Search Results

Identifying learners’ problem-solving strategies from telemetry data is a critical task for serious games. Traditional methods like sequence mining, text replays, and statistical analysis often necessitate labor-intensive manual iterations to ...

Human mobility insights guide domain experts in an array of decisions, including critical infrastructure design, disaster response, epidemic modeling, national security, and policy making. Due to the inherent noise and privacy concerns in real-world ...

Advancements in sensor technology are leading to massive collection of tracking data in sports. There is an increasing interest in analyzing the tracking data to gain competitive advantage. Analyzing and labeling key game moments can provide deep ...

Understanding the skills and proficiency levels required for various roles is crucial for effective workforce planning, learning and development. In this paper, we propose a robust skill proficiency modeling framework that offers a structured method to ...

More researchers are starting to focus on the intersection of artificial intelligence and education as a result of the technology's ongoing advancements. The paper conducts a thorough analysis of the research hotspots, and development lineage in ...

In response to the unique challenges of Mixed Reality (MR) game development, we developed GAMR, an analytics tool specifically designed for MR games. GAMR aims to assist developers in identifying and resolving gameplay issues effectively. It features ...

Learning analytics, blending education theory, psychology, statistics, and computer science, utilizes data about learners and their environments to enhance education. Artificial Intelligence advances this field by personalizing learning and ...

In this paper, we introduce an application for exploring the effect of linguistic features on personalized argument preferences. These individual preferences are derived by measuring the impact of linguistic features on pairwise comparisons ...

Large language models (LLMs) are widely deployed in various downstream tasks, e.g., auto-completion, aided writing, or chat-based text generation. However, the considered output candidates of the underlying search algorithm are under-explored and under-...

In the last years, there has been an increasing amount of data published in different sources, formats, and structures that could be useful for various purposes: learning about a topic, discovering trends, and monitoring public opinion, among others. To ...

Intelligent tutoring systems leverage AI models of expert learning and student knowledge to deliver personalized tutoring to students. While these intelligent tutors have demonstrated improved student learning outcomes, it is still unclear how teachers ...

With the exponential growth of multimedia in various forms, the volume of acquired visual data has dramatically increased while their value intensity remains relatively low. This presents significant challenges in multimedia search and analytics. In this ...

On-device machine learning (ML) moves computation from the cloud to personal devices, protecting user privacy and enabling intelligent user experiences. However, fitting models on devices with limited resources presents a major technical challenge: ...

Scientists studying deep ocean microbial ecosystems use limited numbers of sediment samples collected from the seafloor to characterize important life-sustaining biogeochemical cycles in the environment. Yet conducting fieldwork to sample these extreme ...

The recent explosion in popularity of large language models (LLMs) has inspired learning engineers to incorporate them into adaptive educational tools that automatically score summary writing. Understanding and evaluating LLMs is vital before deploying ...

Direct manipulation interactions on projections are often incorporated in visual analytics applications. These interactions enable analysts to provide incremental feedback to the system in a semi-supervised manner, demonstrating relationships that the ...

Data clustering algorithms are vital in artificial intelligence and data mining to uncover unknown patterns in data sets. This paper investigates the use of data clustering and visualization techniques to analyze medical data sets, with a focus on ...

Scientists use models to further their understanding of phenomena and inform decision-making. A confluence of factors has contributed to an exponential increase in spatial data volumes. In this study, we describe our methodology to identify spatial ...

Anomalies are often indicators of malfunction or inefficiency in various systems such as manufacturing, healthcare, finance, etc. While the literature is abundant in effective detection algorithms due to this practical relevance, autonomous anomaly ...

The massive-scale data generation of positioning (tracking) messages, collected by various surveillance means, has posed new challenges in the field of mobility data analytics in terms of extracting valuable knowledge out of this data. One of these ...