Motivational Social Visualizations for Personalized E-Learning

A large number of educational resources is now available on the Web to support both regular classroom learning and online learning. However, the abundance of available content produces at least two problems: how to help students find the most appropriate resources, and how to engage them into using these resources and benefiting from them. Personalized and social learning have been suggested as potential methods for addressing these problems. Our work presented in this paper attempts to combine the ideas of personalized and social learning. We introduce Progressor + , an innovative Web-based interface that helps students find the most relevant resources in a large collection of self-assessment questions and programming examples. We also present the results of a classroom study of the Progressor +  in an undergraduate class. The data revealed the motivational impact of the personalized social guidance provided by the system in the target context. The interface encouraged students to explore more educational resources and motivated them to do some work ahead of the course schedule. The increase in diversity of explored content resulted in improving students’ problem solving success. A deeper analysis of the social guidance mechanism revealed that it is based on the leading behavior of the strong students, who discovered the most relevant resources and created trails for weaker students to follow. The study results also demonstrate that students were more engaged with the system: they spent more time in working with self-assessment questions and annotated examples, attempted more questions, and achieved higher success rates in answering them

Addictive links: The motivational value of adaptive link annotation

Adaptive link annotation is a popular adaptive navigation support technology. Empirical studies of adaptive annotation in the educational context have demonstrated that it can help students to acquire knowledge faster, improve learning outcomes, reduce navigational overhead, and encourage non-sequential navigation. In this paper, we present our exploration of a lesser known effect of adaptive annotation, its ability to significantly increase students' motivation to work with non-mandatory educational content. We explored this effect and confirmed its significance in the context of two different adaptive hypermedia systems. The paper presents and discusses the results of our work

Adaptive Educational Hypermedia based on Multiple Student Characteristics

The learning process in Adaptive Educational Hypermedia (AEH) environments is complex and may be influenced by aspects of the student, including prior knowledge, learning styles, experience and preferences. Current AEH environments, however, are limited to processing only a small number of student characteristics. This paper discusses the development of an AEH system which includes a student model that can simultaneously take into account multiple student characteristics. The student model will be developed to use stereotypes, overlays and perturbation techniques. Keywords: adaptive educational hypermedia, multiple characteristics, student model

Progressor: Personalized visual access to programming problems

This paper presents Progressor, a visualization of open student models intended to increase the student's motivation to progress on educational content. The system visualizes not only the user's own model, but also the peers' models. It allows sorting the peers' models using a number of criteria, including the overall progress and the progress on a specific topic. Also, in this paper we present results of a classroom study confirming our hypothesis that by showing a student the peers' models and ranking them by progress it is possible to increase the student's motivation to compete and progress in e-learning systems. © 2011 IEEE

A group learning management method for intelligent tutoring systems

In this paper we propose a group management specification and execution method that seeks a compromise between simple course design and complex adaptive group interaction. This is achieved through an authoring method that proposes predefined scenarios to the author. These scenarios already include complex learning interaction protocols in which student and group models use and update are automatically included. The method adopts ontologies to represent domain and student models, and object Petri nets to specify the group interaction protocols. During execution, the method is supported by a multi-agent architecture

Development and Evaluation of an Adaptive Hypermedia System Based on Multiple Student Characteristics

Adaptive Educational Hypermedia systems (AEH) are amongst the most recent types of application to provide individualised instruction to students who undertake online courses. Such systems attempt to adapt to how individuals learn by personalizing instruction for each individual student depending upon one or more “characteristics” of the student. Prior knowledge and learning style have been identified as being prominent characteristics in this process but AEH systems implemented to date have generally been limited to only employing one of these characteristics. Such systems have also been limited in that they are specific to a particular course content and cannot be easily adapted to present different learning materials. This thesis describes the development and evaluation of a new AEH system that provides a generic template for different learning materials as well as a student model that incorporates five distinct student characteristics as an aid to learning: primary characteristics are prior knowledge, learning style and the presence or absence of animated multimedia aids (multimedia mode); secondary characteristics include page background preference and link colour preference. The use of multimedia artefacts as a student characteristic (and hence as an independent variable in this study) has not previously been implemented or evaluated. A separate non-AEH system, identical to the AEH system except for the absence of adaptation to individuals, was developed in parallel as a control. The system development consists of a requirements analysis, design and implementation. The design models including use case diagrams, conceptual design, sequence diagrams, navigation design and presentation design are expressed using Unified Modelling Language (UML). The AEH system which was developed in a generic template implemented using Java Servlets, XHTML, XML, JavaScript and HTML. The generic template is a domain-independent AEH system that has functions of both adaptivity and adaptability. The system was evaluated in an experimental research involving 67 undergraduate engineering students in the Department of Electronics at Yogyakarta State University. The learning material of Analogue Electronics was implemented into both the AEH system and non-AEH systems under seven chapter headings. The participants were randomly divided into an experimental group and a control group. During the 9 weeks of experimentation, the students studied the learning material in two randomly allocated groups, an experimental group using the AEH system and a control group using the non-AEH system. A pre-test was administered to measure initial student knowledge. The student achievement was measured at the end of each chapter of material using a chapter test and at the end of the experimentation as a whole using a post-test. Basic statistical analysis of t-test and Mann-Whitney U were conducted to investigate any difference of student achievement between the two groups. A further detailed analysis using multilevel modelling was conducted to investigate any possible effects of the adaptive parameters on the student achievement. A total of 7 hypotheses were tested during data analysis. Research findings are described as follows. Students who learned using the AEH system performed better significantly than those who learned using the NON-AEH system. The implementation of test repetition as a function of knowledge adaptation in the AEH system increased student achievement significantly. This was found to be the prominent effect. When the effect of test repetition was removed, the implementation of learning style and multimedia mode adaptation in the AEH system was still found to have significant effect upon student performance. Students whose learning style and multimedia preferences were matched with the system (AEH or non-AEH) achieved better results. In terms of the relative merit of each contributing factor toward a student’s achievement, the order of the effects was found to be (1) knowledge, (2) multimedia, and (3) learning style. Whilst repeated knowledge testing is an established cause of improved performance, the positive effects on student performance of using multimedia artefacts over choice of learning style is a new finding