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Course Sequence Recommendation with Course Difficulty Index Using Subset Sum Approximation Algorithms

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1. U. G. C. Guidelines on Adaptation of the Choice Based Credit System. University Grants Commission Bahadurshah Zafar Marg, New Delhi, 110 002.Search in Google Scholar

2. Hasan, M., M. Parvez. Choice-Based Credit System in India: Pros and Cons. – Journal of Education and Practice, Vol. 6, 2015, No 25, pp. 30-33.Search in Google Scholar

3. http://www.vit.ac.in/academics/ffcsSearch in Google Scholar

4. Zafar, S., B. Manjurekar, N. P. Kumar, Z. A. Khan. Effects of FFCS (Fully Flexible Credit System) on Learning Experience and Academic Performance. – Procedia-Social and Behavioral Sciences, Vol. 143, 2014, pp. 4-7.10.1016/j.sbspro.2014.07.347Search in Google Scholar

5. Xu, J., T. Xing, M. Van der Schaar. Personalized Course Sequence Recommendations. – IEEE Transactions on Signal Processing, Vol. 64, October 2016, No 20, pp. 5340-5352.10.1109/TSP.2016.2595495Search in Google Scholar

6. Paura, L., I. Arhipova. Student Dropout Rate in Engineering Education Study Program. – In: Proc. of 15th International Scientific Conference Engineering for Rural Development, Jelgava, Latvia, May 2016, pp. 641-646.Search in Google Scholar

7. Szafran, R. F. The Effect of Academic Load on Success for New College Students: Is Lighter Better? – Research in Higher Education, Vol. 42, 2001, No 1, pp. 27-50.Search in Google Scholar

8. Kori, K., M. Pedaste, H. Altin, E. Tõnisson, T. Palts. Factors that Influence Students’ Motivation to Start and to Continue Studying Information Technology in Estonia. – IEEE Transactions on Education, Vol. 59, 2016, No 4, pp. 255-262.10.1109/TE.2016.2528889Search in Google Scholar

9. Mundfrom, D. J. Estimating Course Difficulty. Ph.D. Dissertation, Statistics, Iowa State Univ., Ames, USA, 1991.Search in Google Scholar

10. Bassiri, D., E. M. Schulz. Constructing a Universal Scale of High School Course Difficulty. – Journal of Educational Measurement, Vol. 40, 2003, No 2, pp. 147-161.10.1111/j.1745-3984.2003.tb01101.xSearch in Google Scholar

11. Banerjee, S., N. J. Rao, C. Ramanathan. Rubrics for Assessment Item Difficulty in Engineering Courses. – In: Proc. of Frontiers in Education Conference (FIE), IEEE, 2015, pp. 1-8.10.1109/FIE.2015.7344299Search in Google Scholar

12. Kaur, K., K. Kaur. Analyzing the Effect of Difficulty Level of a Course on Students Performance Prediction Using Data Mining. – In: Proc. of 1st International Conference on Next Generation Computing Technologies (NGCT), IEEE, September 2015, pp. 756-761.10.1109/NGCT.2015.7375222Search in Google Scholar

13. Liu, J., S. Sha, Q. Zheng, L. Chen. Ranking Difficulty of Knowledge Units Based on Learning Dependency. – In: Proc. of 7th International Conference on e-Business Engineering, IEEE, November 2010, pp. 77-82.10.1109/ICEBE.2010.17Search in Google Scholar

14. Safavi, S. A., K. A. Bakar, R. A. Tarmizi, N. H. Alwi. What Do Higher Education Instructors Consider Useful Regarding Student Ratings of Instruction? Limitations and Recommendations. – Procedia-Social and Behavioral Sciences, Vol. 31, 2012, pp. 653-657.10.1016/j.sbspro.2011.12.119Search in Google Scholar

15. Corelli, A. Direct Vs. Anonymous Feedback: Teacher Behavior in Higher Education, with Focus on Technology Advances. – Procedia-Social and Behavioral Sciences, Vol. 195, 2015, pp. 52-61.10.1016/j.sbspro.2015.06.329Search in Google Scholar

16. Zainudin, S., K. Ahmad, N. M. Ali, N. F. A. Zainal. Determining Course Outcomes Achievement through Examination Difficulty Index Measurement. – Procedia-Social and Behavioral Sciences, Vol. 59, 2012, pp. 270-276.10.1016/j.sbspro.2012.09.275Search in Google Scholar

17. Swart, A. J. Evaluation of Final Examination Papers in Engineering: A Case Study Using Bloom’s Taxonomy. – IEEE Transactions on Education, Vol. 53, 2010, No 2, pp. 257-264.10.1109/TE.2009.2014221Search in Google Scholar

18. Yang, F., F. W. Li, R. W. Lau. A Fine-Grained Outcome-Based Learning Path Model. – IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 44, 2014, No 2, pp. 235-245.10.1109/TSMCC.2013.2263133Search in Google Scholar

19. Pumpuang, P., A. Srivihok, P. Praneetpolgrang, S. Numprasertchai. Using Bayesian Network for Planning Course Registration Model for Undergraduate Students. – In: Proc. of 2nd IEEE International Conference on Digital Ecosystems and Technologies, IEEE, February 2008, pp. 492-496.10.1109/DEST.2008.4635194Search in Google Scholar

