Research topic and aim

The generation and use of digital data and analyses in education comes with promises and opportunities, especially where digital materials allow use of Learning Analytics (LA) as a tool in Data- Based Decision-Making (DBDM). An exploratory systematic scoping review was conducted regarding the use and impact of LA and DBDM in classroom practice to outline aspects related to Digital Learning Material (DLM), teacher usage, and student learning in the context of K-12 mathematics education.

Theoretical framework

LA implies, analysing data to understand and optimise learning and learning environments (Siemens & Baker, 2012). In this study we discuss LA as “a sophisticated form of data driven decision making” (Mandinach & Abrams, 2022, p. 196). Data driven decision making or DBDM is a process used by teachers to make decisions based on data, to implement improvement actions and evaluate these innovations (Schildkamp & Kuiper, 2010). LA in DLM can offer learners adaptive functions embedded in DLMs or provide learners (and teachers) compiled student assessments in relation to learning goals extracted from learning activities (Wise, Zhao & Hausknecht, 2014). We focus on LA-use based on digital data for student learning, for teaching and for teachers’ DBDM.

Methodological design

The methodology used the five-stage framework (Arksey & O’Malley, 2005), identifying the research question, identifying relevant studies, study selection, charting the data, collating, summarizing, and reporting the results. For the analysis, thematic summary and synthesis was used to answer:

RQ1: How are analyses of digital data from DLM used in mathematics education?

RQ2: How do analyses of digital data from DLM impact teaching and learning?

Expected conclusions/findings

3653 records were identified whereof 15 studies were included. Results show that LA-research is an emerging field where LA-applications is used across many contents and curricula standards of K-12 mathematics education. LA-usage supports a wide variety of teachers’ data use. However, teaching by DBDM focused on supervision and guidance. LA-usage have a positive effect on student learning where high-performing students benefit most. Finally, we suggest a definition of an additional class of LA, which we introduce as Guiding analytics for learners.

Relevance to Nordic educational research

Research on using LA and DBDM is essential to support teachers and school leaders to meet today’s demands of utilising data, to be aware of possible unwanted consequences, and to use technology to enhance active learners and students’ ownership of learning.