Personalised correction, feedback, and guidance in an automated tutoring system for skills training

In addition to knowledge, in various domains skills are equally important. Active learning and training are effective forms of education. We present an automated skills training system for a database programming environment that promotes procedural knowledge acquisition and skills training. The system provides support features such as correction of solutions, feedback and personalised guidance, similar to interactions with a human tutor. Specifically, we address synchronous feedback and guidance based on personalised assessment. Each of these features is automated and includes a level of personalisation and adaptation. At the core of the system is a pattern-based error classification and correction component that analyses student input

Interactive correction and recommendation for computer language learning and training

Active learning and training is a particularly effective form of education. In various domains, skills are equally important to knowledge. We present an automated learning and skills training system for a database programming environment that promotes procedural knowledge acquisition and skills training. The system provides meaningful, knowledge-level feedback such as correction of student solutions and personalised guidance through recommendations. Specifically, we address automated synchronous feedback and recommendations based on personalised performance assessment. At the core of the tutoring system is a pattern-based error classification and correction component that analyses student input in order to provide immediate feedback and in order to diagnose student weaknesses and suggest further study material. A syntax-driven approach based on grammars and syntax trees provides the solution for a semantic analysis technique. Syntax tree abstractions and comparison techniques based on equivalence rules and pattern matching are specific approaches

Intelligent and adaptive tutoring for active learning and training environments

Active learning facilitated through interactive and adaptive learning environments differs substantially from traditional instructor-oriented, classroom-based teaching. We present a Web-based e-learning environment that integrates knowledge learning and skills training. How these tools are used most effectively is still an open question. We propose knowledge-level interaction and adaptive feedback and guidance as central features. We discuss these features and evaluate the effectiveness of this Web-based environment, focusing on different aspects of learning behaviour and tool usage. Motivation, acceptance of the approach, learning organisation and actual tool usage are aspects of behaviour that require different evaluation techniques to be used

Automated tutoring for a database skills training environment

The emergence of educational technology and the growth of the Internet, coupled with the rise in the number of students entering third level education, has led to a surge of online courses offered by universities. These online courses may be part of a traditional classroom based course, or they may act as an entire course by themselves. Student engagement, assessment, feedback and guidance are important parts of any course, but have an added importance for one that is presented online. Together, in the absence of a human tutor, they can greatly aid the student in the learning process. We present an automated skills training system for a database programming environment that will promote procedural knowledge acquisition and skills training. An SQL (Structured Query Language) select statement tutoring tool is an integral part of this. Targeted at students with a prior knowledge of database theory, and as part of a blended learning strategy, the system allows the student to practice SQL querying at his own time and pace. This is achieved by providing pedagogical actions that would be offered by a human tutor. Specifically, we refer to synchronous feedback and guidance based on a personalised assessment. Each of these features is automated and includes a level of personalisation and adaptation. A high-level of interaction and engagement exists between the student and the system. Students assume control of their learning experience

A conceptual architecture for interactive educational multimedia

Learning is more than knowledge acquisition; it often involves the active participation of the learner in a variety of knowledge- and skills-based learning and training activities. Interactive multimedia technology can support the variety of interaction channels and languages required to facilitate interactive learning and teaching. A conceptual architecture for interactive educational multimedia can support the development of such multimedia systems. Such an architecture needs to embed multimedia technology into a coherent educational context. A framework based on an integrated interaction model is needed to capture learning and training activities in an online setting from an educational perspective, to describe them in the human-computer context, and to integrate them with mechanisms and principles of multimedia interaction

Systematic review of research on artificial intelligence applications in higher education – where are the educators?

According to various international reports, Artificial Intelligence in Education (AIEd) is one of the currently emerging fields in educational technology. Whilst it has been around for about 30 years, it is still unclear for educators how to make pedagogical advantage of it on a broader scale, and how it can actually impact meaningfully on teaching and learning in higher education. This paper seeks to provide an overview of research on AI applications in higher education through a systematic review. Out of 2656 initially identified publications for the period between 2007 and 2018, 146 articles were included for final synthesis, according to explicit inclusion and exclusion criteria. The descriptive results show that most of the disciplines involved in AIEd papers come from Computer Science and STEM, and that quantitative methods were the most frequently used in empirical studies. The synthesis of results presents four areas of AIEd applications in academic support services, and institutional and administrative services: 1. profiling and prediction, 2. assessment and evaluation, 3. adaptive systems and personalisation, and 4. intelligent tutoring systems. The conclusions reflect on the almost lack of critical reflection of challenges and risks of AIEd, the weak connection to theoretical pedagogical perspectives, and the need for further exploration of ethical and educational approaches in the application of AIEd in higher education

The future of technology enhanced active learning – a roadmap

The notion of active learning refers to the active involvement of learner in the learning process, capturing ideas of learning-by-doing and the fact that active participation and knowledge construction leads to deeper and more sustained learning. Interactivity, in particular learnercontent interaction, is a central aspect of technology-enhanced active learning. In this roadmap, the pedagogical background is discussed, the essential dimensions of technology-enhanced active learning systems are outlined and the factors that are expected to influence these systems currently and in the future are identified. A central aim is to address this promising field from a best practices perspective, clarifying central issues and formulating an agenda for future developments in the form of a roadmap

Quality Assessment for E-learning: a Benchmarking Approach (Third edition)

The primary purpose of this manual is to provide a set of benchmarks, quality criteria and notes for guidance against which e-learning programmes and their support systems may be judged. The manual should therefore be seen primarily as a reference tool for the assessment or review of e-learning programmes and the systems which support them. However, the manual should also prove to be useful to staff in institutions concerned with the design, development, teaching, assessment and support of e-learning programmes. It is hoped that course developers, teachers and other stakeholders will see the manual as a useful development and/or improvement tool for incorporation in their own institutional systems of monitoring, evaluation and enhancement