Hybrid k-means–kNN approach for predicting student performance based on library usage behaviours: A case study in Tanzanian higher education.
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| Title: | Hybrid k-means–kNN approach for predicting student performance based on library usage behaviours: A case study in Tanzanian higher education. |
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| Authors: | Bakiri, Hussein1 (AUTHOR) hussein.bakiri@ifm.ac.tz, Tinabo, Rose1 (AUTHOR) |
| Source: | African Journal of Science, Technology, Innovation & Development. Apr2026, Vol. 18 Issue 2, p208-220. 13p. |
| Subjects: | K-means clustering, Library users, Academic achievement, Higher education, K-nearest neighbor classification, Machine learning |
| Abstract: | The complex relationship between student participation and resource usage is frequently missed by traditional predictors of academic performance, such as past academic records and demographic data. These predictors have limited insight into real-time learning behaviours and often act as lagging indicators compared to behavioural and contextual activities. In contrast, library usage behaviours like study frequency, resource utilization, and reading habits provide dynamic, process-oriented evaluations of student engagement. This study examines the possibility of library usage as a non-traditional predictor by analyzing data on student library usage frequency and the extent to which the student performs the reading, studying, information-seeking, researching, and resource utilization activities to predict student performance. The study uses library usage and likely academic performance data of 1062 students of the five higher learning Institutions collected via online Google Forms. A hybrid method combining k-means clustering and kNN classification algorithms was employed; k-means was explicitly used for clustering processes to perform the initial formulation of 'k'. The clustering identified three distinct clusters: students with predominantly disagreeing responses (students whose performances were not caused by the library usage), those with agreeing responses (students whose performances were caused by the library usage), and a neutral-to-slightly-agreeing (students whose performances showed no relationship with library usage) group. Having obtained a confidence interval of 98.61% and a p-value of 2.2e-16, the results indicate that library usage behaviours can significantly be used to predict student performance. These findings suggest that higher education institutions must reinforce policies encouraging and monitoring active library involvement. The original contribution of this study is the introduction of a machine learning model that uses library usage behaviours as dynamic predictors of student performance, providing a novel perspective over conventional academic and demographic performance indicators. [ABSTRACT FROM AUTHOR] |
| Copyright of African Journal of Science, Technology, Innovation & Development is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 193389027 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Hybrid k-means–kNN approach for predicting student performance based on library usage behaviours: A case study in Tanzanian higher education. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Bakiri%2C+Hussein%22">Bakiri, Hussein</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> hussein.bakiri@ifm.ac.tz</i><br /><searchLink fieldCode="AR" term="%22Tinabo%2C+Rose%22">Tinabo, Rose</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22African+Journal+of+Science%2C+Technology%2C+Innovation+%26+Development%22">African Journal of Science, Technology, Innovation & Development</searchLink>. Apr2026, Vol. 18 Issue 2, p208-220. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22K-means+clustering%22">K-means clustering</searchLink><br /><searchLink fieldCode="DE" term="%22Library+users%22">Library users</searchLink><br /><searchLink fieldCode="DE" term="%22Academic+achievement%22">Academic achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Higher+education%22">Higher education</searchLink><br /><searchLink fieldCode="DE" term="%22K-nearest+neighbor+classification%22">K-nearest neighbor classification</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The complex relationship between student participation and resource usage is frequently missed by traditional predictors of academic performance, such as past academic records and demographic data. These predictors have limited insight into real-time learning behaviours and often act as lagging indicators compared to behavioural and contextual activities. In contrast, library usage behaviours like study frequency, resource utilization, and reading habits provide dynamic, process-oriented evaluations of student engagement. This study examines the possibility of library usage as a non-traditional predictor by analyzing data on student library usage frequency and the extent to which the student performs the reading, studying, information-seeking, researching, and resource utilization activities to predict student performance. The study uses library usage and likely academic performance data of 1062 students of the five higher learning Institutions collected via online Google Forms. A hybrid method combining k-means clustering and kNN classification algorithms was employed; k-means was explicitly used for clustering processes to perform the initial formulation of 'k'. The clustering identified three distinct clusters: students with predominantly disagreeing responses (students whose performances were not caused by the library usage), those with agreeing responses (students whose performances were caused by the library usage), and a neutral-to-slightly-agreeing (students whose performances showed no relationship with library usage) group. Having obtained a confidence interval of 98.61% and a p-value of 2.2e-16, the results indicate that library usage behaviours can significantly be used to predict student performance. These findings suggest that higher education institutions must reinforce policies encouraging and monitoring active library involvement. The original contribution of this study is the introduction of a machine learning model that uses library usage behaviours as dynamic predictors of student performance, providing a novel perspective over conventional academic and demographic performance indicators. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of African Journal of Science, Technology, Innovation & Development is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/20421338.2025.2588228 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 208 Subjects: – SubjectFull: K-means clustering Type: general – SubjectFull: Library users Type: general – SubjectFull: Academic achievement Type: general – SubjectFull: Higher education Type: general – SubjectFull: K-nearest neighbor classification Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: Hybrid k-means–kNN approach for predicting student performance based on library usage behaviours: A case study in Tanzanian higher education. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bakiri, Hussein – PersonEntity: Name: NameFull: Tinabo, Rose IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20421338 Numbering: – Type: volume Value: 18 – Type: issue Value: 2 Titles: – TitleFull: African Journal of Science, Technology, Innovation & Development Type: main |
| ResultId | 1 |
