2021
DOI: 10.1556/606.2021.00374
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Predicting students’ academic performance using a modified kNN algorithm

Abstract: The target (dependent) variable is often influenced not only by ratio scale variables, but also by qualitative (nominal scale) variables in classification analysis. Majority of machine learning techniques accept only numerical inputs. Hence, it is necessary to encode these categorical variables into numerical values using encoding techniques. If the variable does not have relation or order between its values, assigning numbers will mislead the machine learning techniques. This paper presents a modified k-neare… Show more

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Cited by 14 publications
(6 citation statements)
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“…It may be helpful to further investigate the relationship between the metrics and grades that students receive in order to obtain a clearer understanding of the effects of each metric on overall student performance. Moreover, the K-prototype algorithm can be used as a student engagement model since it works with mixed data types for comparisons with K-means models [25,26].…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…It may be helpful to further investigate the relationship between the metrics and grades that students receive in order to obtain a clearer understanding of the effects of each metric on overall student performance. Moreover, the K-prototype algorithm can be used as a student engagement model since it works with mixed data types for comparisons with K-means models [25,26].…”
Section: Discussionmentioning
confidence: 99%
“…K-means was run using k values 2, 3, and 5. The algorithm maximum iteration was set to 25. The details about association rules, minimum support, and confidence values will be provided in the next sections.…”
Section: Modellingmentioning
confidence: 99%
“…KNN algorithm, at its training phase, just stores the dataset, when it gets new data; it classi es that data into a category that is much similar to the new data. [22,23,24,25,26,27] From the above table, it is clear that obtained accuracy for testing is 82%. Also here the performance metrics such as precision, recall, f1-score and support are calculated for each cluster.…”
Section: K-nearest Neighbor (Knn)mentioning
confidence: 99%
“…Peneliti disini menggunakan algoritma Modified K Nearest Neighbor (MKNN) untuk mengkalkulasi jarak nilai tanpa memberikan kode. Pada penelitian menggunakan dataset penilaian siswa dengan 480 data siswa dengan 16 fitur [5].…”
Section: Tinjauan Pustaka 21 Literatur Terdahuluunclassified