Methodological issues in developing a multi-dimensional coding procedure for small group chat communication

In CSCL research, collaboration through chat has primarily been studied in dyadic settings. This article discusses three issues that emerged during the development of a multi-dimensional coding procedure for small group chat communication: a) the unit of analysis and unit fragmentation, b) the reconstruction of the response structure and c) determining reliability without overestimation. Threading, i.e. connections between analysis units, proved essential to handle unit fragmentation, to reconstruct the response structure and for reliability of coding. In addition, a risk for reliability overestimation was illustrated. Implications for analysis methodology in CSCL are discussed

Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings

We study a symmetric collaborative dialogue setting in which two agents, each with private knowledge, must strategically communicate to achieve a common goal. The open-ended dialogue state in this setting poses new challenges for existing dialogue systems. We collected a dataset of 11K human-human dialogues, which exhibits interesting lexical, semantic, and strategic elements. To model both structured knowledge and unstructured language, we propose a neural model with dynamic knowledge graph embeddings that evolve as the dialogue progresses. Automatic and human evaluations show that our model is both more effective at achieving the goal and more human-like than baseline neural and rule-based models.Comment: ACL 201

Dialogism: A Framework for CSCL and a Signature of Collaboration

International audienceAs Computer Supported Collaborative Learning (CSCL) gains a broader usage in multiple educational scenarios facilitated by the use of technology, the need for automated tools capable of detecting and stimulating collaboration increases. We propose a computational model using ReaderBench that assesses collaboration from a dialogical perspective. Accordingly, collaboration emerges from the intertwining of different points of view or, more specifically, from the inter-animation of voices pertaining to different speakers. Collaboration is determined from the intertwining or overlap of voices emitted by different participants throughout the ongoing conversation. This study presents a validation of this model consisting of a comparison between the output of our system and human evaluations of 10 chat conversations, selected from a corpus of more than 100 chats, in which Computer Science students debated on the advantages and disadvantages of CSCL technologies (e.g., chat, blog, wiki, forum, or Google Wave). The human evaluations of the degree of collaboration between the participants and the automated scores showed good overlap as measured by precision, recall, and F1 scores. Our overarching conclusion is that dialogism derived from the overlapping of voices can be perceived as a signature for collaboration

Redes Bayesianas para detección de roles de equipos en aprendizaje colaborativo soportado por computadoras

El trabajo colaborativo soportado por computadoras permite a los estudiantes que se encuentran en lugares remotos trabajar de manera conjunta en el mismo entorno virtual y permite la comunicación de ideas e información entre los integrantes del grupo. Sin embargo, como no todos los estudiantes son iguales, es importante estudiar las características de éstos para construir grupos de trabajo más productivos. La teoría de roles de equipo posibilita obtener buen desempeño en los equipos de trabajo considerando habilidades individuales, combinando las falencias de cada rol con las fortalezas de los otros. Generalmente, las personas tienen que completar extensos cuestionarios para poder determinar sus roles de equipo. En este trabajo, se propone un método alternativo para realizar esta detección a través de un sistema de aprendizaje colaborativo y a partir de la utilización de la técnica de Redes Bayesianas.Computer-supported collaborative learning allows students who are in different places to work together in the same virtual space, and supports the communication of ideas and information among learners. However, as not all students are identical, it is important to study users' characteristics to build more productive teams. Team Roles Theory allows obtaining very good team performance taking into account individual skills, combining the weaknesses of each role with the strengths of others. Originally, people have to complete extensive questionnaires to determine their team role. In this work we propose an alternative method to make this detection through a collaborative learning system and by using a Bayesian Network.Fil: Balmaceda, José María. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Schiaffino, Silvia Noemi. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Godoy, Daniela Lis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentin

Accessibility evaluation of chats and forums in e-learning environments

Proceedings of: 2013 International Conference on Frontiers in Education: Computer Science & Computer Engineering (FECS'13): WORLDCOMP'13, July 22-25, 2013, Las Vegas, Nevada (USA)Collaborative learning is useful for students in their learning process. Nowadays, most e-learning systems include Computer Supported Collaborative Learning (CSCL) tools like chats and forums; however, are they accessible for everybody? This paper presents a heuristic evaluation of accessibility of two CSCL tools (chat and forum) in four web-based, open-source Learning Content Management Systems (LCMS): Moodle, ATutor, dotLRN and Claroline. The evaluation results show that the CSCL tools evaluated present accessibility barriers which are a handicap for many students who want to use the LCMSs Moreover, some recommendations are offered in order to improve the accessibility of the evaluated tools. Considering these recommendations in the development of the evaluated tools, all students could participate actively in the collaborative tasks proposed by teachersThis research work has been supported by the Regional Government of Madrid under the Research Network MA2VICMR (S2009/TIC-1542) and by the Spanish Ministry of Economy under the project MULTIMEDICA (TIN2010-20644-C03-01)Publicad

Validating the Automated Assessment of Participation and of Collaboration in Chat Conversations

International audienceAs Computer Supported Collaborative Learning (CSCL) gains a broader usage as a viable alternative to classic educational scenarios, the need for automated tools capable of supporting tutors in the time consuming process of analyzing conversations becomes more stringent. Moreover, in order to fully explore the benefits of such scenarios, a clear demarcation must be made between participation or active involvement, and collaboration that presumes the intertwining of ideas or points of view with other participants. Therefore, starting from a cohesion-based model of the discourse, we propose two computational models for assessing collaboration and participation. The first model is based on the cohesion graph and can be perceived as a longitudinal analysis of the ongoing conversation, thus accounting for participation from a social knowledge-building perspective. In the second approach, collaboration is regarded from a dialogical perspective as the intertwining or overlap of voices pertaining to different speakers, therefore enabling a transversal analysis of subsequent discussion slices

Utterances Assessment in Chat Conversations

International audienceWith the continuous evolution of collaborative environments, the needs of automatic analyses and assessment of participants in instant messenger conferences (chat) have become essential. For these aims, on one hand, a series of factors based on natural language processing (including lexical analysis and Latent Semantic Analysis) and data-mining have been taken into consideration. On the other hand, in order to thoroughly assess participants, measures as Page's essay grading, readability and social networks analysis metrics were computed. The weights of each factor in the overall grading system are optimized using a genetic algorithm whose entries are provided by a perceptron in order to ensure numerical stability. A gold standard has been used for evaluating the system's performance

Utterances Assessment in Chat Conversations

International audienceWith the continuous evolution of collaborative environments, the needs of automatic analyses and assessment of participants in instant messenger conferences (chat) have become essential. For these aims, on one hand, a series of factors based on natural language processing (including lexical analysis and Latent Semantic Analysis) and data-mining have been taken into consideration. On the other hand, in order to thoroughly assess participants, measures as Page's essay grading, readability and social networks analysis metrics were computed. The weights of each factor in the overall grading system are optimized using a genetic algorithm whose entries are provided by a perceptron in order to ensure numerical stability. A gold standard has been used for evaluating the system's performance