Migration and academic performance in higher education: evidence for Colombia

Aim/Purpose: The main objective of this study is to explore students’ beliefs with regard to social media use (SMU) in higher education and the consequences of such use on the perception of their academic performance. Additionally, the study aims to determine the major influential factors with regard to SMU in student learning settings as a means of enhancing their performance. To achieve these objectives, drawing on the literature related to SMU in higher education settings, a research model has been developed. Background: Social media platforms have led to a significant transformation with regard to the communication landscape in higher education in terms of offering enhanced learning and improved teaching experience. Nevertheless, little is known, particularly in developing countries such as Jordan, as to whether or not the use of such platforms by students in higher education increases the perceptions of their academic performance. Therefore, this study has developed a model to examine the perceptions of higher education students with regard to social media use and its effect on their performance. Methodology: The Structural Equation Modelling approach is used to analyze data collected via an online survey in the form of a questionnaire to examine the use of such a model. The study sample is drawn from undergraduate and postgraduate students from three universities (one public and two private) in Jordan. Convenience sampling is used to collect data. Out of 730 sent questionnaire, 513 responses were received, of which 403 were deemed qualified to be part of the data analysis process. Contribution: This study contributes to the literature on social media in higher education by enhancing our understanding of the perceptions of higher education students on the use of social media in their learning. The tested model can be used as a benchmark for other studies that target the impact of social media on student performance in higher education. Findings: The results reveal that perceptions of (1) usefulness, collaborative learning, enhanced communication, enjoyment, and ease of use of social media have a positive effect on the use of such media in student learning; (2) resource sharing has an insignificant effect on social media use in student learning, and (3) social media use has a positive influence on students’ perceptions of their academic performance. Recommendations for Practitioners: Senior management and policy makers in higher education institutions will have to train faculty members on effective strategies and methods in order to effectively integrate social media into education. This would equip faculty members with the necessary digital skills needed to help them to be fully informed regarding the benefits of social media and its tools in learning and teaching activities and would also allow them to avoid any possible drawbacks. Furthermore, faculty members should reconsider their current techniques and strategies, and adopt new methods in their teaching that encourage students to use social media platforms as part of their learning. For example, they can regularly post discussions and assignments on social media platforms to inculcate the habit of using such platforms among students for educational purposes. Students, on the other hand, should be aware of the implications and potential advantageous aspects of SMU in their learning. This could be done by conducting regular workshops and seminars in the various faculties and schools at universities. Recommendation for Researchers: Researchers are encouraged to investigate additional factors that might influence the use of social media by students as well as faculty members. Specially, an emphasis should be given to identify any potential obstacles that might hinder the use of social media in higher education. Impact on Society: Social media is not only useful for socializing, but also it can be an effective educational tool that enhance students’ performance in higher education. Future Research: Although the collected data support the research model, this study is subjected to various limitations that need to be tackled by further studies. This study is based on the principles of quantitative research design. Data for this study was collected via survey questionnaires. Accordingly, future studies may consider a qualitative research design in order to uncover additional factors that may impact the use of social media on the part of higher education students. This would allow researchers to generate in-depth insights and a holistic understanding of SMU by higher education students. A convenience sampling method was employed to select respondents for this study. The respondents who participated in this study were from three universities (one public and two private) in Jordan. Accordingly, future research is deemed to be necessary to achieve a degree of generalizability regarding the findings of this study.

This paper has been designed to analyze theoretical insights into gender-based academic performance in higher education through the lens of Bourdieu's cultural capital theory. A systematic approach to review the published and available documents in online databases. The researcher reaches the point of saturation by browsing 68 published documents. These documents have been selected using the inclusion criteria on gender differentials in academic performance in higher education. The study findings outline that biological differences are less likely to affect the performance of female and male students rather than shaped by cultural differences. Females’ deprivation of education was due to the traditional and cultural practices in male hegemonic society. The study concludes that the difference in educational performance among female and male students in higher education is not linked with biological characteristics. It has roots in the difference of psycho-social, socio-economic, and cultural factors of socialization among female and male students.

