Markov Chain Analysis for Predicting Help Selection in Metacognitive Help-seeking

M. Sumadyo, H. B. Santoso, D. I. Sensuse, R. F. Aji, A. Hidayati, R. Wahyuni

Research output: Contribution to journalConference articlepeer-review


One of the metacognition techniques is to provide help in solving items using help-seeking. This technique is important in improving metacognitive skills, because it contains an awareness of the need for help, monitoring knowledge and choosing a strategy to get the appropriate help and then evaluating the episode help. Metacognitive skills contain the dimension of metacognitive knowledge. Therefore help-seeking is arranged according to that dimension. However, the help-seeking series have not been able to measure whether the assistance is too excessive to meet the needs of students or even less fulfilling. This study simulates the needs of students for assistance with a help-seeking series arranged in the Markov Chain so that the assistance provided to students in a personal manner will be in accordance with the needs so that the process of finding assistance will be more efficient. The task is given to a number of students to complete the questions with hint steps in the help-seeking series. The Markov Chain are used to predict the tendency of the hint type selection to be resolved in a certain order. The results will help the education facilitator to monitor students in completing the items with the help of hints.

Original languageEnglish
Article number012005
JournalJournal of Physics: Conference Series
Issue number1
Publication statusPublished - 6 Sept 2019
Event2nd International Conference on Mechanical, Electronics, Computer, and Industrial Technology, MECnIT 2018 - Medan, North Sumatera, Indonesia
Duration: 12 Dec 201814 Dec 2018


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