PREDICTING NURSING STUDENTS’ GRADE POINT LEVEL WITH MARKOV CHAIN
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Abstract
The objectives of this research article were to predict the number of students in each score level for the next semester from the changed in the score level of the previous 6 semesters using the Markov chain method which assuming that future changes in the score level will tend to change as in the past. The Markov chain method is using the previous state of change in order to predict the next subsequent states, if changes pattern in the future will tend to have the same pattern of change as in the past. Using the research model that gathered the average score of 96 undergraduate nursing students in the regular semesters from the Year 1, 2 and 3, the total of 6 semesters between the academic years 2018 and 2020. Each semester score was classified into 4 levels, and the status transition probability matrices were generated to predict the number of students at each grade level in the next semester. The results showed that the Markov chain method forecasted difference from the actual in the academic year 2019 to 2020 when using 2018 to 2019 transitions, due to the teaching method had been changed from onsite to online according to the situation of COVID-19 and the difference in the proportion of theoretical and practical courses of different academic years. But if the situations proceed as it used to, the subsequent state will follow the Markov process that continues in accordance with the previous probability transition matrix. Therefore, if the teaching arrangements in the academic year 2021 are not different from the practices in the year 2020, and the combination of lecture subjects and practicum subjects for the fourth year are not much different from the combination in third year subjects, the Markov chain predicted that this group of students for academic year 2021 will have better score levels than the previous academic year 2020.
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References
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