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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).

UMUAI homepage with description of the scope of the journal and instructions for authors.

Springer UMUAI page with online access to the papers.

Latest Results for User Modeling and User-Adapted Interaction

19 September 2021

The latest content available from Springer
  • Multimodal modeling of collaborative problem-solving facets in triads

    Abstract

    Collaborative problem-solving (CPS) is ubiquitous in everyday life, including work, family, leisure activities, etc. With collaborations increasingly occurring remotely, next-generation collaborative interfaces could enhance CPS processes and outcomes with dynamic interventions or by generating feedback for after-action reviews. Automatic modeling of CPS processes (called facets here) is a precursor to this goal. Accordingly, we build automated detectors of three critical CPS facets—construction of shared knowledge, negotiation and coordination, and maintaining team function—derived from a validated CPS framework. We used data of 32 triads who collaborated via a commercial videoconferencing software, to solve challenging problems in a visual programming task. We generated transcripts of 11,163 utterances using automatic speech recognition, which were then coded by trained humans for evidence of the three CPS facets. We used both standard and deep sequential learning classifiers to model the human-coded facets from linguistic, task context, facial expressions, and acoustic–prosodic features in a team-independent fashion. We found that models relying on nonverbal signals yielded above-chance accuracies (area under the receiver operating characteristic curve, AUROC) ranging from .53 to .83, with increases in model accuracy when language information was included (AUROCS from .72 to .86). There were no advantages of deep sequential learning methods over standard classifiers. Overall, Random Forest classifiers using language and task context features performed best, achieving AUROC scores of .86, .78, and .79 for construction of shared knowledge, negotiation/coordination, and maintaining team function, respectively. We discuss application of our work to real-time systems that assess CPS and intervene to improve CPS outcomes.

  • Domain-based Latent Personal Analysis and its use for impersonation detection in social media

    Abstract

    Zipf’s law defines an inverse proportion between a word’s ranking in a given corpus and its frequency in it, roughly dividing the vocabulary into frequent words and infrequent ones. Here, we stipulate that within a domain an author’s signature can be derived from, in loose terms, the author’s missing popular words and frequently used infrequent words. We devise a method, termed Latent Personal Analysis (LPA), for finding domain-based attributes for entities in a domain: their distance from the domain and their signature, which determines how they most differ from a domain. We identify the most suitable distance metric for the method among several and construct the distances and personal signatures for authors, the domain’s entities. The signature consists of both over-used terms (compared to the average) and missing popular terms. We validate the correctness and power of the signatures in identifying users and set existence conditions. We test LPA in several domains, both textual and non-textual. We then demonstrate the use of the method in explainable authorship attribution: we define algorithms that utilize LPA  to identify two types of impersonation in social media: (1) authors with sockpuppets (multiple) accounts and (2) front-users accounts, operated by several authors. We validate the algorithms and employ them over a large-scale dataset obtained from a social media site with over 4000 users. We corroborate these results using temporal rate analysis. LPA  can further be used to devise personal attributes in a wide range of scientific domains in which the constituents have a long-tail distribution of elements.

  • Personalized rehabilitation for children with cerebral palsy

    Abstract

    Over the years, there has been an ongoing increase in the use of virtual gaming (VG) implemented via a range of technologies for the rehabilitation of children with disabilities including cerebral palsy (CP). While many VG-based devices have been developed over the past decade, many have been tested primarily for post-stroke therapy and included limited adaptation capabilities, not to mention personalization. When adaptation was included, it was not implemented dynamically in real-time but rather used to adjust the game parameters before a session. The goal and novel contribution of this study was to examine the potential of dynamic, within-session adaptation of virtual game parameters to support the rehabilitation of children with CP. The iVG4Rehab (Intelligent therapeutic Virtual Gaming System for Rehabilitation) system was designed and developed in collaboration with a team of clinicians. We aimed to demonstrate and evaluate the potential of a personalized virtual gaming system to support and enhance treatment of children with CP by real-time adaptations of game parameters to the children’s abilities and therapeutic needs. Our results validated the hypothesis that personalized system as a tool that has great potential for upper extremity (UE) therapy for children with CP and contributes to a more comprehensive understanding of their underlying performance, usability and kinematics in this unique context.

  • Personalized task difficulty adaptation based on reinforcement learning

    Abstract

    Traditionally, the task difficulty level is often determined by domain experts based on some hand-crafted rules. However, with the adoption of Massive Open Online Courses (MOOCs), it has become harder to manually personalize task difficulty as the system designers are faced with a very large question bank and a user base of individuals with diverse backgrounds and ability levels. This research focuses on developing a data-driven method to adaptively adjust difficulty levels in order to maintain a target user performance level over a series of tasks whose difficulty level is highly variable among different individuals. Specifically, the issue of difficulty adaptation was formulated as a reinforcement learning problem. To ensure responsiveness of the interactive systems, a novel bootstrapped policy gradient (BPG) framework was developed, which can incorporate prior knowledge of difficulty ranking into policy gradient to enhance sample efficiency. To obtain high-quality prior information on difficulty ranking, a clustering-based approach was proposed which can learn a personalized difficulty ranking to capture users’ individual differences. To evaluate the effectiveness of the difficulty adaptation method, we focused on a visual memory training problem with a large question bank and a diverse user base. Specifically, the proposed algorithms were combined and applied to a real-world application consisting of an online visual-spatial memory recall game and were shown to outperform the traditional rule-based adaptation approach in adapting to the slow players while achieving comparable performance in adapting to the fast players.

  • Potential and effects of personalizing gameful fitness applications using behavior change intentions and Hexad user types

    Abstract

    Personalizing gameful applications is essential to account for interpersonal differences in the perception of gameful design elements. Considering that an increasing number of people lead sedentary lifestyles, using personalized gameful applications to encourage physical activity is a particularly relevant domain. In this article, we investigate behavior change intentions and Hexad user types as factors to personalize gameful fitness applications. We first explored the potential of these two factors by analyzing differences in the perceived persuasiveness of gameful design elements using a storyboards-based online study ( \(N=178\) ). Our results show several significant effects regarding both factors and thus support the usefulness of them in explaining perceptual differences. Based on these findings, we implemented “Endless Universe,” a personalized gameful application encouraging physical activity on a treadmill. We used the system in a laboratory study ( \(N=20\) ) to study actual effects of personalization on the users’ performance, enjoyment and affective experiences. While we did not find effects on the immediate performance of users, positive effects on user experience-related measures were found. The results of this study support the relevance of behavior change intentions and Hexad user types for personalizing gameful fitness systems further.