This readme file was generated on 2024-04-02 by Aditi Galada and Fatma Baytar GENERAL INFORMATION Title of Dataset: Galada_Baytar_2024_PBL dataset Data from: Design and evaluation of a problem-based learning VR module for apparel fit correction training Authors: Name: Galada, Aditi ORCID: https://orcid.org/0000-0003-3576-6789 Institution: Cornell University Name: Baytar, Fatma ORCID: https://orcid.org/0000-0001-6666-2902 Institution: Cornell University Date of data collection: 2022-08-29 to 2022-10-01 Geographic location of data collection: Ithaca, NY, USA Information about funding sources that supported the collection of the data: This material is based upon work partially supported by the National Science Foundation under Grant No. 2048022. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. Additional funding was received from the College of Human Ecology Alan D. Mathios Research and Service Grant, and the Department of Human Centered Design at Cornell University. SHARING/ACCESS INFORMATION Licenses/restrictions placed on the data: This dataset is shared under a Creative Commons 1.0 Universal Public Domain Dedication (https://creativecommons.org/publicdomain/zero/1.0/). Links to publications that cite or use the data: To be added. Links to other publicly accessible locations of the data: N/A Links/relationships to ancillary data sets: N/A Was data derived from another source? No. If yes, list source(s): Recommended citation for this dataset: Aditi Galada, Fatma Baytar. (2024) Data from: Design and evaluation of a problem-based learning VR module for apparel fit correction training. [dataset] Cornell University eCommons Repository. https://doi.org/10.7298/8wsv-0q41 DATA & FILE OVERVIEW File List: Galada_Baytar_2024_readme.txt - readme documentation to accompany dataset Galada_Baytar_2024_PBLdataset.csv - This file includes the data colleced from the participants in their assigned VR conditions. Additional related data collected that was not included in the current data package: N/A METHODOLOGICAL INFORMATION Description of methods used for collection/generation of data: At the beginning of the data collection session, the purpose of the study was explained to the participants orally. Participants were also informed with a written consent form, which they read and signed. The training modules were aimed at students as they represent the future workforce in apparel design. No pre-requisite knowledge was expected of the participants or viewers. A quasi-experimental, pretest posttest design was used. Before and after the training participants completed a questionnaire that consisted of demographics, prior pattern making experience, and two objective performance measures, i.e., Apparel Spatial Visualization Test (ASVT) questions adapted from [52] and the Fit Correction Skill Test (FCST) which was developed in a previous study [61]. While the FCST was designed in a 10-question each for the pre-and post-test format, the 20 ASVT questions were equally divided based on their difficulty ranking into pre- and post-test [62].Participants first completed the pre-test, followed by viewing the training module, and answering the post-test. [52] Workman JE, Caldwell LF, Kallal MJ. Development of a test to measure spatial abilities associated with apparel design and product development. Clothing and Textiles Research Journal. 1999;17: 128–133. doi:10.1177/0887302X9901700303 [61] Hirumi A, Appelman B, Rieber L, Eck R Van. Preparing Instructional Designers for Game-Based Learning: Part 1. TechTrends 2010 54:3. 2010;54: 27–37. doi:10.1007/S11528-010-0400-9 [62]Kamis A, Mamat R, Safie NS, Mustapha R. Spatial Visualization Ability Among Apparel Design Students. International Journal of Humanities, Arts, Medicine and Sciences. 2015;3: 15–24. Methods for processing the data: The data was analyzed through JMP Statistical Software. One-tailed paired t-tests were performed to determine whether ASVT and FCST post-test scores were significantly higher than the pre-test scores. To determine the impact of ASVT pre-test scores on the increase in FCST score, a linear regression model was created and the significance of the ASVT pre-test score was analyzed. The impact of gender and its interaction with pre-test scores were studied through another regression model. The main effect of gender and pre-test score was tested in a regression model followed by their interaction in the next model. Instrument- or software-specific information needed to interpret the data: The data presented here as .csv was analyzed through JMP Statistical Software. Standards and calibration information, if appropriate: N/A Environmental/experimental conditions: The users viewed the modules through a monitor and used a mouse to navigate the modules and complete different fit correction tasks by clicking on the desired option. Users were provided with an