaptive learning platform will guide the students to carry out the next learning content and activities that are most suitable for him. When the students encounter the difficulty of the course too high or too low during the learning process, the difficulty level of the course can be automatically adjusted. Teachers can also use its real-time prediction technology to monitor each student's knowledge gaps, adjust them instantly, and provide individualized instruction for each student. In theory, adaptive learning can solve the problem of "teaching students in accordance with their aptitude" in online education and become a complementary learning tool to achieve online education effectiveness evaluation.
The enthusiasm of the domestic capital market for adaptive learning can also be seen that adaptive education is quite necessary for the layout of online education:
On October 12th, K12 Internet Education Company's work box announced the completion of 200 million yuan B + round of financing, founder and CEO Liu Ye said that this round of financing will be mainly used to promote the implementation of AIOC strategy (AI-Oriented-Content), that is, based on Adapt to the content construction of the learning scene.
On October 30th, the AI+ education entrepreneurship project was completed with 100 million rounds of round-robin financing. According to reports, this round of financing extreme class big data will be mainly used to expand more schools and build the next project “EI Super Teacherâ€. Operational optimization of (adaptive learning engine) and to C-side products.
On October 31st, the overseas exam training and the US study abroad service platform Zhixue completed 200 million B+ round financing. Founder and CEO Wei Xiaoliang said that the next step in the intellectual class will be deepening international education and continue to invest in “back-end†products, content and services. And technology (especially in the direction of adaptive learning), and force brand building.
From the point of view of the capital market, some online education institutions will invest most of them in the adaptive platform in the future. Does it imply that domestic online education has entered the adaptive era in the context of market demand and capital promotion?
Let's start with the adaptive learning of online education products in China. The following are the adaptive learning platforms under the various categories of online education:
From the perspective of the functional realization of the adaptive platform, there are basically three types of adaptive platform types in China:
The first category, a self-adaptive platform that combines the content of its own learning
The platform itself has its own curriculum, students can test through the platform in the early stage, obtain their own level of evaluation results, and then obtain content recommendation and learning on the platform according to the evaluation. Taking the dropout as an example, using the means of intelligent adaptation, the knowledge points are segmented, the targeted teaching is strengthened for the weak links of the children, and the data of the interaction between the students and the course content is continuously analyzed according to the students' learning and answering questions.
The second category, the adaptive platform based on the question bank
The platform itself collects a large number of questions, and through the repeated questions of the students on each knowledge point, the students finally grasp the knowledge points. This kind of counseling tool-type platform cuts into the education market from the back words, answers, photos and answers, etc., mostly based on apps, such as 猿 猿 ã€, fluent in English, etc. AI+ education hurdles transform artificial intelligence teaching from the original field. Upgrade products or develop new content models.
The third category, the teaching service class adaptive platform
Through the students' learning content and completion status on the platform, students and teachers can obtain the diagnosis results through such adaptive learning platform. Taking extracurricular tutoring as an example, under ideal conditions, online education has made more choices for off-campus tutoring, and at the same time, solved the problem of uneven distribution of some high-quality educational resources.
It can be seen that although domestic self-adaptation has taken shape in capital and theory, it still belongs to the initial stage, which is basically a combination of testing, feedback, error correction and platform teaching content for students' learning. In comparison, foreign adaptive learning can already predict student learning, which is relatively mature, taking knewton as an example:
Knowton can make a variety of student data to form a learning model, so as to make different learning content recommendations for different student students.
The Knewton platform can predict the overall level of learning and the level of improvement that can be improved by analyzing the overall data, and the forecasting content such as the learning time that you need to raise to such a level.
In terms of learning content and quality of learning, knewton will provide corresponding learning content according to each individual's situation. Students can clearly see the feedback of each question, and the difficulty level of the questions after completing each question will increase or decrease. Unable to complete the problem, knewton can promptly assist the students to complete the course. The data shows that after using knewton, the dropout rate of some schools has dropped significantly, and the pass rate has increased by about 10%.
Therefore, compared with foreign adaptive platforms, domestic adaptive learning still needs to strengthen the following three points in actual operation:
1. It is necessary to construct a knowledge map of the perfect system and label and structure the knowledge point system. At present, there is no platform for realizing a complete knowledge map through big data technology in China. We need to productize the discipline rules that are deposited in the minds of excellent teachers through big data technology.
2. Implement mapping for each learning behavior of the user. The most important thing in adaptive learning is to enhance the learning effect in the continuous feedback and correction of learning behavior. In the process of learning, adaptive learning needs to analyze and predict students' changes to recommend more appropriate content. This requires flexibility and diversification of adaptive learning algorithms.
3. Calculate the best learning path through the algorithm. Adaptive learning is a new paradigm of education technology based on big data. Only by breaking through the underlying technology can we achieve the desired effect. On the one hand, the system needs to deeply understand the “adaptive†problem arising from the current user's personalized learning needs, and at the same time recommend the ideal learning path to the target users, effectively improve the accuracy of the recommended resources, and thus improve the user's learning quality and learning. effect.
Although these domestic enterprises have already started practical combat learning, it seems technically feasible, but it still needs to be verified by large-scale educational practice. However, it is undeniable that online education has opened the era of “adaptive learningâ€. The core of online education in the future will be the application of big data and personalized service. Adaptive learning, as the core of personalized learning, needs to be deep. The continuous improvement of learning and artificial intelligence technology is truly "teaching in accordance with the aptitude".
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