The interplay between institutional integration and self-efficacy in the academic performance of first-year university students: A multigroup approach

Hakyemez, Tuğrul Cabir; Mardikyan, Sona

doi:10.1016/j.ijme.2020.100430

Cited by 13 publications

(10 citation statements)

References 95 publications

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“…Self-efficacy refers to a person's belief in his or her ability to exhibit the qualities and attributes required to achieve a specific goal (Bandura, 1997). Scholars found that international students' self-efficacy is linked to improved academic performance (Hakyemez and Mardikyan, 2021). This also accords with Tinto (2015), who stated that international students must believe in themselves to do well in their studies.…”

Section: Self-e Cacymentioning

confidence: 68%

Academic integration in higher education: A review of effective institutional strategies and personal factors

2022

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Globally, there has been a surge in international students, posing challenges to their host institutions. The review aimed to identify institutional strategies and personal factors that promote international students' academic integration. With an independent systematic review of titles, abstracts, and full texts, 64 studies were sampled and analyzed. The study presented findings through a narrative synthesis across all studies. The review results showed that there have been an increasing number of studies on academic integration over the last decade. Institutional strategies like staff and student interaction, student orientation programs, classroom organization, supervisory support, and learning communities are more effective in promoting healthy academic lives and the academic progress of students. Personal factors, which include financial status, language ability, and self-efficacy in students, were revealed to facilitate healthy academic lives and progress on campus. Prioritizing international students' welfare, providing language adaptation support, and increasing the accessibility of academic staff were suggested strategies for improving the academic lives of international students. The study also recommends that tertiary education systems and other stakeholders take these institutional strategies and personal factors into account when developing higher education policies. This will help international students have better academic lives and stay in school.

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Section: Self-e Cacymentioning

confidence: 68%

Academic integration in higher education: A review of effective institutional strategies and personal factors

2022

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“…In the last decade, multiple studies have been carried out which seek to establish the variables that specifically affect academic performance. Research has been carried out in areas such as psychology, where, apart from demographic data, the influence of variables related to interest, motivation, attendance, integration, self-regulation, commitment, participation, anxiety, and communication on academic performance have been considered (17)(18)(19)(20)(21). From the field of statistics, contributions have been made such as those reflected in (21)(22)(23), which apply statistical models that seek to examine the variables involved in university admission (admission and pre-university exams), proposing a model that involves various interrelated variables in an attempt to predict academic performance.…”

Section: Figure 1 Overview Of Machine Learningmentioning

confidence: 99%

“…The fields or areas of knowledge that have studied the multidimensional variable of academic performance are diverse. This has been approached from the field of psychology (17)(18)(19)(55)(56)(57), which has applied tools related to questionnaires on students' perceptions regarding academic performance, followed mainly by statistical tools that have a much more marked focus on demographic data and their influence on the variable of interest (21,22,58,59). Likewise, research related to data science is important, especially studies that use data mining algorithms and ML applied to the field of education.…”

Section: Contributions and Organizationmentioning

confidence: 99%

Prediction of University-Level Academic Performance through Machine Learning Mechanisms and Supervised Methods

Bravo

Nieves-Pimiento²,

Gonzalez-Guerrero³

2022

Ing.

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Context: In the education sector, variables have been identified which considerably affect students’ academic performance. In the last decade, research has been carried out from various fields such as psychology, statistics, and data analytics in order to predict academic performance. Method: Data analytics, especially through Machine Learning tools, allows predicting academic performance using supervised learning algorithms based on academic, demographic, and sociodemographic variables. In this work, the most influential variables in the course of students’ academic life are selected through wrapping, embedded, filter, and assembler methods, as well as the most important characteristics semester by semester using Machine Learning algorithms (Decision Trees, KNN, SVC, Naive Bayes, LDA), which were implemented using the Python language. Results: The results of the study show that the KNN is the model that best predicts academic performance for each of the semesters, followed by Decision Trees, with precision values that oscillate around 80 and 78,5% in some semesters. Conclusions: Regarding the variables, it cannot be said that a student’s per-semester academic average necessarily influences the prediction of academic performance for the next semester. The analysis of these results indicates that the prediction of academic performance using Machine Learning tools is a promising approach that can help improve students’ academic life allow institutions and teachers to take actions that contribute to the teaching-learning process.

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“…In studying the variation in the degree of relationships between constructs, gender differences are the most reported factors among university students [ 87 ]. Sobieraj and Krämer [ 88 ] investigated gender differences in competence and study motivation regarding STEM subjects, reporting that males perceived more self-efficacy and leadership aspirations than females.…”

Section: Hypothesis Developmentmentioning

confidence: 99%

Relationships between ICT competencies related to work, self-esteem, and self-regulated learning with engineering competencies

Khampirat

2021

PLoS ONE

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The rapid development of advanced technology worldwide has promoted an increase in the need for highly skilled engineers who are adept at applying job-related technologies and have engineering competency (ENcom) to gain knowledge and introduce creative solutions. However, little is known about the underlying mechanism of the associations between ICT competencies related to work (ICT-Work) and the ENcom of engineering students. This study sought to examine the role of ICT-Work on ENcom. Based on the literature, self-esteem and self-regulated learning (SRL) were identified as factors that indicate the effect of ICT-Work on ENcom, while gender was identified as a moderator that conditioned these mediated relationships. The sample consisted of 1,313 undergraduate engineering students from eleven universities in Thailand. The results of structural equation modeling (SEM) showed positive direct and indirect effects of ICT-Work on ENcom, self-esteem, and SRL and confirmed that self-esteem and SRL mediate the impact of ICT-Work on ENcom. Moreover, multigroup SEM revealed no gender differences in the factor loadings and structural path coefficients of ICT-Work on ENcom via self-esteem and SRL. To prepare students for their professional lives in the digital world, educational institutions should emphasize the importance of developing engineering students in ICT-Work and the use of advanced ICT involved in the job.

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The interplay between institutional integration and self-efficacy in the academic performance of first-year university students: A multigroup approach

Cited by 13 publications

References 95 publications

Academic integration in higher education: A review of effective institutional strategies and personal factors

Academic integration in higher education: A review of effective institutional strategies and personal factors

Prediction of University-Level Academic Performance through Machine Learning Mechanisms and Supervised Methods

Relationships between ICT competencies related to work, self-esteem, and self-regulated learning with engineering competencies

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