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This research introduces a novel dataset developed for streaming learning analytics, derived from the Open University Learning Analytics Dataset (OULAD). The dataset incorporates essential temporal in…

This research introduces a novel dataset developed for streaming learning analytics, derived from the Open University Learning Analytics Dataset (OULAD). The dataset incorporates essential temporal information that captures the timing of student interactions with the Virtual Learning Environment (VLE). By integrating these time-based interactions, the dataset enhances the capabilities of stream algorithms, which are particularly well-suited for real-time monitoring and analysis of student learning behaviors.   The dataset consists of 34 features and 1,718,983 samples, encompassing students' demographic information, assessment scores, and interactions with the VLE for a specific time ( T ), corresponding to each student ( S ) within a given course ( C ) and module ( M ). The target classes—'Withdrawn', 'Fail', 'Pass', and 'Distinction'—were encoded as 0, 1, 2, and 3, respectively. Notably, the data exhibits a significant imbalance, with a substantial prevalence of records associated with students who passed the final examination. The class distribution is as follows: 'Pass' (1,022,760 samples), 'Distinction' (308,642 samples), 'Fail' (227,550$ samples), and 'Withdrawn' (160,031 samples).  

This repository contains the dataset and accompanying code used in the manuscript: "Nonlinear Dynamics in Intensive Longitudinal Health Behavior Data: Methodological Rethinking using the 4HAIE Data." …

This repository contains the dataset and accompanying code used in the manuscript: "Nonlinear Dynamics in Intensive Longitudinal Health Behavior Data: Methodological Rethinking using the 4HAIE Data." The dataset originates from the 4HAIE (Healthy Aging in Industrial Environment - Program 4) study, a 12-month intensive longitudinal study designed to examine within-person dynamics of health behaviors and well-being in everyday life (see corresponding protocol paper Elavsky et al., 2021). Dataset Contents The repository includes: Merged analysis dataset linking daily physical activity with corresponding end-of-day life satisfaction assessments. Daily physical activity data collected continuously over approximately 12 months using Fitbit Charge 3 and Fitbit Charge 4 devices. Physical activity is represented by daily step counts. Ecological Momentary Assessment (EMA) data collected during four 14-day measurement bursts distributed across the 12-month study period. Participants completed an end-of-day survey assessing life satisfaction using two items adapted from the Satisfaction with Life Scale (SWLS). Illustrative analysis code demonstrating the generalized additive modeling (GAM) approach used to investigate nonlinear relationships between physical activity and life satisfaction over time. Study Design Participants were monitored continuously for physical activity throughout the study while completing repeated intensive EMA assessments during four separate two-week measurement bursts. This design enabled examination of both long-term behavioral patterns and short-term fluctuations in subjective well-being within individuals. Variables The repository includes variables related to: Participant identifier (de-identified) Date of observation Daily step count (Fitbit) End-of-day life satisfaction ratings (two EMA items) Additional temporal variables required for longitudinal modeling Variable definitions and coding information are provided in the accompanying documentation. Purpose The dataset is intended to illustrate statistical approaches for analyzing intensive longitudinal health behavior data, with particular emphasis on nonlinear modeling of within-person associations using generalized additive models (GAMs). Rather than serving solely as an empirical example, the dataset demonstrates a methodological framework for capturing complex temporal relationships that may not be adequately represented using traditional linear approaches. Associated Publication This dataset accompanies the manuscript: Nonlinear Dynamics in Intensive Longitudinal Health Behavior Data: Methodological Rethinking using the 4HAIE Data. If you use this dataset or the accompanying analysis code, please cite both this Zenodo repository and the associated publication.   References: ELavsky, S., Brabec, M., Maly, M. (in review). Nonlinear Dynamics in Intensive Longitudinal Health Behavior Data: Methodological Rethinking using the 4HAIE Data. Elavsky S, Jandačková V, Knapová L, Vašendová V, Sebera M, Kaštovská B, Blaschová D, Kühnová J, Cimler R, Vilímek D, Bosek T, Koenig J, Jandačka D. Physical activity in an air-polluted environment: behavioral, psychological and neuroimaging protocol for a prospective cohort study (Healthy Aging in Industrial Environment study - Program 4). BMC Public Health. 2021 Jan 12;21(1):126. doi: 10.1186/s12889-021-10166-4. PMID: 33435943; PMCID: PMC7801866. Elavsky S, Brabec M, Maly M, Knapova L, Kastovska B, Sebera M, Ely M, Jandackova VK, Keller J, Pavel M. The temporal dynamics of the association between daily physical activity and life satisfaction. Ann Behav Med. 2025 Jan 4;59(1):kaaf079. doi: 10.1093/abm/kaaf079. PMID: 41224246; PMCID: PMC12757008.

