In recent years, online learning methods have gradually been accepted by more and more people. A large number of online teaching courses and other resources (MOOCs) have also followed. To attract students' interest in learning, many scholars have built recommendation systems for MOOCs. However, students need a variety of different learning…

We studied collaborative skill acquisition in a dynamic setting with the game Co-op Space Fortress. While gaining expertise, the majority of subjects became increasingly consistent in the role they adopted without being able to communicate. Moreover, they acted in anticipation of the future task state. We constructed a collaborative skill…

Research on gamification's effects in educational environments has been a growing domain in recent years. As research has demonstrated the power of gamified systems to effectively motivate learners in educational settings, it has also become clear that not all individuals are motivated in the same way, or to the same extent, by the same gamified…

There has been great progress towards Reinforcement Learning (RL) approaches that can achieve expert performance across a wide range of domains. However, researchers have not yet applied these models to learn expert models for educationally relevant tasks, such as those taught within tutoring systems and educational games. In this paper we explore…

In tutoring systems, a pedagogical policy, which decides the next action for the tutor to take, is important because it determines how well students will learn. An effective pedagogical policy must adapt its actions according to the student's features, such as knowledge, error patterns, and emotions. For adapting difficulty, it is common to…

The current study investigated the effect of moral stories in promoting kindergarteners' sharing behaviour. One hundred eight children were randomly assigned to one of three conditions: two experimental conditions (a moral story with a sharing model and good consequences and a moral story with a selfish model and bad consequences) and a control…

The adaptive learning problem concerns how to create an individualized learning plan (also referred to as a learning policy) that chooses the most appropriate learning materials based on a learner's latent traits. In this article, we study an important yet less-addressed adaptive learning problem--one that assumes continuous latent traits.…

The purpose of this study is to propose an e-learning system model for learning content personalisation based on students' emotions. The proposed system collects learners' brainwaves using a portable Electroencephalogram and processes them via a supervised machine learning algorithm, named K-nearest neighbours (KNN), to recognise real-time…

Several strategies (e.g., performance feedback, video models, tactile prompting) have been found to be effective for improving preservice teachers' use of foundational behavior management skills. However, there is limited research examining these training strategies for promoting preservice clinicians' use of evidence-based behavior management…

This study examined a behavioral intervention package to promote the use of target vocalizations alongside speech-generating device (SGD) mands. Six minimally verbal children with autism spectrum disorder participated, including three with no prior SGD experience. During baseline, SGD responses resulted in access to a preferred item and there was…

This paper studies the use of Reinforcement Learning (RL) policies for optimizing the sequencing of online learning materials to students. Our approach provides an end to end pipeline for automatically deriving and evaluating robust representations of students' interactions and policies for content sequencing in online educational settings. We…

The popularity of formative assessment has increased since the publication of work by Black and Wiliam in 1998. Even though it is a useful teaching tool, in most cases it has only been possible to use it for students with high levels of cognitive and communicative ability. The aim of this article is to propose a modified, personalisable model of…

Self-regulation of cognition and behavior is an important aspect of student learning and academic performance in the 21st-century classroom. The purpose of the chapter is to present how an integrated framework of cyclical phases and developmental levels of self-regulated learning play a significant role in modeling and self-regulatory learning as…

The emergence of signaling systems has been observed in numerous experimental and real-world contexts, but there is no consensus on which (if any) shared mechanisms underlie such phenomena. A number of explanatory mechanisms have been proposed within several disciplines, all of which have been instantiated as credible working models. However, they…

Infants are curious learners who drive their own cognitive development by imposing structure on their learning environment as they explore. Understanding the mechanisms by which infants structure their own learning is therefore critical to our understanding of development. Here we propose an explicit mechanism for intrinsically motivated…