TEACHER MODELING UNTUK MENDUKUNG ADAPTIVE LEARNING DALAM PROSES PEMBELAJARAN FACE TO FACE LEARNING ENVIRONMENTS (KASUS: KELAS X BIDANG STUDI KEAHLIAN TIK SMK NEGERI DI KABUPATEN PONOROGO)

Khafidurrohman Agustianto, Adhistya Erna Permanasari, Indriana Hidayah

Abstract


Setiap siswa memiliki kebutuhan dan karakteristik yang berbeda-beda, sebagai contoh prior knowledge, intellectual level, cognitive traits, dan learning styles, sehingga dibutuhkan proses pembelajaran yang sesuai dengan karakteristik siswa yang berupa adaptive learning (AL). AL secara subtantif berarti suatu pembelajaran yang berfokus pada personal siswa, recommendation-based learning, dan inquire-based learning. Dalam melakukan personalised learning approach, maka diperlukan personalisasi pada learning object (LO) (guru, modul dan mata pelajaran). Sehingga sangat penting untuk memodelkan guru dalam konteks AL, karena guru sebagai salah satu LO memiliki peranan penting terutama dalam konsep blendedlearning..


Full Text:

PDF

References


Suprijono, Cooperative Learning Teori dan Aplikasi PAIKEM, vol. 36. Yogyakarta: Pustaka Pelajar, 2012.

E. Gaudioso, M. Montero, and F. Hernandez-del-Olmo, “Supporting teachers in adaptive educational systems through predictive models: A proof of concept,” Expert Syst. Appl., vol. 39, no. 1, pp. 621–625, Jan. 2012.

J. Bersin, The Blended Learning Handbook, vol. 36. New York: Wiley, 2004.

N. H. Campus and F. Shan, “Blended Learning Trategy Design and Practice,” pp. 3003–3006, 2011.

D. Bath and J. Bourke, Blended Learning. Griffith University, 2010.

R. B. Sachin and M. S. Vijay, “A Survey and Future Vision of Data Mining in Educational Field,” 2012 Second Int. Conf. Adv. Comput. Commun. Technol., pp. 96–100, Jan. 2012.

C. Romero and S. Ventura, “Educational Data Mining: A Review of the State of the Art,” IEEE Trans. Syst. Man, Cybern. Part C (Applications Rev., vol. 40, no. 6, pp. 601–618, Nov. 2010.

G.-Z. Liu, N.-W. Wu, and Y.-W. Chen, “Identifying emerging trends for implementing learning technology in special education: a state-of-the-art review of selected articles published in 2008-2012.,” Res. Dev. Disabil., vol. 34, no. 10, pp. 3618–28, Oct. 2013.

E. Kurilovas, I. Zilinskiene, and V. Dagiene, “Recommending suitable learning scenarios according to learners’ preferences: An improved swarm based approach,” Comput. Human Behav., vol. 30, pp. 550–557, Jan. 2014.

A. Guney and S. Al, “Effective Learning Environments in Relation to Different Learning Theories,” Procedia - Soc. Behav. Sci., vol. 46, pp. 2334–2338, Jan. 2012.

O. Akdemir and T. a. Koszalka, “Investigating the relationships among instructional strategies and learning styles in online environments,” Comput. Educ., vol. 50, no. 4, pp. 1451–1461, May 2008.

T. Adeyinka and S. Mutula, “A proposed model for evaluating the success of WebCT course content management system,” Comput. Human Behav., vol. 26, no. 6, pp. 1795–1805, Nov. 2010.

M. Thurlings, M. Vermeulen, T. Bastiaens, and S. Stijnen, “Understanding feedback: A learning theory perspective,” Educ. Res. Rev., vol. 9, pp. 1–15, Jun. 2013.

N. Hoic-bozic, V. Mornar, I. Boticki, and S. Member, “A Blended Learning Approach to Course Design and Implementation,” vol. 52, no. 1, pp. 19–30, 2009.

“Data mining: concepts and techniques,” Choice Rev. Online, vol. 49, no. 06, pp. 49–3305–49–3305, Feb. 2012.


Refbacks

  • There are currently no refbacks.