A Framework for Personalized Content Recommendations to Support Informal Learning in Massively Diverse Information WIKIS

Personalization has proved to achieve better learning outcomes by adapting to specific learners’ needs, interests, and/or preferences. Traditionally, most personalized learning software systems focused on formal learning. However, learning personalization is not only desirable for formal learning, it is also required for informal learning, which is self-directed, does not follow a specified curriculum, and does not lead to formal qualifications. Wikis among other informal learning platforms are found to attract an increasing attention for informal learning, especially Wikipedia. The nature of wikis enables learners to freely navigate the learning environment and independently construct knowledge without being forced to follow a predefined learning path in accordance with the constructivist learning theory. Nevertheless, navigation on information wikis suffer from several limitations. To support informal learning on Wikipedia and similar environments, it is important to provide easy and fast access to relevant content. Recommendation systems (RSs) have long been used to effectively provide useful recommendations in different technology enhanced learning (TEL) contexts. However, the massive diversity of unstructured content as well as user base on such information oriented websites poses major challenges when designing recommendation models for similar environments. In addition to these challenges, evaluation of TEL recommender systems for informal learning is rather a challenging activity due to the inherent difficulty in measuring the impact of recommendations on informal learning with the absence of formal assessment and commonly used learning analytics. In this research, a personalized content recommendation framework (PCRF) for information wikis as well as an evaluation framework that can be used to evaluate the impact of personalized content recommendations on informal learning from wikis are proposed. The presented recommendation framework models learners’ interests by continuously extrapolating topical navigation graphs from learners’ free navigation and applying graph structural analysis algorithms to extract interesting topics for individual users. Then, it integrates learners’ interest models with fuzzy thesauri for personalized content recommendations. Our evaluation approach encompasses two main activities. First, the impact of personalized recommendations on informal learning is evaluated by assessing conceptual knowledge in users’ feedback. Second, web analytics data is analyzed to get an insight into users’ progress and focus throughout the test session. Our evaluation revealed that PCRF generates highly relevant recommendations that are adaptive to changes in user’s interest using the HARD model with rank-based mean average precision (MAP@k) scores ranging between 100% and 86.4%. In addition, evaluation of informal learning revealed that users who used Wikipedia with personalized support could achieve higher scores on conceptual knowledge assessment with average score of 14.9 compared to 10.0 for the students who used the encyclopedia without any recommendations. The analysis of web analytics data show that users who used Wikipedia with personalized recommendations visited larger number of relevant pages compared to the control group, 644 vs 226 respectively. In addition, they were also able to make use of a larger number of concepts and were able to make comparisons and state relations between concepts

Survey of Personalized Learning Software Systems: A Taxonomy of Environments, Learning Content, and User Models

This paper presents a comprehensive systematic review of personalized learning software systems. All the systems under review are designed to aid educational stakeholders by personalizing one or more facets of the learning process. This is achieved by exploring and analyzing the common architectural attributes among personalized learning software systems. A literature-driven taxonomy is recognized and built to categorize and analyze the reviewed literature. Relevant papers are filtered to produce a final set of full systems to be reviewed and analyzed. In this meta-review, a set of 72 selected personalized learning software systems have been reviewed and categorized based on the proposed personalized learning taxonomy. The proposed taxonomy outlines the three main architectural components of any personalized learning software system: learning environment, learner model, and content. It further defines the different realizations and attributions of each component. Surveyed systems have been analyzed under the proposed taxonomy according to their architectural components, usage, strengths, and weaknesses. Then, the role of these systems in the development of the field of personalized learning systems is discussed. This review sheds light on the field’s current challenges that need to be resolved in the upcoming years

Web 2.0 and its impact on knowledge and business organizations

Today, information overload and the lack of systems that enable locating employees with the right knowledge or skills are common challenges that large organisations face. This makes knowledge workers to re-invent the wheel and have problems to retrieve information from both internal and external resources. In addition, information is dynamically changing and ownership of data is moving from corporations to the individuals. However, there is a set of web based tools that may cause a major progress in the way people collaborate and share their knowledge. This article aims to analyse the impact of ‘Web 2.0’ on organisational knowledge strategies. A comprehensive literature review was done to present the academic background followed by a review of current ‘Web 2.0’ technologies and assessment of their strengths and weaknesses. As the framework of this study is oriented to business applications, the characteristics of the involved segments and tools were reviewed from an organisational point of view. Moreover, the ‘Enterprise 2.0’ paradigm does not only imply tools but also changes the way people collaborate, the way the work is done (processes) and finally impacts on other technologies. Finally, gaps in the literature in this area are outlined