Smartphones may be particularly prone to distracting students, allowing them to spend more time engaging with their devices rather than focusing on academic tasks. The purpose of this study was to explore the impact of the use of smartphones on student academic performance in higher education. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines to guarantee the inclusion of relevant academic materials. We examined 8 out of 299 documents gathered from the Education Resources Information Centre (ERIC), Scopus, and Web of Science (WoS) databases that were published since 2018. During the screening phase, excel files with details about the paper, including title, author and abstract, were exported from the database. Specifically, these papers focused on parameters such as primary, middle and high school students, smartphone addiction. Our bibliometric assessment shows that research on the use of smartphones for specific academic purposes has remained scarce in recent years, and most of the studies are located in Asian countries. The research supports that the academic use of smartphones has a significant positive impact on student academic performance in higher education. It shows that using smartphones for learning purposes has various positive effects on students' learning in and out of the classroom, such as motivating students, increasing their collaboration and interaction, and improving their engagement in learning. Also, the study found positive student attitudes toward the use of smartphone-assisted learning apps. We concluded that smartphone use has a significant positive impact on academic performance.

Socio-economic status and academic performance in higher education: A systematic review

Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation

This research investigates the impact of online learning on student engagement and academic performance in the higher education landscape. In a sample of 300 students from public sector universities in Punjab, Pakistan, the researcher examined online learning experiences, demographic factors, and academic performance. The descriptive statistics revealed moderately positive online learning experiences and moderately favorable academic performance on average, accompanied by significant variability within the sample. Correlation analysis, however, unveiled very weak and often statistically insignificant relationships between these variables, suggesting complex and non-linear dynamics at play. Consequently, we advocate for personalized support, adaptable pedagogical approaches, inclusivity, accessibility, and interdisciplinary collaboration as essential components to enhance online learning. Moreover, this study calls for continued research to unearth the intricate dynamics of online education's influence on student outcomes, driving the evolution of evidence-based practices in higher education. The study underlines the necessity for continued research into online education's complex dynamics and student outcomes. This includes studying new technology, learning analytics, and pedagogical methods to inform evidence-based practices in higher education.

Accurately identifying at-risk students in higher education is crucial for timely interventions. This study presents an AI-based solution for predicting student performance using machine learning classifiers. A dataset of 208 student records from the past two years was preprocessed, and key predictors such as midterm grades, previous semester GPA, and cumulative GPA were selected using information gain evaluation. Multiple classifiers, including Support Vector Machine (SVM), Decision Tree, Naive Bayes, Artificial Neural Networks (ANN), and k-Nearest Neighbors (k-NN), were evaluated through 10-fold crossvalidation. SVM demonstrated the highest performance with an accuracy of 85.1% and an F2 score of 94.0%, effectively identifying students scoring below 65% (GPA < 2.0). The model was implemented in a desktop application for educators, providing both class-level and individual-level predictions. This userfriendly tool enables instructors to monitor performance, predict outcomes, and implement timely interventions to support struggling students. The study highlights the effectiveness of machine learning in enhancing academic performance monitoring and offers a scalable approach for AI-driven educational tools.

Current research investigates the impact of students' E-Learning Attitudes (ELA), Digital Readiness (DR), and Academic Engagement (AE) on their academic performance in higher education. A survey in the form of an online questionnaire was administered to students through the networks established by the research teams. These networks encompassed academic staff from diverse Malaysian private as well as public universities. These academic staff were directed to share the survey link with their undergraduate students through WhatsApp or email. Two hundred eighty-six (286) valid surveys were collected from students at six public and private university campuses in Malaysia that offer courses and have used E-learning for at least one semester as part of COVID-19. In contrast to previous debates and studies, the outcomes of employing structural equation modeling through PLS 4.0 revealed a positive and noteworthy influence of E-learning platforms on students' academic performance. These findings hold broader implications for policymakers in higher education, researchers as well as educators, particularly in terms of considering the potential incorporation of social media tools within higher education, especially in developing nations. This research suggests that educational institutions that embrace change by aligning the goals of both students and instructors to establish a constructive and encouraging online learning atmosphere. This will foster active academic participation and enhance students' academic achievements.

