Student performance is affected by their knowledge which changes dynamically over time. Therefore, employing recurrent neural networks (RNN), which are known to be very good in dynamic time series prediction, can be a suitable approach for student performance prediction. We propose such a neural network architecture containing two modules: (i) a…

Monitoring the progress of student learning is an important part of teachers' data-based decision making. One such tool that can equip teachers with information about students' learning progress throughout the school year and thus facilitate monitoring and instructional decision making is learning progress assessments. In practical contexts and…

With the advancements in information and communications technologies, new teaching approaches arise. In this context, project-based learning (PBL) and science, technology, engineering, the arts, and mathematics model (STEAM) emerge as the most popular in the education field, attributed to their efficacy on students' learning capacity. This article…

Over 10 years of the new college entrance examination reform, the flexible subject selection model has posed challenges to Science, Technology, Engineering, and Math (STEM) majors education in Chinese universities. The relaxation of subject requirements has led to diverse knowledge backgrounds among students, resulting in some lacking a solid…

To prevent students from learning risks and improve teachers' teaching quality, it is of great significance to provide accurate early warning of learning performance to students by analyzing their interactions through an e-learning system. In existing research, the correlations between learning risks and students' changing cognitive abilities or…

Much of learning analytics research has focused on factors influencing model generalizability of predictive models for academic performance. The degree of model generalizability across courses may depend on aspects, such as the similarity of the course setup, course material, the student cohort, or the teacher. Which of these contextual factors…

The integration of web-based discussion tools in education has expanded rapidly, yet their pedagogical use remains largely unstructured, often resulting in fragmented discussions and limited learners' engagement. This gap reflects the absence of empirically grounded instructional models that align digital discourse with principles of collaborative…

The effects of the constructivist-learning model on student outcomes are analyzed in this research study. For this purpose, the results of 19 meta-analysis research focusing on the effects of constructivist learning models on student outcomes are combined with the second-order meta-analysis method. The research included in the process had been…

Teacher absenteeism has high individual and societal costs and triggers a vicious cycle by increasing teacher absenteeism for the remaining teachers. Moreover, teachers' non-attendance disrupts the learning process and affects student motivation and achievement. Teacher absenteeism further exacerbates the increasing teacher shortage observed in…

The purpose of the present study is to analyze the digital competence of Higher Education students, as a function of their academic performance (have either repeated or a not previously), as well as to predict its significant predictors. For this, an ex-post factor and a sample of 17301 students from Chile (Latin America) were utilized. A…

This study investigates recent discourse on "learning gaps" through the lens of Korean teachers' perspectives. Drawing on ecological systems theory and Confucian influences on East Asian education, we analyzed interviews with 24 Korean teachers to explore their perceptions of pandemic-induced learning disparities and their root causes.…

We propose a novel model, Wide & Deep Item Response Theory (Wide & Deep IRT), to predict the correctness of students' responses to questions using historical clickstream data. This model combines the strengths of conventional Item Response Theory (IRT) models and Wide & Deep Learning for Recommender Systems. By leveraging clickstream…

The emergence of online learning has sparked increased interest in predicting learners' academic performance to enhance teaching effectiveness and personalized learning. In this context, we propose a complex model APPMLT-CBT which aims to predict learners' performance in online learning settings. This systemic model integrates cognitive, social,…

In this paper, I discuss the concepts "teaching" and "learning" and investigate the connections between them. Starting from the perspective on teaching and learning expressed in "The European Qualifications Framework" ("EQF"), I analyse three theses on the concept of teaching: the Dewey-thesis, the…

Despite the significance of grit and motivational regulation strategies (MRS) to language learning, limited research has been conducted on their longitudinal interplay. The present study explores the relationship between these two constructs in an English as a second language (L2) learning context through a longitudinal design. This study utilizes…