International Journal of Computer
Trends and Technology

Research Article | Open Access | Download PDF

Volume 53 | Number 1 | Year 2017 | Article Id. IJCTT-V53P105 | DOI : https://doi.org/10.14445/22312803/IJCTT-V53P105

Choice Based Curriculum Design

Krishnendu Bhattacharjee, Aarigin Hazra, Tamasree Biswas, Mousumi Saha

Citation :

Krishnendu Bhattacharjee, Aarigin Hazra, Tamasree Biswas, Mousumi Saha, "Choice Based Curriculum Design," International Journal of Computer Trends and Technology (IJCTT), vol. 53, no. 1, pp. 23-31, 2017. Crossref, https://doi.org/10.14445/22312803/IJCTT-V53P105

Abstract

Data Mining is process of collection of important or relevant data from a pool of data. It is the computational process of discovering patterns in large data sets. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Data mining is the analysis step of the "knowledge discovery in databases" process. Data mining is an important process in our daily life huge amount of data needs to be mined for collecting information. In this paper, we are mainly focusing on the real-life application of data mining. There is a need to allow flexibility in education system, so that students depending upon their interests and aims can choose interdisciplinary, intra-disciplinary and skill-based courses. This can only be possible when choice based credit system (CBCS) is implemented.

Keywords

Choice Based Credit System (CBCS), Choice Based Curriculum Design (CBCD), compulsory subjects, generic elective subjects, calculated weights on subjects.

References

[1] Baker, R., & Yacef, K. (2009). The State of Educational Data mining in 2009: A Review and Future Visions. Journal of Educational Data Mining, 1.
[2] Luan, J. (2002). Data Mining and Knowledge Management in Higher Education- Potential Applications. Paper presented at the Annual Forum for the Association for Institutional Research, Toronto, Ontario, Canada.
[3] Vandamme, J. P., Meskens, N., & Superby, J. F. (2007). Predicting Academic Performance by Data Mining Methods. Education Economics, 15(4), 405-419.
[4] Lin, S.-H. (2012). Data mining for student retention management. J. Comput. Sci. Coll., 27 (4), 92- 99.
[5] Chacon, F., Spicer, D., & Valbuena, A. (2012). Analytics in Support of Student Retention and Success (Research Bulletin 3, 2012ed.). Louisville, CO: Educause Center for Applied Research.
[6] Romero, C., Ventura, S., & García, E. (2008). Data mining in course management systems: Moodle case study and tutorial. Computers & Education, 51(1), 368-384. doi: 10.1016/j.compedu.2007.05.016.
[7] Wang, Y.-h., & Liao, H.-C. (2011). Data mining for adaptive learning in a TESL learning system. Expert Systems with Applications, 38(6), 6480-6485. doi:10.1016/j.eswa.2010.11.098.
[8] Huang, Y.-M., Chen, J.-N., & Cheng, S.-C. (2007). A Method of Cross-Level Frequent Pattern Mining for Web-Based Instruction. Educational Technology & Society, 10(3), 305-319.
[9] Su, J.-m., Tseng, S.-s., Lin, H.-y., & Chen, C.-h. (2011). A personalized learning content adaptation mechanism to meet diverse user needs in mobile learning environments. User Modeling and User - Adapted Interaction, 21(1-2), 5-49.doi: 10.1007/s11257-010-9094-0.
[10] Tamasree Biswas, Mousumi Saha, Soumya Bhattacharyya. An approach to implement Data mining in Services oriented Methodology towards Amelioration of Society. International Journal of Innovations in Engineering and Technology (IJIET). Vol.6 Issue 3 February 2016.