Article: Improving student success rate: an optimisation model for language proficiency prediction based on a random forest classifier Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.36 pp.77 - 94 Abstract: Current language competence assessment relies on inefficient, subjective manual scoring and single tests, lacking accuracy in real-time prediction. This paper proposes a quantitative modelling framework integrating text, speech, and interactive data, transforming language features into computable variables via random forest ensemble learning (optimising feature selection, parameters, and tree structure). To tackle high-dimensional noise, a screening strategy enhances feature importance evaluation and parameter optimisation, boosting prediction robustness. In the sample, the average proficiency score was 57.3: 100% passed reading comprehension, 14.6% struggled with oral fluency, and 59% had tree structure flaws (yet 96.8% achieved high listening accuracy). With an average interaction frequency of 21.4, 79% of low-participation students showed a positive correlation between vocabulary reserve and comprehension ability. Inderscience Publishers - linking academia, business and industry through research

Title: Improving student success rate: an optimisation model for language proficiency prediction based on a random forest classifier

Authors: Xiaonan Yu; Dong Yang

Addresses: Nanchang Normal College of Applied Technology, Nanchang, 330000, China ' Nanchang Normal College of Applied Technology, Nanchang, 330000, China

Abstract: Current language competence assessment relies on inefficient, subjective manual scoring and single tests, lacking accuracy in real-time prediction. This paper proposes a quantitative modelling framework integrating text, speech, and interactive data, transforming language features into computable variables via random forest ensemble learning (optimising feature selection, parameters, and tree structure). To tackle high-dimensional noise, a screening strategy enhances feature importance evaluation and parameter optimisation, boosting prediction robustness. In the sample, the average proficiency score was 57.3: 100% passed reading comprehension, 14.6% struggled with oral fluency, and 59% had tree structure flaws (yet 96.8% achieved high listening accuracy). With an average interaction frequency of 21.4, 79% of low-participation students showed a positive correlation between vocabulary reserve and comprehension ability.

Keywords: student success rate; language competence prediction; random forest classifier; feature selection; parameter optimisation.

DOI: 10.1504/IJICT.2026.153008

International Journal of Information and Communication Technology, 2026 Vol.27 No.36, pp.77 - 94

Received: 15 Dec 2025
Accepted: 19 Jan 2026
Published online: 17 Apr 2026 *

Title: Improving student success rate: an optimisation model for language proficiency prediction based on a random forest classifier

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