arXiv subjects

Ivan Kayongo

Publications and source records attributed to Ivan Kayongo.

4 recordsLinked to original sources

arXiv2025

MakOne: Behavioural Data of University Students' Smart Devices in Uganda

Understanding student behaviour in higher education is essential for improving academic performance, supporting mental well-being, and informing institutional policies. However, most existing behavioural datasets originate from Western institutions and overlook the unique socioeconomic and infrastructural contexts of African institutions, limiting the global applicability of resulting insights. This paper introduces MakOne, a novel multimodal dataset collected over six weeks from 72 students at Makerere University, Kampala, using iLog, a mobile sensing application. The dataset integrates passive smartphone sensor data-including location, physical activity, and screen usage-with ecological momentary assessments (EMAs) that capture students' moods and daily routines. Designed to reflect the lived experiences of students in an African setting, MakOne offers a foundation for research in behaviour modeling, inclusive context-aware system design, mental health analytics, and culturally grounded educational technologies. It contributes a critical African perspective to the growing body of data-driven studies on student behaviour.

cs.CY↗

arXiv2025

DiversityOne: A Multi-Country Smartphone Sensor Dataset for Everyday Life Behavior Modeling

Understanding everyday life behavior of young adults through personal devices, e.g., smartphones and smartwatches, is key for various applications, from enhancing the user experience in mobile apps to enabling appropriate interventions in digital health apps. Towards this goal, previous studies have relied on datasets combining passive sensor data with human-provided annotations or self-reports. However, many existing datasets are limited in scope, often focusing on specific countries primarily in the Global North, involving a small number of participants, or using a limited range of pre-processed sensors. These limitations restrict the ability to capture cross-country variations of human behavior, including the possibility of studying model generalization, and robustness. To address this gap, we introduce DiversityOne, a dataset which spans eight countries (China, Denmark, India, Italy, Mexico, Mongolia, Paraguay, and the United Kingdom) and includes data from 782 college students over four weeks. DiversityOne contains data from 26 smartphone sensor modalities and 350K+ self-reports. As of today, it is one of the largest and most diverse publicly available datasets, while featuring extensive demographic and psychosocial survey data. DiversityOne opens the possibility of studying important research problems in ubiquitous computing, particularly in domain adaptation and generalization across countries, all research areas so far largely underexplored because of the lack of adequate datasets.

cs.CY↗

arXiv2025

A methodology and a platform for high-quality rich personal data

In the last years the pervasive use of sensors, as they exist in smart devices, e.g., phones, watches, medical devices, has increased dramatically the availability of personal data. However, existing research on data collection primarily focuses on the objective view of reality, as provided, for instance, by sensors, often neglecting the integration of subjective human input, as provided, for instance, by user answers to questionnaires. This limits substantially the exploitability of the collected data. In this paper we present a methodology and a platform specifically designed for the collection of a combination of large-scale sensor data and qualitative human feedback. The methodology has been designed to be deployed on top, and enriches the functionalities of, an existing data collection APP, called iLog, which has been used in large scale, worldwide data collection experiments. The main goal is to put the key actors involved in an experiment, i.e., the researcher in charge, the participant, and iLog in better control of the experiment itself, thus enabling a much improved quality and richness of the data collected. The novel functionalities of the resulting platform are: (i) a time-wise representation of the situational context within which the data collection is performed, (ii) an explicit representation of the temporal context within which the data collection is performed, (iii) a calendar-based dashboard for the real-time monitoring of the data collection context(s), and, finally, (iv) a mechanism for the run-time revision of the data collection plan. The practicality and utility of the proposed functionalities are demonstrated by showing how they apply to a case study involving 350 University students.

cs.HC↗

arXiv2024

A Methodology and System For Big-Thick Data Collection

Pervasive sensors have become essential in research for gathering real-world data. However, current studies often focus solely on objective data, neglecting subjective human contributions. We introduce an approach and system for collecting big-thick data, combining extensive sensor data (big data) with qualitative human feedback (thick data). This fusion enables effective collaboration between humans and machines, allowing machine learning to benefit from human behavior and interpretations. Emphasizing data quality, our system incorporates continuous monitoring and adaptive learning mechanisms to optimize data collection timing and context, ensuring relevance, accuracy, and reliability. The system comprises three key components: a) a tool for collecting sensor data and user feedback, b) components for experiment planning and execution monitoring, and c) a machine-learning component that enhances human-machine interaction.

cs.HC↗