Measuring Computational Psychometrics Analysis Motivational Level in Learner’s using Different Parameters through Deep Learning Algorithm

Ashima Bhatnagar Bhatia(1), Kavita Mittal(2),


(1) Vivekananda Institute of Professional Studies
(2) JaganNath University

Abstract


Learning is an ongoing process irrespective of age, gender and geographical location of acquiring new understanding, knowledge, behaviours, skills, values, attitudes, and preferences. Formative assessment methods have emerged and evolved to integrate learning, evaluation and education models. Not only is it critical to understand a learner's skills and how to improve and enhance them, but we also need to consider what the learner is doing; we need to consider navigational patterns. The extended learning and assessment system, a paradigm for doing research, captures this entire view of learning and evaluation systems. The function of computational psychometrics is to facilitating the translation from raw data to meaningful concepts. In this research study, several factors are considered for psychometric analysis of different kinds of learners, and based on a motivational level, many interesting conclusions have been drawn and presented in the result section at the end of the paper. Deep learning model Ludwig Classifier used to calculate, motivational Level is obtained for 100 number of epochs and it is found that the loss is decreasing and in other words, the accuracy of the machine goes on increasing. Each of the categories discussed here has new capabilities, or at the very least expansions of current ones.

Keywords


computational; psychometrics; learning system; evaluation system; skills

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References


Drachsler, H., &Goldhammer, F. (2020). Learning Analytics and eAssessment—Towards Computational Psychometrics by Combining Psychometrics with Learning Analytics. In Radical Solutions and Learning Analytics (pp. 67-80). Springer, Singapore. https://doi.org/10.1007/978-981-15-4526-9_5

M. Anand, A. Velu, and P. Whig, “Prediction of Loan Behaviour with Machine Learning Models for Secure Banking,” Journal of Computer Science and Engineering (JCSE), vol. 3, no. 1, pp. 1–13, 2022. https://doi.org/10.36596/jcse.v3i1.237

Wiswall, M., and Zafar, B. (2015). Determinants of college major choice: identification using an information experiment. Rev. Econ. Stud. 82, 791–824. https://doi.org/10.1093/restud/rdu044

Whitmer, J., Nasiatka, D., and Harfield, T. (2017) “Student interest patterns in learning analytics notifications,” in Blackboard Data Science Research Brief, Blackboard Analytics (Washington, DC), 1–14.

A.P. Ambrosio, C. Xavier, F. Georges, Digital ink for cognitive assessment of computational thinking, IEE Frontiers in Education Conference (2015), pp. 1520-1526, https://doi.org/10.1109/FIE.2014.7044237

MacLaren, B., and Koedinger, K. (2002). “When and why does mastery learning work: instructional experiments with act-r “simstudents”,” in Intelligent Tutoring Systems, eds S. A. Cerri, G. Gouardères, and F. Paraguaçu (Berlin; Heidelberg: Springer), 355–366. http://dx.doi.org/10.1007/3-540-47987-2_39

C. Angeli, J. Voogt, A. Fluck, M. Webb, M. Cox, J. Malyn-Smith, et al. A K-6 computational thinking curriculum framework: Implications for teacher knowledge Journal of Educational Technology & Society, 19 (3) (2016), pp. 47-57

S. Atmatzidou, S. Demetriadis. Advancing students' computational thinking skills through educational robotics: A study on age and gender relevant differences Robotics and Autonomous Systems, 75 (2016), pp. 661-670. http://dx.doi.org/10.1016/j.robot.2015.10.008

K. Brennan, M. Resnick New frameworks for studying and assessing the development of computational thinking American Educational Research Association Meeting, Vancouver, BC (2012) Canada, 1–25

Von Davier, A. A., Deonovic, B. E., Yudelson, M., Polyak, S., & Woo, A. (2019). Computational psychometrics approach to holistic learning and assessment systems. In Frontiers in Education (Vol. 4, p. 69). Frontiers

Teasley, S. D. (2017). Student facing dashboards: one size fits all? Technol. Knowl. Learn. 22, 377–384. http://dx.doi.org/10.1007/s10758-017-9314-3

F. Buitrago Flórez, R. Casallas, M. Hernández, A. Reyes, S. Restrepo, G. Danies,Changing a generation's way of thinking: Teaching computational thinking through programming Review of Educational research, 87 (4) (2017), pp. 834-860. http://dx.doi.org/10.3102/0034654317710096

A. Velu and P. Whig, “Studying the Impact of the COVID Vaccination on the World Using Data Analytics”. Vivekananda Journal of Research, Vol. 10, Issue 1, 147-160.

