This dataset comprises 2,271 entries and provides insights into user interface (UI) and user experience (UX) preferences across various digital platforms. Key information includes user demographics (Name, Age, Gender) and platform preferences (e.g., Twitter, YouTube, Facebook, Website). It captures user experiences and satisfaction levels with various UI/UX elements such as color schemes, visual hierarchy, typography, multimedia usage, and layout design. The dataset also includes evaluations of mobile responsiveness, call-to-action buttons, form usability, feedback/error messages, loading speed, personalization, accessibility, and interactions (like scrolling behavior and gestures). Each UI/UX component is rated on a scale, allowing for quantitative analysis of user preferences and experiences, making this dataset valuable for research in user-centered design and usability optimization.

This dataset provides detailed, event-level records of mobile app feature usage, including user interactions, device context, session information, and user segmentation. It enables product teams and UX researchers to analyze feature adoption rates, engagement patterns, and user cohorts, supporting data-driven decisions for app improvement and user experience optimization.

This dataset comprises 26,261 user reviews of the BCA Mobile app collected from the Google Play Store between June 1, 2023, and May 31, 2024. Each review includes the user's name, the rating they provided (ranging from 1 to 5 stars), the timestamp of when the review was created, and the text content of the review. The dataset is in Indonesian and focuses on feedback from users in Indonesia. This data can be used to perform sentiment analysis, understand user experiences, identify common issues, and assess the overall performance of the BCA Mobile app during the specified timeframe. The reviews are sorted based on the newest first, providing the latest feedback at the top.

A narrative literature review was conducted to identify 50 most cited research articles on user experience (UX). The focus on finding UX definitions that can be divided into dimensions via conceptual analysis to create of a conceptual analysis framework of UX dimensions that can be utilised in analysing different experience concepts for developing the concept of total experience (TX). ACM Digital Library was utilized as the database. Research articles were selected based on the following inclusion and exclusion criteria. In the ACM Digital Library database, “User Experience” was used as a search keyword. The search resulted in a total of 30,723 publications. Due to a large number of publications, the results were sorted so that UX was mentioned in full text, abstract, keywords, and cited by highest. Then the 50 most referenced studies were selected and scanned carefully through titles and abstracts, and then through our research question. The main focus was on finding and selecting articles that focus on UX in a technology-interaction context.  Only publications in which the authors explicitly mention UX in the context of HCI were included. Papers studying UX from a technology driven perspective were not included, because we wanted to focus on how human experiences are created and shaped in technology interaction. Also, publications included for in the analysis had to be original full papers and written in English. Out of the 50 most cited articles, 3 articles were found that we have already cited in our initial framework. At this point, we excluded articles that did not focus on experiences with technology. Also, we excluded 23 research articles that focused either on UX design and evaluation methods, or children´s UX. After that, a full-text review was performed, and 10 research articles were found that focused on UX design in specific technology development context but did not include definition of how UX was defined. The contexts were wearable mobile augmented reality system, driving simulator, mobile devices equipped with Near-Field Communication, viewing sports videos on mobile phones, recommended system, immersive gaming, mobile network performance on social media, a robot privacy and face enrolment system on a humanoid robot, interactive television and panoramic video in CAVE-like environment and head mounted display viewing conditions. Then we selected the articles that were included in this review. A total of 14 primary UX research articles (out of 389 research articles) were included for detailed analysis based on defined inclusion and exclusion criteria.  In the literature review data all the 50 most cited articles are listed and 14 included to further analysis are marked with an asterisk.

The study aimed to investigate the long term impact of experiences in user engagement of a food reporting mobile game app. The study recruited 10 participants, with 8 being able to complete the study. The period consider at least 6 weeks of continuous use of the DigestInn application. A one year licence of the DigestInn mobile app was given for free to each participant. A mixed dataset was collected:

Daily mood reporting: Experience Sampling Method [1] was used to sample daily participants' mood towards their experience using the application. Whatsapp [2] and the visual Pick-A-Mood tool [3] were used to prompt participants daily.

Weekly user engagement reporting: a user engagement scale was used and adjusted for this purpose [4]. The survey was implemented in TypeForm [5]. The prompt/reminder was done through whatsapp via a visual summary of the mood reporting, based on Daily reconstruction method [6]

6 weeks interviews: individual interviews were conducted in person and via Skype. Focus group were conducted in the establishment of Arhnem-Nijmegen Applied Science University. In all cases visual prompts of food and mood reports were presented as probes [6]

Raw data was processed for analysis.

