The directional links between students’ academic motivation and social integration during the first year of higher education

Noyens, Dorien; Donche, Vincent; Coertjens, Liesje; Daal, Tine van; Petegem, Peter Van

doi:10.1007/s10212-017-0365-6

Cited by 58 publications

(46 citation statements)

References 69 publications

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“…Academic motivation refers to stimulating students to learn effectively (Datu & Yang, 2019) and can be defined as the tendency of a student to engage in meaningful and valuable academic activities and to try to achieve the academic results that he targeted (Zeynali et al, 2019). Previous research has demonstrated the importance of students' academic motivation from the first year of higher education (Noyens et al, 2019).…”

Section: Academic Motivationmentioning

confidence: 99%

The academic climate and student-teacher relationship as determinants of academic motivation

Toma

2020

Studia Doctoralia

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In this study we focus on the university students and the factors that influence their academic motivation. The participants were 202 students from different universities and specializations, 167 females and 35 males, aged 18-31 years, M = 21.33, SD = 2.15. We used Perceived Academic Climate Scale (Felner, 1993), Teacher-Student Relationship Scale (Brinkworth et al., 2018), and Academic Motivation Scale (Vallerand et al., 1992). Our objective was to identify the relationship between academic climate, teacher-student relationship (TSR), and academic motivation in the university environment, and to investigate the influence of the career anxiety on the academic motivation of students. The results show that anxiety regarding future career can moderate the relationship between school climate and academic amotivation and also between TSR and academic motivation.

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Section: Academic Motivationmentioning

confidence: 99%

The academic climate and student-teacher relationship as determinants of academic motivation

Toma

2020

Studia Doctoralia

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“…The deferral and early school leaving (or termination) of students' studies may increase the workload of staff and expenditure [7]. In addition, not only the reputation of the school [8] but also the social capital [9] will be lowered as a result of students receiving fail grade.…”

Section: Introductionmentioning

confidence: 99%

Predicting Students’ Performance With School and Family Tutoring Using Generative Adversarial Network-Based Deep Support Vector Machine

et al. 2020

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It has been witnessed that supportive learning has played a crucial role in educational quality enhancement. School and family tutoring offer personalized help and provide positive feedback on students' learning. Predicting students' performance is of much interest which reflects their understanding on the subjects. Particularly it is desired students to manage well in fundamental knowledge in order to build a strong foundation for post-secondary studies and career. In this paper, improved conditional generative adversarial network based deep support vector machine (ICGAN-DSVM) algorithm has been proposed to predict students' performance under supportive learning via school and family tutoring. Owning to the nature of the students' academic dataset is generally low sample size. ICGAN-DSVM offers dual benefits for the nature of low sample size in students' academic dataset in which ICGAN increases the data volume whereas DSVM enhances the prediction accuracy with deep learning architecture. Results with 10-fold cross-validation show that the proposed ICGAN-DSVM yields specificity, sensitivity and area under the receiver operating characteristic curve (AUC) of 0.968, 0.971 and 0.954 respectively. Results also suggest that incorporating both school and family tutoring into the prediction model could further improve the performance compared with only school tutoring and only family tutoring. To show the necessity of ICGAN and DSVM, comparison has been made between ICGAN and traditional conditional generative adversarial network (CGAN). Also, the proposed kernel design via heuristic based multiple kernel learning (MKL) is compared with typical kernels including linear, radial basis function (RBF), polynomial and sigmoid. The prediction of student's performance with and without GAN is presented which is followed by comparison with DSVM and with traditional SVM. The proposed ICGAN-DSVM outperforms related works by 8-29% in terms of performance indicators specificity, sensitivity and AUC.

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“…The researchers in the work [6] emphasized the role of social integration for the academic success of freshmen during their first year at university. The authors suggest to create a friendly learning environment to mitigate the adaptation and to support less motivated students.…”

Section: Introductionmentioning

confidence: 99%

“…Social integration also plays a very important role for the adjustment to university life [6,12,15]. The contacts with fellow students, senior students, and teachers as well as social media [15,16], new friendships [6], peer-monitoring and the fit between secondary school and higher education are extremely relevant to the adjustment of the first year students. Another important factor is academic expectations [12,17].…”

Section: Introductionmentioning

confidence: 99%

Retrospective Assessment and Proposals for the Adaptation of the First Year Students

Monastyrskaya¹,

Medvedeva²,

Katsura³

2019

Proceedings of the Internation Conference on "Humanities and Social Sciences: Novations, Problems, Prospects" (HSSNPP 2019)

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The process of studying at the university requires a freshman to be independent and active, although many freshmen are not prepared for it. We consider several sets of problems associated with the adaptation of the first year students: learning difficulties, difficulties in communication, the starting level (school background), self-organization, fears, and personal adjustment. Based on the analysis of materials from an applied sociological study, the authors conclude that adaptation to the educational process at the university proceeds faster for students with following characteristics: satisfied with their student life, interested in studying, living an active life, informed about the university before entering it, guided by recommendations of relatives or friends, previously enrolled in this university. The original research is based on students' self-assessment. The students make an attempt to assess their own adaptation problems in the first year of study by the second and the third year students. The novelty of the study is to consider the attitudes of students as subjects of the educational process, who are able to independently comprehend the problems of adaptation to the educational process at the university in the first year of study, their own strategies for overcoming difficulties. Another goal of the study is to present an organizational model for helping the first year students to adapt to the university education process as well as to consider opportunities for positive reflection in the process of adaptation to the educational process in the university. The research materials can be useful to educators, administrative staff of universities, psychologists, and freshmen's parents. Keywords-adaptation of the first year students, difficulties of adaptation, problems of communication, satisfaction with studying, adaptation strategies, organizational model of assistance in adaptation of the first year studentsI.The psychological aspect of adaptation is considered by researchers in the context of the relationship with fear, optimism or depression, satisfaction with the process of studies.

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The directional links between students’ academic motivation and social integration during the first year of higher education

Cited by 58 publications

References 69 publications

The academic climate and student-teacher relationship as determinants of academic motivation

The academic climate and student-teacher relationship as determinants of academic motivation

Predicting Students’ Performance With School and Family Tutoring Using Generative Adversarial Network-Based Deep Support Vector Machine

Retrospective Assessment and Proposals for the Adaptation of the First Year Students

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