You might be interested in our Explore - Develop - Understand - Scrutinize - Socialize (EDUSS) framework for self-actualization goals of transparent user modeling. Check out our contribution "Interactive Visualizations of Transparent User Models for Self-Actualization: A Human-Centered Design Approach" that will be published in the Multimodal Technologies and Interaction Journal . A preprint version of the paper can be found here.  

Our paper "Explaining User Models with Different Levels of Detail for Transparent Recommendation: A User Study" got accepted as a full research paper at the workshop on Explainable User Models and Personalized System (ExUM 2022) in conjunction with the conference on User Modeling, Adaptation and Personalization (ACM UMAP 2022). A preprint version of the paper is available here.

Our paper "Is More Always Better? The Effects of Personal Characteristics and Level of Detail on the Perception of Explanations in a Recommender System" got accepted as a full research paper at the conference on User Modeling, Adaptation and Personalization (ACM UMAP 2022). A preprint version of the paper is available here.

The results of the student projects developed in the Human-Computer Interaction (HCI) and Learning Analytics lecture course in the winter semester of 2021/22 are now available, including open-source codes, demos, and advertisement videos.

Teams of students in HCI participated in a two-months project to design and build a prototype around a set of user needs. The teams are tasked with milestones to move them towards a high-fidelity prototype as 
proof-of-concept that is presented in slides and demos at the end of the course. We were impressed with the quality of their projects.

Our paper "A Multi-Dimensional Conceptualization Framework for Personalized Explanations in Recommender Systems" got accepted at the workshop on Adaptive and Personalized Explainable User Interfaces (APEx-UI) in conjunction with the conference on Intelligent User Interfaces (ACM IUI 2022). In this work, we present a multi-dimensional conceptualization framework for personalized explanations in recommender systems, based on five dimensions: WHAT to personalize?, TO WHOM to personalize?, WHO does the personalization?, WHY personalize?, and HOW to personalize?. The paper can be found here.

You might be interested in how to effectively bring together theories and models from self-regulated learning, learning analytics, human-centered design, and information visualization. Check out our contribution "Designing Theory-Driven Analytics-Enhanced Self-Regulated Learning Applications" recently published as a chapter in the book "Visualizations and Dashboards for Learning Analytics. Advances in Analytics for Learning and Teaching" by Springer . In this chapter, we present the Student-Centered Learning Analytics-enhanced Self-Regulated Learning (SCLA-SRL) methodology for building theory-driven LA-enhanced SRL applications for and with learners. A preprint version of the chapter can be found here.

The results of the student projects developed in the Advanced Web Technologies (AWT) lecture course and Interactive Data Exploration and Analytics (IDEA) lab in summer semester 2021 are now available, including open-source codes, demos, and advertisement videos. Our students managed again to provide excellent projects and we are proud of them!

Our paper "Input or Output: Effects of Explanation Focus on the Perception of Explainable Recommendation with Varying Level of Details" has been accepted at the ACM RecSys 21 Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS 2021). The paper can be found here.

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