• Corpus ID: 229349312

BKT-LSTM: Efficient Student Modeling for knowledge tracing and student performance prediction

  title={BKT-LSTM: Efficient Student Modeling for knowledge tracing and student performance prediction},
  author={Sein Minn},
  • Sein Minn
  • Published 22 December 2020
  • Computer Science
  • ArXiv
Recently, we have seen a rapid rise in usage of online educational platforms. The personalized education became crucially important in future learning environments. Knowledge tracing (KT) refers to the detection of students’ knowledge states and predict future performance given their past outcomes for providing adaptive solution to Intelligent Tutoring Systems (ITS). Bayesian Knowledge Tracing (BKT) is a model to capture mastery level of each skill with psychologically meaningful parameters and… 

Student Modeling and Analysis in Adaptive Instructional Systems

A state-of-the-art review of 11 years of research in student modeling, focusing on learner characteristics, learning indicators, and foundational aspects of dissimilar models, shows increased prediction accuracy when using multidimensional learner data to create multimodal models in real-world adaptive instructional systems.

No Task Left Behind: Multi-Task Learning of Knowledge Tracing and Option Tracing for Better Student Assessment

Dichotomous-Polytomous Multi-Task Learning (DP-MTL), a multi-task learning framework that combines KT and OT for more precise student assessment, is proposed and it is shown that the KT objective acts as a regularization term for OT in the DP- MTL framework.

Incremental Knowledge Tracing from Multiple Schools

The results show that learning sequentially with the Self Attentive Knowledge Tracing (SAKT) algorithm is able to achieve considerably similar performance to that of pooling all the data together.

Interpretable Knowledge Tracing: Simple and Efficient Student Modeling with Causal Relations

Interpretable Knowledge Tracing is presented, a simple model that relies on three meaningful features: individual skill mastery, ability profile (learning transfer across skills) and problem difficulty by using data mining techniques and shows better student performance prediction than deep learning based student models without requiring a huge amount of parameters.



Deep Knowledge Tracing and Dynamic Student Classification for Knowledge Tracing

A novel model for knowledge tracing is proposed that captures students' learning ability and dynamically assigns students into distinct groups with similar ability at regular time intervals and combines this information with a Recurrent Neural Network architecture known as Deep Knowledge Tracing.

Deep Knowledge Tracing

The utility of using Recurrent Neural Networks to model student learning and the learned model can be used for intelligent curriculum design and allows straightforward interpretation and discovery of structure in student tasks are explored.

Dynamic Student Classiffication on Memory Networks for Knowledge Tracing

A novel model called Dynamic Student Classification on Memory Networks (DSCMN) for knowledge tracing that enhances existing KT approaches by capturing temporal learning ability at each time interval in student’s long-term learning process is proposed.

Back to the basics: Bayesian extensions of IRT outperform neural networks for proficiency estimation

It is found that IRT-based methods consistently matched or outperformed DKT across all data sets at the finest level of content granularity that was tractable for them to be trained on.

Dynamic Key-Value Memory Networks for Knowledge Tracing

This work introduces a new model called Dynamic Key-Value Memory Networks (DKVMN) that can exploit the relationships between underlying concepts and directly output a student's mastery level of each concept.

Improving Knowledge Tracing Model by Integrating Problem Difficulty

The goal is to investigate a student model that compatible for problems with multiple skills and various concept and recent KT models fail to deal with practices of complex skill composition and variety of concepts included in a problem simultaneously.

More Accurate Student Modeling through Contextual Estimation of Slip and Guess Probabilities in Bayesian Knowledge Tracing

A new method for instantiating Bayesian Knowledge Tracing is offered, using machine learning to make contextual estimations of the probability that a student has guessed or slipped, which allows for more accurate and reliable student modeling in ITSs that use knowledge tracing.

Knowledge tracing: Modeling the acquisition of procedural knowledge

An effort to model students' changing knowledge state during skill acquisition and a series of studies is reviewed that examine the empirical validity of knowledge tracing and has led to modifications in the process.

A review of recent advances in learner and skill modeling in intelligent learning environments

This paper discusses related advancements in modeling other key constructs such as learner motivation, emotional and attentional state, meta-cognition and self-regulated learning, group learning, and the recent movement towards open and shared learner models.

Performance Factors Analysis - A New Alternative to Knowledge Tracing

This paper describes the work to modify an existing data mining model so that it can also be used to select practice adaptively, and compares this new adaptive datamining model (PFA, Performance Factors Analysis) with two versions of LFA and then compares PFA with standard KT.