About this Research Topic
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),
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.
The longitudinal data collected from the assessments throughout the learning process provides researchers with the chance to develop learning models, that can be adopted to track individual growth over time and to evaluate remedial teachings’ effectiveness. Compared to the cross-sectional cognitive diagnostic assessment, the longitudinal cognitive diagnostic assessment is more helpful when aiming to promote students' development.
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.
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.
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.
The longitudinal data collected from the assessments throughout the learning process provides researchers with the chance to develop learning models, that can be adopted to track individual growth over time and to evaluate remedial teachings’ effectiveness. Compared to the cross-sectional cognitive diagnostic assessment, the longitudinal cognitive diagnostic assessment is more helpful when aiming to promote students' development.
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.
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
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