The study aimed to determine if there is a significant relationship between self-esteem and academic performance. For this reason, research on self-esteem was carried out to improve academic performance in higher education. The type of research is applied, being the level of descriptive and correlational research. The research was cross-sectional, since the survey was carried out only once using the technique of the questionnaire and notes. The sample is simple random 56 students. It was shown that emotional and personal self-esteem occupies 64.3% (36) at the highest level; 21.4% (12) represent family self-esteem; 14.3% (8) is the minimum of the study. Therefore, self-esteem improves in the academic performance of students; however, it is necessary that teachers consider emotional and personal self-esteem in their classes. It was determined that more than 50% of the respondents are above 2.00, that is, they have a favorable attitude. On average, the subjects are located at 1.93 (favorable). Likewise, 0.599 scale units deviate from the average. Therefore, self-esteem benefits the student. Finally, it is concluded that there is an adequate significant relationship between self-esteem and the academic performance of university students, since 9.4877 > 4.445a; then, the null hypothesis is accepted and the alternative hypothesis is rejected, that is, it is favorable.

The integration of Information and Communication Technology (ICT) in higher education has emerged as a critical factor in enhancing academic performance and student engagement. ICT tools such as e-learning platforms, virtual classrooms, learning management systems, and educational software offer innovative ways to facilitate teaching and learning processes (Almazroi et al., 2020). These technologies enable students to access vast learning resources, collaborate in real-time, and receive timely feedback, which collectively contribute to improved academic outcomes (Nguyen et al., 2015). Moreover, ICT empowers instructors to adopt student-centered pedagogies, streamline administrative tasks, and personalize learning experiences (Bates, 2019). Despite its advantages, challenges such as digital inequality, lack of ICT skills, and inadequate infrastructure persist in some contexts, limiting its full potential (Buabeng-Andoh, 2012). This paper explores the impact of ICT on academic performance in higher education, highlighting both the transformative potential and the barriers to effective implementation. The study recommends institutional investment in ICT infrastructure and capacity-building programs to maximize the benefits of technology-enhanced learning

PurposeThe purpose of this study is to determine whether students' self-assessment (SSA) could be used as a significant attribute to predict students' future academic achievement.Design/methodology/approachThe authors address how well students can assess their abilities and study the relationship between this ability and demographic properties and previous study performance. The authors present the study results by measuring the relationship between the SSA across five different topics and comparing them with the students' performance in these topics using short tests. The test has been voluntarily taken by more than 300 students planning to enroll in the School of Business Informatics and Mathematics master's programs at the University of Mannheim.FindingsThe study results reveal which attributes are mostly associated with the accuracy level of SSA in higher education. The authors conclude that SSA, it can be valuable in predicting master's students' academic achievement when taking specific measures when designing the predictive module.Research limitations/implicationsDue to time constraints, the study was restricted only to students applying to master's programs at the Faculty of Business Informatics and Mathematics at the University of Mannheim. This resulted in collecting a limited data set. Also, the scope of this study was restricted to testing the accuracy of SSA and did not test using it as an attribute for predicting students' academic achievement.Originality/valuePredicting students' academic performance in higher education is beneficial from different perspectives. The literature reveals that a considerable amount of work is published to analyze and predict academic performance in higher education. However, most of the published work relies on attributes such as demographics, teachers' assessment, and examination scores for performing their prediction while neglecting the use of other forms of evaluation such as SSA or self-evaluation.

