Analytics Dashboard on Talent search Examination Data using Structure of Intellect Model

##plugins.themes.academic_pro.article.main##

VYANKAT VISHNUPANT MUNDE
BINOD KUMAR
ANAGHA VAIDYA
SHAILAJA SHIRWAIKAR

Abstract

The potential of Analytics and Data mining methodologies, that extract useful and actionable information from large data-sets, has transformed one field of scientific inquiry after another. Analytics has been widely applied in Business Organizations as Business Analytics and when applied to education, these methodologies are referred to as Learning Analytics and Educational Data mining. Learning Analytics proposes to collect, measure and analyze data in learning environments to improve teaching and learning process. Educational Data mining (EDM) thrives on existing data collected by learning management systems. The applicability of Learning Analytics and Educational Data mining can be extended to traditional learning processes by suitably combining data collected from technology enabled processes such as Admission and Assessment with data generated from analysis of learning interactions. The intellectual performance of the students can be analyzed using some well known Learning Frameworks. This paper demonstrates the Complete Analytics process from data collection, measurement to Analysis using Guilford’s structure of intellect model. An analytic dashboard provides the necessary information in concise and visual form and in an interactive mode. The analytic process presented on talent examination data can be generalized to similar examinations in traditional educational setup.

##plugins.themes.academic_pro.article.details##

How to Cite
VYANKAT VISHNUPANT MUNDE, BINOD KUMAR, ANAGHA VAIDYA, & SHAILAJA SHIRWAIKAR. (2021). Analytics Dashboard on Talent search Examination Data using Structure of Intellect Model. International Journal of Next-Generation Computing, 12(2), 169–180. https://doi.org/10.47164/ijngc.v12i2.197

