The development of intelligent tutoring systems has long been the focus of applying artificial intelligence and cognitive science in education. A new breed of intelligent learning environments called learning companion systems was developed over a decade ago. In contrast to an intelligent tutoring system, in which a computer mimics an intelligent tutor, the learning companion system assumes two roles, one as an intelligent tutor and another as a learning companion. Motivated by recent interest in agent research and other technologies, this learning companion field has received increasing attention. This study addresses issues that arise from different perspectives on this research effort. With a view to future networked learning environments, the learning companion is redefined for application to a wide spectrum of educational agent research. Accordingly, several subjects that relate to educational agents, and hence learning companions, are identified. #

No abstract

This study presents a model for the early identification of students who are likely to fail in an academic course. To enhance predictive accuracy, sentiment analysis is used to identify affective information from text‐based self‐evaluated comments written by students. Experimental results demonstrated that adding extracted sentiment information from student self‐evaluations yields a significant improvement in early‐stage prediction quality. The results also indicate the limited early‐stage predictive value of structured data, such as homework completion, attendance, and exam grades, due to data sparseness at the beginning of the course. Thus, applying sentiment analysis to unstructured data (e.g., self‐evaluation comments) can play an important role in improving the accuracy of early‐stage predictions. The findings present educators with an opportunity to provide students with real‐time feedback and support to help students become self‐regulated learners. Using the exploring results for improvement in teaching and learning initiatives is important to maintain students' performances and the effectiveness of the learning process.

In self-regulated learning (SRL), students organize, monitor, direct, and regulate their learning. In SRL, monitoring plays a critical role in generating internal feedback and thus adopting appropriate regulations. However, students may have poor SRL processes and performance due to their poor monitoring. Researchers have suggested providing external feedback to facilitate better student SRL. However, SRL involves many meta-cognitive internal processes that are hidden and difficult to observe and measure. This study proposed a SRL model to illustrate the relationship among external SRL tools, internal SRL processes, internal feedback, and external feedback. Based on the model, this study designed a system with SRL tools and open leaner models (OLMs) to assist students in conducting SRL, including self-assessing their initial learning performance (i.e. perceived initial performance and monitoring of learning performance) after listening to a teacher’s lecture, being assessed by and receiving external feedback from the OLM (i.e. actual performance) in the system, setting target goals (i.e. desired performance) of follow-up learning, conducting follow-up learning (i.e. strategy implementation), and evaluating their follow-up learning performance (i.e. perceived outcome performance and strategy outcome monitoring). These SRL tools also externalize students’ internal SRL processes and feedback, including perceived initial, desired, and perceived outcome performances, for investigation. In addition, this study explores the impact of external feedback from the OLM on students’ internal SRL processes and feedback. An evaluation was conducted to record and analyze students’ SRL processes and performance, and a questionnaire was administered to ask students about their SRL processes. There are three main findings. First, the results showed that students often have poor internal SRL processes and poor internal feedback, including poor self-assessment, inappropriate target goals, a failure to conduct follow-up learning, and a failure to achieve their goals. Second, the results revealed that the SRL tools and external feedback from the OLM assisted most students in SRL, including monitoring their learning performance, goal-setting, strategy implementation and monitoring, and strategy outcome monitoring. Third, some students still required further support for SRL.

The interest-driven creator (IDC) theory is being developed as a group endeavor by Asian researchers to articulate a holistic learning design theory for future education in Asia. The theory hypothesizes that students, driven by interest, can be engaged in the creation of knowledge (generating ideas and artifacts). By repeating this creation process in their daily learning routines, they will excel in learning performance, develop twenty-first-century competencies, and form creation habits. We hope that with such practices in education, our future generations will ultimately become lifelong interestdriven creators. In IDC Theory, there are three anchored concepts, namely, interest, creation, and habit. Each anchored concept comprises three component concepts which form a concept loop. For example, the creation loop consists of three component concepts-imitating, combining, and staging. Imitating is concerned with taking in (or inputting) an abundant amount of existing knowledge from the outside world to form one's background knowledge. Combining refers to delivering (or outputting) new ideas or artifacts prolifically by synthesizing existing information encountered in the world and thoughts arising from the students' background knowledge. Staging relates to frequently demonstrating the generated ideas or artifacts to the relevant communities and receiving feedback from these communities to improve the novelty and value of the demonstrated outcomes while gaining social recognition and nurturing positive social emotions. This paper focuses on describing the three components of the creation loop. We provide three case studies to illustrate the creation loop at work, as well as how it intertwines with both the interest and habit loops in supporting students to develop their creation capabilities. In presenting this iteration of the creation concept, an anchored concept in IDC theory, we acknowledge the roles played of imitation, combination, and staging in different learning and education contexts-indeed, there are multiple theories that inform and intersect with it.

Purpose -Exploratory learning is regarded as an important ability for developing knowledge from open environments. During the exploration, learners not only need to acquire new information based on their current interests, but also they need to form new perspectives by incorporating new knowledge into their previous knowledge. This paper seeks to address these issues. Design/methodology/approach -To this end, this paper proposes an approach that includes a concept association bank to recommend related concepts in a domain based on the goal of an exploration. By doing so, learners' knowledge can be expanded beyond their current understanding. An experiment was conducted to investigate how the proposed approach facilitated the learners' exploration. Findings -The results indicated that the concept association bank is a useful mechanism to help learners gain new understanding, including providing exploration directions, reducing complexity and cognitive load, facilitating data-and goal-driven exploration strategies, and commenting on new understanding. The implications of these results are discussed. Originality/value -Current recommendation systems emphasise a data-driven strategy, which seeks isolated pieces of information, instead of suggesting directions related to their exploration goal. The problem with such an approach is that learners' exploration will be limited by their existing knowledge. Thus, this paper presents an approach to support both data-and goal-driven strategies.

The development of information and communication technology changes how, what, who, when, where and why we learn. Unfortunately, little is known of the exact impact that these changes will bring to education. However, we are certain that many new learning and teaching styles which are called learning models in the paper will emerge to cope with the changes in the near future. The present paper describes four spaces of learning models, namely, the future-classroom, the community-based, the structuralknowledge, and the complex-problem learning models, which are specifically designed to integrate the Internet into education.1 With the four spaces of learning models, the present paper may serve two functions. First, it offers a way to integrate an array of different communication technologies (e.g. handheld computer, wireless communication and the Internet) and learning theories into an integrated schema. Secondly, the paper offers a direction concerning how and what to look for in education with the Internet integrated in. #

No abstract