Stefan Trausan-Matu

A polyphonic model and system for inter-animation analysis in chat conversations with multiple participants

Discourse in instant messenger conversations (chats) with multiple participants is often composed of several intertwining threads. Some chat environments for Computer-Supported Collaborative Learning (CSCL) support and encourage the existence of parallel threads by providing explicit referencing facilities. The paper proposes a discourse model for such chats, based on Mikhail Bakhtins dialogic theory. It considers that multiple voices (which do not limit to the participants) inter-animate, sometimes in a polyphonic, counterpointal way. An implemented system is also presented, which analyzes such chat logs for detecting additional, implicit links among utterances and threads and, more important for CSCL, for detecting the involvement (inter-animation) of the participants in problem solving. The system begins with a NLP pipe and concludes with inter-animation identification in order to generate feedback and to propose grades for the learners.

Polyphonic Inter-Animation of Voices in VMT

This chapter introduces a theoretical framework for analyzing collaborative problem solving in chats, based on the concept of polyphony and Bakhtin’s theory of dialog. Polyphony, a notion taken from music theory, may be considered as a general model for interaction and creativity by a group of people (“voices,” in an extended sense) following patterns of counterpoint. As Bakhtin emphasized, polyphony may occur in texts; we will show that it can occur in problem-solving chat texts. One of the features of polyphonic music is its potential development of complex architectures starting from a given theme. Polyphonic structuring of dialogs may transform the interaction into a “thinking device”: Different voices jointly construct a melody (story or solution), sometimes adopting different positions and then generating, idenepsying or solving dissonances (unsound, rickety stories or solutions). Polyphony consists of several “horizontal,” longitudinal melody lines that are “vertically,” transversally integrated. Similarly, in chats, the continuations of utterances are tied together over time providing a melodic line. Simultaneously, they are coordinated with the utterances of others, maintaining the integration toward unity across various themes and variations that sometimes can introduce differences. This chapter also proposes software tools for the visualization of the polyphonic weaving in chats. These tools idenepsy and visualize the explicit and implicit links among utterances, and may determine or visualize the contributions of each participant in a chat.

Textual complexity and discourse structure in computer-supported collaborative learning

Computer-Supported Collaborative Learning (CSCL) technologies play an increasing role simultaneously with the appearance of the Social Web. The polyphonic analysis method based on Bakhtins dialogical model reflects the multi-voiced nature of a CSCL conversation and the related learning processes. We propose the extension of the model and the previous applications of the polyphonic method to both collaborative CSCL chats and individual metacognitive essays performed by the same learners. The model allows a tight correlation between collaboration and textual complexity, all integrated in an implemented system, which uses Natural Language Processing techniques.

ReaderBench: Automated evaluation of collaboration based on cohesion and dialogism

As Computer-Supported Collaborative Learning (CSCL) gains a broader usage, the need for automated tools capable of supporting tutors in the time-consuming process of analyzing conversations becomes more pressing. Moreover, collaboration, which presumes the intertwining of ideas or points of view among participants, is a central element of dialogue performed in CSCL environments. Therefore, starting from dialogism and a cohesion-based model of discourse, we propose and validate two computational models for assessing collaboration. The first model is based on a cohesion graph and can be perceived as a longitudinal analysis of the ongoing conversation, thus accounting for collaboration from a social knowledge-building perspective. In the second approach, collaboration is regarded from a dialogical perspective as the intertwining or synergy of voices pertaining to different speakers, therefore enabling a transversal analysis of subsequent discussion slices.

Event log mining tool for large scale HPC systems

Event log files are the most common source of information for the characterization of events in large scale systems. However the large size of these files makes the task of manual analysing log messages to be difficult and error prone. This is the reason why recent research has been focusing on creating algorithms for automatically analysing these log files. In this paper we present a novel methodology for extracting templates that describe event formats from large datasets presenting an intuitive and user-friendly output to system administrators. Our algorithm is able to keep up with the rapidly changing environments by adapting the clusters to the incoming stream of events. For testing our tool, we have chosen 5 log files that have different formats and that challenge different aspects in the clustering task. The experiments show that our tool outperforms all other algorithms in all tested scenarios achieving an average precision and recall of 0.9, increasing the correct number of groups by a factor of 1.5 and decreasing the number of false positives and negatives by an average factor of 4.

