Katrien Verbert

KU Leuven | ku leuven · Department of Computer Science

While research on explainable AI (XAI) is booming and explanation techniques have proven promising in many application domains, standardised human-centred evaluation procedures are still missing. In addition, current evaluation procedures do not assess XAI methods holistically in the sense that they do not treat explanations’ effects on humans as a...

Background: In families with children with cognitive impairments, both parents and children experience tension and have questions because of a lack of communication and adequate information. Therefore, there is a great need to develop tools that can help bridge the communication gap between patients and caregivers by stimulating conversations and...

While research on explainable AI (XAI) is booming and explanation techniques have proven promising in many application domains, standardised human-centred evaluation procedures are still missing. In addition, current evaluation procedures do not assess XAI methods holistically in the sense that they do not treat explanations' effects on humans as a...

In this paper, we attempt to better understand concerns, needs and expectations of European consumers towards the use of intelligent packaging technologies as this topic appears to need further investigation from a marketing point of view. Thus, this study contributes to the currently limited body of research on the application of smart tag technol...

Researchers have widely acknowledged the potential of control mechanisms with which end-users of recommender systems can better tailor recommendations. However, few e-learning environments so far incorporate such mechanisms, for example for steering recommended exercises. In addition, studies with adolescents in this context are rare. To address th...

Researchers have widely acknowledged the potential of control mechanisms with which end-users of recommender systems can better tailor recommendations. However, few e-learning environments so far incorporate such mechanisms, for example for steering recommended exercises. In addition, studies with adolescents in this context are rare. To address th...

Explainable artificial intelligence is increasingly used in machine learning (ML) based decision-making systems in healthcare. However, little research has compared the utility of different explanation methods in guiding healthcare experts for patient care. Moreover, it is unclear how useful, understandable, actionable and trustworthy these methods...

Systems involving artificial intelligence (AI) are protagonists in many everyday activities. Moreover, designers are increasingly implementing these systems for groups of users in various social and cooperative domains. Unfortunately, research on personalized recommendation systems often reports negative experiences due to a lack of diversity, cont...

Artificial Intelligence (AI) supports many of our everyday activities and decisions. However, personalized algorithmic recommendations often produce adverse experiences due to a lack of awareness, control, or transparency. While research has directed solutions on graphical user interfaces (GUIs), there are no explorations of Tangible User Interface...

Due to their historical nature, humanistic data encompass multiple sources of uncertainty. While humanists are accustomed to handling such uncertainty with their established methods, they are cautious of visualizations that appear overly objective and fail to communicate this uncertainty. To design more trustworthy visualizations for humanistic res...

The rise of ‘big data’ in agrifood has increased the need for decision support systems that harvest the power of artificial intelligence. While many such systems have been proposed, their uptake is limited, for example because they often lack uncertainty representations and are rarely designed in a user-centred way. We present a prototypical visual...

Decision support systems (DSSs) in agriculture are becoming increasingly popular, and have begun adopting visualisations to facilitate insights into complex data. However, DSSs for agriculture are often designed as standalone applications, which limits their flexibility and portability. They also rarely provide interactivity, visualise uncertainty...

Due to the prominent role of recommender systems in our daily lives, it is increasingly important to inform users why certain items are recommended and personalize these explanations to the user. In this study, we explored how explanations in a music recommender system should be designed to fit the preference of different personal characteristics....

Artificial intelligence (AI) is becoming ubiquitous in the lives of both researchers and non-researchers, but AI models often lack transparency. To make well-informed and trustworthy decisions based on these models, people require explanations that indicate how to interpret the model outcomes. This paper presents our ongoing research in explainable...

In the scope of explainable artificial intelligence, explanation techniques are heavily studied to increase trust in recommender systems. However, studies on explaining recommendations typically target adults in e-commerce or media contexts; e-learning has received less research attention. To address these limits, we investigated how explanations a...

Network representation is a crucial topic in historical social network analysis. The debate around their value and connotations, led by humanist scholars, is today more relevant than ever, seeing how common these representations are as support for historical analysis. Force-directed networks, in particular, are popular as they can be developed rela...

Network representation is a crucial topic in historical social network analysis. The debate around their value and connotations, led by humanist scholars, is today more relevant than ever, seeing how common these representations are as support for historical analysis. Force-directed networks, in particular, are popular as they can be developed rela...

Traditionally, laboratory practice aims to establish schemas learned by students in theoretical courses through concrete experiences. However, access to laboratories might not always be available to students. Therefore, it is advantageous to diversify the tools that students could use to train practical skills. This technology report describes the...

Recommender systems are increasingly used in more high risk application domains, including in the domain of Human Resources (HR). These recommender systems help end-users find relevant vacancies out of an abundant overload of available vacancies, but also support other important objectives such as job mobility. Despite the use in industry applicati...

