Amy Soller

Bio: Amy Soller is an academic researcher from Institute for Defense Analyses. The author has contributed to research in topics: Collaborative learning & Educational technology. The author has an hindex of 17, co-authored 22 publications receiving 2004 citations. Previous affiliations of Amy Soller include Mitre Corporation & University of Pittsburgh.

From Mirroring to Guiding: A Review of State of the Art Technology for Supporting Collaborative Learning

Abstract: We review a representative selection of systems that support the management of collaborative learning interaction, and characterize them within a simple classification framework. The framework distinguishes between mirroring systems, which display basic actions to collaborators, metacognitive tools, which represent the state of interaction via a set of key indicators, and coaching systems, which offer advice based on an interpretation of those indicators. The reviewed systems are further characterized by the type of interaction data they assimilate, the processes they use for deriving higher-level data representations, the variables or indicators that characterize these representations, and the type of feedback they provide to students and teachers. This overview of technological capabilities is designed to lay the groundwork for further research into which technological solutions are appropriate for which learning situations.

Supporting Social Interaction in an Intelligent Collaborative Learning System

Abstract: Students learning effectively in groups encourage each other to ask questions, explain and justify their opinions, articulate their reasoning, and elaborate and reflect upon their knowledge. The benefits of collaborative learning, however, are only achieved by active, well- functioning teams. This paper presents a model of collaborative learning designed to help an intelligent collaborative learning system identify and target group interaction problem areas. The model describes potential indicators of effective collaborative learning, and for each indicator, recommends strategies for improving peer interaction. This collaborative learning model drove the design and development of two tools that automate the coding, and aid the analysis of collaborative learning conversation and activity. Empirical evaluation of these tools confirm that effective learning teams are comprised of active participants who demand explanations and justification from their peers. The distribution of conversational skills used by members of a supportive group committed to their teammates' learning is compared to that of an unfocused, unsupportive group. The results suggest that structured, high-level knowledge of student conversation in context may be sufficient for automating the assessment of group interaction, furthering the possibility of an intelligent collaborative learning system that can support and enhance the group learning process.

From mirroring to guiding: A review of the state of art technology for supporting collaborative learning

Abstract: We review systems that support the management of collaborative interaction, and propose a classification framework built on a simple model of coaching. Our framework distinguishes between mirroring systems, which display basic actions to collaborators, metacognitive tools, which represent the state of interaction via a set of key indicators, and coaching systems, which offer advice based on an interpretation of those indicators. The reviewed systems are further characterized by the type of interaction data they assimilate, the processes they use for deriving higher-level data representations, and the type of feedback they provide to users.

Computational Modeling and Analysis of Knowledge Sharing in Collaborative Distance Learning

Abstract: This research aims to support collaborative distance learners by demonstrating how a probabilistic machine learning method can be used to model and analyze online knowledge sharing interactions The approach applies Hidden Markov Models and Multidimensional Scaling to analyze and assess sequences of coded online student interaction These analysis techniques were used to train a system to dynamically recognize (1) when students are having trouble learning the new concepts they share with each other, and (2) why they are having trouble The results of this research may assist an instructor or intelligent coach in understanding and mediating situations in which groups of students collaborate to share their knowledge

Promoting Effective Peer Interaction in an Intelligent Collaborative Learning System

Abstract: Placing students in a group and assigning them a task does not guarantee that the students will engage in effective collaborative learning behavior. The collaborative learning model described in this paper identifies the specific characteristics exhibited by effective collaborative learning teams, and based on these characteristics, suggests strategies for promoting effective peer interaction. The model is designed to help an intelligent collaborative learning system recognize and target group interaction problem areas. Once targeted, the system can take actions to help students collaborate more effectively with their peers, maximizing individual student and group learning.

Artificial neural networks

Abstract: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning

Abstract: Collaborative technologies support group work in project-based environments. In this study, we enhance the technology acceptance model to explain the factors that influence the acceptance of Google Applications for collaborative learning. The enhanced model was empirically evaluated using survey data collected from 136 students enrolled in a full-time degree program that used Google Applications to support project work. According to the research results, determinants of the technology acceptance model are the major factors influencing the adoption of the technology. In addition, the subjective norm represented by peers is found to significantly moderate the relationship between attitude and intention toward the technology. However, our results do not show a significant effect of subjective norms represented by instructors and mass media on students' intentions to use the technology. The ability to share information in the collaborative learning environment is found to influence intention and behavior toward the Google Applications platform.

From Mirroring to Guiding: A Review of State of the Art Technology for Supporting Collaborative Learning

Abstract: We review a representative selection of systems that support the management of collaborative learning interaction, and characterize them within a simple classification framework. The framework distinguishes between mirroring systems, which display basic actions to collaborators, metacognitive tools, which represent the state of interaction via a set of key indicators, and coaching systems, which offer advice based on an interpretation of those indicators. The reviewed systems are further characterized by the type of interaction data they assimilate, the processes they use for deriving higher-level data representations, the variables or indicators that characterize these representations, and the type of feedback they provide to students and teachers. This overview of technological capabilities is designed to lay the groundwork for further research into which technological solutions are appropriate for which learning situations.