Collocated Collaboration Analytics: Principles and Dilemmas for Mining Multimodal Interaction Data

© 2019, Copyright © 2017 Taylor & Francis Group, LLC. Learning to collaborate effectively requires practice, awareness of group dynamics, and reflection; often it benefits from coaching by an expert facilitator. However, in physical spaces it is not always easy to provide teams with evidence to support collaboration. Emerging technology provides a promising opportunity to make collocated collaboration visible by harnessing data about interactions and then mining and visualizing it. These collocated collaboration analytics can help researchers, designers, and users to understand the complexity of collaboration and to find ways they can support collaboration. This article introduces and motivates a set of principles for mining collocated collaboration data and draws attention to trade-offs that may need to be negotiated en route. We integrate Data Science principles and techniques with the advances in interactive surface devices and sensing technologies. We draw on a 7-year research program that has involved the analysis of six group situations in collocated settings with more than 500 users and a variety of surface technologies, tasks, grouping structures, and domains. The contribution of the article includes the key insights and themes that we have identified and summarized in a set of principles and dilemmas that can inform design of future collocated collaboration analytics innovations

Utilizing Online Activity Data to Improve Face-to-Face Collaborative Learning in Technology-Enhanced Learning Environments

학위논문 (박사)-- 서울대학교 대학원 : 융합과학기술대학원 융합과학부(디지털정보융합전공), 2019. 2. Rhee, Wonjong .We live in a flood of information and face more and more complex problems that are difficult to be solved by a single individual. Collaboration with others is necessary to solve these problems. In educational practice, this leads to more attention on collaborative learning. Collaborative learning is a problem-solving process where students learn and work together with other peers to accomplish shared tasks. Through this group-based learning, students can develop collaborative problem-solving skills and improve the core competencies such as communication skills. However, there are many issues for collaborative learning to succeed, especially in a face-to-face learning environment. For example, group formation, the first step to design successful collaborative learning, requires a lot of time and effort. In addition, it is difficult for a small number of instructors to manage a large number of student groups when trying to monitor and support their learning process. These issues can amount hindrance to the effectiveness of face-to-face collaborative learning. The purpose of this dissertation is to enhance the effectiveness of face-to-face collaborative learning with online activity data. First, online activity data is explored to find whether it can capture relevant student characteristics for group formation. If meaningful characteristics can be captured from the data, the entire group formation process can be performed more efficiently because the task can be automated. Second, learning analytics dashboards are implemented to provide adaptive support during a class. The dashboards system would monitor each group's collaboration status by utilizing online activity data that is collected during class in real-time, and provide adaptive feedback according to the status. Lastly, a predictive model is built to detect at-risk groups by utilizing the online activity data. The model is trained based on various features that represent important learning behaviors of a collaboration group. The results reveal that online activity data can be utilized to address some of the issues we have in face-to-face collaborative learning. Student characteristics captured from the online activity data determined important group characteristics that significantly influenced group achievement. This indicates that student groups can be formed efficiently by utilizing the online activity data. In addition, the adaptive support provided by learning analytics dashboards significantly improved group process as well as achievement. Because the data allowed the dashboards system to monitor current learning status, appropriate feedback could be provided accordingly. This led to an improvement of both learning process and outcome. Finally, the predictive model could detect at-risk groups with high accuracy during the class. The random forest algorithm revealed important learning behaviors of a collaboration group that instructors should pay more attention to. The findings indicate that the online activity data can be utilized to address practical issues of face-to-face collaborative learning and to improve the group-based learning where the data is available. Based on the investigation results, this dissertation makes contributions to learning analytics research and face-to-face collaborative learning in technology-enhanced learning environments. First, it can provide a concrete case study and a guide for future research that may take a learning analytics approach and utilize student activity data. Second, it adds a research endeavor to address challenges in face-to-face collaborative learning, which can lead to substantial enhancement of learning in educational practice. Third, it suggests interdisciplinary problem-solving approaches that can be applied to the real classroom context where online activity data is increasingly available with advanced technologies.Abstract i Chapter 1. Introduction １ 1.1. Motivation １ 1.2. Research questions ４ 1.3. Organization ６ Chapter 2. Background ８ 2.1. Learning analytics ８ 2.2. Collaborative learning ２２ 2.3. Technology-enhanced learning environment ２７ Chapter 3. Heterogeneous group formation with online activity data ３５ 3.1. Student characteristics for heterogeneous group formation ３６ 3.2. Method ４１ 3.3. Results ５１ 3.4. Discussion ５９ 3.5. Summary ６４ Chapter 4. Real-time dashboard for adaptive feedback in face-to-face CSCL ６７ 4.1. Theoretical background ７０ 4.2. Dashboard characteristics ８１ 4.3. Evaluation of the dashboard ９４ 4.4. Discussion １０７ 4.5. Summary １１４ Chapter 5. Real-time detection of at-risk groups in face-to-face CSCL １１８ 5.1. Important learning behaviors of group in collaborative argumentation １１８ 5.2. Method １２０ 5.3. Model performance and influential features １２５ 5.4. Discussion １２９ 5.5. Summary １３２ Chapter 6. Conclusion １３４ Bibliography １４０Docto

A case study of collaborative learning among preparatory year students and their teachers at Hail University in Saudi Arabia

