Yafi Rahma Amalia

The positive impact of Telegram bot usage on supporting students' learning processes and outcomes during on-the-job training

Introduction

The positive impact of telegram bot usage on supporting students' learning processes and outcomes during on-the-job training. Discover how Telegram bots significantly boost XI Software Engineering and Game Development students' learning processes and outcomes during on-the-job training. Strong correlation found.

Abstract

This study explores the impact of using Telegram bots on student learning outcomes during the on-the-job training program for XI Software Engineering and Game Development program (PPLG) students in the 2023/2024 even semester, during which students missed direct learning. Using a quantitative correlation approach, data were collected through questionnaires, Telegram bot usage, and learning outcomes. The study involved 71 students from two XI PPLG classes, selected through saturated sampling. Data were analysed using normality, linearity, hypothesis tests, and Pearson product-moment correlation with SPSS 26. The results show a significant relationship between Telegram bot usage and learning outcomes, with a correlation of 0.821 (very strong, sig = 0.000 < 0.05). These findings suggest a potential for developing blended learning models that integrate Telegram bots during the on-the-job training program for students.

Review

This study addresses a highly relevant and timely topic concerning the integration of digital tools, specifically Telegram bots, to support student learning during on-the-job training (OJT), especially when direct instruction is constrained. The research clearly states its objective to investigate the impact of such usage on learning outcomes for vocational students. The quantitative correlation approach provides a straightforward method to explore this relationship, and the reported very strong positive correlation (r=0.821, p<0.05) offers compelling initial evidence for the potential efficacy of Telegram bots in this educational context. While the study's quantitative design and statistical analysis using Pearson product-moment correlation are appropriate for identifying relationships, the abstract would benefit from greater specificity regarding the operationalization of its key variables. Details on *how* "Telegram bot usage" was measured (e.g., frequency of interaction, specific features accessed, nature of tasks performed) and *how* "learning outcomes" were assessed (e.g., specific skill acquisition, performance metrics, assessment scores) are crucial for evaluating the study's rigor and replicability. Furthermore, a brief explanation of the Telegram bot's functionalities and the specific aspects of "missed direct learning" it was designed to address would provide valuable context for interpreting the observed strong correlation. Despite these areas for potential elaboration, the findings offer significant practical implications for the design and implementation of blended learning models within OJT programs, particularly where traditional instructional methods are challenging. The study's recommendation for developing such integrated approaches is well-justified by the strong correlation. Future research building upon these findings could benefit from exploring causal relationships through experimental designs, employing qualitative methods to understand the specific mechanisms through which bots support learning, or investigating student and instructor perceptions to optimize bot features for diverse learning needs. Overall, this paper provides a valuable empirical contribution to the evolving landscape of technology-enhanced education.

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