Crowdsourced real-world sensing: sentiment analysis and the real-time web

The advent of the real-time web is proving both challeng- ing and at the same time disruptive for a number of areas of research, notably information retrieval and web data mining. As an area of research reaching maturity, sentiment analysis oers a promising direction for modelling the text content available in real-time streams. This paper reviews the real-time web as a new area of focus for sentiment analysis and discusses the motivations and challenges behind such a direction

The Business Value of Social Network Technologies: A Framework for Identifying Opportunities for Business Value and an Emerging Research Program

Although social network technologies have been the focus of many articles in the popular and business press, businesses remain unclear about their value. We use theory and data gathered from IT leaders to develop an initial model assessing the value of social network technologies in the business environment. Insights are given into when different features should be used to enhance existing business processes and to provide business value

The Impact of Sentiment-driven Feedback on Knowledge Reuse in Online Communities

Knowledge reuse is of increasing importance for organizations. Despite the extant research, we still do not adequately understand the ways peers are motivated to reuse knowledge with the help of wiki technologies. In this paper, we study the motivation for knowledge reuse in a prominent instance of online social production: Wikipedia. Studying knowledge reuse in Wikipedia is important since Wikipedia has been able to leverage the benefits of efficient knowledge reuse to produce knowledge goods of relatively high quality. Specifically, we explore: 1) how Wikipedia editors (any peer who contributes to developing articles in Wikipedia) communicate their feedback toward each other’s work in peer conversations and 2) to what extent sentiment-driven feedback impacts the level of knowledge reuse in Wikipedia. The results show that displaying sentiment-driven feedback positively influenced the level of knowledge reuse. Our study further shows a significant difference in the level of knowledge reuse between editors who shared mainly positive or mainly negative sentiments. Specifically, displaying mainly positive feedback corresponded to a superior level of knowledge reuse than displaying mainly negative feedback. We contribute to the extant literature of online social production communities in general and Wikipedia in particular by providing a first building block for research on peer feedback’s role in developing and sustaining wiki-based knowledge reuse. We discuss our findings’ implications for theory and practice

Metrics for Analyzing Social Documents to Understand Joint Work

Social Collaboration Analytics (SCA) aims at measuring and interpreting communication and joint work on collaboration platforms and is a relatively new topic in the discipline of Information Systems. Previous applications of SCA are largely based on transactional data (event logs). In this paper, we propose a novel approach for the examination of collaboration based on the structure of social documents. Guided by the ontology for social business documents (SocDOnt) we develop metrics to measure collaboration around documents that provide traces of collaborative activity. For the evaluation, we apply these metrics to a large-scale collaboration platform. The findings show that group workspaces that support the same use case are characterized by a similar richness of their social documents (i.e. the number of components and contributing authors). We also show typical differences in the “collaborativity” of functional modules (containers)

Second language writing online: An update

I last wrote an overview of developments in second language (L2) online writing 10 years ago (Godwin-Jones, 2008). Since that time, there have been significant developments in this area. There has been renewed interest in L2 writing through the wide use of social media, along with the rising popularity of computer-mediated communication (CMC) and telecollaboration (class-based online exchanges). The recognition of writing as a social act has also led to a significant rise in interest in collaborative writing. This has been aided by the popularity of tools providing a shared writing space, such as Google Docs. The importance and recognition of genre in both student work and writing theory have grown considerably among practitioners and researchers. The increased practice of integrating multimedia into writing is reflected in the popularity of multimodal projects, such as digital storytelling. At the same time, digital tools for evaluating writing have become more widely available in the form of digital annotators and automated writing evaluation (AWE) software, which take advantage of advances in corpus linguistics and natural language processing (NLP). In addition, tools for processing and evaluating large data sets enable approaches from data mining that provide valuable insights into writing processes. The variety and, in some cases, the complexity of online writing environments has increased the need for both learner and teacher training