A case study in online formal/informal learning: was it collaborative or cooperative learning?

Developing skills in communication and collaboration is essential in modern design education, in order to prepare students for the realities of design practice, where projects involve multidisciplinary teams, often working remotely. This paper presents a learning activity that focusses on developing communication and collaboration skills of undergraduate design students working remotely and vocational learners based in a community makerspace. Participants were drawn from these formal and informal educational settings and engaged in a design-make project framed in the context of distributed manufacturing. They were given designer or maker roles and worked at distance from each other, communicating using asynchronous online tools. Analysis of the collected data has identified a diversity of working practice across the participants, and highlighted the difficulties that result from getting students to work collaboratively, when not collocated. This paper presents and analysis of participants’ communications, with a view to identify whether they were learning collaboratively, or cooperatively. It was found that engaging participants in joint problem solving is not enough to facilitate collaboration. Instead effective collaboration depends on symmetry within the roles of participants and willingness to share expertise through dialogue. Designing learning activities to overcome the challenges that these factors raise is a difficult task, and the research reported here provides some valuable insight

Conversational Analysis using Utterance-level Attention-based Bidirectional Recurrent Neural Networks

Recent approaches for dialogue act recognition have shown that context from preceding utterances is important to classify the subsequent one. It was shown that the performance improves rapidly when the context is taken into account. We propose an utterance-level attention-based bidirectional recurrent neural network (Utt-Att-BiRNN) model to analyze the importance of preceding utterances to classify the current one. In our setup, the BiRNN is given the input set of current and preceding utterances. Our model outperforms previous models that use only preceding utterances as context on the used corpus. Another contribution of the article is to discover the amount of information in each utterance to classify the subsequent one and to show that context-based learning not only improves the performance but also achieves higher confidence in the classification. We use character- and word-level features to represent the utterances. The results are presented for character and word feature representations and as an ensemble model of both representations. We found that when classifying short utterances, the closest preceding utterances contributes to a higher degree.Comment: Proceedings of INTERSPEECH 201

A Dialogue-Act Taxonomy for a Virtual Coach Designed to Improve the Life of Elderly

This paper presents a dialogue act taxonomy designed for the development of a conversational agent for elderly. The main goal of this conversational agent is to improve life quality of the user by means of coaching sessions in different topics. In contrast to other approaches such as task-oriented dialogue systems and chit-chat implementations, the agent should display a pro-active attitude, driving the conversation to reach a number of diverse coaching goals. Therefore, the main characteristic of the introduced dialogue act taxonomy is its capacity for supporting a communication based on the GROW model for coaching. In addition, the taxonomy has a hierarchical structure between the tags and it is multimodal. We use the taxonomy to annotate a Spanish dialogue corpus collected from a group of elder people. We also present a preliminary examination of the annotated corpus and discuss on the multiple possibilities it presents for further research.The research presented in this paper is conducted as part of the project EMPATHIC that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 769872. The authors would also like to thank the support by the Basque Government through the project IT-1244-19