实现全纳、公平与个性化的学习是面向2035的中国教育现代化追求的核心目标，是世界各国教育创新的关注点，同时，也是落实我国基础教育课程改革的核心理念。然而，以班级为主的课堂教学中，大规模培养与个性化发展无法兼顾的矛盾依然存在，主要表现在：教师仅凭个人精力难以兼顾“面向全体”与“因材施教”；常态教学往往只关注学习者已有认知水平的静态分析，忽视对学生潜在认知发展过程的动态分析，缺乏对学生认知特征与规律的深度挖掘；教师对学习者的诊断与识别更多是建立在以经验为主的静态预设，较难提供适时性的精准教学支持。

维果斯基提出最近发展区的概念，正是解决教学与发展的关系，并由此衍生出动态能力评估。然而，评估最近发展区需要采集大量学习者的数据作为支撑，传统教学中由于缺乏对学习过程中的数据采集和分析手段，难以对学习者的最近发展区进行有效评估，也就难以支持精准教学。运用大数据、人工智能等新兴技术对学习者在线学习数据进行有效采集和分析，为诊断学习者最近发展区，探索教育教学规律，促进数据驱动的精准教学提供技术支持。

基于上述分析，本研究期望针对当前教育中的问题，提出基于最近发展区的精准教学之方法与思路，一方面构建学习者最近发展区的表征模型与诊断方法，精准分析学生当前认知水平，以及可能达到的潜在认知水平。另一方面，教师基于学习者的最近发展区，在最佳的教学时机提供最佳的教学指导，形成满足学生个性化需求的精准教学模式，将因材施教落实在课堂教学。针对上述问题，主要在以下三方面开展研究：

（1）本研究构建学习者最近发展区模型，表征概念学习过程中，学习者在知识、能力、素养多维度的已有水平、潜在发展水平以及达成潜在发展水平所需要的学习支持形态。

（2）基于最近发展区表征模型设计并开发教学应用系统，汇聚学习者学习过程数据，实现学习者最近发展区的量化与可视化呈现，从工程开发的角度将学习者最近发展区模型转化为可实施和应用的功能，使得该系统可以支持精准教学实践。

（3）在基础教育领域开展基于学习者最近发展区的精准教学实践，形成特定教学情境的教学模式，验证了基于最近发展区的精准教学之积极影响和良好效果。

本研究的创新之处在于：

 （1）从学科概念理解视角提出学习者最近发展区表征模型，对学习者最近发展区的表征从概念描述，转向在特定学科领域对学习者认知结构的具体表征。基于最近发展区理论，表征在学习者概念理解过程中，知识结构、学科能力及学科素养等多维度潜在发展水平及所需的学习支持形态。

（2）从动态评估视角实现对学习者潜在认知发展过程与规律的分析与可视化，从对学习者现有能力的静态评价，转向聚焦于学习者学科概念发展潜力的动态评价。基于数据驱动的动态评估理论，实现学习者在学科核心概念理解过程中，其最近发展区的量化和识别，是对现有评估范式的一种创新。

（3）从教学实证研究视角实现基于学习者最近发展区的精准教学实践，将基于最近发展区的教学系统应用在教学实践中，形成特定情境下的精准教学模式，实现了教师依据最近发展区进行科学的精准教学设计，对课堂教学实践产生有意义的影响。

本研究仍然存在不足之处，需要在后续研究中改进和完善，包括利用机器学习、文本挖掘、自动化语义标注等技术来实现知识测试题目和多步骤反馈的编码和标注；利用深度学习技术进行建模研究；利用脑电技术监测学习过程中学习者的内部认知变化，从更多视角分析学习者最近发展区的影响因素。

Realizing all-around, fair and personalized learning is the core goal of the modernization of China's education in 2035, the focus of educational innovation in all countries of the world, and the core concept of implementing the curriculum reform of basic education in China. However, in classroom teaching, the contradiction between large-scale teaching and personalized development still exists, mainly manifested in the following aspects: Teachers cannot balance "education for all" and "education according to one’s personalised ability" by personal energy alone; Normal teaching only focuses on the static analysis of learners' existing cognitive level, neglecting the dynamic analysis of students' potential cognitive development process, and lacking deep analysis of students' cognitive characteristics and rules; Teachers' diagnosis and recognition upon learners are mostly based on static presupposition based on experience, which is difficult to provide timely and precision teaching support.

Vygotsky put forward the concept of the zone of proximal development, which is to solve the relationship between teaching and development, and thus derived from the dynamic assessment. However, the assessment of zone of proximal development needs to collect a large number of learners' data as a support for the reason that traditional teaching is difficult to effectively assess the zone of proximal development of learners or support precision teaching due to the lack of data collection and analysis in the learning process. Using new technologies, such as big data and artificial intelligence, to collect and analyze learners' online learning data effectively provides technical support for diagnosing learners' zone of proximal development, exploring the laws of education and teaching, and promoting data-driven precision teaching.

Based on the above analysis, this study expects to put forward the methods and ideas of precision teaching based on the zone of proximal development. On the one hand, it aims to construct the model and diagnostic method of the learner's zone of proximal development, and to analyze the students' current cognitive level accurately, as well as the potential cognitive level that may be reached. On the other hand, based on the learner's zone of proximal development, teachers provide the best teaching guidance in the best teaching time, forming a precision teaching model to meet the individual needs of students and applying education according to individual needs of students in the classroom. In view of the above-mentioned problems, there are the following three aspects of research:

(1) This study constructs the learner's model of the zone of proximal development, which shows the existing level and potential development level of learners in the dimension of knowledge, ability and literacy in the process of concept learning.

(2) Under the support of the model, this study design and develop the corresponding teaching application system, which aims to collect together the learners' learning process data, to realize the learner's recent development area quantitative calculation method and visual presentation. From the perspective of engineering development, the model of learner's zone of proximal development is transformed into a function that can be implemented and applied, so that the system can support precision teaching practice.

(3) This study aims to carry out the practice of precision teaching based on learners' zone of proximal development in the field of basic education, to form a teaching mode of specific teaching situation, and meanwhile to verify the influence and effect of the model and application system of the zone of proximal development on the development of precision teaching.

The innovations in this study are:

 (1) This paper proposes a representation model of learner's zone of proximal development from the perspective of subject concept understanding. The representation of learner's zone of proximal development changes from concept description to specific representation of learner's cognitive structure in a specific subject field. Based on the theory of zone of proximal development, this paper represents the multidimensional potential development level of knowledge structure, subject ability and subject literacy as well as the learning support form needed in the process of learners' concept understanding.

 (2) From the perspective of dynamic evaluation, it can realize the analysis and visualization of learners' potential cognitive development process and law, from the static evaluation of learners' existing ability to the dynamic evaluation focusing on the development potential of learners' subject concept. Based on the data-driven dynamic assessment theory, it is an innovation of the existing assessment paradigm to realize the quantification and identification of learners' zone of proximal development in the process of understanding core concepts.

(3) From the perspective of teaching empirical research, it realizes the precise teaching practice based on the learners' zone of proximal development, applies the teaching system based on the zone of proximal development in teaching practice, forms the precise teaching mode in specific situations, realizes the teachers' scientific precise teaching design based on the zone of proximal development, and has a significant impact on classroom teaching practice.

There are still some shortcomings which need to be improved and perfected in the follow-up research, including the use of machine learning, text mining, automatic semantic annotation and other technologies to realize the coding and annotation of knowledge test questions and multi-step feedback, the use of deep learning technology for modeling research, the use of EEG technology to monitor the internal cognitive changes of learners in the learning process and the influencing factors of the zone of proximal development are analyzed from more perspectives.