This repository contains data collected for Work Package 1.3: Short-term impacts of ICT use on adult wellbeing, part of the DigiWELL project (Research of Excellence on Digital Technologies and Wellbei…

This repository contains data collected for Work Package 1.3: Short-term impacts of ICT use on adult wellbeing, part of the DigiWELL project (Research of Excellence on Digital Technologies and Wellbeing, CZ.02.01.01/00/22_008/0004583). This study was conducted as a technical pilot (EMA1) to verify the functionality of the research applications used for subsequent EMA studies: Health React (for survey collection) and eBehave (for passive smartphone trace data collection), both developed by the University of Hradec Králové. The seven-day EMA study involved 58 Masaryk University students (58% female; age range: 19–30 years; M = 21.5; SD = 2.3). KeywordsEcological Momentary Assessment, Experience Sampling Method, Technical pilot   For additional information or questions, please contact the contact person: Martin Tancoš (tancos@fss.muni.cz).

This dataset contains aggregated prevalence estimates regarding cigarette smoking among adolescents in the Czech Republic, derived from three waves of the Health Behaviour in School-aged Children (HBS…

This dataset contains aggregated prevalence estimates regarding cigarette smoking among adolescents in the Czech Republic, derived from three waves of the Health Behaviour in School-aged Children (HBSC) study conducted in 2014, 2018, and 2022. The data represents a total sample of 29,525 respondents (14,761 boys and 14,764 girls) across three target age groups: 11, 13, and 15 years old.

This dataset contains aggregated prevalence estimates regarding alcohol consumption among adolescents in the Czech Republic, based on three waves of the Health Behaviour in School-aged Children (HBSC)…

This dataset contains aggregated prevalence estimates regarding alcohol consumption among adolescents in the Czech Republic, based on three waves of the Health Behaviour in School-aged Children (HBSC) study conducted in 2014, 2018, and 2022. The data represents a total sample of 29,525 respondents (14,761 boys and 13,764 girls) across three target age groups: 11, 13, and 15 years old.

This directory contains R scripts, data files, and analysis reports relatedto the analysis of adherence. FILE OVERVIEW DATA FILES* data.xlsx  Main subject-level dataset. * data-fitbit.xlsx  …

This directory contains R scripts, data files, and analysis reports relatedto the analysis of adherence. FILE OVERVIEW DATA FILES* data.xlsx  Main subject-level dataset. * data-fitbit.xlsx  Dataset with detailed Fitbit-derived measures. DATA LOADING AND PREPROCESSING* data.R  R script for loading and preprocessing the main dataset. * data-fitbit.R  R script for loading and preprocessing the detailed Fitbit dataset. DESCRIPTIVE AND BASELINE ANALYSES* summary.Rmd  R Markdown document providing a basic description of the datasets. * baseline.Rmd  R Markdown document with baseline characteristics and missing-value  analysis, including evaluation and imputation of missing values in SWL  (new variable SWLlm.predicted). * baseline-by-burst.Rmd  R Markdown document with baseline characteristics stratified by burst  number. MODELING AND VARIABLE IMPORTANCE* importance.Rmd  R Markdown analysis template for evaluating variable importance using  stepwise regression and random forest models. PROJECT CONFIGURATION AND BUILD SYSTEM* Makefile.R  Project definition for the rmake package; generates the GNU Makefile. * Makefile  File dependencies and compilation commands used by GNU Make to generate  all project results. * Rproject.Rproj  RStudio project file. ------------------------------------------------------------------------------REQUIREMENTS------------------------------------------------------------------------------ 1) INSTALL REQUIRED R PACKAGES Run the following commands in R:   install.packages(c(    "tidyverse", "knitr", "rmake", "caret", "randomForest",    "fastDummies", "broom", "devtools"  ))  devtools::install_github("beerda/hammer")  devtools::install_github("beerda/mbrtools") 2) GENERATE PROJECT ANALYSES Run the analysis pipeline using rmake:   make() This command generates all results defined in the project Makefile.------------------------------------------------------------------------------  

The data come from cross-sectional surveys conducted on representative samples of the non-institutionalised adult population in the Czech Republic in 2017, 2019 and 2022. The data include basic demogr…

The data come from cross-sectional surveys conducted on representative samples of the non-institutionalised adult population in the Czech Republic in 2017, 2019 and 2022. The data include basic demographic data and data from four questionnaires.  Data on mental health problems were assessed using the Mini International Neuropsychiatric Interview (M.I.N.I.) in 2017 and 2022. The Self-identification of Mental Illness (SELF-I) scale was used to assess self-identification as having a mental illness in these years. Stigma associated with mental health was assessed using the Reported and Intended Behaviour Scale (RIBS) and the Community Attitudes towards Mental Illness (CAMI) scale in 2019 and 2022.   Data pocházejí z průřezových šetření provedených na reprezentativních vzorcích neinstitucionalizované dospělé populace v České republice v letech 2017, 2019 a 2022. Data zahrnují základní demografické údaje a údaje ze čtyř dotazníků. Údaje o problémech v oblasti duševního zdraví byly hodnoceny pomocí Mini mezinárodního neuropsychiatrického rozhovoru (M.I.N.I.) v letech 2017 a 2022. K posouzení sebeidentifikace jako osoby s duševním onemocněním byla v těchto letech použita škála Self-identification of Mental Illness (SELF-I). Stigmatizace spojená s duševním zdravím byla hodnocena pomocí škály Reported and Intended Behaviour Scale (RIBS) a škály Community Attitudes towards Mental Illness (CAMI) v letech 2019 a 2022.