Between the Lines: documenting the multiple dimensions of computer supported collaborations

When we consider the possibilities for the design and evaluation of Computer Supported Collaborative Learning (CSCL) we probably constrain the CS in CSCL to situations in which learners, or groups of learners collaborate with each other around a single computer, across a local intranet or via the global internet. We probably also consider situations in which the computer itself acts as a collaborative partner giving hints and tips either with or without the addition of an animated pedagogical agent. However, there are now many possibilities for CSCL applications to be offered to learners through computing technology that is something other than a desktop computer, such as the TV or a digital toy. In order to understand how such complex and novel interactions work, we need tools to map out the multiple dimensions of collaboration using a whole variety of technologies. This paper discusses the evolution of a documentation technique for collaborative interactions from its roots in a situation where a single learner is collaborating with a software learning partner, through its second generation: group use of multimedia, to its current test-bed: young children using digital toys and associated software. We will explore some of the challenges these different learning situations pose for those involved in the evaluation of collaborative learning

Semantic Modeling for Group Formation

Group formation has always been a subject of interest in collaborative learning research. As it is concerned with assigning learners to the groups that maximize their benefits, computer-supported group formation can be viewed in this context as an active personalization for the individual as an entity within the group. While applying this personalization to all students in the class can cause conflicts due to the differences of needs and interests between the individuals, negotiating the allocations to groups to reach consensus can be a very challenging task. The automated process of grouping students while preserving the individual’s personalization needs to be supported by an appropriate learner model. In this paper, we propose a semantic learner model based on the Friend of Friend (FOAF) ontology, a vocabulary for mapping social networks. We discuss the model as we analyse the different types of groups and the learners’ features that need to be modeled for each of these types

Construction and abstraction: contrasting methods of supporting model building in learning science

No description supplie

Learning patterns and learner profiles in learning object design

The questions that Andy Heath has posed are challenging and need more time for reflection than is possible here. The questions posed will inform the research as it develops further. However, in the interests of debate we would like to give our initial replies. We agree in general with Andy Heath's assessment of the limitations of the approach we are adopting. We recognise that this approach uses a very limited response to AccessForAll principles: our Transformation Augmentation and Substitution Service (TASS) is localised, not global, and relies on a limited set of learning patterns matched against a small subset of the potentially infinite set of learner profiles. Our intention is certainly not to reproduce the considerable efforts of the IMS AccessForAll or Dublin Core Adaptability working groups, but to interpret their potential impact on the thinking of courseware designers, tutors and students

A European research agenda for lifelong learning

It is a generally accepted truth that without a proper educational system no country will prosper, nor will its inhabitants. With the arrival of the post-industrial society, in Europe and elsewhere, it has become increasingly clear that people should continue learning over their entire life-spans lest they or their society suffer the dire consequences. But what does this future lifelong learning society exactly look like? And how then should education prepare for it? What should people learn and how should they do so? How can we afford to pay for all this, what are the socio-economic constraints of the move towards a lifelong-learning society? And, of course, what role can and should the educational establishment of schools and universities play? This are questions that demand serious research efforts, which is what this paper argues for

The role of social networks in students’ learning experiences

The aim of this research is to investigate the role of social networks in computer science education. The Internet shows great potential for enhancing collaboration between people and the role of social software has become increasingly relevant in recent years. This research focuses on analyzing the role that social networks play in students’ learning experiences. The construction of students’ social networks, the evolution of these networks, and their effects on the students’ learning experience in a university environment are examined

On Recommendation of Learning Objects using Felder-Silverman Learning Style Model

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The e-learning recommender system in learning institutions is increasingly becoming the preferred mode of delivery, as it enables learning anytime, anywhere. However, delivering personalised course learning objects based on learner preferences is still a challenge. Current mainstream recommendation algorithms, such as the Collaborative Filtering (CF) and Content-Based Filtering (CBF), deal with only two types of entities, namely users and items with their ratings. However, these methods do not pay attention to student preferences, such as learning styles, which are especially important for the accuracy of course learning objects prediction or recommendation. Moreover, several recommendation techniques experience cold-start and rating sparsity problems. To address the challenge of improving the quality of recommender systems, in this paper a novel recommender algorithm for machine learning is proposed, which combines students actual rating with their learning styles to recommend Top-N course learning objects (LOs). Various recommendation techniques are considered in an experimental study investigating the best technique to use in predicting student ratings for e-learning recommender systems. We use the Felder-Silverman Learning Styles Model (FSLSM) to represent both the student learning styles and the learning object profiles. The predicted rating has been compared with the actual student rating. This approach has been experimented on 80 students for an online course created in the MOODLE Learning Management System, while the evaluation of the experiments has been performed with the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The results of the experiment verify that the proposed approach provides a higher prediction rating and significantly increases the accuracy of the recommendation

