Prediction of Academic Performance During Adolescence Based on Socioeconomic, Psychological and Academic Factors

Ahamed, Azad; Mahmood, Navid Tanzeem; Rahman, Rashedur M.

doi:10.1007/978-3-319-56660-3_7

Cited by 3 publications

(4 citation statements)

References 6 publications

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“…In our earlier studies (Ahamed et al, 2016(Ahamed et al, , 2017 we have reported some of our initial and general findings. In this research we have made a depth analysis by discovering rules for a specific group which is formed by different intervals of CGPA.…”

Section: Key Findingsmentioning

confidence: 81%

“…We have used multi-interval discretization as the pre-processing technique to discretize our data set and then we have applied Random forest and Naïve Bays to predict the academic attainment of students. As data were unbalanced, in a later study (Ahamed, Mahmood, and Rahman, 2017) we applied an advanced balancing technique and used principal component analysis (PCA) on balanced data and report performance of classifiers. Due to the continuous nature of our target class, we have had to discretize it before using machine learning techniques.…”

Section: Related Workmentioning

confidence: 99%

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An intelligent system to predict academic performance based on different factors during adolescence

Ahamed

Mahmood

Rahman

2017

Journal of Information and Telecommunication

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Students need to have an effective education to take advantage of all the latest tools available. Even with a proper education, they are failing to reap its benefits; reasons involve social, economic and psychological factors a student faces during their adolescence. Our research is directed towards this particular problem of educational effectiveness. We have surveyed a large number of students across different districts in Bangladesh. Pre-processing was done thoroughly; the use of data balancing, dimensionality reduction, discretization and normalization in combinations has allowed us to derive the best model that could predict the academic performance based on different factors during the adolescence.

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Section: Key Findingsmentioning

confidence: 81%

Section: Related Workmentioning

confidence: 99%

An intelligent system to predict academic performance based on different factors during adolescence

Ahamed

Mahmood

Rahman

2017

Journal of Information and Telecommunication

Self Cite

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“…Using some descriptive analysis, we can have a picture of what our data model's accuracy rate will be, strongly influenced by the dataset. Shakil Ahamed et al. (2017b) used the statistical technique to identify the minority class in the dataset, improved it and then used the technique to forecast performance.…”

Section: Methodsmentioning

confidence: 99%

The impact of big five personality trait in predicting student academic performance

Shaninah,

Mohd Noor

2023

JARHE

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PurposeThe study aims to propose a predictive model that combines personality and demographic factors to predict student academic performance (SAP). This research study works on understanding, enhancing and applying techniques to enhance the prediction of SAP.Design/methodology/approachThe authors gathered information from 305 university students from Al-Zintan University Libya. The study uses a survey questionnaire to collect data on essential variables. The purpose of the questionnaire is to discover variables that affect students' academic performance. The survey questionnaire has 44 closed questions with Likert scale designs that were distributed to a variety of college students at the start of the first semester of 2022. It includes questions about demographics, personality, employment and institutional aspects. The authors proposed a predictive model to identify the main fundamental components, consisting of one dependent variable (SAP) and five independent constructs. The suggested model is tested using partial least squares (PLS) and structural equation modeling (SEM), which perform better than covariance-based structural equation modeling (CB-SEM). PLS-SEM performs well with smaller sample sizes, even for complicated models.FindingsThe study results show that the proposed model accurately predicted the student's academic performance. The personality trait variables are a key factor that determines the actual student's academic performance. The student's academic performance is significantly impacted by each variable in the personality trait variables as well.Originality/valueThe process of validating research was done empirically through the accuracy and efficiency of model performance. The study differs from previous studies in that it accumulated a wide range of factors from different dimensions, including student demographics and personality trait factors. The authors developed a structural equation model to predict students' academic performance.

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“…The effectiveness of mastering courses is greatly influenced by the motives of students' activities, their awareness of the importance of the material being studied, and a steady interest in the subject as the volume of assimilated information increases (Lawanto & Stewardson, 2013;Grebennikova et al, 2018;Litau, 2018). Academic performance is also affected by numerous factors, such as family income, parents qualification and interaction with teachers (Kamal & Ahuja, 2019;Shakil Ahamed, Mahmood, & Rahman, 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Assessing the effects of the modular approach in math learning on independent creativity of university students

Zubova¹

2021

revtee

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Numerous teachers and researchers are studying the issue of improving the effectiveness of university education and offer innovative technologies to address this challenge. Modular learning is among the most relevant and productive teaching techniques. The paper examines how the modular approach in math learning contributes to the development of independent creativity of university students. The testing was performed at Tyumen Industrial University (Russia) among students of the Applied Geology program numbering 28 individuals in the control group and 27 individuals in the experimental group. The students were given a series of lectures to present new material that was later assimilated and reinforced during practical sessions through blocks and chains of preparatory and auxiliary tasks. To demonstrate their knowledge and grasping of the material, the students passed individual tests and exams. Statistical data were processed using Pearson’s chi-squared test and the Wilcoxon signed-rank test. Based on the decision-making rule, the research confirms the hypothesis that the modular approach is more effective in forming students’ independent creativity in math learning if compared with conventional education. The research results demonstrate that 92% of students in the experimental group set about solving advanced math problems, while in the control group this share was 61%. The share of students in the experimental group who prepared research projects increased from 52% to 89%, while in the control group their number decreased from 56% to 43%. Among the avenues for further research is an attempt to implement applied research projects not only in math learning, but also in applied subjects of study throughout the entire training period. The development of independent creativity of university students is expected to have a positive effect on their ability to absorb the material of other courses.

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Prediction of Academic Performance During Adolescence Based on Socioeconomic, Psychological and Academic Factors

Cited by 3 publications

References 6 publications

An intelligent system to predict academic performance based on different factors during adolescence

An intelligent system to predict academic performance based on different factors during adolescence

The impact of big five personality trait in predicting student academic performance

Assessing the effects of the modular approach in math learning on independent creativity of university students

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