ABSTRACT
One of the key promises of Learning Analytics research is to create tools that could help educational institutions to gain a better insight of the inner workings of their programs, in order to tune or correct them. This work presents a set of simple techniques that applied to readily available historical academic data could provide such insights. The techniques described are real course difficulty estimation, dependance estimation, curriculum coherence, dropout paths and load/performance graph. The description of these techniques is accompanied by its application to real academic data from a Computer Science program. The results of the analysis are used to obtain recommendations for curriculum re-design.
- L. Albert. Curriculum design: Finding a balance. Journal of rheumatology, 34(3):458--459, 2007.Google Scholar
- I. Archambault, M. Janosz, J.-S. Fallu, and L. S. Pagani. Student engagement and its relationship with early high school dropout. Journal of adolescence, 32(3):651--670, 2009.Google ScholarCross Ref
- V. V. Busato, F. J. Prins, J. J. Elshout, and C. Hamaker. Intellectual ability, learning style, personality, achievement motivation and academic success of psychology students in higher education. Personality and Individual Differences, 29(6):1057--1068, 2000.Google ScholarCross Ref
- J. P. Campbell, P. B. DeBlois, and D. G. Oblinger. Academic analytics: A new tool for a new era. Educause Review, 42(4):40, 2007.Google Scholar
- J. P. Caulkins, P. D. Larkey, and J. Wei. Adjusting gpa to reflect course difficulty. 1996.Google Scholar
- B. B. de Koning, S. M. Loyens, R. M. Rikers, G. Smeets, and H. T. van der Molen. Generation psy: Student characteristics and academic achievement in a three-year problem-based learning bachelor program. Learning and Individual Differences, 22(3):313--323, 2012.Google ScholarCross Ref
- J. W. Denton, V. Franke, and K. N. Surendra. Curriculum and course design: a new approach using quality function deployment. Journal of Education for Business, 81(2):111--117, 2005.Google ScholarCross Ref
- M. Ester, H.-P. Kriegel, and M. Schubert. Web site mining: a new way to spot competitors, customers and suppliers in the world wide web. In Proceedings of the eighth ACM SIGKDD international conference, pages 249--258. ACM, 2002. Google ScholarDigital Library
- R. Ferguson. Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5):304--317, 2012. Google ScholarDigital Library
- A. A. for the Advancement of Science. Designs for Science Literacy. Oxford University Press, Mar. 2001.Google Scholar
- W. J. Jordan, J. Lara, and J. M. McPartland. Exploring the causes of early dropout among race-ethnic and gender groups. Youth & Society, 28(1):62--94, 1996.Google ScholarCross Ref
- R. B. McNeal Jr. Extracurricular activities and high school dropouts. Sociology of education, pages 62--80, 1995.Google Scholar
- S. Parthasarathy, M. J. Zaki, M. Ogihara, and S. Dwarkadas. Incremental and interactive sequence mining. In Proceedings of the eighth international conference on Information and knowledge management, pages 251--258. ACM, 1999. Google ScholarDigital Library
- J. Pei, J. Han, B. Mortazavi-Asl, and H. Zhu. Mining access patterns efficiently from web logs. In Knowledge Discovery and Data Mining. Current Issues and New Applications, pages 396--407. Springer, 2000. Google ScholarDigital Library
- P. Pukkila, J. DeCosmo, D. C. Swick, and M. Arnold. How to engage in collaborative curriculum design to foster undergraduate inquiry and research in all disciplines. Developing and Sustaining a Research-Supportive Curriculum: A Compendium of Successful Practices, pages 341--357, 2007.Google Scholar
- R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2013.Google Scholar
- R. W. Rumberger. Dropping out of high school: The influence of race, sex, and family background. American Educational Research Journal, 20(2):199--220, 1983.Google ScholarCross Ref
- G. Siemens and P. Long. Penetrating the fog: Analytics in learning and education. Educause Review, 46(5):30--32, 2011.Google Scholar
- K. Singh, M. Granville, and S. Dika. Mathematics and science achievement: Effects of motivation, interest, and academic engagement. The Journal of Educational Research, 95(6):323--332, 2002.Google ScholarCross Ref
- J. A. Sunderman. Curriculum Incubation: Data-driven Innovative Instructional Design. In ASEE Annual Conference, 2012.Google Scholar
- B. G. Tabachnick and L. Fidell. Using Multivariate Statistics: International Edition. Pearson, 2012.Google ScholarDigital Library
- F. S. Turner, D. R. Clutterbuck, C. A. Semple, et al. Pocus: mining genomic sequence annotation to predict disease genes. Genome biology, 4(11):R75--R75, 2003.Google ScholarCross Ref
- P. Wolf. A model for facilitating curriculum development in higher education: A faculty-driven, data-informed, and educational developer--supported approach. New Directions for Teaching and Learning, 2007(112):15--20, 2007.Google ScholarCross Ref
- A. Wolff, Z. Zdrahal, A. Nikolov, and M. Pantucek. Improving retention: predicting at-risk students by analysing clicking behaviour in a virtual learning environment. In Proceedings of the Third International Conference on Learning Analytics and Knowledge, pages 145--149. ACM, 2013. Google ScholarDigital Library
- M. J. Zaki. Spade: An efficient algorithm for mining frequent sequences. Machine learning, 42(1--2):31--60, 2001. Google ScholarDigital Library
Index Terms
- Techniques for data-driven curriculum analysis
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