Advances in multimedia technology and its widespread application in education have made multimedia learning increasingly important. Knowledge Tracing (KT) is the key technology for achieving adaptive multimedia learning, aiming to monitor the degree of ...

Computing education research (CER) is a rapidly advancing discipline, offering vast potential for data-driven, secondary research or replication studies. Although gathering and analyzing data for research seem straightforward, making research data ...

Knowledge Tracing aims to assess student learning states by predicting their performance in answering questions. Different from the existing research which utilizes fixed-length learning sequence to obtain the student states and regards KT as a static ...

Objectives. The purpose of this study is to reveal the importance of informatics in continuing vocational education in Germany. The labour market is a field with diverse data structures and multiple applications, for example connecting jobseekers and ...

In educational settings, a challenge is the lack of linked and labeled data, hindering effective analysis. The integration of ontology facilitates the formulation of educational knowledge concepts, student behaviors, and their relations. ...

Collecting fine-grained programming process data may require students’ informed consent in order to comply with ethical and legal regulations. Previous studies suggest that such consent is not always given in the context of graded introductory ...

To address the limitations of existing deep knowledge tracing models that often consider the factors influencing learners’ knowledge state changes from a single perspective, lacking a comprehensive analysis of multiple dimensions such as problems, ...

The Knowledge Tracing (KT) task aims to track changes in students’ knowledge state based on their historical answer sequences and predict student’s future performance. Existing KT models have two limitations. One is that these models do not fully ...

Many instructors in introductory programming courses experience high dropout and failure rates. Identifying struggling students early is a prerequisite to target this problem. To this end, instructors and learning analytics researchers may leverage ...

Knowledge tracing (KT) is a research area of predicting students’ knowledge states using their interaction data, such as concepts, questions, and responses. Most deep learning-based KT models have suffered from attributions of KT datasets such as ...

Teachers commonly assess students’ knowledge mastery through their submitted programming assignments. However, programming is a dynamic process that involves editing and debugging, and this evaluation may overlook the actual abilities exhibited by ...

Knowledge Tracing (KT) is vital for education, continuously monitoring students' knowledge states (mastery of knowledge) as they interact with online education materials. Despite significant advancements in deep learning-based KT models, existing ...

How to better grasp students’ learning preferences in the environment of rapid development of engineering and science and technology so as to guide them to high-quality learning is one of the important research topics in the field of educational ...

Automated machine learning (AutoML) creates additional opportunities for less advanced users to build and test their own data mining models. Even though AutoML creates the models for the user, there is still technical knowledge and tools needed to ...

This working group aims to identify available datasets within the context of computing education research. One particular area of interest is programming education, and the data in question may include students' steps, progress, or submissions in the ...

What makes an expert+ Beat Saber player? In the field of Educational Data Mining (EDM), there are various techniques for estimating latent skill mastery, such as Bayesian Knowledge Tracing (BKT) and Item Response Theory (IRT). While these techniques can ...

School dropout and academic underachievement have significant effects on economic growth and employment in society. This phenomenon impacts not only the intellectual development of students but also their access to desirable job opportunities, ...

Knowledge tracing (KT) aims to predict students' responses to practices based on their historical question-answering behaviors. However, most current KT methods focus on improving overall AUC, leaving ample room for optimization in modeling sequences of ...

As the core of the Knowledge Tracking (KT) task, assessing students' dynamic mastery of knowledge concepts is crucial for both offline teaching and online educational applications. Since students' mastery of knowledge concepts is often unlabeled, ...

Background and Context: Struggling with programming assignments while learning to program is a common phenomenon in programming courses around the world. Supporting struggling students is a common theme in Computing Education Research (CER), where a wide ...