Development of Modeling System of Motivation and Critical Thinking Skill of Vocational Student

Nanik Anita Mukhlisoh, Khafidurrohman Agustianto, Bety Etika Sari, Syamsiar Kautsar, Wahyu Kurnia Dewanto


The industrial revolution 4.0 had a significant impact on Indonesia, so Indonesia must prepare for that impact. Preparation begins with the improvement of the quality of competence possessed  by  college  graduates.  Polytechnic  is  a  vocational  high  school  that  aims  to  prepare graduates  with  certain  field  competencies  so  as  to  be  able  to  work  in  the  industrial  world professionally. Learning methods relevant to the competence of these graduates are the learning model  of  Student  Centered  Learning  (SCL)  covering  Problem,  Project,  and  Inquiry  Based Learning.  The  three  models  of  learning  require  direct  involvement  of  students  in  learning activities,  where  the  atmosphere  and  conditions  of  the  learning  environment  that  resemble business and industry. One of the important factors affecting student involvement in learning activities is the factor that comes from within the student itself. This study aims to model the level of motivation and critical thinking skills of students to determine the learning model used. The  modeling  results  with  the  Naive  Bayes  Classifier  show  an  accuracy  of 91.667% and 93.617%. The modeling results are used as a variable with the final class of the corresponding learning model. The final result is expected, the system is able to increase the motivation and critical  thinking  skills  of  the  students  for  the  implementation  of  the better learning  process.

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Y. Riyani, “Faktor-faktor yang Mempengaruhi Prestasi Belajar Mahasiswa,” J. EKSOS, vol. 8, no. 1, pp. 19–25, 2012.

A. Kamaei and M. Weisani, “the Relationship Between Achievement Motivation , Critical Thinking and Creative Thinking With,” Indian J. Fundam. Applik lied Life Sci., vol. 3, no. 4, pp. 121–127, 2013.

P. Li, “The Relationship between Motivation and Achievement,” vol. 2, no. 1, p. 123, 2009.

B. Etikasari and H. Suswanto, “The Relationships of Student Critical and Creative Thinking Skills towards Capability of Installation Skill Local Area Network Competence of Vocational Student Computer and Network Engineering Program,” vol. 030038, 2016.

K. Agustianto, A. E. Permanasari, S. S. Kusumawardani, and I. Hidayah, “Design adaptive learning system using metacognitive strategy path for learning in classroom and intelligent

tutoring systems,” in AIP Conference Proceedings, 2016, vol. 1755.

P. Destarianto, B. Etikasari, and K. Agustianto, “Developing Automatic Student Motivation Modeling System,” J. Phys. Conf. Ser., vol. 953, no. 1, 2018.

W. K. Dewanto, K. Agustianto, and B. E. Sari, “Developing thinking skill system for modelling creative thinking and critical thinking of vocational high school student,” J. Phys. Conf. Ser., vol. 953, no. 1, 2018.

S. Irmalia and Anggraini, “Motivasi Belajar dan Faktor-Faktor yang Berpengaruh: Sebuah Kajian Pada Interaksi Pembelajaran Mahasiswa,” Prem. Educ., vol. 1, no. 02, pp. 100–109, 2016.

A. Sri, “Aplikasi Teori Hierarki Kebutuhan Maslow Dalam Meningkatkan Motivasi Belajar Mahasiswa,” J. Online Unika Widya Mandala Madiun, vol. 01, no. 01, p. 82, 2010.

Schunk, Motivasi dalam Pendidikan Teori, Penelitian, dan Aplikasi. Jakarta: Indeks, 2012.

Sujarwo, “Motivasi Berprestasi sebagai Salah Satu Perhatian dalam Memilih Strategi Pembelajaran,” 2008.

E. M. Mursidik, N. Samsiyah, and H. E. Rudyanto, “Kemampuan berpikir kreatif dalam memecahkan masalah matematika open-ended ditinjau dari tingkat kemampuan matematika pada siswa sekolah dasar,” J. Pedagog., vol. 4, no. 1, pp. 23–33, 2015.

Desmita, Psikologi Perkembangan Peserta Didik. Bandung: Remaja Rosdakarya, 2010.

