Visual Learning Analytics (VLA) tools and technologies enable meaningful exchange of information between educational data and teachers. This allows teachers to create meaningful groups of students based on possible collaboration and productive discussions. VLA tools also allow to better understand students' educational demands. Finding similar samples in huge educational datasets, however, involves the use of effective similarity measures that represent the teacher's purpose. In this study, we conducted a user study and improved our web-based VLA tool, Similarity-Based Grouping (SBGTool), to help teachers categorize students into groups based on their similar learning outcomes and activities. SBGTool v2.0 differs from SBGTool due to design changes made in response to teacher suggestions, the addition of sorting options to the dashboard table, the addition of a dropdown component to group the students into classrooms and improvement in some visualizations. To counteract colour-blindness, we have also considered a number of color palettes. By applying SBGTool v2.0, teachers may compare the outcomes of individual students inside a classroom, determine which subjects are the most and least difficult over the period of a week or an academic year, identify the number of correct and incorrect responses for the most difficult and easiest subjects, categorize students into various groups based on their learning outcomes, discover the week with the most interactions for examining students' engagement, and find the relationship between students’ activity and study success. We used 10,000 random samples from the EdNet dataset, a large-scale hierarchical educational dataset consisting of student-system interactions from multiple platforms at the university level, collected over a two-year period, to illustrate the tool's efficacy. Finally, we provide the outcomes of the user study that evaluated the tool's effectiveness. The results revealed that even with limited training, the participants were able to complete the required analysis tasks. Additionally, the participants’ feedback showed that the SBGTool v2.0 gained a good level of support for the given tasks, and it had the potential to assist teachers in enhancing collaborative learning in their classrooms.

Visual Learning Analytics (VLA) uses analytics to monitor and assess educational data by combining visual and automated analysis to provide educational explanations. Such tools could aid teachers in primary and secondary schools in making pedagogical decisions, however, the evidence of their effectiveness and benefits is still limited. With this scoping review, we provide a comprehensive overview of related research on proposed VLA methods, as well as identifying any gaps in the literature that could assist in describing new and helpful directions to the field. This review searched all relevant articles in five accessible databases — Scopus, Web of Science, ERIC, ACM, and IEEE Xplore — using 40 keywords. These studies were mapped, categorized, and summarized based on their objectives, the collected data, the intervention approaches employed, and the results obtained. The results determined what affordances the VLA tools allowed, what kind of visualizations were used to inform teachers and students, and, more importantly, positive evidence of educational interventions. We conclude that there are moderate-to-clear learning improvements within the limit of the studies’ interventions to support the use of VLA tools. More systematic research is needed to determine whether any learning gains are translated into long-term improvements.

There is a significant amount of data available about students and their learning activities in many educational systems today. However, these datasets are frequently spread across several different digital services, making it challenging to use them strategically. In addition, there are no established standards for collecting, processing, analyzing, and presenting such data. As a result, school leaders, teachers, and students do not capitalize on the possibility of making decisions based on data. This is a serious barrier to the improvement of work in schools, teacher and student progress, and the development of effective Educational Technology (EdTech) products and services. Data standards can be used as a protocol on how different IT systems communicate with each other. When working with data from different public and private institutions simultaneously (e.g., different municipalities and EdTech companies), having a trustworthy data pipeline for retrieving the data and storing it in a secure warehouse is critical. In this study, we propose a technical solution containing a data pipeline by employing a secure warehouse—the Swedish University Computer Network (SUNET), which is an interface for information exchange between operational processes in schools. We conducted a user study in collaboration with four municipalities and four EdTech companies based in Sweden. Our proposal involves introducing a data standard to facilitate the integration of educational data from diverse resources in our SUNET drive. To accomplish this, we created customized scripts for each stakeholder, tailored to their specific data formats, with the aim of merging the students’ data. The results of the first four steps show that our solution works. Once the results of the next three steps are in, we will contemplate scaling up our technical solution nationwide. With the implementation of the suggested data standard and the utilization of the proposed technical solution, diverse stakeholders can benefit from improved management, transportation, analysis, and visualization of educational data.

Learning Analytics Dashboards (LADs) can help provide insights and inform pedagogical decisions by supporting the analysis of large amounts of educational data, obtained from sources such as Digital Learning Materials (DLMs). Extracting requirements is a crucial step in developing a LAD, as it helps identify the underlying design problem that needs to be addressed. In fact, determining the problem that requires a solution is one of the primary objectives of requirements extraction. Although there have been studies on the development of LADs for K12 education, these studies have not specifically emphasized the use of a Human-Centered Design (HCD) approach to better comprehend the teachers’ requirements and produce more stimulating insights. In this paper we apply prototyping, which is widely acknowledged as a successful way for rapidly implementing cost-effective designs and efficiently gathering stakeholder feedback, to elicit such requirements. We present a three-step HCD approach, involving a design cycle that employs paper and interactive prototypes to guide the systematic and effective design of LADs that truly meet teacher requirements in primary/secondary education, actively engaging them in the design process. We then conducted interviews and usability testing to co-design and develop a LAD that can be used in classroom’s everyday learning activities. Our results show that the visualizations of the interactive prototype were easily interpreted by the participants, verifying our initial goal of co-developing an easy-to-use LAD.