The study investigates the potential of anxiety clusters in predicting programming performance in two distinct coding environments. Participants comprised 83 second-year programming students who were randomly assigned to either a block-based or a text-based group. Anxiety-induced behaviors were assessed using physiological measures (Apple Watch…

Facebook has been used not only as a popular social network service among college students but also as a platform for promoting learning and teaching effectiveness in different subject areas. While previous studies have demonstrated the utility of Facebook for enhancing student engagement, little is known about whether there are different profiles…

This paper introduces a novel approach to identify at-risk students with a focus on output interpretability through analyzing learning activities at a finer granularity on a weekly basis. Specifically, this approach converts the predicted output from the former weeks into meaningful probabilities to infer the predictions in the current week for…

Previous research has not adequately explored students' behavioral processes when addressing computational thinking (CT) problems of varying difficulty, limiting insights into students' detailed CT development characteristics. This study seeks to fill this gap by employing gamified CT items across multiple difficulty levels to calculate…

Virtual humans are on-screen characters that are often embedded in learning technologies to deliver educational content. Little research has investigated how virtual humans can be used to correct common misconceptions. In this study, we explored how different types of narrative structures, refutation text and expository text, influence perceptions…

With the spread of the COVID-19 pandemic, many universities adopted a hybrid learning model as a substitute for a traditional one. Predicting students' performance in hybrid environments is a complex task because it depends on extracting and analyzing different types of data: log data, self-reports, and face-to-face interactions. Students must…

Educational data mining constitutes a recent research field which gained popularity over the last decade because of its ability to monitor students' academic performance and predict future progression. Numerous machine learning techniques and especially supervised learning algorithms have been applied to develop accurate models to predict…

Educators in higher education institutes often use statistical results obtained from their online Learning Management System (LMS) dataset, which has limitations, to evaluate student academic performance. This study differs from the current body of literature by including an additional dataset that advances the knowledge about factors affecting…

Recently educational robotics has expanded into curriculum beyond traditional STEM fields, and which can also be used to foster computational thinking (CT) skills. Prior research has shown numerous interdisciplinary benefits related to CT, however, these influential factors have often been investigated with relatively few variables. This study…

Learning the basic concepts of programming and its foundations is considered as a challenging task for students to figure out. It is a challenging process for lecturers to learn these concepts, as well. The current literature on programming training abounds with the examples of a wide range of methods employed. Within this context, one of the…

Massive open online courses (MOOCs) show great potential to transform traditional education through the Internet. However, the high attrition rates in MOOCs have often been cited as a scale-efficacy tradeoff. Traditional educational approaches are usually unable to identify such large-scale number of at-risk students in danger of dropping out in…

The objective of this study is to construct a model which explains and predicts the relations of university students' cyberloafing behaviors with demographic and academic variables at computing courses where online social networking sites are utilized for education and is to review whether there is longitudinal effect on these relations in terms…

This study explored relationships between Internet-specific justification beliefs and source evaluation and corroboration during Web search. Fifty university students completed the Internet-Specific Epistemic Justification Inventory (ISEJ), which targeted beliefs concerning the justification of Internet-based knowledge claims about natural science…

Several studies have focused on identifying the significant behavioral predictors of learning performances in web-based courses by examining the log data variables of learning management systems, including time spent on lectures, the number of assignments submitted, and so forth. However, such studies fail to quantify the impact of emotional,…

ChatBot has potential as a language learning tool, especially for learning Chinese vocabulary. This study aimed to investigate the impact of using a newly developed ChatBot to learn Chinese vocabulary by comparing how it works in different learning environments and to explore the ChatBot with reference to the Technology Acceptance Model (TAM).…