Coded transcripts: two students assistant and a code manager processed the transcripts using the software Atlas.ti [7] version 8.4.4. A coding scheme was initially developed, code manager trained the student assistant till a higher than .9 interrelated coder was achieved [8]

Parsed json files: a json file containing the complete dataset of the complete study period was parsed to extract each participants food reports during. First the file was split in 8 files (one for each participant). A python program and a bash script were developed in Mac OSX to parse the json files into .csv files. In excel, .csv files were parsed by means of two Visual Basic macros to obtain a tabular view of the food reports per participant.

[1] Larson, R., & Csikszentmihalyi, M. (2014). The experience sampling method. In Flow and the foundations of positive psychology (pp. 21-34). Springer, Dordrecht. [2] https://www.whatsapp.com [3] Desmet, P., Vastenburg, M., and Romero, N. (2016) Mood measurement with Pick-A-Mood: review of current methods and design of a pictorial self-report scale. Journal Design Research, 14 (3), pp. 241-279 [4] O’Brien, H. L., Cairns, P., & Hall, M. (2018). A practical approach to measuring user engagement with the refined user engagement scale (UES) and new UES short form. International Journal of Human-Computer Studies, 112, 28-39 [5] https://www.typeform.com/ [6] Kahneman, D., Krueger, A. B., Schkade, D. A., Schwarz, N., & Stone, A. A. (2004). A survey method for characterizing daily life experience: The day reconstruction method. Science, 306(5702), 1776-1780. [7] Atlas.ti [8] Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook. sage.

Minimal dataset.csv contains the minimal dataset used for the statistical evaluation and for deriving Figs 3–5.

This dataset is associated with the article "Augmented Reality Apps in Education: Exploring the User Experience through Data Mining". It contains a static collection of all the scripts needed to reproduce the methodology of the paper (methodology folder) and the results obtained and reported in the paper (results folder). The latter include the metadata of all applications scraped, the complete information of the reviews, the results after classification, the file containing the manual classification, and the IRR test. Along with the methodology, the next 3 external references were used, all the files used are linked in this repository and refereed in the corresponding step. 1. Google-Play Scraper: Olano, F. (2020). Google-Play Scraper, version 7.1.2. Retrieved from https://github.com/facundoolano/google-play-scraper#search 2. google-play-scraper: Jo, M. (2020). google-play-scraper, version 0.0.2.2. Retrieved from https://github.com/JoMingyu/google-play-scraper. 3. ARdoc tool: Panichella, S., Di Sorbo, A., Guzman, E., Visaggio, C. A., Canfora, G., & Gall, H. C. (2016). ARdoc: app reviews development oriented classifier. Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering - FSE 2016, (November), 1023–1027. https://doi.org/10.1145/2950290.2983938.

This dataset is associated with the article "Mobile Augmented Reality Apps in Education: Exploring the User Experience through Large-Scale Public Reviews". It contains a static collection of all the scripts needed to reproduce the methodology of the paper (methodology folder) and the results obtained and reported in the paper (results folder). The latter include the metadata of all applications scraped, the complete information of the reviews, the results after classification, the file containing the manual classification, and the IRR test. Along with the methodology, the next 3 external references were used, all the files used are linked in this repository and refereed in the corresponding step. 1. Google-Play Scraper: Olano, F. (2020). Google-Play Scraper, version 7.1.2. Retrieved from https://github.com/facundoolano/google-play-scraper#search 2. google-play-scraper: Jo, M. (2020). google-play-scraper, version 0.0.2.2. Retrieved from https://github.com/JoMingyu/google-play-scraper. 3. AR doc tool: Panichella, S., Di Sorbo, A., Guzman, E., Visaggio, C. A., Canfora, G., & Gall, H. C. (2016). ARdoc: app reviews development-oriented classifier. Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering - FSE 2016, (November), 1023–1027. https://doi.org/10.1145/2950290.2983938.

Data berikut merupakan acuan kami dalam pembuatan paper dengan judul “Analisis User Experience Pada Aplikasi Mobile Bobobox”.