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Personalized Learning in a Virtual Learning Environment Using Modification of Objective Distance

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Abstract

For a virtual learning environment (VLE), assessing student’s competency is important because the proper feedback for students to achieve their goals is re-quired differently. Therefore, it is necessary to have an appropriate measurement for evaluating student's competency so that personalized support can be provided. This study proposes the attribute to determine an individual's competency by modifying the Objective Distance, which is the distance from the current status of a student's competency towards a satisfied competency. In this study, the Objective Distance is modified based on the assumption that the entire course's achievement is obtained from different learning objects' priorities. The proposed attribute was studied to determine to what extent it would represent diverse learners' competencies. The study was done with 55 students in an online learning course through the VLE. The students could choose learning objects freely. The learning enhancement was done through the analysis of pre-test and post-test for all learning objects. The classification results with K-Nearest Neighbor and Artificial Neural Network show that both the original and the modified Objective Distance are effectively used to assess the individual's competency. Overall, the modified Objective Distance performs better than the original one.

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References

  1. Cristea, A. I., & De Mooij, A. (2003). Adaptive course authoring: My online teacher. In 10th International Conference on Telecommunications, 2003. ICT 2003 (Vol. 2, pp. 1762–1769). IEEE.

  2. Hong, C. M., Chen, C. M., Chang, M. H., & Chen, S. C. (2007). Intelligent web-based tutoring system with personalized learning path guidance. In Seventh IEEE international conference on advanced learning technologies, 2007. ICALT 2007 (pp. 512–516). IEEE.

  3. Huang, M., Huang, H., & Chen, M. (2007). Constructing a personalized e-learning system based on genetic algorithm and case-based reasoning approach. Expert Systems with Applications, 33(3), 551–564.

    Article  Google Scholar 

  4. Bai, S. M., & Chen, S. M. (2008). Automatically constructing concept maps based on fuzzy rules for adapting learning systems. Expert Systems with Applications, 35(1–2), 41–49.

    Article  Google Scholar 

  5. Chen, C. M., & Chung, C. J. (2008). Personalized mobile English vocabulary learning system based on item response theory and learning memory cycle. Computers & Education, 51(2), 624–645.

    Article  MathSciNet  Google Scholar 

  6. Bhaskar, M., Das, M. M., Chithralekha, T., & Sivasatya, S. (2010). Genetic algorithm based adaptive learning scheme generation for context aware e-learning. International Journal on Computer Science and Engineering, 2(4), 1271–1279.

    Google Scholar 

  7. Chu, C. P., Chang, Y. C., & Tsai, C. C. (2011). PC 2 PSO: personalized e-course composition based on Particle Swarm Optimization. Applied Intelligence, 34(1), 141–154.

    Article  Google Scholar 

  8. Alshalabi, I. A., Hamada, S., & Elleithy, K. (2015). Automated adaptive learning using smart shortest path algorithm for course units. In Systems, applications and technology conference (LISAT), 2015 IEEE Long Island (pp. 1–5). IEEE.

  9. DwiC, A., & Basuki, A. (2012). Personalized learning path of a web-based learning system. International Journal of Computer Applications, 53(7), 17–22.

    Article  Google Scholar 

  10. Brusilovsky, P., & Henze, N. (2007). Open corpus adaptive educational hypermedia. In The adaptive web (pp. 671–696). Berlin: Springer.

  11. Hwang, G. J., Sung, H. Y., Hung, C. M., & Huang, I. (2013). A learning style perspective to investigate the necessity of developing adaptive learning systems. Educational Technology & Society, 16(2), 188–197.

    Google Scholar 

  12. Chenoweth, T., Corral, K., & Scott, K. (2016). Automated feedback as a convergence tool. Journal of Information Systems Education, 27(1), 7.

    Google Scholar 

  13. Dietz-Uhler, B., & Hurn, J. E. (2013). Using learning analytics to predict (and improve) student success: A faculty perspective. Journal of Interactive Online Learning, 12(1), 17–26.

    Google Scholar 

  14. Lee, I. (2008). Student reactions to teacher feedback in two Hong Kong secondary classrooms. Journal of Second Language Writing, 17(3), 144–164.

    Article  Google Scholar 

  15. Robinson, C. C., & Hullinger, H. (2008). New benchmarks in higher education: Student engagement in online learning. Journal of Education for Business, 84(2), 101–109.

    Article  Google Scholar 

  16. Malgorzata, S. Z., & Waalen, J. K. (2002). The effect of individual learning styles on student outcomes in technology-enabled education. Global Journal of Engineering Education, 6(1), 35–43.

    Google Scholar 

  17. Tomlinson, C. A. (2014). The differentiated classroom: Responding to the needs of all learners. Alexandria: ASCD.

    Google Scholar 

  18. Vargas-Vera, M., & Lytras, M. (2008). Personalized learning using ontologies and semantic web technologies. In World summit on knowledge society (pp. 177–186). Berlin: Springer.