References

  1. Avella, J. T., Kebritchi, M., Nunn, S. G., and Kanai, T. 2016. Learning analyticsmethods, benefits, and challenges in higher education: A systematic literature review.Online Learning 20, 2, 13–29.
  2. Bakharia, A., Corrin, L., De Barba, P., Kennedy, G., Ga?sevic, D. ´ , Mulder, R.,Williams, D., Dawson, S., and Lockyer, L. 2016. A conceptual framework linking learning design with learning analytics. In Proceedings of the Sixth International Conference
  3. on Learning Analytics & Knowledge. 329–338.
  4. Bodily, R. and Verbert, K. 2017. Trends and issues in student-facing learning analyticsreporting systems research. In Proceedings of the seventh international learning analytics & knowledge conference. 309–318.
  5. Brouns, F., Zorrilla Pantaleon, M. E. ´ , Alvarez Saiz, E. E. ´ , Solana-Gonzalez, P., Cobo Ortega, A. ´ , Rocha Blanco, E. R., Collantes Viana, M. ˜ ,Rodr´?guez Hoyos, C., De Lima Silva, M., Marta-Lazo, C., et al. 2015. Eco d2. 5learning analytics requirements and metrics report.
  6. Charleer, S., Moere, A. V., Klerkx, J., Verbert, K., and De Laet, T. 2017. Learninganalytics dashboards to support adviser-student dialogue. IEEE Transactions on Learning Technologies 11, 3, 389–399
  7. Crawley, M. J. 2012. The R book. John Wiley & Sons.Daniel, B. 2015. B ig d ata and analytics in higher education: Opportunities and challenges.British journal of educational technology 46, 5, 904–920.
  8. Fueller, C., Loescher, J., and Indefrey, P. 2013. Writing superiority in cued recall.Frontiers in psychology 4, 764.
  9. Greller, W., Santally, M. I., Boojhawon, R., Rajabalee, Y., and Kevin, R. 2017.Using learning analytics to investigate student performance in blended learning courses.Journal of Higher Education Development–ZFHE 12, 1, 37–63.
  10. Guilford, J. P. 1980. Intelligence education is intelligent education. International Society for
  11. Intelligence Education.
  12. Guilford, J. P. 1982. Cognitive psychology’s ambiguities: Some suggested remedies. Psychological review 89, 1, 48.
  13. Guilford, J. P., Hoepfner, R., et al. 1971. The analysis of intelligence. Mcgraw-hill seriesin psychology.
  14. Hasan, R., Palaniappan, S., Mahmood, S., Abbas, A., Sarker, K. U., and Sattar,M. U. 2020. Predicting student performance in higher educational institutions using video learning analytics and data mining techniques. Applied Sciences 10, 11, 3894.
  15. Hung, J.-L., Rice, K., and Saba, A. 2012. An educational data mining model for onlineteaching and learning. Journal of Educational Technology Development and Exchange.
  16. Iandoli, L., Quinto, I., De Liddo, A., and Shum, S. B. 2014. Socially augmented argumentation tools: Rationale, design and evaluation of a debate dashboard. International Journal of Human-Computer Studies 72, 3, 298–319.
  17. Jivet, I., Scheffel, M., Specht, M., and Drachsler, H. 2018. License to evaluate: Preparing learning analytics dashboards for educational practice. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge. 31–40.
  18. Khalil, M. K. and Elkhider, I. A. 2016. Applying learning theories and instructional design models for effective instruction. Advances in physiology education 40, 2, 147–156.
  19. Leitner, P., Ebner, M., and Ebner, M. 2019. Learning analytics challenges to overcome in higher education institutions. In Utilizing learning analytics to support study success.Springer, 91–104.
  20. Lin˜an, L. C. and P ´ erez, ´ A. A. J. ´ 2015. Educational data mining and learning analytics: differences, similarities, and time evolution. International Journal of Educational Technology in Higher Education 12, 3, 98–112.
  21. Mangaroska, K. and Giannakos, M. 2018. Learning analytics for learning design: A systemMatic literature review of analytics-driven design to enhance learning. IEEE Transactions onLearning Technologies 12, 4, 516–534.
  22. Matloff, N. 2011. The art of R programming: A tour of statistical software design. No Starch Press.
  23. Meeker, M. N. 1969. The structure of intellect, its interpretations and uses. Mining, T. E. D. 2012. Enhancing teaching and learning through educational data mining and learning analytics: An issue brief. In Proceedings of conference on advanced technology for education. 1–64.
  24. Molenaar, I. and Knoop-van Campen, C. A. 2018. How teachers make dashboard information actionable. IEEE Transactions on Learning Technologies 12, 3, 347–355.
  25. Patwa, N., Seetharaman, A., Sreekumar, K., and Phani, S. 2018. Learning analytics:Enhancing the quality of higher education. res j econ 2: 2. of 7, 2.
  26. Ryan, M. M. 2014. The impact collaborative data analysis has on student achievement and teacher practice in high school mathematics classrooms in suburban school districts in the mid-west region of new york.
  27. Schunk, D. H. 2012. Learning theories an educational perspective sixth edition. Pearson.Schwendimann, B. A., Rodriguez-Triana, M. J., Vozniuk, A., Prieto, L. P., Borou jeni, M. S., Holzer, A., Gillet, D., and Dillenbourg, P. 2016. Perceiving learning at
  28. a glance: A systematic literature review of learning dashboard research. IEEE Transactions
  29. on Learning Technologies 10, 1, 30–41.
  30. Siemens, G. and Baker, R. S. d. 2012. Learning analytics and educational data mining: towards communication and collaboration. In Proceedings of the 2nd international conference on learning analytics and knowledge. 252–254.
  31. Sternberg, R. J. and Grigorenko, E. L. 2001. Unified psychology.
  32. Talib, A. M., Alomary, F. O., and Alwadi, H. F. 2018. Assessment of student performance for course examination using rasch measurement model: A case study of information technology fundamentals course. Education Research International 2018.
  33. Vaidya, A., Munde, V., and Shirwaikar, S. 2020. Analytics on talent search examination data. International Journal of Business Intelligence and Data Mining 16, 1, 20–32.
  34. Verbert, K., Govaerts, S., Duval, E., Santos, J. L., Van Assche, F., Parra, G., and Klerkx, J. 2014. Learning dashboards: an overview and future research opportunities.Personal and Ubiquitous Computing 18, 6, 1499–1514.