近日，我系詹沛达博士与我院教育技术系李菲茗教授和美国马里兰大学帕克分校Jiao, Hong (焦红)教授共同出任SSCI期刊Frontiers in Psychology - Quantitative Psychology and Measurement (Q2, IF = 2.086)的客座副主编，并主持Cognitive Diagnostic Assessment for Learning主题。该主题旨在促进心理计量学、教育心理学、教育技术和教育数据挖掘等相关专业的交叉融合。
主题关键词：认知诊断; 学习; 纵向数据分析; 智能导学系统
主题内容如下：
Cognitive Diagnostic Assessment for Learning
【面向学习的认知诊断测评】
(约稿信息见文末)
个人成长/改变长期以来一直是教育、心理和行为研究中的一个活跃话题。近几十年来，为促进学生学习，基于学习评估和认知诊断测评(cognitive diagnostic assessment, CDA)的特点，客观量化学习状态并提供诊断反馈的评估模式越来越受到重视。
Individual growth/change has long been an active topic in educational, psychological and behavioral studies. During the last decades, the assessment pattern of objectively quantifying the learning status and providing diagnostic feedback has been increasingly valued, aiming at promoting students’ learning, based on learning assessment and the characteristics of cognitive diagnostic assessments (CDA).
虽然现有的大多数CDA的研究均提到CDA的主要目的是识别学生在学习状态上的优缺点，并为针对性的补习教学提供指导，但却少有研究对补习教学的效果进行评价。主要原因是目前大多数CDA采用的是横断设计。这意味着在特定的时间点只进行一次评估。这一问题也可反映在作为CDA核心组成部分之一的现有认知诊断模型(cognitive diagnosis models, CDMs)的发展。虽然已有研究人员提出了多种CDM，但大多数都只适用于横断数据分析。
Although most of the existing studies on CDA mentioned that the main objective of CDA is to identify students’ strengths and weaknesses in their learning status and to provide guidance for targeted remedial teachings, still few studies have focused on and evaluated the effectiveness of such remedial teachings. The main reason is that the cross-sectional design is currently adopted by most CDAs. This means that only one assessment is done at a specific point in time. This issue may also reflect on the development of existing cognitive diagnosis models (CDMs) which is one of the core components of CDA. Although various CDMs have been suggested by previous researchers, most of them are only applicable to cross-sectional data analysis.
在整个学习过程中，从测评中收集的纵向数据为研究人员提供了开发学习模型的机会，该类模型可用于跟踪个人随时间的发展和成长，并评估补救教学的效果。与横断CDA相比，纵向CDA在促进学生发展方面更有帮助。
The longitudinal data collected from the assessments throughout the learning process provides researchers with the chance to develop learning models, that can be
本议题的目的是强调在学习中评估和/或改进个人成长(尤其是使用CDA)的问题、实践和方法。
以下摘要突出了本研究主题的范围和期望。
相关研究领域包括教育与心理测量、教育数据挖掘和教育技术。
The purpose of this Research Topic is to highlight issues, practices, and methodologies dealing with the evaluation and/or improvement of individual growth in learning, especially using CDA.
The following summary highlights the scope and expectations of this Research Topic.
Related research areas include educational and psychological measurement, education data mining, and educational technology.
我们欢迎作者提出以下主题，但是，这个列表并不打算限制任何潜在作品的原创性:
1. 提出纵向认知诊断测评数据分析的新模型;
2. 改进现有纵向认知诊断模型的模型参数估计;
3. 采用纵向认知诊断测评，提高个人的学科核心素养;
4. 开发用于学习诊断和跟踪的技术增强创新评估;
5. 提出认知诊断计算机自适应测验的新选题策略;
6. 在认知诊断智能辅导系统中提出新的推荐方法;
7. 提出新的模型或方法来测评高阶思维技能，例如创造力、合作解决问题、领导力、批判性思维等;
8. 根据认知诊断结果，提出有效且系统的干预或补救教学方法。
We welcome authors to address the following themes, however, this list does not intend to limit the originality of any potential work:
1. Suggest new models for longitudinal cognitive diagnostic assessment data analysis;
2. Improve model parameter estimation in current longitudinal cognitive diagnosis models;
3. Adopt longitudinal cognitive diagnostic assessments to improve individual learning in domain-specific key competencies;
4. Develop technology enhanced innovative assessments for learning diagnosis and tracking;
5. Suggest new item-selected strategies in cognitive diagnostic computerized adaptive testings;
6. Suggest new recommendation methods in cognitive diagnostic intelligent tutoring systems;
7. Present new models or methods to assess higher-order thinking skills, e.g., creating, cooperative problem solving, leadership, critical thinking, etc;
8. Propose effective & systematic interventions or remedial teaching methods based on cognitive diagnostic results.
Keywords: cognitive diagnosis, learning tracking, intelligent tutoring system, learning assessment, longitudinal analysis
【约稿信息】
约稿杂志信息：
Frontiers in Psychology
SSCI期刊，JCR二区(Q2)杂志，IF = 2.089，开源期刊(Open Access)
投稿时间节点(dead line):
摘要截止于：2019年9月20日
全文截至于：2020年5月20日
[注：可以先投摘要，然后在全文截止时间前投稿全文]
专题及投稿链接：
https://www.frontiersin.org/research-topics/10482
专题主编：
Peida Zhan (詹沛达)
Department of Psychology, Zhejiang Normal University, China
Feiming Li (李菲茗)
Department of Educational Technology, Zhejiang Normal University, China
Hong Jiao (焦红)
Department of Human Development and Quantitative Methodology, University of Maryland, College Park, USA
文章录用后的相关费用详见：
https://www.frontiersin.org/about/publishing-fees
