Bibliometric Analysis-Based Scientific Article Writing Training for Lecturers

2026

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DOI 10.58524/smartsociety.v6i1.952
Data confidence 70 medium
How this score is calculated

It is built from checkable properties of the record itself: whether the article has a DOI, how many independent sources confirmed its data, whether the required fields are filled in, and how recent the latest confirmation is. Disagreement between sources and a missing identifier lower it. A value of 80 or above means the record was published without further checking; 60 to 79 means it is published at reduced confidence, with a manual check advisable. A record verified by an editor by hand is never pushed below 90 by a recalculation. This scores our data about the article, not the article itself.

Abstract

Enhancing lecturers’ research productivity is a strategic priority in higher education, particularly in institutions where publication performance is constrained by limited research mapping skills and low bibliometric literacy. This study addresses the need to strengthen lecturers’ ability to identify research trends, position manuscripts strategically, and target reputable journals through a structured bibliometric training model. The community service program aimed to develop and evaluate a participatory bibliometric training framework to improve scientific article writing. Eight lecturers from the Diploma III Mechanical Engineering and Applied Bachelor MICE programs at PNJ PSDKU Demak were selected using purposive sampling, based on their active involvement in research activities and interest in improving publication skills. The training consisted of sequential stages: introduction to bibliometric concepts, systematic data retrieval from Scopus, article screening using PRISMA and PICOS, data visualization with VOSviewer and Biblioshiny, and guided manuscript development. Participants’ cognitive improvement was measured using structured pre-test and post-test instruments assessing bibliometric understanding and research mapping skills, administered through an online questionnaire. Data were analyzed using descriptive statistics and the normalized gain (N-Gain) method. Results showed a significant increase in participants’ competencies, with N-Gain scores ranging from 0.72 to 0.80, indicating high improvement. The findings demonstrate that integrating bibliometric mapping into academic writing training enhances strategic research awareness beyond technical writing skills. This model reconceptualizes bibliometric tools as pedagogical instruments and provides a practical, replicable framework for improving lecturers’ publication performance and supporting sustainable quality education.