About this Research Topic
Learning Analytics in education has historically, and to an extent necessarily, tended to adopt an opportunistic approach to data collection and presentation. This has resulted in a multitude of approaches where key performance data on students and teachers are collected and presented for evaluation but where the feedback loop to inform and improve learning has been weak or absent.
To achieve high-quality teaching and learning that takes into account the knowledge, skills, and affective states of students and supports the development of student agency, we need education systems that can foster Self-Regulation of Learning. Using the language of Zimmerman’s (2009) model of self-regulation, we need a system that can facilitate self-reflection and self-evaluation. This is a process that can be readily informed and supported by the use of appropriate Actionable Learning Analytics.
This Research Topic aims to collect high-quality studies that focus on the effective use of Learning Analytics in informing the processes of learning. Drawing on studies from multiple discipline areas and viewpoints, we intend to curate studies that together shed light on the best practices for data use in education. In addition to research driven studies that take a top-down approach to discovering knowledge, we are also interested in teacher-researcher initiated action-research contributions that report on local learnings driven by local needs. Together these articles will contribute to a rich understanding of the many different ways in which learners’ data can be effectively utilised to inform the development of Self-Regulation of Learning and other related concepts including motivation, self-efficacy, and metacognition.
This Research Topic aims to collect original research articles from multiple disciplines and perspectives. Research that reports negative or inconclusive results is welcome to be submitted as a Brief Research Report. As such contributions are actively sought from (but not limited to) researchers looking at applications for Learning Analytics in:
- K-12 Education
- Higher and Further Education
- Vocational Education and Training
- Teacher Education and Development
- Educational Policy
- Educational Technology including Educational Games and Applications
There are three broad themes to be addressed in this Research Topic. Firstly, we welcome contributions that adopt a theoretical perspective and propose mechanisms or frameworks that might be adopted within the constraints of educational systems to facilitate the development of Self-Regulation of Learning and/or related metacognitive skills. Secondly, we seek articles that report empirical studies that focus on the impact of novel applications of learning analytic approaches on student learning outcomes, particularly, but not restricted to, non-cognitive outcomes. Finally, we invite case studies and teacher-researcher action-research reports that describe changes in learners’ attitudes or pedagogical approaches in response to learning analytics or teaching approaches and strategies that are informed by learning analytics.
Priority will be given to articles that demonstrate Actionable Learning Analytics, and papers showcasing how insights from learning analytics can be translated into day-to-day teaching practice in school and higher education settings.
Types of Article Accepted:
- Original Research (maximum word count 12,000, Type A article)
- Brief Research Report (maximum word count 4,000, Type B article)
- Systematic Literature Reviews (following PRISMA or Campbell protocols or similar, maximum word count 12,000, Type A article)
- Mini-review (maximum word count 3,000, Type B article)
- Perspective (maximum word count 3,000, Type B article)
- Curriculum, Instruction, and Pedagogy (maximum word count 5,000, Type B article)
- Teacher-Researcher Action Research report (as Brief research Report, maximum word count 4,000, Type B article)
- Opinion (maximum word count 2,000, Type C article)
Zimmerman, B. J., & Moylan, A. R. (2009). Self-regulation: Where metacognition and motivation intersect. In Handbook of metacognition in education (pp. 299-315). Routledge.
To achieve high-quality teaching and learning that takes into account the knowledge, skills, and affective states of students and supports the development of student agency, we need education systems that can foster Self-Regulation of Learning. Using the language of Zimmerman’s (2009) model of self-regulation, we need a system that can facilitate self-reflection and self-evaluation. This is a process that can be readily informed and supported by the use of appropriate Actionable Learning Analytics.
This Research Topic aims to collect high-quality studies that focus on the effective use of Learning Analytics in informing the processes of learning. Drawing on studies from multiple discipline areas and viewpoints, we intend to curate studies that together shed light on the best practices for data use in education. In addition to research driven studies that take a top-down approach to discovering knowledge, we are also interested in teacher-researcher initiated action-research contributions that report on local learnings driven by local needs. Together these articles will contribute to a rich understanding of the many different ways in which learners’ data can be effectively utilised to inform the development of Self-Regulation of Learning and other related concepts including motivation, self-efficacy, and metacognition.
This Research Topic aims to collect original research articles from multiple disciplines and perspectives. Research that reports negative or inconclusive results is welcome to be submitted as a Brief Research Report. As such contributions are actively sought from (but not limited to) researchers looking at applications for Learning Analytics in:
- K-12 Education
- Higher and Further Education
- Vocational Education and Training
- Teacher Education and Development
- Educational Policy
- Educational Technology including Educational Games and Applications
There are three broad themes to be addressed in this Research Topic. Firstly, we welcome contributions that adopt a theoretical perspective and propose mechanisms or frameworks that might be adopted within the constraints of educational systems to facilitate the development of Self-Regulation of Learning and/or related metacognitive skills. Secondly, we seek articles that report empirical studies that focus on the impact of novel applications of learning analytic approaches on student learning outcomes, particularly, but not restricted to, non-cognitive outcomes. Finally, we invite case studies and teacher-researcher action-research reports that describe changes in learners’ attitudes or pedagogical approaches in response to learning analytics or teaching approaches and strategies that are informed by learning analytics.
Priority will be given to articles that demonstrate Actionable Learning Analytics, and papers showcasing how insights from learning analytics can be translated into day-to-day teaching practice in school and higher education settings.
Types of Article Accepted:
- Original Research (maximum word count 12,000, Type A article)
- Brief Research Report (maximum word count 4,000, Type B article)
- Systematic Literature Reviews (following PRISMA or Campbell protocols or similar, maximum word count 12,000, Type A article)
- Mini-review (maximum word count 3,000, Type B article)
- Perspective (maximum word count 3,000, Type B article)
- Curriculum, Instruction, and Pedagogy (maximum word count 5,000, Type B article)
- Teacher-Researcher Action Research report (as Brief research Report, maximum word count 4,000, Type B article)
- Opinion (maximum word count 2,000, Type C article)
Zimmerman, B. J., & Moylan, A. R. (2009). Self-regulation: Where metacognition and motivation intersect. In Handbook of metacognition in education (pp. 299-315). Routledge.
Keywords: Learning Analytics (LA), Self-Regulation of Learning (SRL), Changing Pedagogies, Metacognitive Development, Learning Sciences
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.