  19. Brusilovsky, P., & Vassileva, J. (2003). Course sequencing techniques for large-scale web-based education. International Journal of Continuing Engineering Education and Life Long Learning, 13(1–2), 75–94.

    Article  Google Scholar 

  20. Chaichumpa, S., Wicha, S. & Temdee, P. (2016). Personalized learning in a virtual learning environment using classification of objective distance. In Global wireless summit. November 27–30, 2016, Aarhus University, Aarhus, Denmark (pp. 411–414).

  21. Chaichumpa, S., & Temdee, P. (2018). Assessment of student competency for personalized online learning using objective distance. International Journal of Innovation and Learning, 23(1), 19–36.

    Article  Google Scholar 

  22. Chaichumpa, S., & Temdee, P. (2018). Multi-agents platform for mobile learning using objective distance based personalisation method. International Journal of Mobile Learning and Organization, 12(3), 293–310.

    Article  Google Scholar 

  23. Kumar, A., Pakala, R., Ragade, R. K., & Wong, J. P. (1998). The virtual learning environment system. In Frontiers in education conference, 1998. FIE'98. 28th annual (Vol. 2, pp. 711–716). IEEE.

  24. Clements, M., & Smalley, M. (2000). Opportunities to enhance the student learning experience using a virtual learning environment. In P. Davies, S. Hodkinson, et al. (Eds.), Innovative approaches to learning and teaching in economics and business higher education, pp. 77–98.

  25. Tucker, A. (2003). A model curriculum for K-12 computer science: Final report of the ACM K-12 task force curriculum committee.

  26. Brusilovsky, P., Eklund, J., & Schwarz, E. (1998). Web-based education for all: a tool for development adaptive courseware. Computer networks and ISDN systems, 30(1–7), 291–300.

    Article  Google Scholar 

  27. Murray, T., Blessing, S., & Ainsworth, S. (Eds.). (2003). Authoring tools for advanced technology learning environments: Toward cost-effective adaptive, interactive and intelligent educational software. Berlin: Springer.

    Google Scholar 

  28. Sangineto, E. (2008). An adaptive e-learning platform for personalized course generation. Architecture Solutions for E-Learning Systems, 262–281.

  29. Gauch, S., Speretta, M., Chandramouli, A., & Micarelli, A. (2007). User profiles for personalized information access. In The adaptive web (pp. 54–89). Springer, Berlin

  30. Yarandi, M., Jahankhani, H., & Tawil, A. R. H. (2013). A personalized adaptive e-learning approach based on semantic web technology. Webology, 10(2), 1.

    Google Scholar 

  31. Chen, C. M., Lee, H. M., & Chen, Y. H. (2005). Personalized e-learning system using item response theory. Computers & Education, 44(3), 237–255.

    Article  Google Scholar 

  32. Brown, G. A., Bull, J., & Pendlebury, M. (2013). Assessing student learning in higher education. Abingdon: Routledge.

    Book  Google Scholar 

  33. Boud, D., & Brew, A. (1995). Developing a typology for learner self assessment practices. Research and development in Higher Education, 18(1), 130–135.

    Google Scholar 

  34. Kovacic, Z. & Green, J. S. (2010). Automated assessment of advanced office skills. The open polytechnic of New Zealand, Private Bag 31914, Lower Hutt, New Zealand.

  35. Baradwaj, B. K. and Pal, S. (2011). Mining educational data to analyze students’ performance. International Journal of Advanced Computer Science and Applications (IJACSA), 2(6).

  36. Liu, J. (2013). The assessment agent system: Design, development, and evaluation. Educational Technology Research and Development, 61(2), 197–215.

    Article  Google Scholar 

  37. Temdee, P. (2014). Ubiquitous learning environment: Smart learning platform with multi-agent architecture. Wireless Personal Communications, 76(3), 627–641.

    Article  Google Scholar 

  38. Hardgrave, B. C., Wilson, R. L., & Walstrom, K. A. (1994). Predicting graduate student success: A comparison of neural networks and traditional techniques. Computers & Operations Research, 21(3), 249–263.

    Article  Google Scholar 

  39. Chen, L. H. (2011). Enhancement of student learning performance using personalized diagnosis and remedial learning system. Computers & Education, 56(1), 289–299.

    Article  Google Scholar 

  40. Shahiri, A. M., & Husain, W. (2015). A review on predicting student’s performance using data mining techniques. Procedia Computer Science, 72, 414–422.

    Article  Google Scholar 

  41. Mayilvaganan, M., & Kalpanadevi, D. (2014). Comparison of classification techniques for predicting the performance of students academic environment. In 2014 international conference on communication and network technologies (ICCNT) (pp. 113–118). IEEE.

