User Modeling and User-Adapted Interaction (UMUAI) provides an interdisciplinary forum for the dissemination of new research results on interactive computer systems that can be adapted or adapt themselves to their current users, and on the role of user models in the adaptation process.

UMUAI has been published since 1991 by Kluwer Academic Publishers (now merged with Springer Verlag).

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Latest Results for User Modeling and User-Adapted Interaction

27 January 2020

The latest content available from Springer
  • Visualising alignment to support students’ judgment of confidence in open learner models


    Knowledge monitoring is a component of metacognition which can help students regulate their own learning. In adaptive learning software, the system’s model of the student can be presented as an open learner model (OLM) which is intended to enable monitoring processes. We explore how presenting alignment, between students’ self-assessed confidence and the system’s model of the student, supports knowledge monitoring. When students can see their confidence and their performance (either combined within one skill meter or expanded as two separate skill meters), their knowledge monitoring and performance improves, particularly for low-achieving students. These results indicate the importance of communicating the alignment between the system’s evaluation of student performance and student confidence in the correctness of their answers as a means to support metacognitive skills.

  • Sentient destination prediction


    In addition to context awareness and proactive behaviour, personalization constitutes one of the most important factors to consider when building digital assistance systems. In the field of location prediction, personal information can lead to a significant improvement of the predictive performance of the respective models. However, most of the existing approaches handle this type of information separate from the actual location and movement data, making them incapable of taking the entire existing underlying dynamics into account. In the presented work, inspired by the Minsky’s frame system theory in the 1970s, we evaluate an adapted ontological construct, which we call context-specific cognitive frame (CSCF), in order to capture the entire experience of a user at a given moment. Moreover, we use CSCFs to associate situations that arise when visiting a certain location with the respective context information as well as the emotions and the personality of the user. We show that our method can be used to provide a flexible, more accurate and therefore more personalized user experience using the example of location prediction.

  • Multistakeholder recommendation: Survey and research directions


    Recommender systems provide personalized information access to users of Internet services from social networks to e-commerce to media and entertainment. As is appropriate for research in a field with a focus on personalization, academic studies of recommender systems have largely concentrated on optimizing for user experience when designing, implementing and evaluating their algorithms and systems. However, this concentration on the user has meant that the field has lacked a systematic exploration of other aspects of recommender system outcomes. A user-centric approach limits the ability to incorporate system objectives, such as fairness, balance, and profitability, and obscures concerns that might come from other stakeholders, such as the providers or sellers of items being recommended. Multistakeholder recommendation has emerged as a unifying framework for describing and understanding recommendation settings where the end user is not the sole focus. This article outlines the multistakeholder perspective on recommendation, highlighting example research areas and discussing important issues, open questions, and prospective research directions.

  • Creating user stereotypes for persona development from qualitative data through semi-automatic subspace clustering


    Personas are models of users that incorporate motivations, wishes, and objectives; These models are employed in user-centred design to help design better user experiences and have recently been employed in adaptive systems to help tailor the personalized user experience. Designing with personas involves the production of descriptions of fictitious users, which are often based on data from real users. The majority of data-driven persona development performed today is based on qualitative data from a limited set of interviewees and transformed into personas using labour-intensive manual techniques. In this study, we propose a method that employs the modelling of user stereotypes to automate part of the persona creation process and addresses the drawbacks of the existing semi-automated methods for persona development. The description of the method is accompanied by an empirical comparison with a manual technique and a semi-automated alternative (multiple correspondence analysis). The results of the comparison show that manual techniques differ between human persona designers leading to different results. The proposed algorithm provides similar results based on parameter input, but was more rigorous and will find optimal clusters, while lowering the labour associated with finding the clusters in the dataset. The output of the method also represents the largest variances in the dataset identified by the multiple correspondence analysis.

  • Impact of inquiry interventions on students in e-learning and classroom environments using affective computing framework


    Effective teaching strategies improve the students’ learning rate within academic learning time. Inquiry-based instruction is one of the effective teaching strategies used in the classrooms. But these teaching strategies are not adapted in other learning environments like intelligent tutoring systems, including auto tutors. In this paper, we propose an automatic inquiry-based instruction teaching strategy, i.e., inquiry intervention using students’ affective states. The proposed model contains two modules: the first module consists of the proposed framework for predicting the unobtrusive multi-modal students’ affective states (teacher-centric attentive and in-attentive states) using the facial expressions, hand gestures and body postures. The second module consists of the proposed automated inquiry-based instruction teaching strategy to compare the learning outcomes with and without inquiry intervention using affective state transitions for both an individual and a group of students. The proposed system is tested on four different learning environments, namely: e-learning, flipped classroom, classroom and webinar environments. Unobtrusive recognition of students’ affective states is performed using deep learning architectures. After student-independent tenfold cross-validation, we obtained the students’ affective state classification accuracy of 77% and object localization accuracy of 81% using students’ faces, hand gestures and body postures. The overall experimental results demonstrate that there is a positive correlation with \(r=0.74\) between students’ affective states and their performance. Proposed inquiry intervention improved the students’ performance as there is a decrease of 65%, 43%, 43%, and 53% in overall in-attentive affective state instances using the inquiry interventions in e-learning, flipped classroom, classroom and webinar environments, respectively.