Interests

In the grand scheme of things, my interests are interdisciplinary, situated at the intersection of analytics, novel methods, open science, and education. I explore temporality, individual differences, and heterogeneity as an overarching analytical framework for personalized, equitable, and debiasing science. My methodological toolkit includes machine learning, mixture models, network science, sequence analysis, process mining, Markov models, psychological networks, temporal networks, meta-analyses, topic modeling, and scientometrics.

The main themes of my research can be summarized in the following:

Modelling the person where learning occurs

My research is dedicated to advancing Precision Explainable Artificial Intelligence (Precise XAI) systems to accurately model individuals to deliver person-specific XAI insights and recommendations with unprecedented granularity. This work prioritizes equity, transparency, and inclusivity while leveraging unobtrusive multimodal data and advanced analytical methods. Through the development of AI techniques and person-specific models, I aim to eliminate bias and unfairness by building adaptive solutions grounded in individual data.

Transition Network Analysis

If anything I am fascinated with, it is the dynamics and time. How and why and for how far a learning event or a process happens and why it progresses, regresses or emerges a new. Therefore, I introduced – with colleagues – a novel method that captures the temporal unfolding of learning process with unprecedented rigor: Transition Network Analysis (TNA). My recent research uses TNA to explore the complex dynamics of human-human, human-AI, and AI-AI interactions to uncover behavioral patterns and processes.

Network Analysis in Education

One of my main areas of research centers around networks and network analysis. I published about networks in collaborative learning, in problem-based learning, in language learning and in computer science education. I also worked on finding best methods for network analysis, performed a large scale empirical investigation of centrality measures and did a meta-analysis on centralities to find out which measures capture students’ achievement.

Temporal Networks

Temporal networks are a new paradigm that has been recently introduced to education. I am passionate about the different possibilities that temporal networks could bring to modeling the relational time dynamics. I published about the basic principles of temporal networks in education, how temporal networks can early predict students’ performance and how network analysis can help reveal the dynamics of collaborative learning across different learning platforms.

Complex Systems

Modeling the complexity of human behavior has always been challenging. The recent introduction of psychological networks has made it possible to model the complexity of human behavior. I work with psychological networks to understand how self-regulated processes work, how they unfold in collaborative learning, and to map idiographic processes. Idiographic analytics seeks to derive insights from individual persons and deliver recommendations based on their own data.

Temporal and Sequence Methods

Longitudinal research is rare, exhaustive and requires advanced methods. However, there is much to learn from how behavior unfolds in time. I have tackled several of these challenges using very large datasets that span a whole program. I explored the longitudinal trajectories of engagement, studied how collaborative roles emerge, persist, transition or evolve over time, and investigated the evolution of learning strategies and the transfer of successful strategies across courses.

Learning Analytics

Learning analytics and quantitative research methods in general are on top of my mind and work. I worked on improving predictive learning analytics, applied single-paper meta-analysis to study which predictors capture students’ performance reliably and consistently. I also used several person-centered methods including latent class analysis, latent profile analysis and clustering, as well as process mining techniques and epistemic network analysis.

Scientometrics and Bibliometrics

My love for networks, visualization and scholarly practices have amounted to a new passion for scientometric and bibliometric studies. I mapped the landscape of research on e.g., computational thinking, games, education technology, Koli calling conference and multiple sclerosis. Altmetrics is closely related to bibliometrics research, where one can see how the public has embraced, discussed or debated a paper.