Collaborative trails in e-learning environments

This deliverable focuses on collaboration within groups of learners, and hence collaborative trails. We begin by reviewing the theoretical background to collaborative learning and looking at the kinds of support that computers can give to groups of learners working collaboratively, and then look more deeply at some of the issues in designing environments to support collaborative learning trails and at tools and techniques, including collaborative filtering, that can be used for analysing collaborative trails. We then review the state-of-the-art in supporting collaborative learning in three different areas – experimental academic systems, systems using mobile technology (which are also generally academic), and commercially available systems. The final part of the deliverable presents three scenarios that show where technology that supports groups working collaboratively and producing collaborative trails may be heading in the near future

Improving Hybrid Brainstorming Outcomes with Scripting and Group Awareness Support

Previous research has shown that hybrid brainstorming, which combines individual and group methods, generates more ideas than either approach alone. However, the quality of these ideas remains similar across different methods. This study, guided by the dual-pathway to creativity model, tested two computer-supported scaffolds – scripting and group awareness support – for enhancing idea quality in hybrid brainstorming. 94 higher education students,grouped into triads, were tasked with generating ideas in three conditions. The Control condition used standard hybrid brainstorming without extra support. In the Experimental 1 condition, students received scripting support during individual brainstorming, and students in the Experimental 2 condition were provided with group awareness support during the group phase in addition. While the quantity of ideas was similar across all conditions, the Experimental 2 condition produced ideas of higher quality, and the Experimental 1 condition also showed improved idea quality in the individual phase compared to the Control condition

Maximizing the Benefits of Collaborative Learning in the College Classroom

abstract: This study tested the effects of two kinds of cognitive, domain-based preparation tasks on learning outcomes after engaging in a collaborative activity with a partner. The collaborative learning method of interest was termed "preparing-to-interact," and is supported in theory by the Preparation for Future Learning (PFL) paradigm and the Interactive-Constructive-Active-Passive (ICAP) framework. The current work combined these two cognitive-based approaches to design collaborative learning activities that can serve as alternatives to existing methods, which carry limitations and challenges. The "preparing-to-interact" method avoids the need for training students in specific collaboration skills or guiding/scripting their dialogic behaviors, while providing the opportunity for students to acquire the necessary prior knowledge for maximizing their discussions towards learning. The study used a 2x2 experimental design, investigating the factors of Preparation (No Prep and Prep) and Type of Activity (Active and Constructive) on deep and shallow learning. The sample was community college students in introductory psychology classes; the domain tested was "memory," in particular, concepts related to the process of remembering/forgetting information. Results showed that Preparation was a significant factor affecting deep learning, while shallow learning was not affected differently by the interventions. Essentially, equalizing time-on-task and content across all conditions, time spent individually preparing by working on the task alone and then discussing the content with a partner produced deeper learning than engaging in the task jointly for the duration of the learning period. Type of Task was not a significant factor in learning outcomes, however, exploratory analyses showed evidence of Constructive-type behaviors leading to deeper learning of the content. Additionally, a novel method of multilevel analysis (MLA) was used to examine the data to account for the dependency between partners within dyads. This work showed that "preparing-to-interact" is a way to maximize the benefits of collaborative learning. When students are first cognitively prepared, they seem to make the most efficient use of discussion towards learning, engage more deeply in the content during learning, leading to deeper knowledge of the content. Additionally, in using MLA to account for subject nonindependency, this work introduces new questions about the validity of statistical analyses for dyadic data.Dissertation/ThesisPh.D. Educational Psychology 201

Domino: exploring mobile collaborative software adaptation

Social Proximity Applications (SPAs) are a promising new area for ubicomp software that exploits the everyday changes in the proximity of mobile users. While a number of applications facilitate simple file sharing between co–present users, this paper explores opportunities for recommending and sharing software between users. We describe an architecture that allows the recommendation of new system components from systems with similar histories of use. Software components and usage histories are exchanged between mobile users who are in proximity with each other. We apply this architecture in a mobile strategy game in which players adapt and upgrade their game using components from other players, progressing through the game through sharing tools and history. More broadly, we discuss the general application of this technique as well as the security and privacy challenges to such an approach

Location-based and contextual mobile learning. A STELLAR Small-Scale Study

This study starts from several inputs that the partners have collected from previous and current running research projects and a workshop organised at the STELLAR Alpine Rendevous 2010. In the study, several steps have been taken, firstly a literature review and analysis of existing systems; secondly, mobile learning experts have been involved in a concept mapping study to identify the main challenges that can be solved via mobile learning; and thirdly, an identification of educational patterns based on these examples has been done. Out of this study the partners aim to develop an educational framework for contextual learning as a unifying approach in the field. Therefore one of our central research questions is: how can we investigate, theorise, model and support contextual learning