easy-to-navigate environment through a semi-immersive experience. The training modules included PBL learning activities where the user was given a brief explanation of the garment fit issue that needs to be rectified, shown the simulated garment with fit issues, and given three potential pattern modification options one of which would correct the fit issue. The users could interact with the model and view it from different angles to better understand the garment fit. If the user selected the wrong pattern modification option, immediate audio-visual feedback was given, which was expected to reinforce learning. In case the user chose the right pattern modification option, audio-visual feedback indicated that the right option had been chosen. The user was then directed to the next step where the 3D garment simulations with the before and after the pattern modifications were shown along with the correct pattern modification as well as an explanation of why the pattern change corrected the fit. Once all the fit issues in a garment had been rectified, the participants were shown their score and directed back to the main menu to work on the next garment. Describe any quality-assurance procedures performed on the data: N/A People involved with sample collection, processing, analysis and/or submission: Aditi Galada, Fatma Baytar DATA-SPECIFIC INFORMATION FOR: Galada_Baytar_2024_PBLdataset.csv Number of variables: 17 Number of cases/rows: 40 Variable List: A. Name: Participant code (1-60) Description: Unique identifier assigned to each participant in the study allowing for easy reference and tracking throughout the research process. B. Name:Sex assigned at birth (M/ W) Description: Indicates the sex assigned to each participant at birth, categorized as either male (M) or female (W). It provides demographic information about the gender distribution among the study participants. C. Name:Race (based on U.S. Census Bureau categorization) (Asian, White, Black or African American) Description: Categorized based on the U.S. Census Bureau classification system and includes options such as Asian, White, Black or African American. This variable provides information about the racial diversity among the study participants. D. Name:Condition (Desktop VR, Immersive VR) Description: Describes the experimental condition to which each participant was assigned during the study. Participants were either assigned to the Desktop VR condition, where they interacted with semi-immersive virtual reality using a computer screen and mouse for navigation, or the Immersive VR condition, where they experienced full immersion using a VR headset and controller, allowing for physical movement within the virtual environment. E. Name:Pattern Making Experience (Yes/ No) Description: Indicates whether participants have prior experience in apparel pattern making, with options for both "Yes" and "No". It provides insight into the participants' familiarity with the subject matter before engaging in the study. F. Name:Pre-test ASVT Description: The Pre-test ASVT (Apparel Spatial Visualization Test) is a standardized assessment consisting of 10 questions designed to evaluate participants' spatial visualization skills specific to apparel-related tasks. This test was administered before the training session to assess participants' baseline abilities in this area. Each participant's score on this test ranges from 0 to 10. G. Name:Pre-test FCST Description: The Pre-test FCST (Fit Correction Skill Test) is a standardized assessment comprising 10 questions aimed at evaluating participants' proficiency in correcting fit issues in apparel. It was administered prior to the training session to gauge participants' initial competency in this aspect of apparel design. Each participant's score on this test ranges from 0 to 10. H. Name:Post-test ASVT Description: The Post-test ASVT (Apparel Spatial Visualization Test) is a follow-up assessment consisting of 10 questions administered after the training session to measure any improvements in participants' spatial visualization skills related to apparel tasks. It provides data on the effectiveness of the training intervention in enhancing this skill. Each participant's score on this test ranges from 0 to 10. I. Name:Post-test FCST Description: The Post-test FCST (Fit Correction Skill Test) is a follow-up assessment comprising 10 questions administered after the training session to assess any enhancements in participants' ability to correct fit issues in apparel. It helps evaluate the efficacy of the training program in improving participants' fit correction skills. Each participant's score on this test ranges from 0 to 10. Missing data codes: N/A Specialized formats or other abbreviations used: problem-based learning (PBL) virtual reality (VR) Apparel Spatial Visualization Test (ASVT) Fit Correction Skill Test (FCST)