20. Pumpuang, P., A. Srivihok, P. Praneetpolgrang. Comparisons of Classifier Algorithms: Bayesian Network, C4. 5, Decision Forest and NBTree for Course Registration Planning Model of Undergraduate Students. – In: Proc. of IEEE International Conference on Systems, Man and Cybernetics, IEEE, October 2008, pp. 3647-3651.10.1109/ICSMC.2008.4811865Search in Google Scholar

21. Wang, X., F. Yuan. Course Recommendation by Improving BM25 to Identity Students’ Different Levels of Interests in Courses. – In: Proc. of 2009 International Conference on New Trends in Information and Service Science, IEEE, June 2009, pp. 1372-1377.10.1109/NISS.2009.104Search in Google Scholar

22. Garrido, A., L. Morales. e-Learning and Intelligent Planning: Improving Content Personalization. – IEEE Revista Iberoamericana de Tecnologias del Aprendizaje, Vol. 9, 2014, No 1, pp. 1-7.10.1109/RITA.2014.2301886Search in Google Scholar

23. Parameswaran, A. G., H. Garcia-Molina, J. D. Ullman. Evaluating, Combining and Generalizing Recommendations with Prerequisites. – In: Proc. of 19th ACM International Conference on Information and Knowledge Management, ACM, October 2010, pp. 919-928.10.1145/1871437.1871555Search in Google Scholar

24. Parameswaran, A., P. Venetis, H. Garcia-Molina. Recommendation Systems with Complex Constraints: A Course Recommendation Perspective. – ACM Transactions on Information Systems (TOIS), Vol. 29, 2011, No 4, Art. No 20.10.1145/2037661.2037665Search in Google Scholar

25. Betancur, L., B. M. Rottman, E. Votruba-Drzal, C. Schunn. Analytical Assessment of Course Sequencing: The Case of Methodological Courses in Psychology. – Journal of Educational Psychology, Vol. 111, 2019, No 1, pp. 91-103.10.1037/edu0000269Search in Google Scholar

26. Chen, C. M., C. Y. Liu, M. H. Chang. Personalized Curriculum Sequencing Utilizing Modified Item Response Theory for Web-Based Instruction. – Expert Systems with Applications, Vol. 30, 2006, No 2, pp. 378-396.10.1016/j.eswa.2005.07.029Search in Google Scholar

27. Bridges, C., J. Jared, J. Weissmann, A. Montanez-Garay, J. Spencer, C. G. Brinton. Course Recommendation as Graphical Analysis. – In: Proc. of 52nd Annual Conference on Information Sciences and Systems (CISS), IEEE, March 2018, pp. 1-6.10.1109/CISS.2018.8362325Search in Google Scholar

28. Morrow, T., A. R. Hurson, S. S. Sarvestani. A Multi-Stage Approach to Personalized Course Selection and Scheduling. – In: Proc. of 2017 IEEE International Conference on Information Reuse and Integration (IRI), San Diego, CA, 2017, pp. 253-262.10.1109/IRI.2017.58Search in Google Scholar

29. Cucuringu, M., C. Z. Marshak, D. Montag, P. Rombach. Rank Aggregation for Course Sequence Discovery. – In: Proc. of International Workshop on Complex Networks and Their Applications, Springer, Cham., November 2017, pp. 139-150.10.1007/978-3-319-72150-7_12Search in Google Scholar

30. Segal, A., Y. B. David, J. J. Williams, K. Gal, Y. Shalom. Combining Difficulty Ranking with Multi-Armed Bandits to Sequence Educational Content. – In: Proc. of International Conference on Artificial Intelligence in Education, Springer, Cham., June 2018, pp. 317-321.10.1007/978-3-319-93846-2_59Search in Google Scholar

31. Gunji, A. B., B. B. B. V. L. Deepak, C. R. Bahubalendruni, D. B. B. Biswal. An Optimal Robotic Assembly Sequence Planning by Assembly Subsets Detection Method Using Teaching Learning-Based Optimization Algorithm. – IEEE Transactions on Automation Science and Engineering, Vol. 15, 2018, No 3, pp. 1369-1385.10.1109/TASE.2018.2791665Search in Google Scholar

32. Caprara, A., H. Kellerer, U. Pferschy. The Multiple Subset Sum Problem. – SIAM Journal on Optimization, Vol. 11, 2000, No 2, pp. 308-319.10.1137/S1052623498348481Search in Google Scholar

33. Caprara, A., H. Kellerer, U. Pferschy. A PTAS for the Multiple Subset Sum Problem with Different Knapsack Capacities. – Information Processing Letters, Vol. 73, 2000, No 3-4, pp. 111-118.10.1016/S0020-0190(00)00010-7Search in Google Scholar

34. Wisneski, J. E., G. Ozogul, B. A. Bichelmeyer. Investigating the Impact of Learning Environments on Undergraduate Students’ Academic Performance in a Prerequisite and Post-Requisite Course Sequence. – The Internet and Higher Education, Vol. 32, 2017, pp. 1-10.10.1016/j.iheduc.2016.08.003Search in Google Scholar

35. Adjei, S. A., A. F. Botelho, N. T. Heffernan. Predicting Student Performance on Post-Requisite Skills Using Prerequisite Skill Data: An Alternative Method for Refining Prerequisite Skill Structures. – In: Proc. of 6th International Conference on Learning Analytics & Knowledge, April 2016, ACM, pp. 469-473.10.1145/2883851.2883867Search in Google Scholar

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