This study introduces an AI-powered data storytelling agent designed to support data science educators by automatically curating high-quality, real-world data stories. The system streamlines the discovery of relevant instructional examples for specific teaching activities, including assignments, quizzes, classroom discussions, and case studies, by utilizing automated classification and narrative analysis. Our prototype significantly reduces instructor preparation time while improving the diversity, quality, and pedagogical alignment of curated stories. This innovation enables educators to more efficiently source, annotate, and deploy impactful data narratives tailored to their teaching and research objectives.

Problem solving is one of the most important 21st century skills. However, effectively coaching young students in problem solving is challenging because teachers must continuously monitor their cognitive and affective states, and make real-time pedagogical interventions to maximize their learning outcomes. It is an even more challenging task in social environments with limited human coaching resources. To lessen the cognitive load on a teacher and enable affect-sensitive intelligent tutoring, many researchers have investigated automated cognitive and affective detection methods. However, most of the studies use culturally-sensitive indices of affect that are prone to social editing such as facial expressions, and only few studies have explored involuntary dynamic behavioral signals such as gross body movements. In addition, most current methods rely on expensive labelled data from trained annotators for supervised learning. In this paper, we explore a semi-supervised learning framework that can learn low-dimensional representations of involuntary dynamic behavioral signals (mainly gross-body movements) from a modest number of short time series segments. Experiments on a real-world dataset reveal a significant advantage of these representations in discriminating cognitive disequilibrium and flow, as compared to traditional complexity measures from dynamical systems literature, and demonstrate their potential in transferring learned models to previously unseen subjects.

Problem solving is one of the most important 21st century skills. However, effectively coaching young students in problem solving is challenging because teachers must continuously monitor their cognitive and affective states and make real-time pedagogical interventions to maximize students' learning outcomes. It is an even more challenging task in social environments with limited human coaching resources. To lessen the cognitive load on a teacher and enable affect-sensitive intelligent tutoring, many researchers have investigated automated cognitive and affective detection methods. However, most of the studies use culturally-sensitive indices of affect that are prone to social editing such as facial expressions, and only few studies have explored involuntary dynamic behavioral signals such as gross body movements. In addition, most current methods rely on expensive labelled data from trained annotators for supervised learning. In this paper, we explore a semi-supervised learning framework that can learn low-dimensional representations of involuntary dynamic behavioral signals (mainly gross-body movements) from a modest number of short time series segments. Experiments on a real-world dataset reveal a significant utility of these representations in discriminating cognitive disequilibrium and flow and demonstrate their potential in transferring learned models to previously unseen subjects.

Engaged learners are effective learners. Even though it is widely recognized that engagement plays a vital role in learning effectiveness, engagement remains to be an elusive psychological construct that is yet to find a consensus definition and reliable measurement. In this study, we attempted to discover the plausible operational definitions of engagement within an online learning context. We achieved this goal by first deriving a set of interpretable features on dynamics of eyes, head and mouth movement from facial landmarks extractions of video recording when students interacting with an online tutoring system. We then assessed their predicative value for engagement which was approximated by synchronized measurements from commercial EEG brainwave headset worn by students. Our preliminary results show that those features reduce root mean-squared error by 29% compared with default predictor and we found that the random forest model performs better than a linear regressor.

It often takes years of hands-on practice to build operational problem solving skills for a data scientist to be sufficiently competent to tackle real world problems. In this research, we explore a new scalable technology-enhanced learning (TEL) platform that enables accelerated apprenticeship process via a repository of caselets - small but focused case studies with scaffolding questions and feedback. In this paper, we report rationales of the design, caselet authoring process, and the planned experiment with cohorts of students who will use caselets while taking graduate level data science courses.

We propose a data driven method for decomposing population level learning curve models into mutually exclusive distinctive groups each consisting of similar learning trajectories. We validate this method on six knowledge components from the log data from an online tutoring system ASSIST-ment. Preliminary analysis reveals interpretable patterns of "skill growth" that correlate with students' performance in the subsequently administered state standardized tests.

This paper evaluates trade-offs between agility and reliability of predictions arising due to sparseness of data modeled with dynamic social networks. We use real field data from food safety domain to illustrate the discussion. We model food production facilities as one type of entities in a social network evolving in time. Another type of entities denotes various specific strains of Salmonella. Two entities are linked in the graph if a microbial test of food sample conducted at the specific food facility over specific period of time turns out positive for the particular pathogen. We use a computationally efficient latent space model to predict future occurrences of pathogens in individual facilities. Empirical results indicate predictive utility of the proposed representation. However, sparseness of data limits the attainable agility of predictions. We identify exploiting recency of data and using the known patterns in it, such as seasonality, as plausible means of battling the challenge of sparseness.

Displacement estimation is a key step in the evaluation of tissue elasticity by quasistatic strain imaging. An efficient approach may incorporate a tracking strategy whereby each estimate is initially obtained from its neighbours' displacements and then refined through a localized search. This increases the accuracy and reduces the computational expense compared with exhaustive search. However, simple tracking strategies fail when the target displacement map exhibits complex structure. For example, there may be discontinuities and regions of indeterminate displacement caused by decorrelation between the pre- and post-deformation radio frequency (RF) echo signals. This paper introduces a novel displacement tracking algorithm, with a search strategy guided by a data quality indicator. Comparisons with existing methods show that the proposed algorithm is more robust when the displacement distribution is challenging.