References: [1] Mirja, S., Singh, S. P. (2014). Effectiveness of Student Support Services Provided by Indira Gandhi National Open University (IGNOU). Mediterranean Journal of Social Sciences, 5(26) 124-132. [2] Hughes, S. (2016). Student Support in Higher Education 2016/17: a guide for constituents. Cardiff, CF: National Assembly for Wales.Retrieved from https://dera.ioe.ac.uk/26546/2/faq16-007-web-english_Redacted.pdf. [3] Chuah, P., Lim, P. (2018). Applying quality tools to improve student retention supporting process: a case study from WOU. Asian Association of Open Universities Journal,13(1) 60-72. [4] APQC. (2019). Education Process Classification Framework V.7.2.1. Retrieved from https://www.apqc.org/knowledgebase/ download/384560/Education_v721_021019.pdf [5] Huddleston, Jr, T. (2000). Enrollment management. New Directions for Higher Education, 2000(111) 65-73. [6] Clark, M., Fine, M. B., Scheuer, C. L. (2017). Relationship quality in higher education marketing: the role of social media engagement. Journal of Marketing for Higher Education, 27(1) 40-58. [7] Van Barneveld, A., Arnold, K. E., Campbell, J. P. (2012). Analytics in higher education: Establishing a common language. EDUCAUSE learning initiative, 1(1) 1-11. [8] Bichsel, J. (2012). Analytics in Higher Education: Benefits, Barriers, Progress, and Recommendations (Research Report). Louisville, CO: EDUCAUSE Centre for Applied Research. Retrieved from https://www.researchgate.net/publication/ 281111069_Analytics_in_Higher_Education_Benefits_Barriers_Progress_and_Recommendations. [9] de Freitas, S., Gibson, D., Du Plessis, C., Halloran, P., Williams, E., Ambrose, M., Arnab, S. (2015). Foundations of dynamic learning analytics: Using university student data to increase retention. British Journal of Educational Technology, 46(6) 1175- 1188. [10] Hu, Y. H., Lo, C. L., Shih, S. P. (2014). Developing early warning systems to predict students’ online learning performance. Computers in Human Behavior, 36, 469-478. [11] Yadav, S. K., Bharadwaj, B., Pal, S. (2012). Data mining applications: A comparative study for predicting student’s performance. International Journal of Innovative Technology and Creative Engineering, 1(12) 13-19. [12] Bloom, B. S. (1968). Learning for Mastery. UCLA- CSEIP evaluation Comment. 1 (2) 1-12. |