The continuous development of Educational Data Mining (EDM) and Learning Analytics (LA) technologies has provided more effective technical support for accurate early warning and interventions for student academic performance. However, the existing body of research on EDM and LA needs more empirical studies that provide feedback interventions, and…

Modern technology is necessary and important for improving the quality of education. While machine learning algorithms to support students remain limited. Thus, it is necessary to inspire educational scholars and educational technologists. This research therefore has three main targets: to educate the holistic context of rural education…

Since schools increasingly use digital platforms that provide educational data in digital formats, teacher data use, and data literacy have become a focus of educational research. One main challenge is whether teachers use digital data for pedagogical purposes, such as informing their teaching. We conducted a survey study with N = 1059 teachers in…

This paper describes a partnership-based approach for analyzing data from a learning management system (LMS) used by students in grades 6-12. The goal of the partnership was to create indicators for the ways in which students navigated digital learning activities, referred to as playlists, that were comprised of resources, pre-assessments, and…

The rise of big data technology provides direction and support for the reform and development of education. Big data technology can realize the inventory management and effective dynamic monitoring of schools, students, and teachers. It is conducive to comprehensively and accurately controlling the development of teaching activities, injecting new…

Machine learning methods for predictive analytics have great potential for uncovering trends in educational data. However, simple linear models still appear to be most widely used, in part, because of their interpretability. This study aims to address the issues of interpretability of complex machine learning classifiers by conducting feature…

Automatic machine learning (AutoML) methods automate the time-consuming, feature-engineering process so that researchers produce accurate student models more quickly and easily. In this paper, we compare two AutoML feature engineering methods in the context of the National Assessment of Educational Progress (NAEP) data mining competition. The…

Science literacy, which is included in Programme for International Student Assessment (PISA) as an assessment area, is an important research and discussion area of science education literature with all its dimensions. In this study, the clustering results of the students from 34 Organization for Economic Cooperation and Development (OECD)…

Analysis of programming process data has become popular in computing education research and educational data mining in the last decade. This type of data is quantitative, often of high temporal resolution, and it can be collected non-intrusively while the student is in a natural setting. Many levels of granularity can be obtained, such as…

Data-Driven Learning (DDL) is a method for learning languages that involves analyzing language usage trends and finding patterns in language data, utilizing technology and statistics. One of the key benefits of DDL is that it allows students to focus on the most relevant and useful language data for their needs. Data-driven learning is an…

Recognizing that children's learning outcomes generally remain low, in its recent 2017--2021 Education and Skills Sector Plan (ESSP) the Government of Zambia prioritized improving learning outcomes through strategies that addressed gaps in education system quality, access, equity and efficiency. What resources and contextual factors are associated…

This chapter reviews actionable data use--both as an umbrella term and as a specific concept--developed in three different traditions that data/information can inform and guide P-20 educational practice toward better outcomes. The literatures reviewed are known as data-driven decision making (DDDM), education data mining (EDM), and learning…

The proceedings of the 18th edition of Latin American Conference on Learning Technologies (LACLO) demonstrates the developments in the research of learning science, learning resources, challenges and solutions. This Proceedings book showcases a collection of quality articles that explores and discusses trending topics in education in the upcoming…