Yogyakarta, August 28, 2026 – The Library Science Study Program held a Lecturer Research Strengthening Workshop themed "Building a Data-Driven Research Culture by Strengthening Quantitative Methodology in the AI Era." The event took place on Friday, August 28, 2026, from 8:30 a.m. to 2:00 p.m. Western Indonesian Time (WIB) at the Grand Rohan Jogja Hotel.

The workshop featured Prof. Dr. Sugiyono, M.Pd., an expert in research methods, as the keynote speaker. It brought together 30 participants, including lecturers from the Library Science Study Program, administrative staff, students, and academic representatives from UIN Raden Fatah Palembang. All registered participants attended.

In his opening remarks, Head of the Library Science Study Program M. Ainul Yaqin, S.Pd., M.Ed., Ph.D., thanked everyone involved in organizing the workshop. He said stronger skills in quantitative research methodology are essential to help lecturers and students carry out research systematically.

"Research must follow clear stages, from formulating the problem, defining variables and hypotheses, and determining the sample to developing instruments and analyzing data," M. Ainul Yaqin said.

He also stressed the importance of producing research that is novel, valid, reliable, objective, and of real benefit. He said high-quality research would contribute to the development of library science and raise the quality of academic publications.

11 Key Topics Covered

In his presentation, Prof. Dr. Sugiyono explained key aspects of quantitative research methodology. His material covered 11 key topics, from the definition of research methods to how a quantitative research report is structured.

Participants learned about the characteristics of quantitative, qualitative, and mixed-methods research, as well as several other approaches, including Research and Development (R&D), action research, evaluation research, and policy research. The speaker also explained the philosophical foundations of quantitative research: rationalism, positivism, and postpositivism.

The discussion also covered the survey research process, sources of research problems, quantitative research design, literature reviews, conceptual frameworks, and hypothesis formulation. In the research design session, participants were introduced to survey, experimental, ex post facto, and case study designs.

Sampling techniques were also discussed, including both probability and non-probability sampling. The speaker explained how to determine sample size, including the use of Cochran's formula when the population size is unknown.

Participants also learned how to develop and test research instruments. This part covered writing operational definitions of variables, choosing indicators, drafting instrument items, testing validity, and testing reliability with techniques such as Cronbach's alpha, Spearman-Brown, KR-20, and test-retest.

Strengthening Data Analysis

The workshop also built participants' skills in data collection and analysis. The data collection techniques discussed included questionnaires, tests, and documentation. Quantitative data analysis was explained through descriptive and inferential statistics.

Participants were introduced to a range of analytical techniques, including the mean, standard deviation, percentages, the t-test, ANOVA, Spearman correlation, Kendall's tau, regression, path analysis, and Structural Equation Modeling (SEM). The speaker stressed that the choice of statistical technique must match the type of data and the form of the research hypothesis.

In the next session, Prof. Dr. Sugiyono explained the structure of a quantitative research report. It has five chapters: introduction, theoretical framework, research methods, results and discussion, and conclusions and recommendations.

Using AI Responsibly

A highlight of the workshop was a demonstration of how AI prompts can support the research process. The demonstration showed how to generate research titles for undergraduate and master's levels, draft background sections, and formulate research questions.

In the closing session, the speaker said ChatGPT and other AI tools should be used as aids, not as substitutes for researchers. AI can help generate ideas, process information, and prepare first drafts. However, researchers must still understand, critically evaluate, verify, and synthesize the results in their own words and with their own thinking.

Using AI wisely is expected to support originality, quality, and academic responsibility. This way, AI can help strengthen a data-driven research culture without weakening researchers' critical role or integrity.

Through this workshop, the Library Science Study Program aims to build a research culture that is systematic, data-driven, and responsive to new technology, while helping to improve the quality of publications and advance library science. (MBF)