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Moiz Rasheed

dblp:359/3268 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Virtual and augmented reality · 77% Visualization and visual analytics · 23%
Human-computer interaction and pervasive computing
1 paper
Learning and educational technologies · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Virtual and augmented reality
mixed reality
0.912025
Visualizing Causality in Mixed Reality for Manual Task Learning: A Study · IEEE Trans. Vis. Comput. Graph. 2025
Visualization and visual analytics › visual analytics › visual analysis
causality visualization
0.312025
Visualizing Causality in Mixed Reality for Manual Task Learning: A Study · IEEE Trans. Vis. Comput. Graph. 2025

Methods — techniques the papers use, named apart from their topics

user study · 1.7hierarchical task decomposition · 1.7
YearPublicationVenuePosition
2025 Visualizing Causality in Mixed Reality for Manual Task Learning: A Study
abstract
Mixed Reality (MR) is gaining prominence in manual task skill learning due to its in-situ, embodied, and immersive experience. To teach manual tasks, current methodologies break the task into hierarchies (tasks into subtasks) and visualize not only the current subtasks but also the future ones that are causally related. We investigate the impact of visualizing causality within an MR framework on manual task skill learning. We conducted a user study with 48 participants, experimenting with how presenting tasks in hierarchical causality levels (no causality, event-level, interaction-level, and gesture-level causality) affects user comprehension and performance in a complex assembly task. The research finds that displaying all causality levels enhances user understanding and task execution, with a compromise of learning time. Based on the results, we further provide design recommendations and in-depth discussions for future manual task learning systems.
Rahul Jain 0018, Jingyu Shi, Andrew Benton, Moiz Rasheed, Hyungjun Doh, Subramanian Chidambaram, Karthik Ramani
IEEE Trans. Vis. Comput. Graph.4