The survey reports, citing Jung et al., that students' language proficiency as side information was effective in promoting several KT models, including DKT, DKVMN and SAKT.¶

Claim · All claims
Evidence · 1 study · review r2 · q2
Currency · newest study 2021 · not yet checked against its sources · not reviewed by a person · overdue for a recheck since 2024: tool and model accuracy changes quickly

Subclaims¶

q2 i? The review reports language proficiency, extracted by Elo rating score and time window features, was demonstrated effective in promoting DKT, DKVMN and SAKT. → Shuanghong Shen 2021

Evidence¶

Shuanghong Shen 2021¶

Shuanghong Shen, Qi Liu, Zhenya Huang, Yonghe Zheng, Minghao Yin, Minjuan Wang, and Enhong Chen. (2021). A Survey of Knowledge Tracing: Models, Variants, and Applications. https://arxiv.org/abs/2105.15106 doi:10.48550/arxiv.2105.15106

q2 · i? · review · r2

Narrative review of Jung et al.: "the language proficiency information is demonstrated to be effective in promoting several KT models, including DKT, DKVMN, and SAKT." The survey adds that it alleviated the cold-start problem; no statistics are printed.

"the language proficiency information is demonstrated to be effective in promoting several KT models, including DKT, DKVMN, and SAKT."

Discussion¶

Related Claims¶

• The survey reports, citing Pavlik et al., that no single knowledge tracing model was always the best, and that a better model must consider multiple student features and the learning context. — the general claim this finding bears on, with the studies behind it
• The survey reports, citing Mongkhonvanit et al., that DKT achieved better performance when binarized MOOC video-engagement covariates were incorporated. — related
• The survey reports, citing Pu and Becker, that removing students' repeated interactions on the same exercises reduced AKT's performance to close to that of DKVMN. — related