Polyphonic support for collaborative learning

This paper argues that one reason for the success of collaborative problem solving where individual attempts failed is the polyphonic character of work in small groups. Polyphony, a concept taken from music, may occur in chats for problem solving, transforming dialog into a “thinking device”: Different voices jointly construct a melody (story, or solution) and other voices adopt differential positions, identifying dissonances (unsound, rickety stories or solutions). This polyphonic interplay may eventually make clear the correct (“sound”) construction. The paper illustrates the polyphonic character of collaborative problem solving using chats. It also proposes prototyped software tools for facilitating polyphony in chats.

Improving topic evaluation using conceptual knowledge

The growing number of statistical topic models led to the need to better evaluate their output. Traditional evaluation means estimate the models fitness to unseen data. It has recently been proven than the output of human judgment can greatly differ from these measures. Thus the need for methods that better emulate human judgment is stringent. In this paper we present a system that computes the conceptual relevance of individual topics from a given model on the basis of information drawn from a given concept hierarchy, in this case WordNet. The notion of conceptual relevance is regarded as the ability to attribute a concept to each topic and separate words related to the topic from the unrelated ones based on that concept. In multiple experiments we prove the correlation between the automatic evaluation method and the answers received from human evaluators, for various corpora and difficulty levels. By changing the evaluation focus from a statistical one to a conceptual one we were able to detect which topics are conceptually meaningful and rank them accordingly.

Automatic assessment of collaborative chat conversations with PolyCAFe

The wider acceptance and usage of instant messaging (chat) represents one of the consequences of undertaking Computer-Supported Collaborative Learning (CSCL) practices in formal education settings. However, the difficulty of analyzing these textual artifacts of learners in order to offer them feedback represents a serious problem in further extending the usage of chat conversations. PolyCAFe is a system that was designed to support the tutors and to provide automatic feedback for the learners engaged in collaborative chat conversations and discussion forums. The architecture of the system is presented by focusing on two key components: the assessment of the utterances and of the collaborative discourse. PolyCAFes effectiveness has been proved in a validation experiment with students and tutors from a University course. The main findings from this trial, together with the conclusions of domain experts verifying the accuracy of the assessment provided by PolyCAFe, are also analyzed and commented in detail.

Adaptive event prediction strategy with dynamic time window for large-scale HPC systems

In this paper, we analyse messages generated by different HPC large-scale systems in order to extract sequences of correlated events which we lately use to predict the normal and faulty behaviour of the system. Our method uses a dynamic window strategy that is able to find frequent sequences of events regardless on the time delay between them. Most of the current related research narrows the correlation extraction to fixed and relatively small time windows that do not reflect the whole behaviour of the system. The generated events are in constant change during the lifetime of the machine. We consider that it is important to update the sequences at runtime by applying modifications after each prediction phase according to the forecasts accuracy and the difference between what was expected and what really happened. Our experiments show that our analysing system is able to predict around 60% of events with a precision of around 85% at a lower event granularity than before.

PolyCAFe—automatic support for the polyphonic analysis of CSCL chats

Chat conversations and other types of online communication environments are widely used within CSCL educational scenarios. However, there is a lack of theoretical and methodological background for the analysis of collaboration. Manual assessing of non-moderated chat discussions is difficult and time-consuming, having as a consequence that learning scenarios have not been widely adopted, neither in formal education nor in informal learning contexts. An analysis method of collaboration and individual participation is needed. Moreover, computer-support tools for the analysis and assessment of these conversations are required. In this paper, we start from the “polyphonic framework” as a theoretical foundation suitable for the analysis of textual and even gestural interactions within collaborative groups. This framework exploits the notions of dialogism, inter-animation and polyphony for assessing interactions between participants. The basics of the polyphonic framework are discussed and a systematic presentation of the polyphonic analysis method is included. Then, we present the PolyCAFe system, which provides tools that support the polyphonic analysis of chat conversations and online discussion forums of small groups of learners. Natural Language Processing (NLP) is used in order to identify topics, semantic similarities and links between utterances. The detected links are then used to build a graph of utterances, which forms the central element for the polyphonic analysis and for providing automatic feedback and support to both tutors and learners. Social Network Analysis is used for computing quantitative measures for the interactions between participants. Two evaluation experiments have been undertaken with PolyCAFe. Learners find the system useful and efficient. In addition to these advantages, tutors reflecting on the conversation can provide quicker manual feedback.