As one of the fundamental tasks in the online learning platform, interactive course recommendation (ICR) aims to maximize the long-term learning efficiency of each student, through actively exploring and exploiting the student's feedbacks, and accordingly conducting personalized course recommendation. Recently, deep reinforcement learning (DRL)…

The global outbreak of the new coronavirus epidemic has promoted the development of intelligent education and the utilization of online learning systems. In order to provide students with intelligent services, such as cognitive diagnosis and personalized exercises recommendation, a fundamental task is the concept tagging for exercises, which…

In recent years, there has been an increasing trend of utilizing artificial intelligence (AI) methodologies over traditional statistical methods for predicting student performance in e-learning contexts. Notably, many researchers have adopted AI techniques without conducting a comprehensive investigation into the most appropriate and accurate…

With the application of virtual venues in the field of education, numerous educational empirical studies have examined the impact of deep learning in the learning environment of virtual venues, but the conclusions are not always in agreement. The present study adopted the meta-analysis method and RStudio software to test the overall effect of 45…

The most critical parameter of audio and video information output is the playback speed, which affects many viewing or listening metrics, including when learning using tutoring systems. However, the availability of quantitative models for personalized playback speed control considering the learner's personal traits is still an open question. The…

Pedagogical agents (PAs) are a crucial aspect of the e-learning environment. A PA is defined as a virtual character presented on an interface, and they are designed to promote student learning. PAs have been widely discussed in academic papers. However, an appropriate analysis framework has not been proposed because of the diversity and complexity…

In interactive e-learning environments such as Intelligent Tutoring Systems, pedagogical decisions can be made at different levels of granularity. In this work, we focus on making decisions at "two levels": whole problems vs. single steps and explore three types of granularity: "problem-level only" ("Prob-Only"),…

The evolving landscape of higher education demands integrating advanced technologies to foster engaging and inclusive learning environments. This paper examines the practical integration of Stellarium, a virtual planetarium software, and ChatGPT, an AI conversational agent, in an asynchronous online undergraduate astronomy course. Stellarium…

Adaptive learning has garnered researchers' interest. The main issue within this field is how to select appropriate learning objects (LOs) based on learners' requirements and context, and how to combine the selected LOs to form what is known as an adaptive learning path. Heuristic and metaheuristic approaches have achieved significant progress on…

For offering adaptive learning to learners in intelligent tutoring systems, one of the fundamental tasks is knowledge tracing (KT), which aims to assess learners' learning states and make prediction for future performance. However, there are two crucial issues in deep learning-based KT models. First, the knowledge concepts are used to predict…

In this article, smart learning environment (SLE) is defined as a hybrid learning system that provides learners and other stakeholders with a joyful learning process while achieving learning outcomes as a result of the employed intelligent tools and techniques. From literature study, existing SLE models and frameworks are difficult to understand…

Reading comprehension is essential for students' ability to build knowledge. Students' comprehension abilities can be enhanced by providing students with deliberate practice and formative feedback on reading comprehension strategies. iSTART is an Intelligent Tutoring System (ITS) that is designed to provide instruction in reading strategies with…

This study explored how a private, southeastern college of education (COE) supported students in passing the Praxis II examinations. Using Knowles' (1988) adult learning theory and Bandura's (1977) self-efficacy theory, the researchers examined students' experiences with various COE-provided resources and their impact on Praxis II scores. A…

This study explored how a private, southeastern college of education (COE) supported students in passing the Praxis II examinations. Using Knowles' (1988) adult learning theory and Bandura's (1977) self-efficacy theory, the researchers examined students' experiences with various COE-provided resources and their impact on Praxis II scores. A…

To better understand the self-regulated learning process in online learning environments, this research applied a data mining method, the two-layer hidden Markov model (TL-HMM), to explore the patterns of learning activities. We analyzed 25,818 entries of behavior log data from an intelligent tutoring system. Results indicated that students with…