Publication Type : Conference Paper
Thematic Areas : Learning-Technologies
Publisher : Machine Learning for Predictive Analysis, Springer Singapore.
Source : Machine Learning for Predictive Analysis, Springer Singapore, Singapore (2021)
Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096440546&doi=10.1007%2f978-981-15-7106-0_27&partnerID=40&md5=4f23ba6f8de05d0aaddd858db06b9192
ISBN : 9789811571060
Keywords : Feature-aware student knowledge tracing (FAST), hidden Markov model, Intelligent tutoring system
Campus : Amritapuri
School : Department of Computer Science and Engineering, School of Engineering
Center : Technologies & Education (AmritaCREATE), Amrita Center For Research in Analytics
Department : Computer Science
Year : 2021
Abstract : In many Indian rural schools, individual students do not receive adequate attention due to the high student–teacher ratio. It is anonerous task for teachers to assess their students' knowledge levels, and identify their deficient areas of learning. An Intelligent Tutoring System (ITS) enables the teacher to create a report on topics, which students need to study in more detail. Knowledge tracing approaches are a good option for the generation of such reports. For the current paper, we analyze first-grade students from 28 schools, who use Amrita Learning, an ITS developed by Amrita University. There were 211,275 responses obtained in a single academic year. The performance of three knowledge tracing approaches were compared using this dataset: standard Bayesian Knowledge Tracing, Feature-Aware Student Knowledge Tracing (FAST) and Hidden Markov Model (HMM). We find that the HMM approach marginally outperforms the other two methods.
Cite this Research Publication : Georg Gutjahr, Chandrashekar, P., M. Nair, G., Haridas, M., and Prof. Prema Nedungadi, “Comparison of Hidden Markov Models and the FAST Algorithm for Feature-Aware Knowledge Tracing”, in Machine Learning for Predictive Analysis, Singapore, 2021.