References
- 1Adhatrao, K, Gaykar, A, Dhawan, A, Jha, R and Honrao, V. 2013. Predicting Students’ Performance Using Id3 and C4.5 Classification Algorithms. International Journal of Data Mining and Knowledge Management Process, 3(5): 39–52. DOI: 10.5121/ijdkp.2013.3504
- 2Ameri, S. 2015. Survival Analysis Approach For Early Prediction Of Student Dropout. PhD thesis, Wayne State University.
- 3Ameri, S, Fard, MJ, Chinnam, RB and Reddy, CK. 2016. Survival Analysis Based Framework for Early Prediction of Student Dropouts. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, CIKM, 16: 903–912. New York, NY, USA:
ACM . DOI: 10.1145/2983323.2983351 - 4Arsad, PM, Buniyamin, N and Manan, J-lA. 2013. A neural network students’ performance prediction model (NNSPPM). 2013 IEEE International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA), (
November ): 1–5. - 5Aulck, L, Aras, R, Li, L, Heureux, CL, Lu, P and West, J. 2017. STEM-ming the Tide: Predicting STEM attrition using student transcript data. In: Proceedings of ACM Knowledge Discovery and Data Mining Conference. Nova Scotia, Canada.
- 6Aulck, L, Velagapudi, N, Blumenstock, J and West, J. 2016. Predicting Student Dropout in Higher Education. In: ICML Workshop on #Data4Good: Machine Learning in within the Open Polytechnic of New Zealand, relying Social Good Applications. New York, NY, USA.
- 7Babu, AR. 2015. Comparative Analysis of Cascadeded Multilevel Inverter for Phase Disposition and Phase Shift Carrier PWM for Different Load. Indian Journal of Science and Technology, 8(April): 251–262. DOI: 10.17485/ijst/2015/v8iS7/70151
- 8Bani, MJ and Haji, M. 2017. College Student Retention: When Do We Losing Them? In: Proceedings of the World Congress on Engineering and Computer Science. Tehran, IRAN.
- 9Beck, HP and Davidson, WD. 2016. Establishing an Early Warning System: Predicting Low Grades in College Students from Survey of Academic Orientations … Research in Higher Education, 42(December 2001).
- 10Brundage, A. 2014. The use of early warning systems to promote success for all students.
- 11Center for Digital Technology and Management. 2015.
The Future of Education Trend Report 2015 . Technical report, Munich, Germany. - 12Chen, JF, Hsieh, HN and Do, QH. 2014. Predicting student academic performance: A comparison of two meta-heuristic algorithms inspired by cuckoo birds for training neural networks. Algorithms, 7(4): 538–553. DOI: 10.3390/a7040538
- 13Chen, Y, Chen, Q, Zhao, M, Boyer, S, Veeramachaneni, K and Qu, H. 2017. DropoutSeer: Visualizing learning patterns in Massive Open Online Courses for dropout reasoning and prediction. 2016 IEEE Conference on Visual Analytics Science and Technology, VAST 2016 – Proceedings, 111–120.
- 14Deng, L and Yu, D. 2014. Deep Learning: Methods and Applications. Foundations and Trends® in Signal Processing, 7(3–4): 197–387. DOI: 10.1561/2000000039
- 15Durairaj, M and Vijitha, C. 2014. Educational data mining for prediction of student performance using clustering algorithms. International Journal of Computer Science and Information Technologies (IJCSIT), 5(4): 5987–5991.
- 16Elbadrawy, A, Polyzou, A, Ren, Z, Sweeney, M, Karypis, G and Rangwala, H. 2016. -okay-Predicting Student Performance Using Personalized Analytics. Computer, 49(4): 61–69. DOI: 10.1109/MC.2016.119
- 17Erik, G. 2014. Introduction to Supervised Learning.
- 18Fei, M and Yeung, D-Y. 2015. Temporal Models for Predicting Student Dropout in Massive Open Online Courses. 2015 IEEE International Conference on Data Mining Workshop (ICDMW), 256–263. DOI: 10.1109/ICDMW.2015.174
- 19Gao, T. 2015. Hybrid classification approach of SMOTE and instance selection for imbalanced datasets. PhD thesis, Iowa State University.
- 20Gray, G, McGuinness, C and Owende, P. 2014. An application of classification models to predict learner progression in tertiary education. 2014 4th IEEE International Advance Computing Conference (IACC), 549–554. DOI: 10.1109/IAdCC.2014.6779384
- 21Halland, R, Igel, C and Alstrup, S. 2015. High-School Dropout Prediction Using Machine Learning: A Danish Large-scale Study. ESANN 2015 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, (
April ): 22–24. - 22Hu, Q and Rangwala, H. 2017. Enriching Course-Specific Regression Models with Content Features for Grade Prediction. In: Proceedings of ACM SIGKDD. Nova Scotia, Canada. DOI: 10.1109/DSAA.2017.74
- 23Hung, JL, Wang, MC, Wang, S, Abdelrasoul, M, Li, Y and He, W. 2017. Identifying At-Risk Students for Early Interventions – A Time-Series Clustering Approach. IEEE Transactions on Emerging Topics in Computing, 5(1): 45–55. DOI: 10.1109/TETC.2015.2504239
- 24Iam-On, N and Boongoen, T. 2017. Generating descriptive model for student dropout: A review of clustering approach. Human-centric Computing and Information Sciences, 7(1): 1. DOI: 10.1186/s13673-016-0083-0
- 25Iqbal, Z, Qadir, J, Mian, AN and Kamiran, F. 2017. Machine Learning Based Student Grade Prediction: A Case Study.
