Guiding the Students in High School by Using Machine Learning

Ababneh, Mustafa Abdel-Karim; Aljarrah, Aayat; Karagozlu, Damla; Özdamlı, Fezile

doi:10.18421/tem101-48

Cited by 5 publications

(4 citation statements)

References 32 publications

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“…In addition, a good employment match would reduce their risks of changing new positions. Some studies have attempted to investigate the use of students' academic performance and social behavior with several attributes using data mining techniques [Athani et al (2017)], [Na, W. (2020)], analysis of educational big data using machine learning for guiding the students in high school [Ababneh et al (2021)] and optimize of agent-user matching process using a machine learning algorithm [Avdagić-Golub et al (2020)]. The latest research was conducted with 4,634 students from 16 colleges and collected data from their use of campus smart cards for almost three years (from 2010/09/01 to 2014/06/30).…”

Section: Related Workmentioning

confidence: 99%

The Effective Prediction Model Based on Employment Positions for Computer Curriculums Using Classification Techniques

Silprachawong¹,

Kaewchuea²,

Srisawat³

2021

INDJCSE

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Data mining techniques were used to build a prediction model prototype based on employment positions for computer majors in the Faculty of Science and Technology, Suratthani Rajabhat University. This study investigated the impact factors of employment positions of computer careers, compared model performances that consisted of six classification techniques and developed a web application using the effective prediction model that could be implemented in four majors using data collected from the graduates of sessions 2016-2020. The results of this research demonstrated that factors of seven courses and cumulative grade point average predicted employment positions. The models with best performances were rule-based and naive Bayes methods with a classification accuracy of 89.66%, precision of 90.48%, and recall of 90.00%. Therefore, a web application of the prediction model was developed using PHP programming. In practice, this application can suitably predict employment positions for computer majors.

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Section: Related Workmentioning

confidence: 99%

The Effective Prediction Model Based on Employment Positions for Computer Curriculums Using Classification Techniques

Silprachawong¹,

Kaewchuea²,

Srisawat³

2021

INDJCSE

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“…In fact, the rise of Artificial Intelligence (AI) has facilitated the development of a series of predictive models based on electronic online assessment and LMS tools. Faculty members are free to intervene and avoid student failures during learning, teaching, and assessment processes [5,[18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Using Ensemble Learning Algorithms to Predict Student Failure and Enabling Customized Educational Paths

Smirani

Yamani

Menzli

et al. 2022

Scientific Programming

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One of the challenges in e-learning is the customization of the learning environment to avoid learners’ failures. This paper proposes a Stacked Generalization for Failure Prediction (SGFP) model to improve students’ results. The SGFP model mixes three ensemble learning classifiers, namely, Light Gradient Boosting Machine (LGBM), eXtreme Gradient Boosting machine (XGB), and Random Forest (RF), using a Multilayer Perceptron (MLP). In fact, the model relies on high-quality training and testing datasets that are collected automatically from the analytic reports of the Blackboard Learning Management System (i.e., analytic for learn (A4L) and full grade center (FGC) modules. The SGFP algorithm was validated using heterogeneous data reflecting students’ interactivity degrees, educational performance, and skills. The main output of SGFP is a classification of students into three performance-based classes (class A: above average, class B: average, class C: below average). To avoid failures, the SGFP model uses the Blackboard Adaptive Release tool to design three learning paths where students have to follow automatically according to the class they belong to. The SGFP model was compared to base classifiers (LGBM, XGB, and RF). The results show that the mean and median accuracies of SGFP are higher. Moreover, it correctly identified students’ classifications with a sensitivity average of 97.3% and a precision average of 97.2%. Furthermore, SGFP had the highest F1-score of 97.1%. In addition, the used meta-classifier MLP has more accuracy than other Artificial Neural Network (ANN) algorithms, with an average of 97.3%. Once learned, tested, and validated, SGFP was applied to students before the end of the first semester of the 2020-2021 academic year at the College of Computer Sciences at Umm al-Qura University. The findings showed a significant increase in student success rates (98.86%). The drop rate declines from 12% to 1.14% for students in class C, for whom more customized assessment steps and materials are provided. SGFP outcomes may be beneficial for higher educational institutions within fully online or blended learning schemas to reduce the failure rate and improve the performance of program curriculum outcomes, especially in pandemic situations.

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“…That may happen because of the difficulty of analyzing the emotions in real-time and for the difficulty of monitoring students in a well-done way. Eventually, the lack of emotional interaction between the teacher and the learner affects seriously the process of learning [7], [8].…”

Section: Introductionmentioning

confidence: 99%

“…With the rapid changes and developments in the world of technology, especially in the field of artificial intelligence, machine learning, and deep learning, it has become possible to train machine learning and deep learning models to guide the students, know students' feelings and reactions [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Techniques for Distance Education: A Systematic Literature Review

Aljarrah¹,

Ababneh²,

Karagozlu³

et al. 2021

TEM Journal

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In the current era, education, like other fields, relies heavily on big data. Moreover, artificial intelligence, including affective computing, is one of the most essential and popular technologies adopted by educational institutions to process and analyze big data. In this systematic review, many previous research types related to improving educational systems using artificial intelligence techniques were studied, such as: deep learning, machine learning, and affective computing. This systematic review aims to identify the gaps in students' emotional understanding in distance education systems. The world has recently witnessed the spread of educational processes for distance learning, especially in the university and the enormous open online courses (MOOCs). Besides, the COVID-19 pandemic has been involved in changing all educational processes to a distance learning system. The results indicated that these systems recorded a high success rate. However, the teacher does not fully understand the student’s emotional state during the educational session. It also lacks monitoring or monitoring during the electronic exams, which are electronic exams. So, it is a widespread problem in distance learning.

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Guiding the Students in High School by Using Machine Learning

Cited by 5 publications

References 32 publications

The Effective Prediction Model Based on Employment Positions for Computer Curriculums Using Classification Techniques

The Effective Prediction Model Based on Employment Positions for Computer Curriculums Using Classification Techniques

Using Ensemble Learning Algorithms to Predict Student Failure and Enabling Customized Educational Paths

Artificial Intelligence Techniques for Distance Education: A Systematic Literature Review

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