Search results

Interest is currently growing in open social learner modeling (OSLM), which means making peer models and a learner's own model visible to encourage users in e-learning. The purpose of this study is to examine students' views about the OSLM in an e-learning system.

This case study was conducted with 40 undergraduate students enrolled in advanced programming and database management system courses. A Likert-type questionnaire and open-ended questions were used to obtain the students' views. System usage data were also analyzed to ensure the richness and diversity of the overall data set.

The quantitative data of the students' views were analyzed with descriptive statistics; the results are presented as graphics. The qualitative data of the students' views were examined by content analysis to derive themes. These themes are organized into four subtopics: the students' positive views, their negative views, their improvement suggestions and their preferences about using similar OSLM visualizations in other e-learning systems. The students' subjective views are discussed in the context of their recorded interactions with the system.

Competition due to seeing peer models was considered by participants both as positive and negative features of the learning system. So, this study revealed that, the ways to combine peer learner models to e-learning systems that promote positive competition without resulting social pressure, still need to be explored.

By combining open learner models with open peer models, OSLM enhances the learning process in three different ways: it supports self-regulation, encourages competition and empowers self-evaluation. To take advantage of these positive contributions, practitioners should consider enhancing e-learning systems with both own learner and peer model features.

Despite increasing interest in OSLM studies, several limitations and problems must be addressed such as sparsity of data and lack of study of different contexts and cultures. To date, no published study in this area exists in Turkey. The purpose of this study is to fill this gap by examining OSLM features in an e-learning system from the perspectives of Turkish students by using both their system interaction data and their subjective views.

Mini‐languages for teaching principles of programming ‐ such as Karel the Robot ‐ were once used in top computer science departments to provide a “gentle introduction” to programming for computer science majors. The paper builds a case for the use of mini‐languages in the context of introductory programming courses for non‐computer science major. We present a study that explored the use of Karel to teach introductory programming for information science majors.

Social navigation is an emerging trend for navigation in hypermedia. With social navigation, users can be guided through large volumes of learning content by cues which integrate the browsing history of past users. Earlier papers have shown that social navigation is suitable for navigation not only in classic hypermedia but also in time‐based learning media like web lectures by presenting prototype implementations. The purpose of this paper is to report on user experiences with social navigation for web lectures in the classroom.

This paper presents results obtained from a two‐term classroom study with a social navigation interface for web lectures. The study comprises both log file analysis and student questionnaires. The interface used in the study implements a footprint‐based social navigation approach for time‐based continuous media such as web lectures.

The results of the user study show that social navigation cues significantly affect user lecture navigation, causing users to pay more attention to the material previously explored by other users. The users' subjective feedback on the usefulness of the social navigation cues and related navigation components was significantly positive.

Social navigation has primarily been implemented and researched in traditional text‐ and picture‐based hypermedia. This paper presents an actual user study of footprint‐based social navigation for web lectures. The results of this study are relevant to both practitioners who want to use social navigation in web lectures and researchers who want to improve and research navigation approaches for time‐based media.

The goal of the research is to explore whether the use of higher‐level semantic features can help us to build better self‐organising map (SOM) representation as measured from a human‐centred perspective. The authors also explore an automatic evaluation method that utilises human expert knowledge encapsulated in the structure of traditional textbooks to determine map representation quality.

Two types of document representations involving semantic features have been explored – i.e. using only one individual semantic feature, and mixing a semantic feature with keywords. Experiments were conducted to investigate the impact of semantic representation quality on the map. The experiments were performed on data collections from a single book corpus and a multiple book corpus.

Combining keywords with certain semantic features achieves significant improvement of representation quality over the keywords‐only approach in a relatively homogeneous single book corpus. Changing the ratios in combining different features also affects the performance. While semantic mixtures can work well in a single book corpus, they lose their advantages over keywords in the multiple book corpus. This raises a concern about whether the semantic representations in the multiple book corpus are homogeneous and coherent enough for applying semantic features. The terminology issue among textbooks affects the ability of the SOM to generate a high quality map for heterogeneous collections.

The authors explored the use of higher‐level document representation features for the development of better quality SOM. In addition the authors have piloted a specific method for evaluating the SOM quality based on the organisation of information content in the map.

The purpose of this paper is to introduce the Opencast Community, a global community of individuals, institutions, and commercial stakeholders exchanging knowledge about all matters relevant in the context of academic video and promoting projects in this context. It also gives an overview of the most prominent of these projects, Opencast Matterhorn – a community‐driven open source solution for producing, managing, and distributing academic video.

