Profile transformation in mobile technology based educational systems : a thesis presented in partial fulfillment of the requirements for the degree of Master of Information Science in Information Systems at Massey University, Palmerston North, New Zealand

In order to meet the learning needs from various types of students, computer aided education systems try to include new methods to provide personalized education to every student. From the early 1970s, a lot of adaptive educational systems have been created to provide training on a variety of subjects. Combined with the Internet, the adaptive educational systems have become web-based and even more popular. Recently, the development of mobile technology has made the web-based adaptive educational systems accessible through mobile phones. It is necessary that the students can also receive adaptive educational contents on mobile phones. This research project investigated the possible student's preference differences between Personal Computer (PC) and mobile phone, and then proposed a student profile transformation framework to address such differences. This research project conducted two surveys on the student profile transformation between PC and mobile phone. A demo web-based educational system that could be accessed from both PC and mobile phone was also developed for participants of the surveys to give more real and precise responses. Based on Felder-Silverman Learning Style Theory (Felder, 1993; Felder & Silverman, 1988) and the results of the surveys, this thesis proposes a student profile template and a student profile transformation framework, which both fully considered the influences of device capabilities and locations on students' preferences on mobile phones. Furthermore, the proposed framework integrates a solution for unsupported preferences and preference conflicts. By implementing the proposed template and framework, the students' preference changes between PC and mobile phone are automatically updated according to various device capabilities and locations, and then the students can receive adaptive educational contents that meet their updated preferences

Adaptive hypermedia for education and training

Adaptive hypermedia (AH) is an alternative to the traditional, one-size-fits-all approach in the development of hypermedia systems. AH systems build a model of the goals, preferences, and knowledge of each individual user; this model is used throughout the interaction with the user to adapt to the needs of that particular user (Brusilovsky, 1996b). For example, a student in an adaptive educational hypermedia system will be given a presentation that is adapted specifically to his or her knowledge of the subject (De Bra & Calvi, 1998; Hothi, Hall, & Sly, 2000) as well as a suggested set of the most relevant links to proceed further (Brusilovsky, Eklund, & Schwarz, 1998; Kavcic, 2004). An adaptive electronic encyclopedia will personalize the content of an article to augment the user's existing knowledge and interests (Bontcheva & Wilks, 2005; Milosavljevic, 1997). A museum guide will adapt the presentation about every visited object to the user's individual path through the museum (Oberlander et al., 1998; Stock et al., 2007). Adaptive hypermedia belongs to the class of user-adaptive systems (Schneider-Hufschmidt, Kühme, & Malinowski, 1993). A distinctive feature of an adaptive system is an explicit user model that represents user knowledge, goals, and interests, as well as other features that enable the system to adapt to different users with their own specific set of goals. An adaptive system collects data for the user model from various sources that can include implicitly observing user interaction and explicitly requesting direct input from the user. The user model is applied to provide an adaptation effect, that is, tailor interaction to different users in the same context. In different kinds of adaptive systems, adaptation effects could vary greatly. In AH systems, it is limited to three major adaptation technologies: adaptive content selection, adaptive navigation support, and adaptive presentation. The first of these three technologies comes from the fields of adaptive information retrieval (IR) and intelligent tutoring systems (ITS). When the user searches for information, the system adaptively selects and prioritizes the most relevant items (Brajnik, Guida, & Tasso, 1987; Brusilovsky, 1992b)

Integration of computer-aided language learning into formal university-level L2 instruction

This paper presents our experience from pilot studies оn integration of intelligent learning and tutoring tools into official curricula for foreign/second-language (L2) learning. We report specifically on initial studies with learners of Russian as a second language at major universities in Italy and in Finland. An important challenge in both of these educational situations is the heterogeneous nature of the student contingent, including the presence of a sizable proportion of ‘heritage’ learners. Furthermore, the groups are often very large, which motivates the integration of an ICALL system. We describe the first integration attempt, an analysis of the emerging aspects and problems, and the design of a new experiment, which is on-going and takes into account the lessons learned. To the best of our knowledge, this is the first report on large-scale ICALL studies involving substantial numbers of ‘high-stakes’ learners of Russian at the intermediate-to-advanced levels – i.e., learners beyond the elementary level.Peer reviewe

Artificial intelligent based teaching and learning approaches: A comprehensive review

The goal of this study is to investigate the potential effects that Artificial intelligence (AI) could have on education. The narrative and framework for investigating AI that emerged from the preliminary research served as the basis for the study’s emphasis, which was narrowed down to the use of AI and its effects on administration, instruction, and student learning. According to the findings, artificial intelligence has seen widespread adoption and use in education, particularly by educational institutions and in various contexts and applications. The development of AI began with computers and technologies related to computers; it then progressed to web-based and online intelligent education systems; and finally, it applied embedded computer systems in conjunction with other technologies, humanoid robots, and web-based chatbots to execute instructor tasks and functions either independently or in partnership with instructors. By utilizing these platforms, educators have been able to accomplish a variety of administrative tasks. In addition, because the systems rely on machine learning and flexibility, the curriculum and content have been modified to match the needs of students. This has led to improved learning outcomes in the form of higher uptake and retention rates

Solving The Problem of Adaptive E-Learning By Using Social Networks

This paper propose an enhanced E-Learning Social Network Exploiting Approach focused around chart model and clustering algorithm, which can consequently gathering dispersed e-learners with comparative premiums and make fitting suggestions, which can at last upgrade the collective learning among comparable e-learners. Through closeness revelation, trust weights overhaul and potential companions change, the algorithm actualized a programmed adjusted trust association with progressively upgraded fulfillments. Keywords: Relations, Adaptive E-Learning, Clustering , Social Network , E-learning ,  and Collaborative Learnin

Future challenges in intelligent tutoring systems: a framework

Intelligent Tutoring Systems (ITS) provide the benefits of one-on-one instruction in an automatic way and cost effectively, keeping in mind their multidisciplinary nature. The challenge remains on transporting to com-puters the expertise, skills and mode of action of the human tutor, overcoming space, time, socio-economical and environmental restrictions. ITS appear as a form of deployment of this issue and have been object of an increasing research. This paper aims to establish some characteristics, properties and functions that an ITS should provide, and the possible contributions that the different fields of research can make, proposing a multi-domain and multidisciplinary framework to address the research in this field. The framework incorpo-rates a knowledge base where data and knowledge related to the problem are maintained and a model base re-lated to student, teaching and environmental issues together with pedagogical perspectives

Math E-Tutor: Towards A Better Self-Learning Environment.

Math E-Tutor is a web-based tutoring system which aims to create a virtual teaching and learning environment that suits today's trend. This system focuses on the mathematical syllabus of lower secondary school which uses multiple choice questions (MCQ) as the mode of evaluation, known as Penilaian Menengah Rendah (PMR) in Malaysia

Design and implementation of an intelligent web-based interactive language learning system

[[abstract]]The design and implementation of an Intelligent Web-based Interactive Language Learning (IWiLL) system to support English learning on the Internet is described. We designed two kinds of learning environments: (1) interactive English writing environment and (2) mining movies for real English. These are intended to improve learners’ basic language skills such as listening, reading, and writing. In addition, the system also offers authoring tools that facilitate teachers’ content preparation. The system not only provides multimedia learning environments for users, but also builds a learner corpus, an archive of annotated English texts written by learners for whom English is a second language. Further analysis of the learner corpus creates the potential to detect the users’ persistent errors and then provide adequate help to the users.[[notice]]補正完畢[[conferencetype]]國際[[conferencedate]]20010822~20010825[[iscallforpapers]]Y[[conferencelocation]]Japan, Toky