Introduction to the special issue on advances in multimedia and educational technology

With the advances in communication and information, multimedia and other new technologies, the paradigm of education has been shifting from the conventional paper-based instruction to technology-embedded instruction. Educational institutions around the world have been incorporating new technologies into existing curriculum and course design in order to reinforce traditional textbook-based and classroom-based instruction. These technology-mediated learning environments have helped learners interact with contents, peers, and teachers. Now, thanks to the development of digital devices such as mobile phones and smart pads, ubiquitous learning following anywhere and anytime principles has become the new educational trend. The objective of this Special Issue is to investigate and explore the impact and outcome of integrating new technologies into the field of education and training. Articles for this Special Issue address a range of education and training settings, focusing on the theory, practice, and development of multimedia and educational technology. Particularly, there is a focus on pedagogical applications of new technologies in order to offer innovative ideas and insight in the fields of education and training. The first paper entitled “Utilising behavioural analytics in a blended programming learning environment,” (Paredes, Huang and Hsiao) reports on a study of the investigation of students’ learning effectiveness in programming learning. To achieve this, they utilised Web Programming Grading Assistant, which was a homegrown educational web application that provided augmented grading and feedback giving interfaces for handwritten assignments. By analysing the relation between students’ effort and academic performances, they concluded several educational implications. The second paper “Combining object detection and causality mining for efficient development of augmented reality-based on-the-job training systems in hotel management” (Koo, Lee and Kwon) proposes an AR-based On-thejob training system which employs text mining and object detection. In the system, syntactic expression of causality is learned and stored in the casual