To make predictions and explore large datasets, healthcare is increasingly applying advanced algorithms of artificial intelligence. However, to make well‐considered and trustworthy decisions, healthcare professionals require ways to gain insights in these algorithms' outputs. One approach is visual analytics, which integrates humans in decision‐mak...

Background Mild cognitive impairment (MCI), the intermediate cognitive status between normal cognitive decline and pathological decline, is an important clinical construct for signaling possible prodromes of dementia. However, this condition is underdiagnosed. To assist monitoring and screening, digital biomarkers derived from commercial off-the-sh...

The Hexad user types model is often used in the gamification community to tailor gamified systems. However, most often, it requires users to fill out a questionnaire, preventing an automated adaptation of the interactive system. For this reason, we explored the potential of using mobile banking data to automate the profiling of Hexad user types. In...

People's trust in prediction models can be affected by many factors, including domain expertise like knowledge about the application domain and experience with predictive modelling. However, to what extent and why domain expertise impacts people's trust is not entirely clear. In addition, accurately measuring people's trust remains challenging. We...

People's trust in prediction models can be affected by many factors, including domain expertise like knowledge about the application domain and experience with predictive modelling. However, to what extent and why domain expertise impacts people's trust is not entirely clear. In addition, accurately measuring people's trust remains challenging. We...

Decision support systems have become increasingly popular in the domain of agriculture. With the development of automated machine learning, agricultural experts are now able to train, evaluate and make predictions using cutting edge machine learning (ML) models without the need for much ML knowledge. Although this automated approach has led to succ...

Systematic reviews of gamification research often focus on effects on motivation and engagement. Fewer studies systematically investigate the effect of gamification on `adherence', the extent to which individuals use a gamified service and experience its content, as envisioned by the creators, to derive a certain benefit. To this end, this paper pr...

BACKGROUND Type 2 Diabetes Mellitus (T2DM) is a common cause of mortality worldwide: each year, the chronic disease kills over one million people, making it the ninth leading cause of death. The growing use of smartphone applications (apps) led to focusing on mobile health, which is increasingly oriented towards self-care of T2DM. With smartphone a...

Systematic reviews of gamification research often focus on effects on motivation and engagement. Fewer studies systematically investigate the effect of gamification on ‘adherence’, the extent to which individuals use a gamified service and experience its content, as envisioned by the creators, to derive a certain benefit. To this end, this paper pr...

Background Health recommender systems (HRSs) offer the potential to motivate and engage users to change their behavior by sharing better choices and actionable knowledge based on observed user behavior. Objective We aim to review HRSs targeting nonmedical professionals (laypersons) to better understand the current state of the art and identify bot...

Clinical reports, as unstructured texts, contain important temporal information. However, it remains a challenge for natural language processing (NLP) models to accurately combine temporal cues into a single coherent temporal ordering of described events. In this paper, we present TIEVis, a visual analytics dashboard that visualizes event-timelines...

In online learning, teachers need constant feedback about their students’ progress and regulation needs. Learning Analytics Dashboards for process-oriented feedback can be a valuable tool for this purpose. However, few such dashboards have been proposed in literature, and most of them lack empirical validation or grounding in learning theories. We...

Background: Mild cognitive impairment (MCI) is a condition that entails a slight yet noticeable decline in cognition that exceeds normal age-related changes. Older adults living with MCI have a higher chance of progressing to dementia, which warrants regular cognitive follow-up at memory clinics. However, due to time and resource constraints, this...

Decision support systems have become increasingly popular in the domain of agriculture. With the development of Automated Machine Learning, agricultural experts are now able to train, evaluate and make predictions using cutting edge machine learning (ML) models without the need for much ML knowledge. Although this automated approach has led to succ...

This exploratory study provides a deeper understanding of employees when they are asked to use a personalized meal recommendation application in the workplace. Motivational design techniques were integrated into 25 alternative designs and evaluated. Our initial results show that participants appreciated the designs and highlighted the importance of...

Gamification researchers deem adolescents a particularly interesting audience for tailored gamification. However, empirical validation of popular player typologies and personality trait models thus far has been limited to adults. As adolescents exhibit complex behaviours that differ from older adults, these models may need adaptation. To that end,...

Algorithms are present in many of our everyday activities. However, there is generally low awareness of their presence among users, and there are various conceptualizations to define them. Additionally, algorithms are often both complex and opaque. These characteristics raise challenges when applying co-design activities to the interaction design o...

User beliefs about algorithmic systems are constantly co-produced through user interaction and the complex socio-technical systems that generate recommendations. Identifying these beliefs is crucial because they influence how users interact with recommendation algorithms. With no prior work on user beliefs of algorithmic video recommendations, prac...

Information visualization (infovis) is a powerful tool for exploring rich datasets. Within humanistic research, rich qualitative data and domain culture make traditional infovis approaches appear reductive and disconnected, leading to low adoption. In this paper, we use a multi-step approach to scrutinize the relationship between infovis and the hu...

