Dimensionality Reduction (DR) scatterplot layouts have become a ubiquitous visualization tool for analyzing multidimensional datasets. Despite their popularity, such scatterplots suffer from occlusion, especially when informative glyphs are used to represent data instances, potentially obfuscating critical information for the analysis under execution. Different strategies have been devised to address this issue, either producing overlap-free layouts that lack the powerful capabilities of contemporary DR techniques in uncovering interesting data patterns or eliminating overlaps as a post-processing strategy. Despite the good results of post-processing techniques, most of the best methods typically expand or distort the scatterplot area, thus reducing glyphs’ size (sometimes) to unreadable dimensions, defeating the purpose of removing overlaps. This article presents Distance Grid (DGrid) , a novel post-processing strategy to remove overlaps from DR layouts that faithfully preserves the original layout's characteristics and bounds the minimum glyph sizes. We show that DGrid surpasses the state-of-the-art in overlap removal (through an extensive comparative evaluation considering multiple different metrics) while also being one of the fastest techniques, especially for large datasets. A user study with 51 participants also shows that DGrid is consistently ranked among the top techniques for preserving the original scatterplots’ visual characteristics and the aesthetics of the final results.

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.

As the complexity of Machine Learning (ML) models increases and their application in different (and critical) domains grows, there is a strong demand for more interpretable and trustworthy ML. A direct, model-agnostic, way to interpret such models is to train surrogate models—such as rule sets and decision trees—that sufficiently approximate the original ones while being simpler and easier-to-explain. Yet, rule sets can become very lengthy, with many if-else statements, and decision tree depth grows rapidly when accurately emulating complex ML models. In such cases, both approaches can fail to meet their core goal—providing users with model interpretability. To tackle this, we propose DeforestVis, a visual analytics tool that offers summarization of the behavior of complex ML models by providing surrogate decision stumps (one-level decision trees) generated with the Adaptive Boosting (AdaBoost) technique. DeforestVis helps users to explore the complexity vs fidelity trade-off by incrementally generating more stumps, creating attribute-based explanations with weighted stumps to justify decision making, and analyzing the impact of rule overriding on training instance allocation between one or more stumps. An independent test set allows users to monitor the effectiveness of manual rule changes and form hypotheses based on case-by-case analyses. We show the applicability and usefulness of DeforestVis with two use cases and expert interviews with data analysts and model developers.

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.

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.

We present a conceptual framework for the development of visual interactive techniques to formalize and externalize trust in machine learning (ML) workflows. Currently, trust in ML applications is an implicit process that takes place in the user-s mind. As such, there is no method of feedback or communication of trust that can be acted upon. Our framework will be instrumental in developing interactive visualization approaches that will help users to efficiently and effectively build and communicate trust in ways that fit each of the ML process stages. We formulate several research questions and directions that include: 1) a typology/taxonomy of trust objects, trust issues, and possible reasons for (mis)trust; 2) formalisms to represent trust in machine-readable form; 3) means by which users can express their state of trust by interacting with a computer system (e.g., text, drawing, marking); 4) ways in which a system can facilitate users- expression and communication of the state of trust; and 5) creation of visual interactive techniques for representation and exploration of trust over all stages of an ML pipeline.

Visual Learning Analytics (VLA) tools (such as dashboards) serve as a centralized hub for monitoring and analyzing educational data. Dashboards can assist teachers in data-informed pedagogical decision-making and/or students in following their own learning progress. However, the design of VLA tools should include features of trust in order to make analytics overt among its users. In this study, we propose a framework for the development of VLA tools from beginning to end that describes how we intend to develop the digital and technical infrastructure in our project for teachers. With that aim, we offer one scenario describing how data is managed, transferred, analyzed, and visualized by teachers. The suggested framework intends to make it easier for developers to understand the various steps involved in co-designing and developing a reliable VLA tool and to comprehend the importance of the teacher’s participation in design. VLA tools developed based on the proposed framework have the potential to assist teachers in understanding and analyzing educational data, monitoring students’ learning paths based on their learning outcomes and activities, simplifying regular tasks, and giving teachers more time to support teaching/learning and growth.

Bagging and boosting are two popular ensemble methods in machine learning (ML) that produce many individual decision trees. Due to the inherent ensemble characteristic of these methods, they typically outperform single decision trees or other ML models in predictive performance. However, numerous decision paths are generated for each decision tree, increasing the overall complexity of the model and hindering its use in domains that require trustworthy and explainable decisions, such as finance, social care, and health care. Thus, the interpretability of bagging and boosting algorithms—such as random forest and adaptive boosting—reduces as the number of decisions rises. In this paper, we propose a visual analytics tool that aims to assist users in extracting decisions from such ML models via a thorough visual inspection workflow that includes selecting a set of robust and diverse models (originating from different ensemble learning algorithms), choosing important features according to their global contribution, and deciding which decisions are essential for global explanation (or locally, for specific cases). The outcome is a final decision based on the class agreement of several models and the explored manual decisions exported by users. We evaluated the applicability and effectiveness of VisRuler via a use case, a usage scenario, and a user study. The evaluation revealed that most users managed to successfully use our system to explore decision rules visually, performing the proposed tasks and answering the given questions in a satisfying way.

Streaming data applications are becoming more common due to the ability ofdifferent information sources to continuously capture or produce data, such as sensors and social media. Although there are recent advances, most visualization approaches, particularly Dimensionality Reduction (DR) techniques, cannot be directly applied in such scenarios due to the transient nature of streaming data. A few DR methods currently address this limitation using online or incremental strategies, continuously updating the visualization as data is received. Despite their relative success, most impose the need to store and access the data multiple times to produce a complete projection, not being appropriate for streaming where data continuously grow. Others do not impose such requirements but cannot update the position of the data already projected, potentially resulting in visual artifacts. This paper presents Xtreaming, a novel incremental DR technique that continuously updates the visual representation to reflect new emerging structures or patterns without visiting the high-dimensional data more than once. Our tests show that in streaming scenarios where data is not fully stored in-memory, Xtreaming is competitive in terms of quality compared to other streaming and incremental techniques while being orders of magnitude faster.

The machine learning (ML) life cycle involves a series of iterative steps, from the effective gathering and preparation of the data—including complex feature engineering processes—to the presentation and improvement of results, with various algorithms to choose from in every step. Feature engineering in particular can be very beneficial for ML, leading to numerous improvements such as boosting the predictive results, decreasing computational times, reducing excessive noise, and increasing the transparency behind the decisions taken during the training. Despite that, while several visual analytics tools exist to monitor and control the different stages of the ML life cycle (especially those related to data and algorithms), feature engineering support remains inadequate. In this paper, we present FeatureEnVi, a visual analytics system specifically designed to assist with the feature engineering process. Our proposed system helps users to choose the most important feature, to transform the original features into powerful alternatives, and to experiment with different feature generation combinations. Additionally, data space slicing allows users to explore the impact of features on both local and global scales. FeatureEnVi utilizes multiple automatic feature selection techniques; furthermore, it visually guides users with statistical evidence about the influence of each feature (or subsets of features). The final outcome is the extraction of heavily engineered features, evaluated by multiple validation metrics. The usefulness and applicability of FeatureEnVi are demonstrated with two use cases and a case study. We also report feedback from interviews with two ML experts and a visualization researcher who assessed the effectiveness of our system.