Analyzing groups’ problem-solving process to characterize collaboration within groups

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Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

INVESTIGATING SEARCH PROCESSES IN COLLABORATIVE EXPLORATORY WEB SEARCH

People are often engaged in collaboration in workplaces or daily life due to the complexity of tasks. In the information seeking and retrieval environment, the task can be as simple as fact-finding or a known-item search, or as complex as exploratory search. Given the complex nature of the information needs, exploratory searches may require the collaboration among multiple people who share the same search goal. For instance, students may work together to search for information in a collaborative course project; friends may search together while planning a vacation. There are demands for collaborative search systems that could support this new format of search (Morris, 2013). Despite the recognized importance of understanding search process for designing successful search system (Bates, 1990; M. Hearst, 2009), it is particularly difficult to study collaborative search process because of the complex interactions involved. In this dissertation, I propose and demonstrate a framework of investigating search processes in the collaborative exploratory search. I designed a laboratory-based user study to collect the data, compared two search conditions: individual search and collaborative search as well as two task types through the study. I first applied a novel Hidden Markov Model approach to analyze the search states in the collaborative search process, the results of which provide a holistic picture of the collaborative search process. I then investigated two important components in the collaborative search process – query behaviors and communications. The findings reveal the characteristics of query and communication patterns in the collaborative search. It also suggests that although the collaboration between two people on search did not achieve a higher performance than two individuals, the collaboration indeed make people feel more satisfied with their performance and less stressed. The results of this study not only provide implications for designing effective collaborative search systems, but also show valuable research directions and methodologies for other researchers

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

Human–agent team dynamics: a review and future research opportunities

Humans teaming with intelligent autonomous agents is becoming indispensable in work environments. However, human–agent teams pose significant challenges, as team dynamics are complex arising from the task and social aspects of human–agent interactions. To improve our understanding of human–agent team dynamics, in this article, we conduct a systematic literature review. Drawing on Mathieu et al.’s (2019) teamwork model developed for all-human teams, we map the landscape of research to human–agent team dynamics, including structural features, compositional features, mediating mechanisms, and the interplay of the above features and mechanisms. We reveal that the development of human–agent team dynamics is still nascent, with a particular focus on information sharing, trust development, agents’ human likeness behaviors, shared cognitions, situation awareness, and function allocation. Gaps remain in many areas of team dynamics, such as team processes, adaptability, shared leadership, and team diversity. We offer various interdisciplinary pathways to advance research on human–agent teams

E-Learning

Technology development, mainly for telecommunications and computer systems, was a key factor for the interactivity and, thus, for the expansion of e-learning. This book is divided into two parts, presenting some proposals to deal with e-learning challenges, opening up a way of learning about and discussing new methodologies to increase the interaction level of classes and implementing technical tools for helping students to make better use of e-learning resources. In the first part, the reader may find chapters mentioning the required infrastructure for e-learning models and processes, organizational practices, suggestions, implementation of methods for assessing results, and case studies focused on pedagogical aspects that can be applied generically in different environments. The second part is related to tools that can be adopted by users such as graphical tools for engineering, mobile phone networks, and techniques to build robots, among others. Moreover, part two includes some chapters dedicated specifically to e-learning areas like engineering and architecture

Human vs. supervised machine learning: Who learns patterns faster?

The capabilities of supervised machine learning (SML), especially compared to human abilities, are being discussed in scientific research and in the usage of SML. This study provides an answer to how learning performance differs between humans and machines when there is limited training data. We have designed an experiment in which 44 humans and three different machine learning algorithms identify patterns in labeled training data and have to label instances according to the patterns they find. The results show a high dependency between performance and the underlying patterns of the task. Whereas humans perform relatively similarly across all patterns, machines show large performance differences for the various patterns in our experiment. After seeing 20 instances in the experiment, human performance does not improve anymore, which we relate to theories of cognitive overload. Machines learn slower but can reach the same level or may even outperform humans in 2 of the 4 of used patterns. However, machines need more instances compared to humans for the same results. The performance of machines is comparably lower for the other 2 patterns due to the difficulty of combining input features