The paper will demonstrate that Opencast Matterhorn is satisfying institutional needs to manage audiovisual content more efficiently as video is becoming a significant resource in research and education. Furthermore, the paper highlights that Opencast Matterhorn as a product and as a project is open for contributions from the research community and provides an excellent environment for the integration of research results from media analysis, multimedia authoring, search technologies, and other related fields.

Opencast Matterhorn provides a scalable open source solution for universities to manage academic video. Its service‐oriented architecture makes it customizable to institutional needs and open for contributions from users as well as media research.

The paper provides an insight to the idea of Opencast, the Opencast Community, and Opencast Matterhorn – and how they will help academic institutions to better manage and exploit the full richness of educational video.

The purpose of this paper is to present a user interface for web lectures for engaging with other users while working with video based learning content. The application allows its users to ask questions about the content and to get answers from those users that currently online are more familiar with it. The filtering is based on the evaluation of past user interaction data in time‐based media.

The work is implemented as a prototype application in the context of the Opencast Matterhorn project – an open source based project for producing, managing and distributing academic video content. The application compares users viewing behavior and allows communication with others that are good candidates to answer questions.

Different filtering approaches for identifying suitable candidates are being discussed that foster past interactions in time‐based media.

The paper shows that web lectures can benefit from user awareness ideas and presents examples of how learners can benefit from the knowledge of other users who are working with the same video based content.

User awareness has become an important feature in today's Web 2.0 experience. The paper discusses different user awareness models and explains how they can be adapted to time‐based video content. The presented work is available as a plug‐in for the Opencast Matterhorn project.

In this article the authors will focus on the state of the art on information filtering and recommender systems based on trust. Then the authors will represent a variety of filtering and recommendation techniques studied in different literature, like basic content filtering, collaborative filtering and hybrid filtering. The authors will also examine different trust-based recommendation algorithms. It will ends with a summary of the different existing approaches and it develops the link between trust, sustainability and recommender systems.

Methodology of this study will begin with a general introduction to the different approaches of recommendation systems; then define trust and its relationship with recommender systems. At the end the authors will present their approach to “trust-based recommendation systems”.

The purpose of this study is to understand how groups of users could improve trust in a recommendation system. The authors will examine how to evaluate the performance of recommender systems to ensure their ability to meet the needs that led to its creation and to make the system sustainable with respect to the information. The authors know very well that selecting a measure must depend on the type of data to be processed and user interests. Since the recommendation domain is derived from information search paradigms, it is obvious to use the evaluation measures of information systems.

The authors presented a list of recommendations systems. They examined and compared several recommendation approaches. The authors then analyzed the dominance of collaborative filtering in the field and the emergence of Recommender Systems in social web. Then the authors presented and analyzed different trust algorithms. Finally, their proposal was to measure the impact of trust in recommendation systems.

Lecture recording provides learning material for local and distance education. The TeleTeachingTool uses the very flexible screen recording technique to capture virtually any material displayed during a presentation. With its built‐in annotation system teachers can add freehand notes and emphasize important parts. Unlike other screen recorders, our implementation offers slide‐based navigation, full text search and annotated scripts, which are obtained by automated post‐production. This article presents how automated analysis generates indices for slide‐based navigation on the fly and how to achieve live interlinkage of annotations with slides so that annotations disappear when a slide is changed and are made visible again when returning to that slide later during presentation, although screen recorders generally do not provide an association of annotations with slides.

The purpose of this paper is to discuss how learning styles and theories are currently used within personalised adaptable e‐learning adaptive systems. This paper then aims to describe the e‐learning platform iLearn and how this platform is designed to incorporate learning styles as part of the personalisation offered by the system.

The paper discusses how learning styles and theories are currently being used within the area of adaptive e‐learning and describes current research within this area. This paper then gives an overview of the iLearn project and describes how iLearn is using the VARK learning style to enhance the platform's personalisation and adaptability for the learner. This research also describes the system's design and how the learning style is incorporated into the system design and semantic framework within the learner's profile.

The findings describe how the final implemented iLearn platform intends to address the issues found with the limited personalisation within common learning management systems and intends to provide the learner with a personalised learning experience.

Adaptability and personalisation are large research areas, however, many limitations have been found during the current research. This research project, therefore adds value to this by proposing a system which will address the current personalisation limitations.