IndexIntroductionResult and DiscussionConclusionAlthough there are measures of students' online learning readiness published in behavioral science and educational literature and journals, very few scales are available to measure students' readiness for online learning, particularly in Indonesia. The purpose of this study was to validate a tool for preparing high school students for online learning. The Online Learning Readiness Scale aimed to measure online learning readiness among Indonesian students. Data from 271 high school students (male = 126, female = 145) in Yogyakarta, Indonesia were subjected to an Item Response Theory (IRT) analysis, and the psychometric properties of the scale were examined using the generalized model of partial credit (GPCM). The results of the IRT analysis using GPCM revealed excellent psychometric characteristics of the Indonesian version of this scale. Evidence of construct validity is presented. The scores on the students' online learning readiness scale were very reliable. Implications for future research are discussed. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an original essay IntroductionThe Republic of Indonesia is a country that has a very large and strategic geographical area, flanked by two oceans and two continents where the conditions of most of the area consists of oceans and islands that number around 17,000 islands. This condition has caused various obstacles to national development, including the development of road infrastructure, educational facilities, healthcare facilities, and Internet networks that have not been distributed equally from Sabang (far west) to Merauke (far east). Human resource development in this case is the world of education, an important part and it cannot be released like this. The demands of the progress of the times and technology encourage the government and all parties involved directly or indirectly to synergize, adapt and innovate in the face of this millennial era. In the digital age like today, the use of technology has dominated the world of education. The development of education today in Indonesia is necessary in order to bring about changes and innovations in order to catch up with other countries. The first national exam that still uses paper or paper media will be replaced with an online or online national exam system. This can be demonstrated when Indonesia started to organize computer-based national exams. The use of computers is like providing an opportunity for ease in administering exams. The facilities provided include administrative ones such as no manual marking, manual collection of answer sheets, slow turnaround time in Jakarta where the UN is marked and the most important is on the psychometric side where this can be the starting point to administer the exam with Computerize Adaptive Tests (CAT). The implementation of online national exams in all regions of Indonesia will certainly face many challenges. One of the challenges is preparing students to take the exam. Learning readiness is studied in so many researches, none in Indonesia. They define readiness for online learning in three aspects: (1) student preference for forms of instruction such as face-to-face classroom instruction; (2) student confidence in using electronic communication for learning and, in particular, competence andconfidence in the use of the Internet and computer-mediated communication; and (3) the ability to be independently engaged in learning. Although there have been studies focused on developing online student readiness assessment tools, they appear to have ignored an important detail about the psychometric quality of these tools. The participants in this study were high school students with a total of 271 students, 126 female and 145 male students aged between 15 and 18 years old selected with non-probability sampling technique. The consideration in using the sampling technique is due to the time limitations to create a sampling frame that contains data on active students actively attending school, so the sampling technique that allows it to be used is non-probability. Other characteristics that are taken into consideration when determining research participants are high school students who will take computer-based or online exams. The willingness of the interviewees to participate in this research is voluntary. Generalized Partial Credit Model (GPCM) IRT is a powerful modeling approach used to evaluate the psychometric properties of survey questionnaires with categorical (ordered and unordered) responses. IRT models are similar to factor analytic models in that both provide information about the dimensionality and fit of the model. A key difference between IRT and factor analytic approaches is how data is treated. While factor analysis methods examine the covariances between individual items, IRT models examine the overall response patterns of all items. As a consequence of evaluating item response models, the parameter estimates obtained provide information about how the items function. This type of information can be especially helpful during the survey development process. Furthermore, factor analytic approaches construct a linear relationship between the factor score and the item response. This contrasts with the IRT approach, which constructs a nonlinear relationship between latent traits and item responses. The Likert scale format is an extension of the dichotomous response format and is used when more information can be obtained than with a dichotomous format. In elementary applications, categories in the expected order are evaluated with successive integers, starting at zero (0), and a person's scores on multiple items of a test or questionnaire are added together to characterize a person. In more advanced applications, a probabilistic model, such as GPCM, is applied. A major advantage of using polytomized scored items is that they generally cover a broader range of the ability scale with sufficient information relative to an assessment with the same number of dichotomous items. For polytomous response models, parameter values ​​should be interpreted with the help of graphical presentations. Inspecting the graphs of ICRF, IRF, and element information functions for each element is an essential element analysis procedure. Result and Discussion The analysis of 9 items of the Online Learning Readiness Scale (OLRS) showed that the model fitting Pearson Chi-square obtained was 4355 .967, df = 1952917, p-value = 1.000 and Chi-square of probability = 720.460, df = 1952917 and p-value = 1.000 where both of these results indicate that the assumption of unidimensionality of the generalized partial credit model (GPCM) in this study was fulfilled. If the model is not suitable, then the use of GPCM must be changed to other models such as the model of.