This research assesses the student engagement mediating effect on the interaction between feedback offered through generative AI and academic performance in higher education. Understanding the impact of AI-generated feedback on student performance is increasingly important as technology is integrated into educational systems. Information was gathered using a structured questionnaire administered to 432 students, of which 311 provided usable responses. Hypothesized relationships were tested using regression analysis. Findings indicate that the use of AI-fed-back especially increases academic performance and student engagement has a mediating effect. Increased engagement due to prompt and tailored AI responses leads to improved motivation, enhanced learning, and academic performance. The active involvement of students during the application of AI systems into education is crucial as per the findings of this research. The study offers additional evidence paving the way for policy guidelines about the application of AI technology in education to support students’ performance. The study discusses the need to design AI feedback systems for active users emphasizing the role of generative AI on the higher education landscape.

Research has reported equivocal results regarding the relationship between study time investment and academic performance in higher education. In the setting of the active, assignment-based teaching approach at Hasselt University (Belgium), the present study aimed (a) to further clarify the role of study time in academic performance, while taking into account student characteristics (e.g. gender, prior domain knowledge), and (b) to examine the relation between a number of student and course characteristics and study time. Data included course-specific study time recordings across the entire term, grades for 14 courses, expert ratings of six course characteristics, and other data from the records of 168 freshmen in business economics. For most courses, study time predicted grades, even beyond student characteristics. However, there were differential results depending on the course considered, stressing the importance of examining relations at course level instead of globally across courses. As to study time, course characteristics were strong predictors.

Microlearning is an innovative pedagogy which is the process of learning through small-sized, well-planned learning units and short-term learning activities. The objective of this study was to conduct a systematic review and meta-analysis to evaluate the impact of microlearning compared to macro-learning on the academic performance of students enrolled in higher education. Studies conducted on microlearning in higher education, in which the academic performance in theoretical examinations following microlearning method was evaluated quantitatively and compared with macro-learning. Studies which were reported in English language were included in this review. Ten databases were searched including SCOPUS, EBSCOhost, Emerald, JSTOR, Taylor & Francis, PubMed (MEDLINE), Oxford University Press, ERIC, ACM and IEEE Xplore. The search retrieved 602 studies and 12 studies were included in the systematic review. Cochrane’s risk of bias tool was used for the risk of bias assessment of the included studies. Five studies were included in the meta-analysis which was conducted using the RevMan 5.4 software. Meta-analysis showed a higher academic performance in students learned using microlearning (n=344) compared to the students learned using macro-learning (n=310) (p = 0.03). The overall mean difference in academic performance in relation to post-test scores in theoretical examinations between microlearning and macro-learning groups was 12.6 (95% CI: 1.2 - 23.9). Microlearning has contributed to a substantial increase in academic performance among students in higher education compared to macro-learning. Microlearning can increase academic performance of students by reducing cognitive load, providing flexible learning environment, promoting self-directed learning and by providing timely feedback.

Creating a classroom environment for learners to participate actively and engage is an integral element of a comprehensive educational initiative for actively learning students in higher education institutions. Students collaborate to attain goals through collaborative learning. The idea behind collaborative learning is that students may help each other learn and build a deeper comprehension of the subject matter by working together. The study examines the influence of collaborative learning on the academic performance of students at B.Ed. Levels focusing on social factors such as interaction with peers, interaction with teachers, and social media usage influence CL and collaborative learning to improve the student's academic performance. Social constructivism theory was used to observe student performance. Data have been collected through questionnaires from four private universities. Findings were evaluated through SPSS version 22; the composite reliability of the instrument was measured as α=0.954. The results of the regression analysis confirmed and accepted all three hypotheses. It can be concluded that all three independent variables - student interaction with peers, interaction with teachers, and social media use- positively impact collaborative learning and help students improve their academic performance and achieve their goals. The results of this study suggest that collaborative learning is an effective approach to enhancing academic Performance in higher education (B.Ed. Honors) and that social factors play an important role in promoting collaboration among students. The findings have implications for teaching and learning practices in higher education, where collaborative learning may be especially beneficial. Overall, this research highlights the importance of collaborative learning in promoting academic development in students.