2025
Rahman, Yeaminur
Adapting Eco-Driving Feedback and Historical Visualization for Vessel Dashboards Masters Thesis
2025.
Abstract | Links | BibTeX | Tags: behaviour change, climate, dashboard, geospatial analytics, mobile, navigation, peripheral vision, simulation, training, virtual environment, visualization, wayfinding
@mastersthesis{nokey,
title = {Adapting Eco-Driving Feedback and Historical Visualization for Vessel Dashboards},
author = {Yeaminur Rahman},
url = {https://hdl.handle.net/10222/85531},
year = {2025},
date = {2025-11-25},
urldate = {2025-11-25},
abstract = {Maritime navigation is a significant source of greenhouse gas emissions. While large-scale cargo shipping is the major contributor, smaller maritime operations, including patrolling, fishing, public transit, and recreation, present unique challenges and opportunities for power management. Fuel consumption, power conversion, and environmental data can permit environmentally conscious and cost-effective decision-making when driving a boat. To achieve this, we need to understand how best to integrate such data into boat dashboard interfaces. In this work, we design an Eco Dashboard inspired by eco-driving feedback dashboards in the automotive industry, as well as a variant of the Eco Dashboard that additionally visualizes historical route and fuel consumption data (Eco + Historical Dashboard). In an experimental simulation (N = 30) involving 12 experienced mariners and 18 novices, we compared both interfaces with a typical boat dashboard that presented fuel and speed. Our findings suggest that dashboards incorporating historical data, alongside eco-driving features, improve fuel efficiency and decision-making, particularly for non-experienced users. The Eco Dashboard supported real-time adjustments during complex navigation, whereas the Eco + Historical Dashboard enhanced route planning and confidence in longer-term decisions. Participants also reported greater confidence and reduced cognitive load when using these systems. These results provide valuable insights for the future design of maritime dashboard systems, offering a pathway to more effective and environmentally conscious navigation tools.},
keywords = {behaviour change, climate, dashboard, geospatial analytics, mobile, navigation, peripheral vision, simulation, training, virtual environment, visualization, wayfinding},
pubstate = {published},
tppubtype = {mastersthesis}
}
Maritime navigation is a significant source of greenhouse gas emissions. While large-scale cargo shipping is the major contributor, smaller maritime operations, including patrolling, fishing, public transit, and recreation, present unique challenges and opportunities for power management. Fuel consumption, power conversion, and environmental data can permit environmentally conscious and cost-effective decision-making when driving a boat. To achieve this, we need to understand how best to integrate such data into boat dashboard interfaces. In this work, we design an Eco Dashboard inspired by eco-driving feedback dashboards in the automotive industry, as well as a variant of the Eco Dashboard that additionally visualizes historical route and fuel consumption data (Eco + Historical Dashboard). In an experimental simulation (N = 30) involving 12 experienced mariners and 18 novices, we compared both interfaces with a typical boat dashboard that presented fuel and speed. Our findings suggest that dashboards incorporating historical data, alongside eco-driving features, improve fuel efficiency and decision-making, particularly for non-experienced users. The Eco Dashboard supported real-time adjustments during complex navigation, whereas the Eco + Historical Dashboard enhanced route planning and confidence in longer-term decisions. Participants also reported greater confidence and reduced cognitive load when using these systems. These results provide valuable insights for the future design of maritime dashboard systems, offering a pathway to more effective and environmentally conscious navigation tools.
Amirkandeh, Melika Besharati
A Comparative Evaluation of Natural Language and Dashboard Interfaces for Visualizing Real-Time Monitoring Data Masters Thesis
2025.
Abstract | Links | BibTeX | Tags: chat interface, dashboard, visualization
@mastersthesis{nokey,
title = {A Comparative Evaluation of Natural Language and Dashboard Interfaces for Visualizing Real-Time Monitoring Data},
author = {Melika Besharati Amirkandeh},
url = {https://hdl.handle.net/10222/85441},
year = {2025},
date = {2025-09-23},
urldate = {2025-09-23},
abstract = {Natural Language Interfaces (NLIs) are emerging as an alternative to Dashboard Interfaces for data visualization, allowing users to formulate queries using conversational input rather than structured commands. In a controlled study (N=24) we compare an NLI-driven chatbot and a commercial visualization dashboard (RealFishPro) for a set of randomized analytics tasks involving real-time monitoring data. We find no difference in System Usability Scale (SUS) scores and no difference in task accuracy scores between interface conditions. NASA TLX scores show significantly higher mental and temporal demand when using the chatbot vs. the dashboard, and the chatbot yielded significantly higher task times overall. This pattern shows that while NLIs are flexible, they often impose greater cognitive effort and slower interaction compared to dashboards. Participant feedback indicated complementary strengths: NLIs were praised for simplicity and adaptability, dashboards for precision and clarity. These findings suggest that hybrid solutions integrating natural language and traditional interfaces could enhance data exploration and decision-making.},
keywords = {chat interface, dashboard, visualization},
pubstate = {published},
tppubtype = {mastersthesis}
}
Natural Language Interfaces (NLIs) are emerging as an alternative to Dashboard Interfaces for data visualization, allowing users to formulate queries using conversational input rather than structured commands. In a controlled study (N=24) we compare an NLI-driven chatbot and a commercial visualization dashboard (RealFishPro) for a set of randomized analytics tasks involving real-time monitoring data. We find no difference in System Usability Scale (SUS) scores and no difference in task accuracy scores between interface conditions. NASA TLX scores show significantly higher mental and temporal demand when using the chatbot vs. the dashboard, and the chatbot yielded significantly higher task times overall. This pattern shows that while NLIs are flexible, they often impose greater cognitive effort and slower interaction compared to dashboards. Participant feedback indicated complementary strengths: NLIs were praised for simplicity and adaptability, dashboards for precision and clarity. These findings suggest that hybrid solutions integrating natural language and traditional interfaces could enhance data exploration and decision-making.