User Modeling and User-Adapted Interaction最新文献

Conversational recommender systems aim at recommending the most relevant information for users based on textual or spoken dialogues, through which users can communicate their preferences to the system more efficiently. Argumentative conversational recommender systems represent a kind of deliberation dialogue in which participants share their specific beliefs in the respective representations of the common ground, to act towards a common goal. The goal of such systems is to present appropriate supporting arguments to their recommendations to show the interlocutor that a specific item corresponds to their manifested interests. Here, we present a cross-disciplinary argumentation-based conversational recommender model based on cognitive pragmatics. We also present a dialogue simulator to investigate the quality of the theoretical background. We produced a set of synthetic dialogues based on a computational model implementing the linguistic theory and we collected human evaluations about the plausibility and efficiency of these dialogues. Our results show that the synthetic dialogues obtain high scores concerning their naturalness and the selection of the supporting arguments.

Adaptive learning systems offer a personalised digital environment that continually adjusts to the learner and the material, with the goal of maximising learning gains. Whenever such a system encounters a new learner, or when a returning learner starts studying new material, the system first has to determine the difficulty of the material for that specific learner. Failing to address this “cold-start” problem leads to suboptimal learning and potential disengagement from the system, as the system may present problems of an inappropriate difficulty or provide unhelpful feedback. In a simulation study conducted on a large educational data set from an adaptive fact learning system (about 100 million trials from almost 140 thousand learners), we predicted individual learning parameters from response data. Using these predicted parameters as starting estimates for the adaptive learning system yielded a more accurate model of learners’ memory performance than using default values. We found that predictions based on the difficulty of the fact (“what”) generally outperformed predictions based on the ability of the learner (“who”), though both contributed to better model estimates. This work extends a previous smaller-scale laboratory-based experiment in which using fact-specific predictions in a cold-start scenario improved learning outcomes. The current findings suggest that similar cold-start alleviation may be possible in real-world educational settings. The improved predictions can be harnessed to increase the efficiency of the learning system, mitigate the negative effects of a cold start, and potentially improve learning outcomes.

In recent years, we have seen a significant proliferation of e-learning platforms. E-learning platforms allow teachers to create digital courses in a more effective and time-saving way, but several flaws hinder their actual success. One main problem is that teachers have difficulties finding and combining open-access learning materials that match their specific needs precisely when there are so many to choose from. This paper proposes a new strategy for creating digital courses that use learning objects (LOs) as primary elements. The idea consists of using an intelligent chatbot to assist teachers in their activities. Defined using RASA technology, the chatbot asks for information about the course the teacher has to create based on her/his profile and needs. It suggests the best LOs and how to combine them according to their prerequisites and outcomes. A chatbot-based recommendation system provides suggestions through BERT, a machine-learning model based on Transformers, to define the semantic similarity between the entered data and the LOs metadata. In addition, the chatbot also suggests how to combine the LOs into a final learning path. Finally, the paper presents some preliminary results about tests carried out by teachers in creating their digital courses.

This paper introduces a novel approach for leveraging inertial data to discern expertise levels in motor skill execution, specifically distinguishing between experts and beginners. By implementing inertial data transformation and fusion techniques, we conduct a comprehensive analysis of motor behaviour. Our approach goes beyond conventional assessments, providing nuanced insights into the underlying patterns of movement. Additionally, we explore the potential for utilising this data-driven methodology to aid novice practitioners in enhancing their performance. The findings showcase the efficacy of this approach in accurately identifying proficiency levels and lay the groundwork for personalised interventions to support skill refinement and mastery. This research contributes to the field of motor skill assessment and intervention strategies, with broad implications for sports training, physical rehabilitation, and performance optimisation across various domains.

Personalization in online learning environments has been extensively studied at various levels, ranging from adaptive hints during task-solving to recommending whole courses. In this study, we focus on recommending learning activities (sequences of homogeneous tasks). We argue that this is an important yet insufficiently explored area, particularly when considering the requirements of large-scale online learning environments used in practice. To address this gap, we propose a modular rule-based framework for recommendations and thoroughly explain the rationale behind the proposal. We also discuss a specific application of the framework.

In this study, we propose a novel SeqMF model to solve the problem of predicting the next app launch during mobile device usage. Although this problem can be represented as a classical collaborative filtering problem, it requires proper modification since the data are sequential, the user feedback is distributed among devices, and the transmission of users’ data to aggregate common patterns must be protected against leakage. According to such requirements, we modify the structure of the classical matrix factorization model and update the training procedure to sequential learning. Since the data about user experience are distributed among devices, the federated learning setup is used to train the proposed sequential matrix factorization model. One more ingredient of our approach is a new privacy mechanism that guarantees the protection of the sent data from the users to the remote server. To demonstrate the efficiency of the proposed model, we use publicly available mobile user behavior data. We compare our model with sequential rules and models based on the frequency of app launches. The comparison is conducted in static and dynamic environments. The static environment evaluates how our model processes sequential data compared to competitors. The dynamic environment emulates the real-world scenario, where users generate new data by running apps on devices. Our experiments show that the proposed model provides comparable quality with other methods in the static environment. However, more importantly, our method achieves a better privacy-utility trade-off than competitors in the dynamic environment, which provides more accurate simulations of real-world usage.

