Assessing the language of chat for teamwork dialogue

In technology enhanced language learning, many pedagogical activities involve students in online discussion such as synchronous chat, in order to help them practice their language skills. Besides developing the language competency of students, it is also crucial to nurture their teamwork competencies for today's global and complex environment. Language communication is an important glue of teamwork. In order to assess the language of chat for teamwork dimensions, several text mining methods are pos sible. However, difficulties arise such as pre-processing being a black box and classification approaches and algorithms being dependent on the context. To address these issues, the study will evaluate and explain preprocessing and classification methods used to analyze teamwork dialogue from a dataset of chat data. Analytics methods evaluated in this study provide a direction for assessing the language of chat for teamwork dialogue and can help extend the work of technology enhanced language learning to n ot only focus on academic competency, but on the communication aspect too

Achievements and challenges in learning analytics in Spain: The view of SNOLA

[EN] As in other research fields, the development of learning analytics is influenced by the networks of researchers that contribute to it. This paper describes one of such networks: the Spanish Network of Learning Analytics (SNOLA). The paper presents the research lines of the members of SNOLA, as well as the main challenges that learning analytics has to address in the next few years as perceived by these researchers. This analysis is based on SNOLA’s archival data and on a survey carried out to the current members of the network. Although this approach does not cover all the activity related to learning analytics in Spain, the results provide a representative overview of the current state of research related to learning analytics in this context. The paper describes these trends and the main challenges, among which we can point out the need to adopt an ethical commitment with data, to develop systems that respond to the requirements of the end users, and to reach a wider institutional impact.[ES] Tal y como ocurre en otros campos de investigación, el desarrollo de la analítica del aprendizaje está influido por las redes de investigadores que contribuyen al mismo. Este artículo describe una de estas redes: la Red Española de Analítica de Aprendizaje (SNOLA). El artículo presenta las líneas de investigación de los miembros de SNOLA, así como los principales retos que la analítica del aprendizaje tiene que afrontar en los próximos años desde la visión de estos investigadores. Este análisis está basado en datos de archivo de SNOLA y en una encuesta realizada a los actuales miembros de la red. Aunque esta aproximación no cubre toda la actividad relacionada con analítica del aprendizaje en España, los resultados proporcionan una visión general representativa del estado de la investigación relacionada con analítica del aprendizaje en dicho contexto. El artículo muestra cuáles son estas tendencias y los principales retos, entre los que se encuentran la necesidad de adoptar un compromiso ético con los datos, desarrollar sistemas que respondan a las necesidades de los usuarios y alcanzar mayor impacto institucional.SIMinisterio de Ciencia, Innovación y Universidades ( RED2018-102725-T)Ministerio de Ciencia, Innovación y Universidades (TIN2016-80172-R)Junta de Castilla y León (VA257P18)Gobierno Vasco (IT980-16)Generalitat de Catalunya (2017SGR1619)Gobierno de la Comunidad de Madrid (S2018/TCS-4307

Logros y retos en analítica del aprendizaje en España: La perspectiva de SNOLA

As in other research fields, the development of learning analytics is influenced by the networks of researchers that contribute to it. This paper describes one of such networks: the Spanish Network of Learning Analytics (SNOLA). The paper presents the research lines of the members of SNOLA, as well as the main challenges that learning analytics has to address in the next few years as perceived by these researchers. This analysis is based on SNOLA’s archival data and on a survey carried out to the current members of the network. Although this approach does not cover all the activity related to learning analytics in Spain, the results provide a representative overview of the current state of research related to learning analytics in this context. The paper describes these trends and the main challenges, among which we can point out the need to adopt an ethical commitment with data, to develop systems that respond to the requirements of the end users, and to reach a wider institutional impact.Tal y como ocurre en otros campos de investigación, el desarrollo de la analítica del aprendizaje está influido por las redes de investigadores que contribuyen al mismo. Este artículo describe una de estas redes: la Red Española de Analítica de Aprendizaje (SNOLA). El artículo presenta las líneas de investigación de los miembros de SNOLA, así como los principales retos que la analítica del aprendizaje tiene que afrontar en los próximos años desde la visión de estos investigadores. Este análisis está basado en datos de archivo de SNOLA y en una encuesta realizada a los actuales miembros de la red. Aunque esta aproximación no cubre toda la actividad relacionada con analítica del aprendizaje en España, los resultados proporcionan una visión general representativa del estado de la investigación relacionada con analítica del aprendizaje en dicho contexto. El artículo muestra cuáles son estas tendencias y los principales retos, entre los que se encuentran la necesidad de adoptar un compromiso ético con los datos, desarrollar sistemas que respondan a las necesidades de los usuarios y alcanzar mayor impacto institucional

Study of the flexibility of a learning analytics tool to evaluate teamwork competence acquisition in different contexts

