Evaluation of social personalized adaptive E-Learning environments : end-user point of view

The use of adaptations, along with the social affordances of collaboration and networking, carries a great potential for improving e-learning experiences. However, the review of the previous work indicates current e-learning systems have only marginally explored the integration of social features and adaptation techniques. The overall aim of this research, therefore, is to address this gap by evaluating a system developed to foster social personalized adaptive e-learning experiences. We have developed our first prototype system, Topolor, based on the concepts of Adaptive Educational Hypermedia and Social E-Learning. We have also conducted an experimental case study for the evaluation of the prototype system from different perspectives. The results show a considerably high satisfaction of the end users. This paper reports the evaluation results from end user point of view, and generalizes our method to a component-based evaluation framework

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

User-centred design of flexible hypermedia for a mobile guide: Reflections on the hyperaudio experience

A user-centred design approach involves end-users from the very beginning. Considering users at the early stages compels designers to think in terms of utility and usability and helps develop the system on what is actually needed. This paper discusses the case of HyperAudio, a context-sensitive adaptive and mobile guide to museums developed in the late 90s. User requirements were collected via a survey to understand visitors’ profiles and visit styles in Natural Science museums. The knowledge acquired supported the specification of system requirements, helping defining user model, data structure and adaptive behaviour of the system. User requirements guided the design decisions on what could be implemented by using simple adaptable triggers and what instead needed more sophisticated adaptive techniques, a fundamental choice when all the computation must be done on a PDA. Graphical and interactive environments for developing and testing complex adaptive systems are discussed as a further step towards an iterative design that considers the user interaction a central point. The paper discusses how such an environment allows designers and developers to experiment with different system’s behaviours and to widely test it under realistic conditions by simulation of the actual context evolving over time. The understanding gained in HyperAudio is then considered in the perspective of the developments that followed that first experience: our findings seem still valid despite the passed time

Evaluation of social personalized adaptive E-Learning environments : end-user point of view

The use of adaptations, along with the social affordances of collaboration and networking, carries a great potential for improving e-learning experiences. However, the review of the previous work indicates current e-learning systems have only marginally explored the integration of social features and adaptation techniques. The overall aim of this research, therefore, is to address this gap by evaluating a system developed to foster social personalized adaptive e-learning experiences. We have developed our first prototype system, Topolor, based on the concepts of Adaptive Educational Hypermedia and Social E-Learning. We have also conducted an experimental case study for the evaluation of the prototype system from different perspectives. The results show a considerably high satisfaction of the end users. This paper reports the evaluation results from end user point of view, and generalizes our method to a component-based evaluation framework

Towards a competency model for adaptive assessment to support lifelong learning

Adaptive assessment provides efficient and personalised routes to establishing the proficiencies of learners. We can envisage a future in which learners are able to maintain and expose their competency profile to multiple services, throughout their life, which will use the competency information in the model to personalise assessment. Current competency standards tend to over simplify the representation of competency and the knowledge domain. This paper presents a competency model for evaluating learned capability by considering achieved competencies to support adaptive assessment for lifelong learning. This model provides a multidimensional view of competencies and provides for interoperability between systems as the learner progresses through life. The proposed competency model is being developed and implemented in the JISC-funded Placement Learning and Assessment Toolkit (mPLAT) project at the University of Southampton. This project which takes a Service-Oriented approach will contribute to the JISC community by adding mobile assessment tools to the E-framework

Personalised trails and learner profiling in an e-learning environment

This deliverable focuses on personalisation and personalised trails. We begin by introducing and defining the concepts of personalisation and personalised trails. Personalisation requires that a user profile be stored, and so we assess currently available standard profile schemas and discuss the requirements for a profile to support personalised learning. We then review techniques for providing personalisation and some systems that implement these techniques, and discuss some of the issues around evaluating personalisation systems. We look especially at the use of learning and cognitive styles to support personalised learning, and also consider personalisation in the field of mobile learning, which has a slightly different take on the subject, and in commercially available systems, where personalisation support is found to currently be only at quite a low level. We conclude with a summary of the lessons to be learned from our review of personalisation and personalised trails

Using decision trees for discovering problems on adaptive courses

Copyright by AACE. Reprinted from the World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, Nov 17, 2008, with permission of AACE (http://www.aace.org).Adaptive Hypermedia Systems personalize the learning experience of each user, by providing learning materials adapted to his/her needs, preferences, personal characteristics, etc. The goal is to make the learning process easier or more efficient. However, on the teacher side the improvement and evaluation of these systems are difficult tasks, especially when there are multiple student profiles or huge amount of interaction data of students. In this work, data mining methods, and specifically decision trees, are used for helping in both improvement and evaluation. Our work consists of analyzing two data sets by using decision trees. The first data set contains the interaction data of 24 real students, and the second data set is composed of synthetic data about 100 students. The results of these analyses demonstrated that 24 students is a small data set when decision trees are used. However, the tree showed information relating to the practical activities in which students had more problems for completing them providing useful feedback to the course designer.This work has been funded by Spanish Ministry of Science and Education through the HADA project TIN2007-64718. Cesar Vialardi is also funded by Fundación Carolina