A virtual diary companion

Chatbots and embodied conversational agents show turn based conversation behaviour. In current research we almost always assume that each utterance of a human conversational partner should be followed by an intelligent and/or empathetic reaction of chatbot or embodied agent. They are assumed to be alert, trying to please the user. There are other applications which have not yet received much attention and which require a more patient or relaxed attitude, waiting for the right moment to provide feedback to the human partner. Being able and willing to listen is one of the conditions for being successful. In this paper we have some observations on listening behaviour research and introduce one of our applications, the virtual diary companion

Applications of Kort Spiral Learning Method on Learners Behaviour Based on Wavelet Transform Method(DWT) in E-Learning Environment

This paper is planning to address one of the important difficulties faced by the e-learning communities, that is, capturing of human emotion accurately both of a tutor and learner in e-learning sceanario. In this paper, an approach for human emotion recognition system based on Discrete Wavelet Transform (DWT) on korts spiral model of learning on learners and tutors is presented. The affective pedagogy is one of the important component in effective learning model. The Korts model helps us to understand the effectiveness of learners emotion in the learning environment. The Korts model can be better implemented by means of human emotion recognition system based on DWT method. The classification of human emotional state is achieved by extracting the energies from all sub-bands of DWT. The robust K-Nearest Neighbor (K-NN) is constructed for classification. The evaluation of the system is carried on using JApanese Female Facial Expression (JAFFE) database. Experimental results show that the proposed DWT based human emotion recognition system produces more accurate recognition rate which applied on Korts learning model we can able to produce the optimal e-learning environment(OELE)

Intelligent Mentoring Systems for Making Meaning from Work Experience

This position paper presents a forward-looking view on addressing a long standing professional learning challenge faced by higher educational institutions, namely assisting students to make meaning from work-based experience and develop as reflexive professionals. We suggest that a synergetic approach, building on existing research in professional lifelong learning and intelligent learning environments and taking advantage of new opportunities provided by emerging technologies, will underpin a new breed of intelligent mentoring systems for professional learning. They will foster the learners’ meaning making process, as well as assist tutors in their roles as coaches/mentors

When to generate hedges in peer-tutoring interactions

This paper explores the application of machine learning techniques to predict where hedging occurs in peer-tutoring interactions. The study uses a naturalistic face-to-face dataset annotated for natural language turns, conversational strategies, tutoring strategies, and nonverbal behaviours. These elements are processed into a vector representation of the previous turns, which serves as input to several machine learning models. Results show that embedding layers, that capture the semantic information of the previous turns, significantly improves the model's performance. Additionally, the study provides insights into the importance of various features, such as interpersonal rapport and nonverbal behaviours, in predicting hedges by using Shapley values for feature explanation. We discover that the eye gaze of both the tutor and the tutee has a significant impact on hedge prediction. We further validate this observation through a follow-up ablation study.Comment: In Proceedings of the 16th Annual Conference ub Discourse and Dialogue (SIGDIAL). Sept 11-15, Prague Czechi

ATL-BP: a student engagement dataset and model for affect transfer learning for behavior prediction

IIS-1551572 - National Science Foundationhttp://10.0.4.85/TBIOM.2022.3210479Published versio