Task-Conditioned Early Prediction of Navigation Failure in
Information Architecture Evaluation
Kharchenko Raisa1,*, Rahul Chauhan2, Andino Maseleno3
1North-West Institute of Management, RANEPA, Russia
2Unitedworld Institution of Management, Karnavati University, Gandhinagar, India
3Institut Bakti Nusantara, Lampung, Indonesia
Emails: kh9044947155r@gmail.com; rahulchauhan@karnavatiuniversity.edu.in; andino.maseleno@ibnus.ac.id
Abstract
The interaction logs which researchers collected during their information-architecture evaluation process contain detailed
proof which shows how users select between successful and unsuccessful navigation routes. The predictive signal displays its
initial appearance during task execution yet users exhibit different navigation patterns depending on their current task and
interface they are using. The researchers of this study developed an early navigation failure prediction system which uses
public interaction data to create task-specific prefix classification models. The study analyzes data from an open dataset
which includes 180 participants completing 1800 tasks across six testing conditions that evaluate tree testing and highfidelity
prototype navigation. A prefix-structural encoder works together with a regularized task-conditioned logistic model
which predicts success based on the first k navigation actions. The researchers assessed model performance through
participant-specific validation using three different machine learning techniques which included random forest, extra trees, and
gradient boosting. The optimal configuration achieved 0.7833 accuracy, 0.7513 balanced accuracy, 0.8350 F1-score, and
0.7949 ROC–AUC performance at k = 3. The horizon analysis demonstration shows that predictive signals become
accessible after users complete their first three actions. The ablation study proves that task conditioning functions as an
essential component. The study results demonstrate that early trace analytics enable quick identification of navigation
failures in information-architecture research while providing a useful method for customized assessment during usability
testing.
Keywords: Information-architecture evaluation; Navigation patterns; Task-specific prefix classification models