Data for: Task Completion Among Nursing Students in Intelligent Educational Environments
Description
Background and Purpose This dataset supports the study "Determinants of Task Completion Among Nursing Students in Intelligent Educational Environments: A Longitudinal Repeated-Measures Study." It examines multilevel factors (activity type, instructor identity, course nature, time limitation) influencing nursing students' task completion across pre-class, in-class, and post-class phases in an intelligent educational environment. Data Collection and Participants Data were collected from September 2024 to June 2025 at a medical university in Guangzhou, China. The study included 119 third-year undergraduate nursing students who completed tasks via the SuperStarLearn platform over one academic semester. A total of 90 tasks were distributed across theoretical and non-theoretical courses. Each student responded only to tasks assigned to their class, contributing 24 to 34 task-level observations. The final dataset comprises 3,527 valid observations after removing 5 duplicate records. Variables Included The dataset includes eight variables per observation: Student ID: Unique identifier (numeric) Task ID: Unique task identifier (numeric) Activity Type: Temporal placement (1=Pre-class, 2=In-class, 3=Post-class) Course Nature: Course type (1=Theoretical, 2=Non-theoretical) Instructor ID: Anonymized instructor identifier (1-6) Sex: Student sex (1=Male, 2=Female) Time Limitation: Deadline status (1=Time-limited, 2=Non-time-limited) Completion Status: Task completion (0=Not completed, 1=Completed) All personally identifiable information has been anonymized. The dataset supports replication of original analyses, secondary research on student engagement, and methodological training in Generalized Estimating Equations (GEE). Key Findings from the Original Study Analysis revealed activity type as the strongest predictor of task completion. Pre-class (99.6%) and in-class (96.5%) activities showed significantly higher completion rates than post-class activities (66.7%). Instructor identity and course nature also emerged as significant determinants, while sex showed no statistically significant association. Non-time-limited tasks had nearly threefold higher odds of completion compared to time-limited tasks (OR=2.94, 95% CI: 1.90-4.54). Data Files The dataset is provided as a Microsoft Excel (.xlsx) file containing two sheets: a Data sheet with all 3,527 observations, and a Codebook sheet describing each variable and its coding scheme. The data have been cleaned and formatted for immediate use in statistical software. Usage and License This dataset is made available under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Users may share and adapt the material for any purpose, provided appropriate credit is given to the original authors. Please cite both the associated manuscript and this dataset when using the data. For questions, contact the corresponding author.
Files
Institutions
- Guangzhou Medical UniversityGuangdong, Guangzhou