  42. Tuparova, D., & Tuparov, G. (2010). Automated real-live performance-based assessment of ICT skills. Procedia-Social and Behavioral Sciences, 2(2), 4747–4751.

    Article  Google Scholar 

  43. Ballantine, J. A., Larres, P. M., & Oyelere, P. (2007). Computer usage and the validity of self-assessed computer competence among first-year business students. Computers & Education, 49(4), 976–990.

    Article  Google Scholar 

  44. Temdee, P., & Intayoad, W. (2014). Discovering and analyzing learning pattern on web based learning using social network analysis. In Signal and information processing association annual summit and conference (APSIPA), 2014 Asia-Pacific (pp. 1–4). IEEE.

  45. MacLeod, J. E., Luk, A., & Titterington, D. M. (1987). A re-examination of the distance-weighted k-nearest neighbor classification rule. IEEE Transactions on Systems, Man, and Cybernetics, 17(4), 689–696.

    Article  Google Scholar 

  46. Lykourentzou, I., Giannoukos, I., Mpardis, G., Nikolopoulos, V., & Loumos, V. (2009). Early and dynamic student achievement prediction in e-learning courses using neural networks. Journal of the Association for Information Science and Technology, 60(2), 372–380.

    Google Scholar 

  47. Dasarasthy, B. V. (1991). Nearest neighbor pattern classification techniques. IEEE Computer Society Press.

  48. Shakhnarovich, G., Darrell, T., & Indyk, P. (2008). Nearest-neighbor methods in learning and vision. IEEE Transactions on Neural Networks, 19(2), 377.

    Article  Google Scholar 

  49. Tanner, T., & Toivonen, H. (2010). Predicting and preventing student failure–using the k-nearest neighbour method to predict student performance in an online course environment. International Journal of Learning Technology, 5(4), 356–377.

    Article  Google Scholar 

  50. Minaei-Bidgoli, B., Kashy, D. A., Kortemeyer, G., & Punch, W. F. (2003, November). Predicting student performance: an application of data mining methods with an educational web-based system. In Frontiers in education, 2003. FIE 2003 33rd annual (Vol. 1, pp. T2A-13). IEEE.

  51. Kubat, M. (1999). Neural networks: a comprehensive foundation by Simon Haykin, Macmillan, 1994. The Knowledge Engineering Review, 13(4), 409–412.

    Article  Google Scholar 

  52. Yukselturk, E., Ozekes, S., & Türel, Y. K. (2014). Predicting dropout student: an application of data mining methods in an online education program. European Journal of Open, Distance and E-learning, 17(1), 118–133.

    Article  Google Scholar 

  53. Zhang, G. P. (2000). Neural networks for classification: a survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 30(4), 451–462.

    Article  Google Scholar 

  54. Paliwal, M., & Kumar, U. A. (2009). A study of academic performance of business school graduates using neural network and statistical techniques. Expert Systems with Applications, 36(4), 7865–7872.

    Article  Google Scholar 

  55. Rashid, T. A., & Aziz, N. K. (2016). Student academic performance using artificial intelligence. ZANCO Journal of Pure and Applied Sciences, 28(2).

  56. Oladokun, V. O., Adebanjo, A. T., & Charles-Owaba, O. E. (2008). Predicting students’ academic performance using artificial neural network: A case study of an engineering course. The Pacific Journal of Science and Technology, 9(1), 72–79.

    Google Scholar 

  57. ICDL/ECDL. ECDL (European Computer Driving Licence) and ICDL (International Computer Driving Licence). http://ecdl.org/ [Accessed February 2018 2015].

  58. Patankar, B., & Chavda, V. (2014). A comparative study of decision tree, Naive Bayesian and KNN classifier in data mining. International Journal of Advanced Research in Computer Science and Software Engineering, 4(12), 776–779.

    Google Scholar 

  59. Brame, M. (2007). Principles of data mining (pp. 173–176). Berlin: Springer.

    Google Scholar 

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Authors and Affiliations

  1. Digital Business Technology Program, Chiangrai Vocational College, Chiang Rai, Thailand

    Sataworn Chaichumpa

  2. Computer and Communication Engineering for Capacity Building Research Center, School of Information Technology, Mae Fah Luang University, Chiang Rai, Thailand

    Santichai Wicha & Punnarumol Temdee

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  1. Sataworn Chaichumpa
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Correspondence to Punnarumol Temdee.

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Chaichumpa, S., Wicha, S. & Temdee, P. Personalized Learning in a Virtual Learning Environment Using Modification of Objective Distance. Wireless Pers Commun 118, 2055–2072 (2021). https://doi.org/10.1007/s11277-021-08126-7

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