O. Debdi, M. Paredes-Velasco, and J. A. Velazquez-Iturbide, “Influence of Pedagogic Approaches and Learning Styles on Motivation and Educational Efficiency of Computer

Science Students,” Rev. Iberoam. Tecnol. del Aprendiz., vol. 11, no. 3, pp. 213–218, 2016.

D. Fonseca, X. Canaleta, and A. Climent, “Evaluación de la usabilidad y la satisfacción del estudiante de formación profesional en función de su motivación inicial.”

P. Khongchai and P. Songmuang, “Implement of salary prediction system to improve student motivation using data mining technique,” 2016 11th Int. Conf. Knowledge, Inf. Creat. Support

Syst., pp. 1–6, 2016.

J. N. Purwaningsih and Y. Suwarno, “Predicting students achievement based on motivation in vocational school using data mining approach,” 2016 4th Int. Conf. Inf. Commun. Technol., vol. 4, no. c, pp. 1–5, 2016.

B. L. Shoop, “Developing Critical Thinking, Creativity and Innovation Skills,” Proc. SPIE Int. Soc. Opt. Eng., vol. 9289, pp. 928904–928904, 2014.

J. W. Chang, T.-I. Wang, M.-C. Lee, C.-Y. Su, and P.-C. Chang, “Impact of Using Creative Thinking Skills and Open Data on Programming Design in a Computer-Supported Collaborative Learning Environment,” 2016 IEEE 16th Int. Conf. Adv. Learn. Technol., pp. 396–400, 2016.

L. Y. Tokman and R. Yamacli, “Web (

L. N. S. P. Goteti and G. V. Madhuri, “Assessing soft and higher order thinking skills amongstudents using a rubric and progressive reflection,” Proc. 2013 IEEE Int. Conf. MOOC, Innov.

Technol. Educ. MITE 2013, pp. 332–334, 2013.

H. Li, J. Liu, X. Yang, J. Xiao, and G. Yang, “An Empirical Study on Developing HigherOrder Thinking Skills of Primary Students with E Schoolbag,” 2016 Int. Symp. Educ. Technol., vol. 1, pp. 44–49, 2016.

S. Huang, K. H. Muci-Kuchler, M. D. Bedillion, M. D. Ellingsen, and C. M. Degen, “Systems thinking skills of undergraduate engineering students,” pp. 1–5, 2015.

Z. Deng, W. Huang, R. Dong, and P. Wen, “Exploration of ability development of engineering and computational thinking skills in software engineering majors,” Proc. 2009 4th Int. Conf. Comput. Sci. Educ. ICCSE 2009, pp. 1665–1668, 2009.

P. Guleria and M. Sood, “Predicting Student Placements Using Bayesian Classification,” Third Int. Conf. Image Infonnation Process. Predict., pp. 109–112, 2015.

Z. Liu, “Cox’s proportional hazards model with Lp penalty for biomarker identification and survival prediction,” Proc. - 6th Int. Conf. Mach. Learn. Appl. ICMLA 2007, pp. 624–628,

T. Mahboob, S. Irfan, and A. Karamat, “A machine learning approach for student assessment in E-learning using Quinlan’s C4.5, Naive Bayes and Random Forest algorithms,” Proc. 2016

th Int. Multi-Topic Conf. INMIC 2016, 2017.

K. Maharani, T. B. Adji, N. A. Setiawan, and I. Hidayah, “Comparison Analysis of Data Mining Methodology and Student Performance Improvement Influence Factors in Small Data

Set,” pp. 169–174, 2015.

T. Devasia, Vinushree T P, and V. Hegde, “Prediction of students performance using Educational Data Mining,” 2016 Int. Conf. Data Min. Adv. Comput., pp. 91–95, 2016.

D. Oreški, M. Konecki, and L. Milić, “Estimating profile of successful IT student: Data mining approach,” 2017 40th Int. Conv. Inf. Commun. Technol. Electron. Microelectron. MIPRO 2017 - Proc., pp. 723–727, 2017.

A. Kawano and E. Isogai, “A Model and Evaluation Method of Learning Motivation in the Education and Training of Professional Engineers,” 2016 IEEE Int. Conf. Teaching, Assessment, Learn. Eng., no. December, pp. 311–318, 2016.

F. A. Gunawan, “Fuzzy-Mamdani Inference System in Predicting the Corelation Between Learning Method , Discipline and Motivation with Student ’ s Achievement,” pp. 1–6, 2016.


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