Tracking the dynamic nature of learner individual differences: Initial results from a longitudinal study

Individual differences (IDs) have long been considered one of the most important factors explaining variable rates and outcomes in second language acquisition (Dewaele, 2013). While traditional operationalizations of IDs have, explicitly or implicitly, assumed that IDs are static traits that are stable through time, more recent research inspired by complex dynamic systems theory (Larsen-Freeman, 1997, 2020) demonstrates that many IDs are dynamic and variable through time and across contexts, a theme echoed throughout the current issue. This study reports the initial semester of a diachronic project investigating the dynamicity of four learner IDs: motivation, personality, learning and cognitive styles, and working memory. In the initial semester, data from 323 participants in their first year of university-level Spanish were collected and analyzed to determine what type of variability may be present across learners with respect to the four IDs studied at one time point and to discern possible learner profiles in the data or patterns via which the data may be otherwise meaningfully described. The results revealed four types of learner profiles present in the dataset

The role of the “Inter-Life” virtual world as a creative technology to support student transition into higher education

The shape of Higher Education (HE) in the UK and internationally is changing, with wider access policies leading to greater diversity and heterogeneity in contemporary student populations world-wide. Students in the 21st Century are often described as “fragmented”; meaning they are frequently working whilst participating in a full time Degree programme. Consequently, those in the HE setting are required to become “future ready” which increasingly involves the seamless integration of new digital technologies into undergraduate programmes of teaching and learning. The present study evaluated the effectiveness of the “Inter-Life” three-dimensional virtual world as a suitable Technology Enhanced Learning (TEL) tool to support the initial stages of transition from school into university. Our results demonstrate that Inter-Life is “fit for purpose” in terms of the robustness of both the educational and technical design features. We have shown that Inter-Life provides a safe space that supports induction mediated by active learning tasks using learner-generated, multi-modal transition tools. In addition, through the provision of private spaces, Inter-Life also supports and fosters the development of critical reflective thinking skills. However, in keeping with the current literature in the field, some of the students expressed a wish for more training in the functional and social skills required to navigate and experience the Inter-Life virtual world more effectively. Such findings resonate with the current debate in the field which challenges the notion of “digital natives”, but the present study has also provided some new evidence to support the role of virtual worlds for the development of a suitable community to support students undergoing transition to university

Using styles for more effective learning in multicultural and e-learning environments

Purpose – This Special Issue contains selected papers from the thirteenth annual European Learning Styles Information Network (ELSIN) conference held in Ghent, Belgium in June 2008. One of the key aims of ELSIN is to promote understanding of individual learning and cognitive differences through the dissemination of international multidisciplinary research about learning and cognitive styles and strategies of learning and thinking. Design/methodology/approach – Three papers within this special issue consider how style differences can inform the development of e-learning opportunities to enhance the learning of all (Vigentini; Kyprianidou, Demetriadis, Pombortsis and Karatasios; Zhu, Valcke and Schellens). The influence of culture on learning is also raised in the paper of Zhu and colleagues and those of Sulimma and Eaves which both focus more directly on cultural influences on style, learning and teaching. Findings – A number of key themes permeate the studies included in this Special Edition such as: the nature of styles; the intrinsic difficulty of isolating style variables from other variables impacting on performance; inherent difficulties in choosing the most appropriate style measures; the potential of e-learning to attend to individual learning differences; the role of culture in informing attitudes and access to learning; the development of constructivist learning environments to support learning through an understanding of individual differences; and most importantly how one can apply such insights about individual differences to inform and enhance instruction. Originality/value – The papers in this Special Issue contribute to enhanced knowledge about the value of style differences to design constructive learning environments in multicultural and e-learning contexts