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

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• Abstract

Predictive models of student success in Massive Open Online Courses (MOOCs) are a critical component of effective content personalization and adaptive interventions. In this article we review the state of the art in predictive models of student success in MOOCs and present a categorization of MOOC research according to the predictors (features), prediction (outcomes), and underlying theoretical model. We critically survey work across each category, providing data on the raw data source, feature engineering, statistical model, evaluation method, prediction architecture, and other aspects of these experiments. Such a review is particularly useful given the rapid expansion of predictive modeling research in MOOCs since the emergence of major MOOC platforms in 2012. This survey reveals several key methodological gaps, which include extensive filtering of experimental subpopulations, ineffective student model evaluation, and the use of experimental data which would be unavailable for real-world student success prediction and intervention, which is the ultimate goal of such models. Finally, we highlight opportunities for future research, which include temporal modeling, research bridging predictive and explanatory student models, work which contributes to learning theory, and evaluating long-term learner success in MOOCs.

• Abstract

Recommender Systems have to deal with a wide variety of users and user types that express their preferences in different ways. This difference in user behavior can have a profound impact on the performance of the recommender system. Users receive better (or worse) recommendations depending on the quantity and the quality of the information the system knows about them. Specifically, the inconsistencies in users’ preferences impose a lower bound on the error the system may achieve when predicting ratings for one particular user—this is referred to as the magic barrier. In this work, we present a mathematical characterization of the magic barrier based on the assumption that user ratings are afflicted with inconsistencies—noise. Furthermore, we propose a measure of the consistency of user ratings (rating coherence) that predicts the performance of recommendation methods. More specifically, we show that user coherence is correlated with the magic barrier; we exploit this correlation to discriminate between easy users (those with a lower magic barrier) and difficult ones (those with a higher magic barrier). We report experiments where the recommendation error for the more coherent users is lower than that of the less coherent ones. We further validate these results by using two public datasets, where the necessary data to identify the magic barrier is not available, in which we obtain similar performance improvements.

• Abstract

A novel user interface (UI) design based on the personality characteristics of users was proposed and examined in a mobile learning context. It was argued that differences in personality can stimulate individuals’ information processing capabilities in according to their display preferences, thus an effective visual experience. The personality characteristics and design preferences of 87 students (37 male, and 50 female) were collected and analysed. The clustering result (using k-means algorithm) revealed two potential personality types, which we call the neuroticism and the extra-conscientiousness groups. Then, an interface was designed for each personality group using the association rules method. An eye-tracking device was used to record changes in participants’ eye-pupil diameter and fixation duration, and thus examine their cognitive load and attention. The participants’ eye movement data of each group showed that their visual experience was significantly improved when using the interface designed based on their personality characteristics. This work offers some important design and practical insights to the human–computer interaction and the design of mobile device UI.

• Abstract

The conflicting evidence in the literature on energy feedback as a driver for energy behaviour change has lead to the realization that it is a complex problem and that interventions must be proposed and evaluated in the context of a tangled web of individual and societal factors. We put forward an integrated agent-based computational model of energy consumption behaviour change interventions based on personal values and energy literacy, informed by research in persuasive technologies, environmental, educational and cognitive psychology, sociology, and energy education. Our objectives are: (i) to build a framework to accommodate a rich variety of models that might impact consumption decisions, (ii) to use the simulation as a means to evaluate persuasive technologies in-silico prior to deployment. The model novelty lies in its capacity to connect the determinants of energy related behaviour (values, energy literacy and social practices) and several generic design strategies proposed in the area of persuasive technologies within one framework. We validate the framework using survey data and personal value and energy consumption data extracted from a 2-year field study in Exeter, UK. The preliminary evaluation results demonstrate that the model can predict energy saving behaviour much better than a random model and can correctly estimate the effect of persuasive technologies. The model can be embedded into an adaptive decision-making system for energy behaviour change.

• Abstract

Systems and services we employ in our daily life have increasingly been augmented with motivational designs which fall under the classes of (1) gamification, (2) quantified-self and (3) social networking features that aim to help users reach their goals via motivational enforcement. However, users differ in terms of their orientation and focus toward goals and in terms of the attributes of their goals. Therefore, different classes of motivational design may have a differential fit for users. Being able to distinguish the goal profiles of users, motivational design could be better tailored. Therefore, in this study we investigate how different goal foci (outcome and focus), goals orientation (mastery, proving, and avoiding), and goal attributes (specificity and difficulty) are associated with perceived importance of gamification, social networking and quantified-self features. We employ survey data ( $$\mathrm{N}=167$$ ) from users of HeiaHeia; a popular exercise encouragement app. Results indicate that goal-setting related factors of users and attributes of goals are connected with users’ preference over motivational design classes. In particular, the results reveal that being outcome-focused is associated with positive evaluations of gamification and quantified-self design classes. Users with higher proving-orientation perceived gamification and social networking design classes as more important, users with lower goal avoidance-orientation perceived social networking design as more important, whereas users with higher mastery-orientation perceived quantified-self design more important. Users with difficult goals were less likely to perceive gamification and social networking design important, whereas for users with high goal specificity quantified-self features were important. The findings provide insights for the automatic adaptation of motivational designs to users’ goals. However, more research is naturally needed to further investigate generalizability of the results.