Dataset of Intrinsic Motivation Students During Pandemic Learning in South Sumatera, Indonesia.
One factor that determines the success of online learning in higher education is students' level of intrinsic motivation. Thus, understanding intrinsic motivation's capabilities are essential for achieving successful education during this pandemic. This paper presents data to estimate the intrinsic motivation of students in South Sumatra based on their gender, age, and level of education. The data contains 22 items and 1037 respondents. The respondents were from several regions in South Sumatra, Indonesia. One thousand thirty-seven data respondents were analyzed using the Rasch model by Winsteps Version 3.73. This data can generate students' intrinsic motivation and build a recommender for policy learning during pandemic COVID-19 in Higher Education.
Steps to reproduce
The method for obtaining data that fit is using the Rasch model. Some fit indexes pro-vided in the Rasch analysis are ZSTD Person Infit, ZSTD Person Outfit, MNSQ Infit Person, MNSQ Person Outfit, ZSTD Infit Item, ZSTD Outfit Item, MNSQ Infit Item, MNSQ Outfit Item. From 1037 out of total respondents were analyzed. The respondents who met the criteria. 1. Outfit Mean Square (MNSQ) received: 0.5 < MNSQ < 1.5 2. Z-Standard Outfit (ZSTD) value received: –2.0 < ZSTD < + 2.0 3. Outfit Point Value Correlation (Pt Mean Corr.) Value: 0.4 < Pt Measure Corr. < 0.85. So, if fit data are collected, there are 599 fit total respondents. The rest of the respondents who did not fit were 438 people consisting of 2 people indicated as outliers (one people from the maximum extreme score group and one person from the minimum extreme score group), 1 person were indicated not filling in the questionnaire. 435 entered the most misfitting responses and the most unexpected responses. Then, 599 respondents were grouped according to their level of motivation based on demographic data.