Rohit B. Kaliwal
*,
Santosh. L. Deshpande
- Dept. of CSE, VTU, Belagavi-590018, Karnataka, India
Abstract
An Intelligent Tutoring Systems is a type of knowledge-based system whose main agenda is to efficiently supplement a human tutor with a machine. Dissimilar to conventional classroom teaching, Intelligent Tutoring Systems (ITSs) have the ability to fit according to the necessity of an individual learner. More emphasis has been laid on various types of e-learning systems. In this work, a probability-based ITSs system is proposed consisting of four models specifically the learner�s behaviour model, pedagogical model, knowledge base model and learner assessment model. The importance has been given to the learner assessment model where an element of uncertainty has been introduced and handled by the Bayesian Belief Network (BBN). The purpose of the learner assessment model is to rightly detect the knowledge level of each learner based on their reply to the level of questions, where the level of questions is random to the process of assessment to the learner. The uncertainty factor has been defined in terms of success and failure parameters. Success is the probability that a learner of low cleverness level gives a right reply to a level of questions and is increased by a small probability of 0.07, whereas Failure is the probability that a learner of high cleverness level gives a wrong reply to a level of questions and is reduced by a small probability of 0.04. In this work during an assessment of the knowledge level of a learner, the system has incorporated the uncertainty factors of Success and Failure with the help of Bayes� rule and has found promising results that take into account the possibility of Success or Failure.
References
- Nazeeh Ghatasheh, �Knowledge Level Assessment in e-Learning Systems Using Machine Learning and User Activity Analysis�,International Journal of Advanced Computer Science and Applications, Vol. 6, No. 4, 2015.
- Mahbobe Bani Asad Askari, Mostafa Ghazizadeh Ahsaee, �Bayesian network structure learning based on cuckoo search algorithm�, 6th Iranian Joint Congress on Fuzzy and Intelligent Systems, 2018.
- M. J. Rosenberg, �E-Learning: Strategies for Delivering Knowledge in the Digital Age�, New York, NY, USA: McGraw-Hill, Inc., 2002.
- Rohit B Kaliwal, Santosh L Deshpande, �Efficiency of Probabilistic Network Model for Assessment in E-Learning System�, International Journal of Recent Technology and Engineering (IJRTE), ISSN: 2277-3878 (Online), Volume-9 Issue-3, September 2020. Page No: 562-566.
- Baisakhi Chakraborty and Meghamala Sinha, �Student Evaluation Model Using Bayesian Network In An Intelligent E-Learning System�, IIOAB Journal, 2016, Vol. 7, ISSN: 0976-3104.
- Oktariani Nurul Pratiwi, Yenie Syukriyah, �Question Classification for e-Learning Using Machine Learning Approach�, International Conference on ICT for Smart Society (ICISS), 2020.
- D Baker, Ryan SJ, Albert T Corbett, and Vincent Aleven, �More accurate student modeling through contextual estimation of slip and guess probabilities in bayesian knowledge tracing�, Intelligent Tutoring Systems. Springer Berlin Heidelberg,2008.
- Nabila Khodeir, Nayer Wanas and Nadia Hegazy, �Bayesian Based Student Knowledge Modeling In Intelligent Tutoring Systems�,6th IEEE International Conference on E-Learning in Industrial Electronics (ICELIE), 2012.
- Anderson H, Koedinger M, �Intelligent tutoring goes to school in the Big City�, International Journal of Artificial Intelligence in Education, pp. 30�43, 1997.
- Juan-Diego Zapata-Rivera, Jim E. Greer, �Interacting with Inspectable Bayesian Student Models�, International Journal of Artificial Intelligence in Education, 2004.
- Rohit. B. K, �Mobile Agents Based Network Monitoring�, National Conference on RIICTEM-2015, PG Centre, VTU, Kalaburagi, 2015.
- Sonali Shankar, Bishal Dey Sarkar, Sai Sabitha, Deepti Mehrotra, �Performance Analysis of Student Learning Metric using K-Mean Clustering Approach�,6th International Conference Cloud System and Big Data Engineering, 2016.
- Abdulghani Ali Al-Hattami, �E-Assessment of Students Performance During the E-Teaching and Learning�, International Journal of Advanced Science and Technology, ISSN: 2005-4238, Vol. 29, No. 8, pp. 1537-1547, 2020.
- Conati C., Gertner A., Vanlehn K, �Using Bayesian networks to manage uncertainty in student modelling�, Journal of User Modeling and User-Adapted Interaction, 12(4), 371�417, 2002.
- JM Agosta E Millan and JL Perez de la Cruz, �Bayesian student modelling and the problem of parameter specification�, British Journal of Educational Technology, 2001.
- Cristina Conati and Kurt VanLehn Pola,�A student modeling framework for probabilistic on-line assessment of problem solving performance�, Proceedings of the 5th International Conference on User Modeling, 1996.