Mangaroska, K., Vesin, B., & Giannakos, M. (2019, July). Elo-rating method: towards adaptive assessment in e-learning. In 2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT). Vol. 2161, pp. 380-382. https://doi.org/10.1109/ICALT.2019.00116

C. Cachero, P. Barra, S. Melia, O. Lopez, Impact of programming exposure on the development of computational thinking capabilities: An empirical study IEEE Access, 8 (2020), pp. 72316-72325. https://doi.org/10.1109/ACCESS.2020.2987254

W.K. Campbell, C. Sedikides, Self-threat magnifies the self-serving bias: A meta-analytic integration, Review of General Psychology, 3 (1) (1999), pp. 23-43. https://doi.org/10.1037/1089-2680.3.1.23

M. Cutumisu, C. Adams, C. Lu, A scoping review of empirical research on recent computational thinking assessments Journal of Science Education and Technology, 28 (6) (2019), pp. 651-676. https://doi.org/10.1007/s10956-019-09799-3

J. Del Olmo-Muñoz, R. Cózar-Gutiérrez, J.A. González-Calero Computational thinking through unplugged activities in early years of primary education Computers & Education, 150 (2020). https://doi.org/10.1016/j.compedu.2020.103832

H.Y. Durak, M. Saritepeci, Analysis of the relation between computational thinking skills and various variables with the structural equation model, Computers & Education, 116 (2018), pp. 191-202, https://doi.org/10.1016/j.compedu.2017.09.004

H.B.G. Ganzeboom, P. M. de Graaf, D.J. Treiman, A standard international socio-economic index of occupational status, Social Science Research, 21 (1) (1992), pp. 1-56. https://doi.org/10.1016/0049-089X(92)90017-B

R.M. Gonyea, Self-reported data in institutional research: Review and recommendations, New Directions for Institutional Research (127) (2005), pp. 73-89. https://doi.org/10.1002/ir.156

Pelánek, R. (2017). Bayesian knowledge tracing, logistic

models, and beyond: an overview of learner modeling techniques. User Model. User Adapt. Interact. 1–38.

S. Grover, R. Pea, Computational thinking in K-12: A review of the state of the field, Educational Researcher, 42 (1) (2013), pp. 38-43. https://doi.org/10.3102/0013189X12463051

V. S. Grover, R. Pea, S. Cooper, Designing for deeper learning in a blended computer science course for middle school students, Computer Science Education, 25 (2) (2015), pp. 199-237. https://doi.org/10.1080/08993408.2015.1033142

Polyak, S. T., von Davier, A. A., and Peterschmidt, K. (2017). Computational psychometrics for the measurement of collaborative problem solving skills. Front. Psychol. 8:2029. doi: https://doi.org/10.3389/fpsyg.2017.02029

J.F. Hair, J.J. Risher, M. Sarstedt, C.M. Ringle, When to use and how to report the results of PLS-SEM, European Business Review, 31 (1) (2019), pp. 2-24. https://doi.org/10.1108/EBR-11-2018-0203

Von Davier, A. A. (2017). Computational psychometrics in support of collaborative educational assessments. Journal of Educational Measurement, 54(1), 3-11

R. R. Nadikattu, S. M. Mohammad, and P. Whig, “Novel economical social distancing smart device for covid-19,” International Journal of Electrical Engineering and Technology (IJEET), 11(4):204-217, 2020. http://dx.doi.org/10.34218/IJEET.11.4.2020.023

H. Jeon, H. Oh and J. Lee, "Machine Learning based Fast Reading Algorithm for Future ICT based Education," 2018 International Conference on Information and Communication Technology Convergence (ICTC), 2018, pp. 771-775. https://doi.org/10.1109/ICTC.2018.8539447

J.L. Howard, M. Gagné, J.S. Bureau, Testing a continuum structure of self-determined motivation: A meta-analysis, Psychological Bulletin, 143 (12) (2017), pp. 1346-1377. https://doi.org/10.1037/bul0000125

P. Whig, R. R. Nadikattu, and A. Velu, “COVID-19 pandemic analysis using application of AI,” Healthcare Monitoring and Data Analysis Using IoT: Technologies and Applications, p. 1, 2022. http://dx.doi.org/10.1049/PBHE038E_ch1


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