Melody Siadaty

A Semantic Web-enabled Tool for Self-Regulated Learning in the Workplace

Self-regulated learning processes have a potential to enhance the motivation of knowledge workers to take part in learning and knowledge building activities, and thus contribute to the resolution of an important research challenge in workplace learning. An equally important research challenge for successful completion of each step of a self-regulatory process is to enable learners to be aware of characteristics of their organizationally embedded learning context. In this paper, we describe how a combination of pedagogy and Semantic Web-based technologies can be utilized to address the above two challenges. Specifically, we demonstrate the proposed solution through the Learning Pal tool which leverages ontologies to support self-regulation in organizational learning.

Can Educators Develop Ontologies Using Ontology Extraction Tools: An End-User Study

The recent research demonstrated several important benefits in the use of semantic technologies in development of technology-enhanced environments. The one underlying assumption for most of the current approaches is that there is a domain ontology. The second unspoken assumption follows that educators will build domain ontologies for their courses. However, ontologies are hard to build. Ontology extraction tools aim to overcome this problem. We have conducted an empirical study with educators where they used current ontology tools to extract ontologies from their existing course material. The results are reported for the IT and non-IT educators.

Learn-B: a social analytics-enabled tool for self-regulated workplace learning

In this design briefing, we introduce the Learn-B environment, our attempt in designing and implementing a research prototype to address some of the challenges inherent in workplace learning: the informal aspect of workplace learning requires knowledge workers to be supported in their self-regulatory learning (SRL) processes, whilst its social nature draws attention to the role of collective in those processes. Moreover, learning at workplace is contextual and on-demand, thus requiring organizations to recognize and motivate the learning and knowledge building activities of their employees, where individual learning goals are harmonized with those of the organization. In particular, we focus on the analytics-based features of Learn-B, illustrate their design and current implementation, and discuss how each of them is hypothesized to target the above challenges.

Leveraging semantic technologies for harmonization of individual and organizational learning

For a successful learning organization, it is of crucial importance to have successful methods for stimulation and sharing of working and learning activities of their employees. However, there are two important challenges to be addressed: i) combination of individual and organizational incentives that motivate employees to take part in knowledge building and sharing activities; and ii) structuring of learning and knowledge building activities and their outcomes in a representation that can assure unambiguous knowledge sharing. To address these challenges, we propose a framework of individual and organizational factors for knowledge sharing and a set of ontologies that provides a systematic and interlinked representation of concepts of individual and organizational learning. On top of these proposals, we developed and here present a software solution, which has been evaluated through a case study conducted in a large enterprise context.

Ontology Extraction Tools: An Empirical Study with Educators

Recent research in Technology-Enhanced Learning (TEL) demonstrated several important benefits that semantic technologies can bring to the TEL domain. An underlying assumption for most of these research efforts is the existence of a domain ontology. The second unspoken assumption follows that educators will build domain ontologies for their courses. However, ontologies are hard to build, especially for not-tech-savvy users. Tools for ontology extraction from text aim to overcome this problem. We have conducted an empirical study with educators, both from Information Technology (IT) and non-IT domains, where they used current ontology extraction tools to build domain ontologies for their courses from their existing course material. Based on the obtained study results we have drawn conclusions about the existing ontology extraction tools and provided recommendations for their future development so that they can be beneficial for the TEL domain.

Measuring the impact of technological scaffolding interventions on micro-level processes of self-regulated workplace learning

This paper reports on the findings of an exploratory study in which the effects of technological scaffolding interventions on micro-level processes of self-regulated learning in the workplace were investigated. Empirical research in the workplace has been much less represented than in formal education. Even less research is available that aimed to identify which technological scaffolding interventions, out of those available in a learning environment, had the highest influence on specific micro-level process of self-regulated learning. This paper reports on the findings of a case study conducted in the naturalistic settings of two organizations in Europe (N?=?53) for the period of two months. Trace data about the events of engagement with the technological scaffolding interventions and micro-level processes of self-regulated learning were collected. Both a transition graph based analysis of the temporal dependencies of the collected events and multiple linear regression analyses showed that an intervention that promoted social awareness had consistently the highest effect on all the micro-level processes used in the study. This intervention was followed by the intervention that offered system-generated recommendations about learning paths, learning activities and knowledge assets to stimulate engagement into the micro-level processes within the forethought or preparatory phase of self-regulated learning. These findings suggest that both the social and organizational contexts should be taken into account when developing interventions aimed at supporting the forethought and engagement phases. Further discussion about research, methodological, and learning technology design implications is provided. Effects of technological scaffolds on self-regulated workplace learning are studied.Protocol based on trace data is used for measurement in an exploratory study.Transition graphs and multiple linear regression are used for analysis.Scaffolds promoting social awareness had consistently the highest effect.System-generated recommendations of learning resources had also high effects.

Online Presence in Adaptive Learning on the Social Semantic Web

In adaptive learning environments, this exchangeof online presence data cannot be considered isolatedfrom the overall learning context. However, there isyet no systematic solution to exchanging andintegrating online presence data from diverse instantmessaging and social networking applications. Toaddress this issue, we propose an ontology-basedapproach to sharing online presence data in adaptivelearning environments through the use of the OnlinePresence Ontology. This ontology is integrated into theLearning Object Context Ontology framework, whichallows for capturing and unambiguous representationof all relevant data about students online presence,their mutual interactions, as well as their interactionswith learning resources. On top of this ontologyframework, we have developed innovative, context-awarelearning services presented in this paper. Theuse of these services is demonstrated in a learningenvironment for studying software patterns.

Semantic Technologies for Socially-Enhanced Context-Aware Mobile Learning

In this paper, we present an extended ontological framework, m-LOCO, in which potentials of mobile devices as content delivery media have been utilized in the design of a context-aware learning environment, where the learning content and/or the learning environment are matched to the needs and requirements of the learning context. Moreover, we suggest the affordance of a socially-enhanced self-regulated learning approach as the most efficient pedagogical roadmap for ubiquitous learning environments, with the goal of helping the learners to be more productive through fostering both their individual and social collaborative competencies and expertise.

Self-regulated learners and collaboration: how innovative tools can address the motivation to learn at the workplace?

This paper describes an innovative motivational approach combined with new tools to support self-regulated learners, stressing the importance of social embeddedness and collaboration for learners at work. We suggest that the social context plays an important role when it comes to defining learning goals, adapting ones strategies to social norms, and collaboratively creating knowledge with colleagues and peers. A first assessment of this motivational approach and early prototypes was conducted within the IntelLEO project by the end-users of three heterogeneous business cases. The results of this evaluation in the biggest business case are presented in this paper.

Let's Meet: Integrating Social and Learning Worlds

The widespread welcome to the social web in recentyears has led to the emergence of a new source of userinformation which is a result of users’ everydayactivities and contributions on social networks.Leveraging this information within learningenvironments bears beneficial opportunities that cani) bring the educational (what learners learn ineducational settings) and social (what they performwithin their social networks) worlds closer, and ii)support further recommendation and personalizationservices based on more ‘real’ data about learners.This information, however, is neither explicitlyavailable nor formally represented on the social webparadigm, an attribute of the social web regarded asbeing a “walled garden”. In this paper, we propose aframework that leverages semantic web technologiesto locate and extract such data, originating frommultiple resources on the social web, to supportadvanced learning services such as personalized peerrecommendation.