The concept of collaborative learning (CL) relates to the educational use of small groups, in which students work together to maximise their learning and to teach and learn from each other as much as possible, after receiving guidelines and instructions from their teachers. Collaborative learning in Saudi higher education (SHE) has been promoted at the government level in recent years as part of a trend to increase the adoption of e-learning. The policy also aligns with educational reforms and the drive to make the Saudi economy more competitive and diverse. Nevertheless, it is still enforcing itself to become a norm in the teaching and learning process as it is a radical shift from the traditional centralised decision making in educational settings and teacher-centred teaching, which indicate a high power distance structure. Therefore, this study investigates the perceptions of preparatory year students and teachers at Hail University regarding the implementation of CL. A qualitative research methodology was adopted. Data were gathered from observations, six focus groups (composed of five students in each group) and individual interviews with 12 teachers on the foundation year. The findings of this study indicated two modalities for deploying CL: traditional CL (TCL/non-computer- supported collaborative learning [CSCL]) and computer-supported CL (CSCL) in Saudi higher Education. Furthermore, the results showed that CL indeed provides personal, social, and academic benefits. It is still, however, marred by challenges such that effective implementation is curtailed and thus does not produce positive learning outcomes among students. Overall, given the cultural background, the preference for retaining a high power distance, and what teachers and students are accustomed to, the study suggests further research be conducted to implement an form of CL adapted to suit Saudi culture

A Design-Based Research Study Examining The Impact Of Collaboration Technology Tools In Mediating Collaboration

ABSTRACT A DESIGN-BASED RESEARCH STUDY EXAMINING THE IMPACT OF COLLABORATION TECHNOLOGY TOOLS IN MEDIATING COLLABORATION by KECIA J. WADDELL December 2015 Advisor: Dr. Monica W. Tracey Major: Instructional Technology Degree: Doctor of Philosophy Interactive collaboration technologies have expanded users\u27 capabilities to collaborate and have driven pedagogical paradigm shifts toward more learner-centered and interactive teaching and learning. Online learners may be not sufficiently prepared for the level of collaboration fluency expected by a globally competitive digital distributed knowledge economy. This is largely due in part by how collaboration technologies is used towards impacting learning goals and outcomes in practice by online learners themselves or by deliberate instructional design of the online environment. The purpose of this design-based research study was three-fold: (1) examine collaboration by exploring the perceptions of adult online learners regarding collaboration technology use and of a series instructional intervention videos that supported tool use; (2) track the iterative design, development, implementation, and evaluation of instructional screencasts designed to demonstrate and support the use of dynamic text editor functions and multimedia features for authentic collaboration learning tasks and learner-driven discussion board communication in two online discussion forum platforms: Blackboard Learn (BB) and Google Groups (GG); and (3) determine the impact of the instructional intervention on our educational problem identified as a behavior: organic learner-driven online discussion board collaboration. Participants were purposive sample of online learners enrolled in five graduate-level instructional technology online courses. Quantitative survey and qualitative reflective journal data was gathered in a three phased feedback loop. Findings indicated that collaboration is first a mindset supported not only by collaboration technology tools or learner technological self-efficacy, but by deliberate instructional design mediated by the cultural environment and the social context of the activity system

Dynamic measuring tools for online discourse

When evaluating participation within an Asynchronous Learning Network (ALN), current best practices include counting messages and reviewing participant surveys. To understand the impact of more advanced dynamic measurement tools for use within an ALN, a web-based tool, known as iPET (the integrated Participation Evaluation Tool), was created. iPET, which leverages Social Network Analysis and Information Visualization techniques, was then evaluated via an empirical study. This research demonstrates that using a tool such as iPET increases participation within an ALN without increasing facilitator workload. Due to the fact that active online discussion is a key factor in the success of an ALN, this research demonstrates that dynamic measuring tools for online participation can help ensure a positive outcome within an online learning environment

Understanding Technology Mediated Learning in Higher Education: A Repertory Grid Approach

Given the considerable opportunities that Web 2.0 technologies are seen to present for the enhancement of learning and teaching, understanding what motivates today’s students to use this technology in their learning is crucial. Drawing from technology mediated learning (TML) and Uses and Gratifications (U&G) perspectives, this study investigates university students’ motivations for using Web 2.0 technologies in learning. The Repertory Grid Interview technique (RGT) is used to interview 16 participants and capture their technology use motivations. A grounded approach was used to resolve eleven categories of motivations: Access and Content Control, Accessibility, Communication Efficiency, Communication Mode, Communication Quality, Course Management, Information Seeking, Interaction, Learning Capability, Managing Contents, and Self-Disclosure. The findings suggest that today’s students have different motivations for using technologies when it comes to learning

Enhancing Free-text Interactions in a Communication Skills Learning Environment

Learning environments frequently use gamification to enhance user interactions.Virtual characters with whom players engage in simulated conversations often employ prescripted dialogues; however, free user inputs enable deeper immersion and higher-order cognition. In our learning environment, experts developed a scripted scenario as a sequence of potential actions, and we explore possibilities for enhancing interactions by enabling users to type free inputs that are matched to the pre-scripted statements using Natural Language Processing techniques. In this paper, we introduce a clustering mechanism that provides recommendations for fine-tuning the pre-scripted answers in order to better match user inputs

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