Research literacy and its predictors among university students and graduates identified by machine learning and spatial analysis

The landscape of academic publishing has evolved dramatically, leading to a surge in publications and journals. The 'publish or perish' culture has resulted in undesirable practices, such as many researchers publishing in predatory journals due to institutional pressures and lack of awareness. While numerous studies have investigated knowledge of predatory journals, overall research literacy has remained underexplored. This study is the first to assess research literacy comprehensively, incorporating GIS and machine learning techniques alongside traditional statistical analyses. This study utilized a cross-sectional survey method with a questionnaire collecting information on socio-demographics, academic information, research training and experience, and research literacy. Traditional statistical analyses were performed using SPSS, while machine learning models were developed with Python and Google Colab. Supervised classification algorithms and mapping with R statistical software's 'bangladesh' package. The findings revealed that over half of the participants had poor research literacy. Significant predictors of higher research literacy included satisfaction with research courses at university education, research course taken outside university , and research-related professional engagement. Machine learning analysis identified that taking research courses outside of university was the most impactful factor for research literacy, while researchers within family members had minimal influence. The Random Forest and CatBoost models performed strongly in predicting literacy, achieving accuracy rates of 73.04% and 71.57%, respectively, and precision values of 73.29% and 71.69%, respectively, with low log loss values of 0.57 and 0.56. GIS-based spatial analyses revealed regional disparities in research literacy (χ²=9.234, p = 0.236), with certain divisions exhibiting a higher prevalence of lower literacy. This study highlights that a substantial portion of the participants lack research literacy, which is associated with multiple factors. The findings suggest the need for intervention programs to enhance research practices and awareness among students and professionals, fostering a culture of academic excellence.

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Publication Details

Journal
Scientific Reports
Published
2025-10-13
DOI
https://doi.org/10.1038/s41598-025-19488-4
Primary Topic
scientometrics and bibliometrics research
Type
article
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article

Research literacy and its predictors among university students and graduates identified by machine learning and spatial analysis

Pintu Chandra Shil, Nitai Roy, Md Mehedi Hasan, Pronab Das et al.
Scientific Reports
scientometrics and bibliometrics research
article

Research literacy and its predictors among university students and graduates identified by machine learning and spatial analysis

Pintu Chandra Shil, Nitai Roy, Md Mehedi Hasan, Pronab Das, Firoj Al‐Mamun, Jannatul Ferdaus, Md. Zahidul Hasan, David Gozal, Sharmin Akter, Mohammed A. Mamun, Moneerah Mohammad ALmerab, Md. Omar Faruk, Sabrina Aktar, Aysha Siddiky, Md Junayed Hasan, Sadikur Rahman, Anonna Haque, Mohammad Kibria, Md Emran Hasan, Md Maruf Khan, Milan Kumar Das, Sajib Kumar Nath, Momotaj Begum, Abdul Kaium, Md Shakil Ahamed, Tamim Ikram, Marjia Khan Trisha, Tasnim B. K. Chowdhury, Md. Hasibul Islam Jitu, Rubiya Wazed, Md. Abu Huraira, Naoroj Muntashir, Arpita Howlader Tisa, Sree Siddarth Shankar Devnath Satu, Md. Abdulla Hell Kafi Patowary, Kulsuma Bahar Bethi, Umme Zaida Misma
article en

Abstract

The landscape of academic publishing has evolved dramatically, leading to a surge in publications and journals. The 'publish or perish' culture has resulted in undesirable practices, such as many researchers publishing in predatory journals due to institutional pressures and lack of awareness. While numerous studies have investigated knowledge of predatory journals, overall research literacy has remained underexplored. This study is the first to assess research literacy comprehensively, incorporating GIS and machine learning techniques alongside traditional statistical analyses. This study utilized a cross-sectional survey method with a questionnaire collecting information on socio-demographics, academic information, research training and experience, and research literacy. Traditional statistical analyses were performed using SPSS, while machine learning models were developed with Python and Google Colab. Supervised classification algorithms and mapping with R statistical software's 'bangladesh' package. The findings revealed that over half of the participants had poor research literacy. Significant predictors of higher research literacy included satisfaction with research courses at university education, research course taken outside university , and research-related professional engagement. Machine learning analysis identified that taking research courses outside of university was the most impactful factor for research literacy, while researchers within family members had minimal influence. The Random Forest and CatBoost models performed strongly in predicting literacy, achieving accuracy rates of 73.04% and 71.57%, respectively, and precision values of 73.29% and 71.69%, respectively, with low log loss values of 0.57 and 0.56. GIS-based spatial analyses revealed regional disparities in research literacy (χ²=9.234, p = 0.236), with certain divisions exhibiting a higher prevalence of lower literacy. This study highlights that a substantial portion of the participants lack research literacy, which is associated with multiple factors. The findings suggest the need for intervention programs to enhance research practices and awareness among students and professionals, fostering a culture of academic excellence.

Scientific ReportsVol. 15(1)
Princess Nourah bint Abdulrahman University (SA), Shahjalal University of Science and Technology (BD), North South University (BD), Bangladesh University of Engineering and Technology (BD), Bangladesh Medical University (BD), University of Dhaka (BD), National University Bangladesh (BD), Patuakhali Science and Technology University (BD), Noakhali Science and Technology University (BD), University of South Asia (BD), Nanjing University of Science and Technology (CN), Bangladesh Open University (BD), Mawlana Bhashani Science and Technology University (BD), Chattogram Veterinary and Animal Sciences University (BD), Jahangirnagar University (BD), Primeasia University (BD), Premier University (BD), University of Missouri (US), Marshall University (US), Northeastern University (CN)
Openalex Percentile: Top 71%
scientometrics and bibliometrics research
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