Learning analytics tools and methodologies aim to facilitate teachers and/or decision makers with information and knowledge about what is happening in virtual learning environments in a straightforward and effortless way. However, it is necessary to apply these tools and methodologies in different contexts with a similar success, that is, that they should be flexible and portable enough. There exist several learning analytics tools that only works properly with very specific versions of learning platforms. In this paper, the authors aim to evaluate the flexibility and portability of a methodology and a learning analytics tool that supports individual assessment of teamwork competence. In order to do so the methodology and the tool are applied in a similar course from two different academic contexts. After the experiment, it is possible to see that the learning analytics tool seems to work properly and the suggested new functionalities are similar in both contexts. The methodology can be also applied but results could be improved if some meetings are carried out to check how team works are progressing with their tasks

Towards Predictable Process and Consequence Attributes of Data-Driven Group Work: Primary Analysis for Assisting Teachers with Automatic Group Formation

Data-driven platforms with rich data and learning analytics applications provide immense opportunities to support collaborative learning such as algorithmic group formation systems based on learning logs. However, teachers can still get overwhelmed since they have to manually set the parameters to create groups and it takes time to understand the meaning of each indicator. Therefore, it is imperative to explore predictive indicators for algorithmic group formation to release teachers from the dilemma with explainable group formation indicators and recommended settings based on group work purposes. Employing learning logs of group work from a reading-based university course, this study examines how learner indicators from different dimensions before the group work connect to the subsequent group work processes and consequences attributes through correlation analysis. Results find that the reading engagement and previous peer ratings can reveal individual achievement of the group work, and a homogeneous grouping strategy based on reading annotations and previous group work experience can predict desirable group performance for this learning context. In addition, it also proposes the potential of automatic group formation with recommended parameter settings that leverage the results of predictive indicators

Multimodal collaborative workgroup dataset and challenges

© 2017, CEUR-WS. All rights reserved. This work presents a multimodal dataset of 17 workgroup sessions in a collaborative learning activity. Workgroups were conformed of two or three students using a tabletop application in a co-located space. The dataset includes time-synchronized audio, video and tabletop system's logs. Some challenges were identified during the collection of the data, such as audio participation identification, and user traces identification. Future work should explore how to overcome the aforementioned difficulties

Complex network approaches to small team analysis. Conflict and gender

En un contexto donde los equipos juegan un papel cada vez más importante, es de gran importancia entender el conflicto y desarrollar herramientas de diagnóstico para evitarlo. Aquí, investigamos empíricamente si es posible predecir cuantitativamente futuros conflictos en equipos pequeños utilizando modelos de estructura de red social. Analizamos los datos de 16 equipos pequeños. Encontramos que los modelos basados en redes complejas anticipan el conflicto exitosamente mientras que los modelos utilizados tradicionalmente no lo hacen. También presentamos un estudio sobre las diferencias de género en el liderazgo. Este estudio tiene como objetivo entender los mecanismos detrás del proceso de evaluación del liderazgo. Analizamos los datos de 45 evaluaciones de líderes (33% mujeres). Encontramos que las mujeres líderes son mejor evaluadas que los líderes masculinos al principio. Más tarde, esta ventaja se desvanece junto con el efecto sorprendente de tener una líder femenino. También encontramos que la agrupación de la red de líderes mujeres crece significativamente más que la agrupación de líderes hombres.In a context where teams play an increasingly important role, it is of major importance to understand conflict and to develop diagnostic tools to avert it. Here, we investigate empirically whether it is possible to quantitatively predict future conflict in small teams using models of social network structure. We analyze data of 16 small teams. We find that models based on complex networks successfully anticipate conflict whereas traditionally used models do not. We also present a study about gender differences on leadership. This study aims to understand the mechanisms behind the leadership evaluation process. We analyze data of 45 leader evaluations (33% women). We find that female leaders are better evaluated than male leaders at the beginning. Later, this advantage vanishes together with the surprising effect of having a female leader. We also find that the network clustering of female leaders grows significantly more than the clustering of male leaders

Beyond Surveys: Analyzing Software Development Artifacts to Assess Teaching Efforts

This Innovative Practice Full Paper presents an approach of using software development artifacts to gauge student behavior and the effectiveness of changes to curriculum design. There is an ongoing need to adapt university courses to changing requirements and shifts in industry. As an educator it is therefore vital to have access to methods, with which to ascertain the effects of curriculum design changes. In this paper, we present our approach of analyzing software repositories in order to gauge student behavior during project work. We evaluate this approach in a case study of a university undergraduate software development course teaching agile development methodologies. Surveys revealed positive attitudes towards the course and the change of employed development methodology from Scrum to Kanban. However, surveys were not usable to ascertain the degree to which students had adapted their workflows and whether they had done so in accordance with course goals. Therefore, we analyzed students' software repository data, which represents information that can be collected by educators to reveal insights into learning successes and detailed student behavior. We analyze the software repositories created during the last five courses, and evaluate differences in workflows between Kanban and Scrum usage