- 26Jordan, MI and Mitchell, TM. 2015. Machine learning: Trends, perspectives, and prospects. Science, 349(6245): 255–260. DOI: 10.1126/science.aaa8415
- 27Joseph, HR. 2014. Promoting education: A state of the art machine learning framework for feedback and monitoring E-Learning impact. 2014 IEEE Global Humanitarian Technology Conference – South Asia Satellite, GHTC-SAS 2014, 251–254. DOI: 10.1109/GHTC-SAS.2014.6967592
- 28Kartal, OO. 2015. Using Survival Analysis to Investigate the Persistence of Students in an Introductory Information Technology Course at Metu. PhD thesis, The Middle East Technical University.
- 29Kotsiantis, SB. 2012. Use of machine learning techniques for educational proposes: A decision support system for forecasting students’ grades. Artificial Intelligence Review, 37(4): 331–344. DOI: 10.1007/s10462-011-9234-x
- 30Kumar, M, Singh, AJ and Handa, D. 2017. Literature Survey on Educational Dropout Prediction. I.J. Education and Management Engineering, 2(March): 8–19. DOI: 10.5815/ijeme.2017.02.02
- 31Lakkaraju, H, Aguiar, E, Shan, C, Miller, D, Bhanpuri, N, Ghani, R and Addison, KL. 2015. A Machine Learning Framework to Identify Students at Risk of Adverse Academic Outcomes. KDD, 1909–1918. DOI: 10.1145/2783258.2788620
- 32Lan, AS, Studer, C and Baraniuk, RG. 2014.
Time-varying Learning and Content Analytics via Sparse Factor Analysis . In: KDD’14 ACM. New York, USA. DOI: 10.1145/2623330.2623631 - 33Latif, A, Choudhary, AI and Hammayun, AA. 2015. Economic Effects of Student Dropouts: A Comparative Study. Journal of Global Economics, 03(02): 2–5.
- 34Lee, K. 2017. Large-Scale and Interpretable Collaborative Filtering for Educational Data.
- 35Lei, C and Li, KF. 2015. Academic Performance Predictors. In: Proceedings – IEEE 29th International Conference on Advanced Information Networking and Applications Workshops, WAINA 2015. DOI: 10.1109/WAINA.2015.114
- 36Li, Y, Wang, J, Ye, J and Reddy, CK. 2016. A Multi-Task Learning Formulation for Survival Analysis. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – KDD’16, 1715–1724. DOI: 10.1145/2939672.2939857
- 37Liang, J, Li, C and Zheng, L. 2016. Machine learning application in MOOCs: Dropout prediction. ICCSE 2016 – 11th International Conference on Computer Science and Education (ICCSE): 52–57.
- 38Lin, WJ and Chen, JJ. 2013. Class-imbalanced classifiers for high-dimensional data. Briefings in Bioinformatics, 14(1): 13–26. DOI: 10.1093/bib/bbs006
- 39Longadge, R, Dongre, SS and Malik, L. 2013. Class imbalance problem in data mining: Review. International Journal of Computer Science and Network, 2(1): 83–87.
- 40López, V, Fernández, A, García, S, Palade, V and Herrera, F. 2013. An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics. Information Sciences, 250: 113–141. DOI: 10.1016/j.ins.2013.07.007
- 41Mgala, M. 2016. Investigating Prediction Modelling of Academic Performance for Students in Rural Schools in Kenya. PhD thesis, University of Cape Town.
- 42Mgala, M and Mbogho, A. 2015. Data-driven Intervention-level Prediction Modeling for Academic Performance. Proceedings of the Seventh International Conference on Information and Communication Technologies and Development, 2: 1–8. DOI: 10.1145/2737856.2738012
- 43Młynarska, E, Greene, D and Cunningham, P. 2016. Time series clustering of Moodle activity data. CEUR Workshop Proceedings, 1751: 104–115.
- 44Mosha, D. 2014. Assessment of Factors behind Dropout in Secondary Schools in Tanzania. A Case of Meru District in Tanzania. PhD thesis, Open University of Tanzania.
- 45Mun, S, Shin, M, Shon, S, Kim, W, Han, D and Ko, H. 2017. DNN transfer learning based non-linear feature extraction for acoustic event classification. IEICE Transactions on Information and Systems, E100D(9): 1–4. DOI: 10.1587/transinf.2017EDL8048
- 46Natek, S and Zwilling, M. 2014. Expert Systems with Applications Student data mining solution knowledge management system related to higher education institutions. Expert Systems with Applications, 41: 6400–6407. DOI: 10.1016/j.eswa.2014.04.024
- 47Nunn, S, Avella, JT, Kanai, T and Kebritchi, M. 2016. Learning Analytics Methods, Benefits, and Challenges in Higher Education: A Systematic Literature Review. Online Learning, 20(2): 13–29. DOI: 10.24059/olj.v20i2.790
- 48Patron, R. 2014.
