A Prediction-based Curriculum Analysis using the Modified Artificial Bee Colony Algorithm

Abstract

Due to the vast amount of students' information and the need of quick retrieval, establishing databases is one of the top lists of the IT infrastructure in learning institutions. However, most of these institutions do not utilize them for knowledge discovery which can aid in informed decision-making, investigation of teaching and learning outcomes, and development of prediction models in particular. Prediction models have been utilized in almost all areas and improving the accuracy of the model is sought-after this study. Thus, the study presents a Scoutless Rule-driven binary Artificial Bee Colony (SRABC) as a searching strategy to enhance the accuracy of the prediction model for curriculum analysis. Experimental verification revealed that SRABC paired with K-Nearest Neighbor (KNN) increases the prediction accuracy from 94.14% to 97.59% than paired with Support Vector Machine (SVM) and Logistic Regression (LR). SRABC is efficient in selecting 14 out of 60 variables through majority voting scheme using the data of the BSIT students of Davao Del Norte State College (DNSC),

Recent advances in Predictive Learning Analytics: A decade systematic review (2012–2022)

“… A second source of data was Student Information Systems; as stated by Hasan et al ( 2020 ), 772 students’ academic information were collected from a private institution’s system in Oman. Meanwhile, Cutad and Gerardo ( 2019 ) used a dataset including 343 students records from the Enrollment Information System of the Information Technology Students at Davao Del Norte State College in the Philippines. Questionnaires are another data source; as an example, Tuononen and Parpala ( 2021 ) employed an electronic questionnaire to gather responses for analysis from 1019 students who had completed their bachelor’s or master’s degrees from the Faculty of Arts and the Faculty of Social Sciences in Finland.…”

“…A second source of data was Student Information Systems; as stated by Hasan et al ( 2020 ), 772 students’ academic information were collected from a private institution’s system in Oman. Meanwhile, Cutad and Gerardo ( 2019 ) used a dataset including 343 students records from the Enrollment Information System of the Information Technology Students at Davao Del Norte State College in the Philippines.…”

“…To predict learners’ performance, K Nearest Neighbor (K-NN) algorithm was employed (El Aouifi et al, 2021 ), while it was used by Cutad and Gerardo ( 2019 ) for curriculum analysis.…”

Prediction-Based Model for Student Dropouts using Modified Mutated Firefly Algorithm

“…After the generation of fireflies, each firefly with its corresponding light intensity undergoes a discretization process to mark the candidate firefly with either 1 or 0 [54] and [55]. The binary values are essential as these are the basis for fireflies to qualify in the selection phase [56] as shown in ( 4):…”