SkillCrest: A Skill Assessment System Using Deep Knowledge Tracing and User Feedback Sentiment Analysis

Vaisakh, K V; Ravi, Akash; Aakash, K O; Sai, A.M. Abhishek; Bhaskar, Jasmine

doi:10.1109/gcat52182.2021.9587697

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…Sequential (Tian et al, 2021), (Mandlazi et al, 2021), (Vaisakh et al, 2021), (Lai et al, 2021) Memory (Sun et al, 2021), (Wan et al, 2021), (Qin et al, 2021), (S. Liu, Zou, et al, 2021) Textual (Q. ) Attention (Wang et al, 2021), (Zeng et al, 2021), (C. Liu, 2021, (Wu et al, 2021), (Y. , (Gan et al, n.d.) Graph (Yang et al, 2021), (Junrui Zhang et al, 2021), (Nakagawa et al, 2021) Other (He, 2021), (Ma et al, 2021), (Song et al, 2021), (Yin Wong et al, 2022), (Song et al, 2022) The table also contains an additional category at the end, this incorporates proposed models that either missed or novel perspectives, some of them are briefly explained: (He, 2021) proposed an extensible embedding framework to synthesize skill, exercise, performance factors and side factors.…”

Section: Category Publicationsmentioning

confidence: 99%

Knowledge tracing: A brief overview of the advances done since 2021 using deep learning techniques

Naranjo¹,

Stoffová²,

Zhu³

2022

JTIE

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This paper aims to describe briefly the direction followed by the models based on deep learning techniques attempting to solve the Knowledge Tracing (KT) task since 2021. The goal of the KT task is to model a student's knowledge level based on their responses to a series of activities (known as interactions) provided by a learning platform. A summary of the advances made during the last year within the research area will be presented in this manuscript, with a unique focus on those models that use neural networks to provide a solution. The interest in that segment of models stems from the fact that they have achieved the highest performance over solutions based on probabilistic or logistic models. This theoretical analysis facilitates the understanding of the current state of the art for new researchers of this area. Furthermore, a brief analysis of the classification will be made accompanied with a description of relevant models. The contribution includes the timeline chart followed by the KT models, a short comparison between recently proposed taxonomies for the models within the research area, a brief description of relevant models published since 2021 falling outside any category included in the taxonomies. Lastly a short discussion of findings, possible new applications of the research area and conceivable future directions are discussed.

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Section: Category Publicationsmentioning

confidence: 99%

Knowledge tracing: A brief overview of the advances done since 2021 using deep learning techniques

Naranjo¹,

Stoffová²,

Zhu³

2022

JTIE

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“…The main objective of BKT is to determine whether a learner has successfully gained proficiency (i.e., answered items or tasks correctly) in a set of skills represented as binary variables (one per skill). BKT has emerged as a successful model in this domain, yielding significant advancements [13,14].…”

Section: Introductionmentioning

confidence: 99%

An Introduction to Bayesian Knowledge Tracing with pyBKT

Bulut

Shin

Yildirim-Erbasli

et al. 2023

Psych

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This study aims to introduce Bayesian Knowledge Tracing (BKT), a probabilistic model used in educational data mining to estimate learners’ knowledge states over time. It also provides a practical guide to estimating BKT models using the pyBKT library available in Python. The first section presents an overview of BKT by explaining its theoretical foundations and advantages in modeling individual learning processes. In the second section, we describe different variants of the standard BKT model based on item response theory (IRT). Next, we demonstrate the estimation of BKT with the pyBKT library in Python, outlining data pre-processing steps, parameter estimation, and model evaluation. Different cases of knowledge tracing tasks illustrate how BKT estimates learners’ knowledge states and evaluates prediction accuracy. The results highlight the utility of BKT in capturing learners’ knowledge states dynamically. We also show that the model parameters of BKT resemble the parameters from logistic IRT models.

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SkillCrest: A Skill Assessment System Using Deep Knowledge Tracing and User Feedback Sentiment Analysis

Cited by 2 publications

References 12 publications

Knowledge tracing: A brief overview of the advances done since 2021 using deep learning techniques

Knowledge tracing: A brief overview of the advances done since 2021 using deep learning techniques

An Introduction to Bayesian Knowledge Tracing with pyBKT

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