Abstract

Achieving safe collaboration between humans and robots in an industrial work-cell requires effective communication. This can be achieved through a robot perception system developed using data-driven machine learning. The challenge for human–robot communication is the availability of extensive, labelled datasets for training. Due to the variations in human behaviour and the impact of environmental conditions on the performance of perception models, models trained on standard, publicly available datasets fail to generalize well to domain and application-specific scenarios. Thus, model personalization involving the adaptation of such models to the individual humans involved in the task in the given environment would lead to better model performance. A novel framework is presented that leverages robust modes of communication and gathers feedback from the human partner to auto-label the mode with the sparse dataset. The strength of the contribution lies in using in-commensurable multimodes of inputs for personalizing models with user-specific data. The personalization through feedback-enabled human–robot communication (PF-HRCom) framework is implemented on the use of facial expression recognition as a safety feature to ensure that the human partner is engaged in the collaborative task with the robot. Additionally, PF-HRCom has been applied to a real-time human–robot handover task with a robotic manipulator. The perception module of the manipulator adapts to the user’s facial expressions and personalizes the model using feedback. Having said that, the framework is applicable to other combinations of multimodal inputs in human–robot collaboration applications.

Persuasive strategies have been widely operationalized in systems or applications to motivate behaviour change across diverse domains. However, no empirical evidence exists on whether or not persuasive strategies lead to certain emotions to inform which strategies are most appropriate for delivering interventions that not only motivate users to perform target behaviour but also help to regulate their current emotional states. We conducted a large-scale study of 660 participants to investigate if and how individuals including those at different stages of change respond emotionally to persuasive strategies and why. Specifically, we examined the relationship between perceived effectiveness of individual strategies operationalized in a system and perceived emotional states for participants at different stages of behaviour change. Our findings established relations between perceived effectiveness of strategies and emotions elicited in individuals at distinct stages of change and that the perceived emotions vary across stages of change for different reasons. For example, the reward strategy is associated with positive emotion only (i.e. happiness) for individuals across distinct stages of change because it induces feelings of personal accomplishment, provides incentives that increase the urge to achieve more goals, and offers gamified experience. Other strategies are associated with mixed emotions. Our work links emotion theory with behaviour change theories and stages of change theory to develop practical guidelines for designing personalized and emotion-adaptive persuasive systems.

We present CPSCoach 2.0, an automated system that provides feedback, instructional scaffolding, and practice to help individuals improve three collaborative problem-solving (CPS) skills drawn from a theoretical CPS framework: construction of shared knowledge, negotiation/coordination, and maintaining team function. CPSCoach 2.0 was developed and tested in the context of computer-mediated collaboration (video conferencing) with an educational game. It automatically analyzes users’ speech during a round of collaborative gameplay to provide personalized feedback and to select a target CPS skill for improvement. After multiple cycles of iterative testing and refinement, we tested CPSCoach 2.0 in a user study where 21 dyads (n = 42) completed four rounds of feedback and scaffolding embedded within five rounds of game-play in a single session. Using a quasi-experimental matching procedure, we found that the use of CPSCoach 2.0 was associated with improvement in CPS skill development compared to matched controls. Further, users found the automated feedback to be moderately accurate and had positive perceptions of the system, and these impressions were stronger for those who received higher scores overall. Results demonstrate the use of automated feedback and instructional scaffolds to support the development of CPS skills.

Tracing a student’s knowledge state is critical for teaching and learning. Knowledge tracing aims to accurately predict student performance by analyzing historical records on online education platforms. Most studies have focused on a student’s skill with interactions sequence to predict the probability of correctly answering the latest question. However, they still suffer from the challenge of information sparsity and student forgetting. Specifically, the relationship between question and skill, and the features related to question texts have not been integrated to enrich information exploration. Besides, modeling forgetting behavior remains a challenge in assessing a student’s learning gains. In this paper, we present a novel model, namely Informative Representations for Forgetting-Robust Knowledge Tracing (IFKT). IFKT utilizes a light graph convolutional network to capture various relational structures via embedding propagation. Then, the embeddings are assembled with rich interaction features separately as the powerful representation. Furthermore, attention weights assignments are individualized using the relative positions, in addition to the relevance between the current question with historical interaction representations. Finally, we compare IFKT against seven knowledge tracing baselines on three real-world benchmark datasets, demonstrating the superiority of the proposed model.