Prediction of social media effects on students’ academic performance using Machine Learning Algorithms (MLAs)

Nti, Isaac Kofi; Akyeramfo-Sam, Samuel; Bediako-Kyeremeh, Bright; Agyemang, Sylvester

doi:10.1007/s40692-021-00201-z

Cited by 30 publications

(15 citation statements)

References 43 publications

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“…From posting announcements to holding live lectures, social media platform can be used in a variety of ways in the classroom. This study should be used a benchmark for other studies on students using social media, how they feel after use (Isaac et al 2021). It recommends that social media use by students should be encouraged at all level of education.…”

Section: Recommendationsmentioning

confidence: 99%

Ascertaining the Effect of Social Network on Student’s Mood and Time Management with Respect to Gender in Nigerian Universities.

Ezugwu,

Nworgu,

Uzo

et al. 2024

IJLTEMAS

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One of the most popular pastimes among university students are reading which has an impact on their everyday lives is social networking. Social networking sites have become deeply embedded in the lives of students as a result of their ease of access and technological advancement. This study sought to determine whether a student's number of hours spent online is gender-independent, as well as whether a student's mood (angry/depressed) after utilizing social media is gender-independent. The inquiry was conducted as part of a study of students' responses to questionnaire questions. The results of the chi-square test of independence were used to determine the data's independence across groups. The findings revealed that a student's inclination to become angry or unhappy after utilizing social media is not reliant on their gender. Furthermore, it was observed that the time students’ use on the internet is not gender-specific. The total time the students’ spend on the internet is irrelevant to their gender, according to one study, and the change in a student's mood after utilizing social networking is likewise unrelated to gender.

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Section: Recommendationsmentioning

confidence: 99%

Ascertaining the Effect of Social Network on Student’s Mood and Time Management with Respect to Gender in Nigerian Universities.

Ezugwu,

Nworgu,

Uzo

et al. 2024

IJLTEMAS

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“…Input Features and Performance Focus Studies Performance Prediction using preadmission data High school GPA, GAT and Admission test scores (predict performance at the end of first year of undergrad program) (Tan et al, 2022), (Martínez-Navarro et al, 2021), (Alharthi, 2021), (Erika B. Varga, 2021), (Adekitan and N-Osaghae, 2019) Performance Prediction using academic data of undergrad program's initial years GPA of first-second year of undergrad program and grades in a some courses (predict graduating GPA) (Hashim et al, 2020), (Qazdar et al, 2019), (Miguéis et al, 2018), (Asif et al, 2017), (Hoffait and Schyns, 2017), (Jia and Maloney, 2015) Performance Prediction using low-cost variables Class participation, resource availability, heterogeneity, and class strength (predict future academic performance) (Tomasevic et al, 2020), (Yousafzai et al, 2020), (Xu et al, 2019), (Helal et al, 2018), (Sandoval et al, 2018), (Thiele et al, 2016), (Xing et al, 2015) Performance Prediction using non-academic variables in addition to academic data Behavioral and emotional characteristics, social and demographic features (forecast future academic performance) (Wild et al, 2023), (Kukkar et al, 2023), (Yao et al, 2019), (Nti et al, 2022), (Karagiannopoulou et al, 2021), (Keser and Aghalarova, 2021), (Fernandes et al, 2019), (Thiele et al, 2016)…”

Section: Factors For Categorizing Research Studiesmentioning

confidence: 99%

“…According to some studies, only academic factors are not enough for performance pre diction, but socio-demographic factors can be beneficial too (Thiele et al, 2016;Wild et al, 2023;Kukkar et al, 2023;Nti et al, 2022). Yao et al (2019) predicted the performance of undergraduate students based on their behavior in school.…”

Section: Performance Prediction In Coursesmentioning

confidence: 99%

Enhancing Student Performance Prediction via Educational Data Mining on Academic data

Alamgir¹,

Akram²,

Karim³

et al. 2023

Informatics in Education

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Educational data mining is widely deployed to extract valuable information and patterns from academic data. This research explores new features that can help predict the future performance of undergraduate students and identify at-risk students early on. It answers some crucial and intuitive questions that are not addressed by previous studies. Most of the existing research is conducted on data from 2-3 years in an absolute grading scheme. We examined the effects of historical academic data of 15 years on predictive modeling. Additionally, we explore the performance of undergraduate students in a relative grading scheme and examine the effects of grades in core courses and initial semesters on future performances. As a pilot study, we analyzed the academic performance of Computer Science university students. Many exciting discoveries were made; the duration and size of the historical data play a significant role in predicting future performance, mainly due to changes in curriculum, faculty, society, and evolving trends. Furthermore, predicting grades in advanced courses based on initial pre-requisite courses is challenging in a relative grading scheme, as students’ performance depends not only on their efforts but also on their peers. In short, educational data mining can come to the rescue by uncovering valuable insights from academic data to predict future performance and identify the critical areas that need significant improvement.

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“…Academic performance has different research approaches, including the evaluation of the effect of the use of social networks on academic performance using predictive models. For instance, the use of social networks in classes partially affects students' academic performance [15,16]. Likewise, Ref.…”

Section: Related Workmentioning

confidence: 99%

Visual-Predictive Data Analysis Approach for the Academic Performance of Students from a Peruvian University

et al. 2022

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The academic success of university students is a problem that depends in a multi-factorial way on the aspects related to the student and the career itself. A problem with this level of complexity needs to be faced with integral approaches, which involves the complement of numerical quantitative analysis with other types of analysis. This study uses a novel visual-predictive data analysis approach to obtain relevant information regarding the academic performance of students from a Peruvian university. This approach joins together domain understanding and data-visualization analysis, with the construction of machine learning models in order to provide a visual-predictive model of the students’ academic success. Specifically, a trained XGBoost Machine Learning model achieved a performance of up to 91.5% Accuracy. The results obtained alongside a visual data analysis allow us to identify the relevant variables associated with the students’ academic performances. In this study, this novel approach was found to be a valuable tool for developing and targeting policies to support students with lower academic performance or to stimulate advanced students. Moreover, we were able to give some insight into the academic situation of the different careers of the university.

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Prediction of social media effects on students’ academic performance using Machine Learning Algorithms (MLAs)

Cited by 30 publications

References 43 publications

Ascertaining the Effect of Social Network on Student’s Mood and Time Management with Respect to Gender in Nigerian Universities.

Ascertaining the Effect of Social Network on Student’s Mood and Time Management with Respect to Gender in Nigerian Universities.

Enhancing Student Performance Prediction via Educational Data Mining on Academic data

Visual-Predictive Data Analysis Approach for the Academic Performance of Students from a Peruvian University

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