User beliefs about algorithmic systems are constantly co-produced through user interaction and the complex socio-technical systems that generate recommendations. Identifying these beliefs is crucial because they influence how users interact with recommendation algorithms. With no prior work on user beliefs of algorithmic video recommendations, prac...

Open educational resources (OER) can contribute to democratize education by providing effective learning experiences with lower costs. Nevertheless, the massive amount of resources currently available in OER repositories makes it difficult for teachers and learners to find relevant and high‑quality content, which is hindering OER use and adoption....

There is a need of ensuring that learning (ML) models are interpretable. Higher interpretability of the model means easier comprehension and explanation of future predictions for end‐users. Further, interpretable ML models allow healthcare experts to make reasonable and data‐driven decisions to provide personalized decisions that can ultimately lea...

Despite the success of academic advising dashboards in several higher educational institutions (HEI), these dashboards are still under‐explored in Latin American HEI's. To close this gap, three different Latin American universities adapted an existing advising dashboard, originally deployed at the KU Leuven to their own context. In all three cases,...

This paper presents a case study on the adoption and the impact of new modules in a learning analytics dashboard supporting the dialogue between student advisors and students when advising on a study plan for the next academic semester in Escuela Superior Politecnica del Litoral, a higher education institute in Ecuador. The impact and the adoption...

Many Latin‐American institutions recognise the potential of learning analytics (LA). However, the number of actual LA implementations at scale remains limited, notwithstanding considerable effort made to formulate guidelines and frameworks to support the LA policy development. Guidance on how to coordinate the interaction between the LA policymakin...

Music recommender systems typically offer a “one-size-fits-all” approach with the same user controls and visualizations for all users. However, the effectiveness of interactive interfaces for music recommender systems is likely to be affected by individual differences. In this paper, we first conduct a comprehensive literature review of interactive...

Learning analytics dashboards are at the core of the LAK vision to involve the human into the decision-making process. The key focus of these dashboards is to support better human sense-making and decision-making by visualising data about learners to a variety of stakeholders. Early research on learning analytics dashboards focused on the use of vi...

BACKGROUND Health recommender systems (HRSs) offer the potential to motivate and engage users to change their behavior by sharing better choices and actionable knowledge based on observed user behavior. OBJECTIVE We aim to review HRSs targeting nonmedical professionals (laypersons) to better understand the current state of the art and identify bot...

In this study, we present a case study involving two self-service dashboards providing feedback on learning and study skills and on academic achievement. These dashboards were offered to first-year university students in several study programmes in Flanders, Belgium. Data for this study were collected using usage tracking (N = 2875) and a survey ta...

Critiquing-based recommender systems aim to elicit more accurate user preferences from users' feedback toward recommendations. However, systems using a graphical user interface (GUI) limit the way that users can critique the recommendation. With the rise of chatbots in many application domains, they have been regarded as an ideal platform to build...

We report on an exploratory study for assessing cognitive aging based on the acquisition and modeling of player data of commercial off-the-shelf games. To this end, candidates for digital biomarkers of cognitive performance were captured via FreeCell, from three distinctive age groups (18-25, 40-55, 65+) from 52 participants, playing for a total of...

To start medical or dentistry studies in Flanders, prospective students need to pass a central admission test. A blended program with four Small Private Online Courses (SPOCs) was designed to support those students. The logs from the platform provide an opportunity to delve into the learners’ interactions and to develop predictive models to forecas...

The dynamics of the labor market and the tasks with which jobs are being composed are continuously evolving. Job mobility is not evident, and providing effective recommendations in this context has also been found to be particularly challenging. In this paper, we present Labor Market Explorer, an interactive dashboard that enables job seekers to ex...

Many of our daily activities and decisions are driven by algorithms. This is particularly evident in our interactions with contemporary cultural content, where recommender algorithms deal with most of their access, production, and distribution. In this context, the Algorithmic Experience (AX) design framework emerged to guide the design of users’ e...

Researchers have reported a lack of experience and low graph literacy as significant problems when making visual analytics applications available to a general audience. Therefore, it is fundamental to understand the strengths and weaknesses of different visualizations in the decision-making process. This paper explores the benefits and challenges o...

Decision Support Systems (DSSs) are used in precision agriculture to provide feedback to a variety of stakeholders, including farmers, advisers, researchers and policymakers. However, increments in the amount of data might lead to data quality issues, and as these applications scale into big, real-time monitoring systems the problem gets even more...

The most common surgery for pectus patients is associated with severe acute and persistent postoperative pain. It is, therefore, important that patients are adequately monitored after their surgery to prevent the chronification of pain. This paper provides a deeper understanding of young adolescents when they are asked to use a telemonitoring platf...

The GDPR has a significant impact on the way users interact with technologies, especially the everyday platforms used to personalize news and related forms of information. This paper presents the initial results from a study whose primary objective is to empirically test those platforms' level of compliance with the so-called 'right to explanation'...