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

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

Computer aided learning for entry level accountancy students

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Developing Student Model for Intelligent Tutoring System

The effectiveness of an e-learning environment mainly encompasses on how efficiently the tutor presents the learning content to the candidate based on their learning capability. It is therefore inevitable for the teaching community to understand the learning style of their students and to cater for the needs of their students. One such system that can cater to the needs of the students is the Intelligent Tutoring System (ITS). To overcome the challenges faced by the teachers and to cater to the needs of their students, e-learning experts in recent times have focused in Intelligent Tutoring System (ITS). There is sufficient literature that suggested that meaningful, constructive and adaptive feedback is the essential feature of ITSs, and it is such feedback that helps students achieve strong learning gains. At the same time, in an ITS, it is the student model that plays a main role in planning the training path, supplying feedback information to the pedagogical module of the system. Added to it, the student model is the preliminary component, which stores the information to the specific individual learner. In this study, Multiple-choice questions (MCQs) was administered to capture the student ability with respect to three levels of difficulty, namely, low, medium and high in Physics domain to train the neural network. Further, neural network and psychometric analysis were used for understanding the student characteristic and determining the student’s classification with respect to their ability. Thus, this study focused on developing a student model by using the Multiple-Choice Questions (MCQ) for integrating it with an ITS by applying the neural network and psychometric analysis. The findings of this research showed that even though the linear regression between real test scores and that of the Final exam scores were marginally weak (37%), still the success of the student classification to the extent of 80 percent (79.8%) makes this student model a good fit for clustering students in groups according to their common characteristics. This finding is in line with that of the findings discussed in the literature review of this study. Further, the outcome of this research is most likely to generate a new dimension for cluster based student modelling approaches for an online learning environment that uses aptitude tests (MCQ’s) for learners using ITS. The use of psychometric analysis and neural network for student classification makes this study unique towards the development of a new student model for ITS in supporting online learning. Therefore, the student model developed in this study seems to be a good model fit for all those who wish to infuse aptitude test based student modelling approach in an ITS system for an online learning environment. (Abstract by Author

Factors shaping the evolution of electronic documentation systems

The main goal is to prepare the space station technical and managerial structure for likely changes in the creation, capture, transfer, and utilization of knowledge. By anticipating advances, the design of Space Station Project (SSP) information systems can be tailored to facilitate a progression of increasingly sophisticated strategies as the space station evolves. Future generations of advanced information systems will use increases in power to deliver environmentally meaningful, contextually targeted, interconnected data (knowledge). The concept of a Knowledge Base Management System is emerging when the problem is focused on how information systems can perform such a conversion of raw data. Such a system would include traditional management functions for large space databases. Added artificial intelligence features might encompass co-existing knowledge representation schemes; effective control structures for deductive, plausible, and inductive reasoning; means for knowledge acquisition, refinement, and validation; explanation facilities; and dynamic human intervention. The major areas covered include: alternative knowledge representation approaches; advanced user interface capabilities; computer-supported cooperative work; the evolution of information system hardware; standardization, compatibility, and connectivity; and organizational impacts of information intensive environments

TEACHING OF COMPUTER AIDED DESIGN SYSTEMS

The objective of this paper is to analyze and describe computer-aided design training and its aspects. A traditional and technology-supported learning process is described with the purpose of analyzing computer-aided design training and provision of knowledge assessment, and identifying problems in the CAD system training. The article analyzes the learning process by defining its objectives, the necessity of student characterization, motivation analysis, the necessity of feedback and other basic components; it analyzes the training methods, provides insight into technology-supported learning process, identifies the provision and types of computer-aided design training and knowledge assessment as well as describes feedback and its role in the training process.Elaboration of an intellectual learning system would solve the problems associated with lack of feedback, lack of adaptivity and the emergence of plagiarism (since only the end result of the design is subjected to the test and not the whole process of creating it, it is easy to pass another’s work for one’s own). A solution to these problems would facilitate the work of the teacher and improve student learning outcomes

M-Learning: Content Tool for Accounting

Independency of time and space are often named as the main advantages of e-Learning. The basic requirement of e-Learning is a Personal Computer (PC), and therefore a real independency of time and space is not given. Even with a notebook these independencies are not fulfilled, because a real independency of time and space means learning wherever and whenever you want to learn. Hardly anyone carries his notebook or his PC with him all the time. Due to certain requirements eLearning fulfils just partly this demand of independency. Mobile learning (MLearning), the next generation of the computer-aided and multimedia-based learning, is based on mobile phones. The market penetration of mobile phones in Malaysia is at a level of 81% and the numbers are rising. It can be said, that the great majority of the population has a mobile phone and carries it with them most of the time. Because of this fact the independency of time and space for learning is fulfilled to nearly a hundred percent. As a consequence the main advantage of mobile learning is learning wherever and whenever you want to learn. You can use idle periods for learning. For example: The times while you are traveling, while you are waiting for the bus or while you are waiting at the restaurant or at the trainstation. Nearly every unused and wasted time can now be used for efficient and effective learning. Therefore mobile learning will be an important instrument for lifelong learning, because it will help us to use our time more efficiently

Intelligent tutoring systems for systems engineering methodologies

The general goal is to provide the technology required to build systems that can provide intelligent tutoring in IDEF (Integrated Computer Aided Manufacturing Definition Method) modeling. The following subject areas are covered: intelligent tutoring systems for systems analysis methodologies; IDEF tutor architecture and components; developing cognitive skills for IDEF modeling; experimental software; and PC based prototype