Analyzing collaborative knowledge construction: multiple methods for integrated understanding

Documenting collaborative knowledge construction is critical for research in computer-supported collaborative learning. Because this is a multifaceted phenomenon, mixed methods are necessary to construct a good understanding of collaborative interactions, otherwise there is a risk of being overly reductionistic. In this paper I use quantitative methods of verbal data analysis, qualitative analysis, and techniques of data representation to characterize two successful knowledge building interactions from a sociocultural perspective. In the first study, a computer simulation helped mediate the interaction and in the second, a student-constructed representation was an important mediator. A fine-grained turn-by-turn analysis of the group discussions was supplemented with qualitative analysis of larger units of dialogue. In addition, chronological representations of discourse features and tool-related activity were used in study 2 to gain an integrated understanding of how a student-generated representation mediated collaborative knowledge construction. It is only by mixing methods that collaborative knowledge construction can be well characterized.     

Query based model learning and stable tracking of a robot arm using radial basis function network

In the context of a robot manipulator, a generalized neural emulator over the complete workspace is very difficult to obtain because of dimensionally insufficient training data. A query based learning algorithm is proposed in this paper that can generate new examples where control inputs are independent of states of the system. This algorithm is centered around the concept of network inversion using an extended Kalman filtering based algorithm. This is a novel idea since robot manipulator is an open loop unstable system and generation of control input independent of state is a research issue for neural model identification. Two trajectory independent stable control schemes have been designed using the neural emulator. One of the control schemes uses forward-inverse-modeling approach to update the controller parameters adaptively following Lyapunov function synthesis technique. The proposed scheme is trajectory independent unlike the back-propagation scheme. The second type of controller predicts the minimum variance estimate of control action using recall process (network inversion) and the control law is derived following a Lyapunov function synthesis approach so that the closed loop system consisting of controller and neural emulator remains stable. The simulation experiments show that the model validation approach is efficient and the proposed control schemes guarantee stable accurate tracking.     

HIGH‐DIMENSIONAL LEARNING FRAMEWORK FOR ADAPTIVE DOCUMENT FILTERING*

We investigate the unique requirements of the adaptive textual document filtering problem and propose a new high‐dimensional on‐line learning framework, known as the REPGER (relevant feature pool with good training example retrieval rule) algorithm to tackle this problem. Our algorithm possesses three characteristics. First, it maintains a pool of selective features with potentially high predictive power to predict document relevance. Second, besides retrieving documents according to their predicted relevance, it also retrieves incoming documents that are considered good training examples. Third, it can dynamically adjust the dissemination threshold throughout the filtering process so as to maintain a good filtering performance in a fully interactive environment. We have conducted experiments on three document corpora, namely, Associated Press, Foreign Broadcast Information Service, and Wall Street Journal to compare the performance of our REPGER algorithm with two existing on‐line learning algorithms. The results demonstrate that our REPGER algorithm gives better performance most of the time. Comparison with the TREC (Text Retrieval Conference) adaptive text filtering track participants was also made. The result shows that our REPGER algorithm is comparable to them.     

Converting numerical classification into text classification

Consider a supervised learning problem in which examples contain both numerical- and text-valued features. To use traditional feature-vector-based learning methods, one could treat the presence or absence of a word as a Boolean feature and use these binary-valued features together with the numerical features. However, the use of a text-classification system on this is a bit more problematic—in the most straight-forward approach each number would be considered a distinct token and treated as a word. This paper presents an alternative approach for the use of text classification methods for supervised learning problems with numerical-valued features in which the numerical features are converted into bag-of-words features, thereby making them directly usable by text classification methods. We show that even on purely numerical-valued data the results of text classification on the derived text-like representation outperforms the more naive numbers-as-tokens representation and, more importantly, is competitive with mature numerical classification methods such as C4.5, Ripper, and SVM. We further show that on mixed-mode data adding numerical features using our approach can improve performance over not adding those features.     