Early school dropouts in developing countries: An equity issue? The Uruguayan case . University of Uruguay, P13. - 49Pernkopf, F, Peharz, R and Tschiatschek, S. 2013. Introduction to Probabilistic Graphical Models Introduction. Graz, Austria.
- 50Poh, N and Smythe, I. 2015. To what extend can we predict students’ performance? A case study in colleges in South Africa. IEEE SSCI 2014–2014 IEEE Symposium Series on Computational Intelligence – CIDM 2014: 2014 IEEE Symposium on Computational Intelligence and Data Mining, Proceedings, 416–421.
- 51President’s Office and Government, Regional Administration and Local. 2016. Pre-Primary, Primary and Secondary Education Statistics in Brief 2016 The United Republic of Tanzania President’s Office Regional Administration and Local Government. Technical report.
- 52Prieto, LP, Rodríguez-Triana, MJ, Kusmin, M and Laanpere, M. 2017. Smart school multimodal dataset and challenges. CEUR Workshop Proceedings, 1828: 53–59.
- 53Rakesh, A, Christoforaki, M, Gollapudi, S, Kannan, A, Kenthapad, K and Swaminathan, A. 2014. Mining Videos from the Web for Electronic Textbooks. Microsoft Research.
- 54Ramachandra, V and Way, K. 2018. Deep Learning for Causal Inference.
- 55Rovira, S, Puertas, E and Igual, L. 2017. Data-driven system to predict academic grades and dropout. PLOS ONE, 12(2): 1–21. DOI: 10.1371/journal.pone.0171207
- 56Sales, A, Balby, L and Cajueiro, A. 2016. Exploiting Academic Records for Predicting Student Drop Out: a case study in Brazilian higher education. Journal of Information and Data Management, 7(2): 166–180.
- 57Santana, MA, Costa, EB, Neto, BFS, Silva, ICL and Rego, JBA. 2015. A predictive model for identifying students with dropout profiles in online courses. CEUR Workshop Proceedings, 1446.
- 58Sathya, R and Abraham, A. 2013. Comparison of Supervised and Unsupervised Learning Algorithms for Pattern Classification. International Journal of Advanced Research in Artificial Intelligence, 2(2): 34–38. DOI: 10.14569/IJARAI.2013.020206
- 59Shahidul, SM and Karim, AHMZ. 2015. Factors contributing to school dropout among the girls: a review of literature. European Journal of Research and Reflection in Educational Sciences, 3(2): 25–36.
- 60Shahiri, AM, Husain, W and Rashid, NA. 2015. A Review on Predicting Student’s Performance Using Data Mining Techniques. Procedia Computer Science, 72: 414–422. DOI: 10.1016/j.procs.2015.12.157
- 61TAMISEMI. 2004. The United Republic of Tanzania Ministry of Education and Culture. 2004–2009.
- 62Thammasiri, D, Delen, D, Meesad, P and Kasap, N. 2014. A critical assessment of imbalanced class distribution problem: The case of predicting freshmen student attrition. Expert Systems with Applications, 41(2): 321–330. DOI: 10.1016/j.eswa.2013.07.046
- 63UNESCO. 2011. UNESCO Global Partnership for Girls’ and Women’s Education- One Year On.
- 64US Department of Education. 2016. Definition of Early Warning Systems Research on Early Warning Systems Issue Brief: Early Warning Systems. Technical Report September.
- 65Wang, P, Li, Y and Reddy, CK. 2017a. Machine Learning for Survival Analysis: A Survey. ACM Comput. Surv. Article, 1(1): 38.
- 66Wang, W, Yu, H and Miao, C. 2017b. Deep Model for Dropout Prediction in MOOCs. Proceedings of the 2nd International Conference on Crowd Science and Engineering – ICCSE’17, 26–32. DOI: 10.1145/3126973.3126990
- 67Waters, AE, Studer, C and Baraniuk, RG. 2014. Sparse Factor Analysis for Learning and Content Analytics. Journal of Machine Learning Research, 15: 1959–2008.
- 68Xu, J, Moon, KH and van der Schaar, M. 2017. A Machine Learning Approach for Tracking and Predicting Student Performance in Degree Programs. IEEE Journal of Selected Topics in Signal Processing, 11(5): 742–753. DOI: 10.1109/JSTSP.2017.2692560
- 69Yang, D, Piergallini, M, Howley, I and Rose, C. 2014. Forum Thread Recommendation for Massive Open Online Courses. Proceedings of the 7th International Conference on Educational Data Mining (EDM), 257–260.
- 70Yudelson, MV, Koedinger, KR and Gordon, GJ. 2013. Individualized Bayesian Knowledge Tracing Models.
