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

The latest content available from Springer
  • Affective learning: improving engagement and enhancing learning with affect-aware feedback


    This paper describes the design and ecologically valid evaluation of a learner model that lies at the heart of an intelligent learning environment called iTalk2Learn. A core objective of the learner model is to adapt formative feedback based on students’ affective states. Types of adaptation include what type of formative feedback should be provided and how it should be presented. Two Bayesian networks trained with data gathered in a series of Wizard-of-Oz studies are used for the adaptation process. This paper reports results from a quasi-experimental evaluation, in authentic classroom settings, which compared a version of iTalk2Learn that adapted feedback based on students’ affective states as they were talking aloud with the system (the affect condition) with one that provided feedback based only on the students’ performance (the non-affect condition). Our results suggest that affect-aware support contributes to reducing boredom and off-task behavior, and may have an effect on learning. We discuss the internal and ecological validity of the study, in light of pedagogical considerations that informed the design of the two conditions. Overall, the results of the study have implications both for the design of educational technology and for classroom approaches to teaching, because they highlight the important role that affect-aware modelling plays in the adaptive delivery of formative feedback to support learning.

  • Preface to the UMUAI special issue on the impact of learner modeling
  • Learner modeling for adaptive scaffolding in a Computational Thinking-based science learning environment


    Learner modeling has been used in computer-based learning environments to model learners’ domain knowledge, cognitive skills, and interests, and customize their experiences in the environment based on this information. In this paper, we develop a learner modeling and adaptive scaffolding framework for Computational Thinking using Simulation and Modeling (CTSiM)—an open ended learning environment that supports synergistic learning of science and Computational Thinking (CT) for middle school students. In CTSiM, students have the freedom to choose and coordinate use of the different tools provided in the environment, as they build and test their models. However, the open-ended nature of the environment makes it hard to interpret the intent of students’ actions, and to provide useful feedback and hints that improves student understanding and helps them achieve their learning goals. To address this challenge, we define an extended learner modeling scheme that uses (1) a hierarchical task model for the CTSiM environment, (2) a set of strategies that support effective learning and model building, and (3) effectiveness and coherence measures that help us evaluate student’s proficiency in the different tasks and strategies. We use this scheme to dynamically scaffold learners when they are deficient in performing their tasks, or they demonstrate suboptimal use of strategies. We demonstrate the effectiveness of our approach in a classroom study where one group of 6th grade students received scaffolding and the other did not. We found that students who received scaffolding built more accurate models, used modeling strategies effectively, adopted more useful modeling behaviors, showed a better understanding of important science and CT concepts, and transferred their modeling skills better to new scenarios.

  • Enhancing learning outcomes through self-regulated learning support with an Open Learner Model


    Open Learner Models (OLMs) have great potential to support students’ Self-Regulated Learning (SRL) in Intelligent Tutoring Systems (ITSs). Yet few classroom experiments have been conducted to empirically evaluate whether and how an OLM can enhance students’ domain level learning outcomes through the scaffolding of SRL processes in an ITS. In two classroom experiments with a total of 302 7th- and 8th-grade students, we investigated the effect of (a) an OLM that supports students’ self-assessment of their equation-solving skills and (b) shared control over problem selection, on students’ equation-solving abilities, enjoyment of learning with the tutor, self-assessment accuracy, and problem selection decisions. In the first, smaller experiment, the hypothesized main effect of the OLM on students’ learning outcomes was confirmed; we found no main effect of shared control of problem selection, nor an interaction. In the second, larger experiment, the hypothesized main effects were not confirmed, but we found an interaction such that the students who had access to the OLM learned significantly better equation-solving skills than their counterparts when shared control over problem selection was offered in the system. Thus, the two experiments support the notion that an OLM can enhance students’ domain-level learning outcomes through scaffolding of SRL processes, and are among the first in-vivo classroom experiments to do so. They suggest that an OLM is especially effective if it is designed to support multiple SRL processes.

  • Editorial note