一种基于神经网络集成的规则学习算法

将神经网络集成与规则学习相结合，提出了一种基于神经网络集成的规则学习算法．该算法以神经网络集成作为规则学习的前端，利用其产生出规则学习所用的数据集，在此基础上进行规则学习．在UCl机器学习数据库上的实验结果表明，该算法可以产生泛化能力非常强的规则．     

Learning mixtures of point distribution models with the EM algorithm

This paper demonstrates how the EM algorithm can be used for learning and matching mixtures of point distribution models. We make two contributions. First, we show how shape-classes can be learned in an unsupervised manner. We present a fast procedure for training point distribution models using the EM algorithm. Rather than estimating the class means and covariance matrices needed to construct the PDM, the method iteratively refines the eigenvectors of the covariance matrix using a gradient ascent technique. Second, we show how recognition by alignment can be realised by fitting a mixture of linear shape deformations. We evaluate the method on the problem of learning the class-structure and recognising Arabic characters.     

School experience course with multimedia in teacher education

Abstract This paper describes an interactive and self-paced multimedia tutorial programme that provides pre-service teachers with a complete range of school system and teaching strategies. Situated learning has been used as the theoretical framework to produce the multimedia tutorial for a pre-service teachers' School Experience course. The programme allows pre-service teachers to implement approaches appropriate for teaching a K-12 curriculum. Two methods of information delivery were investigated: traditional lecturing and multimedia. The results show that using the multimedia tutorial leads to a positive difference in the School Experience course over participants in traditional lecturing. Pre-service teachers who participated in the multimedia group responded positively to a multimedia approach. This study concludes that using multimedia in teacher education enriches pre-service teachers' learning and provides them with an opportunity to view and critique various teaching methods and classroom activities collectively.     

Web-based collaborative inquiry learning

Abstract This study proposes a web-based collaborative inquiry learning system. This system uses the World-wide web (WWW) as a source of knowledge exploration, and provides exploratory problems to guide students to think and explore. A concept map is used as a tool of anchoring and representing knowledge during inquiry process. In the process of learning, learners are allowed to exchange the evidence they have collected, their personal opinions, and the concept maps that they have built. In order to effectively integrate the inquiry learning, collaborative learning, and concept map in the system, this study proposes a collaborative inquiry learning model and related learning activities. Two studies were constructed based on the collaborative inquiry learning model to investigate students' learning processes in the collaborative inquiry learning on the web.     

Easy Algorithm Animation on the Web

The animation environment Jeliot automates visualization of algorithms over the Internet. The user can visualize algorithms of his own, not merely the selection supplied by a service provider. Jeliot is based on self-animating data types: the user selects the data objects of the source code to be visualized, and Jeliot produces the animation based on operations performed on those types. We present the design of Jeliot and the principles of its implementation. In addition, we discuss the educational applications of Jeliot.     

Congregation Formation in Multiagent Systems

We present congregating both as a metaphor for describing and modeling multiagent systems (MAS) and as a means for reducing coordination costs in large-scale MAS. When agents must search for other agents to interact with, congregations provide a way for agents to bias this search towards groups of agents that have tended to produce successful interactions in the past. This causes each agent's search problem to scale with the size of a congregation rather than the size of the population as a whole. In this paper, we present a formal model of a congregation and then apply Vidal and Durfee's CLRI framework [24] to the congregating problem. We apply congregating to the affinity group domain, and show that if agents are unable to describe congregations to each other, the problem of forming optimal congregations grows exponentially with the number of agents. The introduction of labelers provides a means of coordinating agent decisions, thereby reducing the problem's complexity. We then show how a structured label space can be exploited to simplify the labeler's decision problem and make the congregating problem linear in the number of labels. We then present experimental evidence demonstrating how congregating can be used to reduce agents' search costs, thereby allowing the system to scale up. We conclude with a comparison to other methods for coordinating multiagent behavior, particularly teams and coalitions.