Recognizing Students At-Danger With Early Intervention Using Machine Learning Techniques
Main Article Content
Abstract
Students in online courses require attention as there is no much interaction between the teacher and student compared to traditional instructing methods. Due to the increase in advent of massive open online courses, there is a need to focus on identifying students at danger of withdrawl or failure. As the count of students enrolling in an online course is huge it’s quite difficult to find out specific students who are at-danger of failure/withdrawal from the course. There is a need to alleviate this problem by identifying those students and help academic instructors offer support to them. The major contribution of this work is to analyze the risk associated with the dropout of student in order to aid instructors in delivering the intensive intervention support to student who is at verge of quitting from the course. The main objective is to track student performance and provide valuable information to the educator to subsequent the courses according to their learning achievement and also help academic advisors to detect the student having low academic achievement records and encourage the candidates. Data collected from OULAD datset is analyzed with the help at -risk prediction model is to identify whether a student is at verge of withdrawal or not.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Ragha Al-Shabandari,Abir Jaafar Hussain,Panos Liatsis,Robert Keight, “Detecting At-Risk Students With Early Intervention Using Machine Learning”,October 2019.
Luis M.Romero-Rodriguez,Maria Soledad Ramirez-Montoya, and Jaime Ricardo Valenzuela Gonzalez,“Gamification in MOOCs: Engagement Application Test in Energy Sustainability Courses” ,March 2019.
W.Xing and Ddu, “Dropout prediction in MOOCs for personalized intervention,” J. Educ. Comput. Res., vol. 57, no. 3,pp. 547570, 2018.
O.Zughoul,F.Momani,O.H.Almasri,A.A.Zaidan,B.B.Zaidan ,M.A.Alsalem,O.S.Albahri,and M.Hashim, “Comprehensive insights into the criteria of student performance in various educational domains,” IEEE Access, vol. 6, pp. 73245-73264, 2018.
J.L.Hung,M.C.Wanf,S.Wang,M.Abdelrasul,Y.Li, and W.He, “Ídentifying at-risk students for early interventions A time-series clustering approach”, IEEE Trans. Emerg. Topics Comput., vol. 5, no. 1,pp. 4555, Jan./Mar. 2017.
R.Al-Shabandar,A.Hussain,A.Laws,R.Keight,J.Lunn,N.Radi, “Machine learning approaches to predict learning outcomes in massive open online courses”, in Proc. Int. Joint Conf. Neural Netw. (IJCNN),May 2017, pp. 713-720.
H.B.Sharpio,C.H.Lee,N.E.W.Roth,K.Li,M.Cetinkaya-Rundel,andD.A.Canelas, “Understanding massive open online course(MOOC) student experience :An examination of attitudes,motivations,and barriers,”Comput. Educ., vol. 110, pp. 35-50, Jul. 2017.
J.Sinclair and S.Kalvala, “Student engagement in massive open online courses,”. J. Learn. Technol., vol. 11, no. 3, pp. 218-237,2016.
E.Summary, “Haravard and MITx:Two years of open online courses”, Electron. J., no. 10, pp. 1-37, 2015.
]Uma .N.Dulhare, Udai Kumar Kudikyala, “Using Scrum and Wikis to Manage Student Major
Projects”,2015.
J.Reich,S.Nesterko,D.Seaton,T.Mullaney,J.Waldo,I.Chuang, and A.D.Ho, “PH207x:Health in numbers &PH278x:Human health and global environmental change, ”USA, Tech. Rep., 2014.
Dr. K.Udai Kumar,Dr. Uma N. Dulhare, “Enhancing Engineering Education in an Indian
Education System: WebBlogs”,2014.
M. R. Ghaznavi, A. Keikha, and N.-M. Yaghoubi, “The impact of information and communication technology (ICT) on educational improvement,''Int. Educ. Stud., vol. 4, no. 2, pp. 116-125, 2011.
M.Barak,A.Watted,and H.Haick, “Motivation to learn massive open online courses:Examining aspects of language and social engagement,” Edu., vol. 94, pp. 49-60, Mar. 2016.
C.Geigle and C.Zhai, “Modeling MOOC student behavior with two layer hidden Markov models”in Proc. 4th ACM Conf. Learn. Scale, 2017,pp. 205-208.
Altair , “Improve Retail Store Performance Through In-StoreAnalytics”,2019. Available: https://www.datawatch.com/in-action/usecases/retail-in-store-analytics/
D.S.Chaplot,E.Rhim, and J.Kim, “Predicting student attrition in MOOCs using sentiment analysis and neural networks,” in Proc. 17th Int.Conf. Artif. Intell. Educ., 2015, pp. 7-12.
J.He,J.Bailey,B.I.P.Rubinstein, and R.Zhang, “Identifying at-risk students in massive open online courses,” in Proc. 29th AAAI Conf. Artif.Intell., 2015, pp. 1749-1755.
B.Minaei-Bidgoli,D.A.Kashy,G.Kortemeyer, and W.F.Punch, “Predicting student performance:An application of data mining methods with an educational web-based system,” in Proc. 33rd Annu. Frontiers Educ.(FIE), vol. 1, Dec. 2003, pp. T2A13T2A18.
A.Ho,J.Reich,S.Nesterko,D.Seaton,T.Mullaney,J.Waldo,and I.Chaung, “Harvardx and MITx:The first year of open online courses,fall 2012-summer 2013,” SSRN Electron. J., no. 1, pp. 1-33, 2014.
K.Kuzilek,M.Hlosta,D.Herrmanniva,Z.Zdrahal,and A.Wolff, “OU analyse:Analysing at-risk students at the open university,” inProc. 5th Int. Learn. Anal. Knowl. (LAK), 2015, pp. 1-16.
J.W.D.Seaton,J.Reich,S.Nesterko,and T.Mullaney, “Introduction to Computer Science and Programming MITx on edX-2012 Fall,” York, NY, USA, 2014.
P.F.Mitros,K.K.Afridi,G.J.Sussman,C.J.Terman,J.K.White,L.Fischer, and A.Agarwal, “Teaching electronic circuits online:Lessons from MITx’s 6.002x on edX,” in Proc. IEEE Int. Symp. Circuits Syst.,May 2013, pp. 2763-2766.