Exploring Teachers Perceptions on Modeling Effort Demanded by CSCL Designs with Explicit Artifact Flow Support

Producción CientíficaArtifact flow represents an important aspect of teaching/learning processes, especially in CSCL situations in which complex relationships may be found. However, explicit modeling of CSCL processes with artifact flow may increase the cognitive load and associated effort of the teachers-designers and therefore decrease the efficiency of the design process. The empirical study, reported in this paper and grounded on mixed methods, provides evidence of the effort overload when teachers are involved in designing CSCL situations in a controlled environment. The results of the study illustrate the problem through the subjective perception of the participating teachers, complemented with objective parameters, such as time consumed, errors committed, uncertainty and objective complexity metrics.Ministerio de Economía, Industria y Competitividad (Project Project TIN2014-53199-C3- 2-R)Junta de Castilla y León (programa de apoyo a proyectos de investigación - Ref. VA277U14)Junta de Castilla y León (programa de apoyo a proyectos de investigación – Ref. VA082U16

A Pedagogical Application Framework for Synchronous Collaboration

Designing successful collaborative learning activities is a new focus of research within the E-Learning community. The social dimension inside the traditional face-to-face collaborative learning is important and must be included in the online learning designs. In this thesis, we introduce the concept of Pedagogical Application Frameworks, and describe Beehive, a pedagogical application framework for synchronous collaborative learning. Beehive guides teachers in reusing online collaborative learning activities based on well-known pedagogical designs, to accomplish their educational objectives within a certain educational setting, and also simplifies the development of new pedagogical collaboration designs. Beehive’s conceptual model has four abstraction layers: Pedagogical Techniques, Collaboration Task patterns, CSCL Components, and CSCL script. By following the framework’s guidelines and specifications, developers will place the control of designing pedagogical collaboration tools in the teacher’s hand rather than in the software designer’s

A Pedagogical Application Framework for Synchronous Collaboration

Designing successful collaborative learning activities is a new focus of research within the E-Learning community. The social dimension inside the traditional face-to-face collaborative learning is important and must be included in the online learning designs. In this thesis, we introduce the concept of Pedagogical Application Frameworks, and describe Beehive, a pedagogical application framework for synchronous collaborative learning. Beehive guides teachers in reusing online collaborative learning activities based on well-known pedagogical designs, to accomplish their educational objectives within a certain educational setting, and also simplifies the development of new pedagogical collaboration designs. Beehive’s conceptual model has four abstraction layers: Pedagogical Techniques, Collaboration Task patterns, CSCL Components, and CSCL script. By following the framework’s guidelines and specifications, developers will place the control of designing pedagogical collaboration tools in the teacher’s hand rather than in the software designer’s

Identity-Guided Collaborative Learning for Cloth-Changing Person Reidentification

Cloth-changing person reidentification (ReID) is a newly emerging research topic that is aimed at addressing the issues of large feature variations due to cloth-changing and pedestrian view/pose changes. Although significant progress has been achieved by introducing extra information (e.g., human contour sketching information, human body keypoints, and 3D human information), cloth-changing person ReID is still challenging due to impressionable pedestrian representations. Moreover, human semantic information and pedestrian identity information are not fully explored. To solve these issues, we propose a novel identity-guided collaborative learning scheme (IGCL) for cloth-changing person ReID, where the human semantic is fully utilized and the identity is unchangeable to guide collaborative learning. First, we design a novel clothing attention degradation stream to reasonably reduce the interference caused by clothing information where clothing attention and mid-level collaborative learning are employed. Second, we propose a human semantic attention and body jigsaw stream to highlight the human semantic information and simulate different poses of the same identity. In this way, the extraction features not only focus on human semantic information that is unrelated to the background but also are suitable for pedestrian pose variations. Moreover, a pedestrian identity enhancement stream is further proposed to enhance the identity importance and extract more favorable identity robust features. Most importantly, all these streams are jointly explored in an end-to-end unified framework, and the identity is utilized to guide the optimization. Extensive experiments on five public clothing person ReID datasets demonstrate that the proposed IGCL significantly outperforms SOTA methods and that the extracted feature is more robust, discriminative, and clothing-irrelevant