Toward Predicting Student’s Academic Performance Using Artificial Neural Networks (ANNs)

Abstract

Student performance is related to complex and correlated factors. The implementation of a new advancement of technologies in educational displacement has unlimited potentials. One of these advances is the use of analytics and data mining to predict student academic accomplishment and performance. Given the existing literature, machine learning (ML) approaches such as Artificial Neural Networks (ANNs) can continuously be improved. This work examines and surveys the current literature regarding the ANN methods used in predicting students’ academic performance. This study also attempts to capture a pattern of the most used ANN techniques and algorithms. Of note, the articles reviewed mainly focused on higher education. The results indicated that ANN is always used in combination with data analysis and data mining methodologies, allowing studies to assess the effectiveness of their findings in evaluating academic achievement. No pattern was detected regarding selecting the input variables as they are mainly based on the context of the study and the availability of data. Moreover, the very limited tangible findings referred to the use of techniques in the actual context and target objective of improving student outcomes, performance, and achievement. An important recommendation of this work is to overcome the identified gap related to the only theoretical and limited application of the ANN in a real-life situation to help achieve the educational goals.

Assessment and Evaluation of Different Machine Learning Algorithms for Predicting Student Performance

“…This work is conducted to assess the main ML algorithms and key attributes in student performance prediction. Several approaches [ 8 – 13 ] were followed, along with various strategies and steps proposed by references [ 10 , 11 ] in performing this survey work. These include (a) formulation of research questions, (b) eligibility criteria, (c) information source/search strategy, and finally (d) study selection.…”

Prediksi Prestasi Akademik Mahasiswa Bekerja Paruh Waktu Menggunakan Artificial Neural Network

“…ANN merupakan polimorfik dalam formasi struktural dan paralel dalam perhitungan algoritma, dan dapat digambarkan sebagai sistem elemen pemrosesan yang saling berhubungan erat yang bisa bekerja secara paralel komputasi. Sebuah ANN terdiri dari lapisan input yang bersifat sebagai variabel independen, satu atau lebih lapisan tersembunyi, dan variabel lapisan keluaran [13] D. Pengujian Model.…”

Prediction of Graduate Learners’ Academic Achievement in an Online Learning Environment Using a Blended Trauma Course

“…Online predictors can be the number of times the learner logged into the course, the number of activities that the learner completed on the platform, and the time spent on the platform. 8 …”

“…Online predictors can be the number of times the learner logged into the course, the number of activities that the learner completed on the platform, and the time spent on the platform. 8 As continuous monitoring is an important aspect of effective online teaching, it was recommended in the literature to carry out more studies to explore the predicting factors either personal or educational variables. Studying the variables that can predict the academic achievement of learners in distance education can improve the achievement of learners.…”