Revisiting the Tinto&amp;#39;s Theoretical Dropout Model

Nicoletti, Maria do Carmo

doi:10.5539/hes.v9n3p52

Cited by 37 publications

(19 citation statements)

References 24 publications

(54 reference statements)

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“…The MDP models the dropout event as a temporal process based on information from two subjacent intertwined subprocesses, the academic and the social, since both co-exist in higher-level institutions. As pointed out by Nicoletti (2019), in his both papers Spady presents a very detailed discussion about the variety of issues as well as the many variables involved in dropout processes, and provides, as support for his analysis and comments, results found in a large number of empirical and theoretical works in the literature.…”

Section: Earlier Modelling Of Higher Education Dropoutmentioning

confidence: 75%

A Machine Learning-Based Computational System Proposal Aiming at Higher Education Dropout Prediction

Nicoletti¹,

Oliveira²

2020

HES

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In the literature related to higher education, the concept of dropout has been approached from several perspectives and, over the years, its definition has been influenced by the use of diversified semantic interpretations. In a general higher education environment dropout can be broadly characterized as the act of a student engaged in a course leaving the educational institution without finishing the course. This paper describes the proposal of the architecture of a computational system, PDE (Predicting Dropout Events), based on machine learning (ML) algorithms and specifically designed for predicting dropout events in a higher level educational environment. PDE’s main subsystem implements a group of instance-based learning (IBL) algorithms which, taking into account a particular university-course environment, and based on log files containing descriptions of previous dropouts events, is capable to predict when a student already engaged in the course, is prone to dropout, so preventive measures could be quickly implemented.

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Section: Earlier Modelling Of Higher Education Dropoutmentioning

confidence: 75%

A Machine Learning-Based Computational System Proposal Aiming at Higher Education Dropout Prediction

Nicoletti¹,

Oliveira²

2020

HES

Self Cite

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“…Academic failure is characterized by an inconsistent student engagement, which sheds light on a problematic situation. It does not consist of a single event, but is the final phase in a dynamic, cumulative, and multifactorial process of student disengagement (Chipchase et al, 2017;De Witte et al, 2013;Nicoletti, 2019). Particularly, more current studies show that the process of disengagement that leads to drop out begins during the first months of the first academic year (Hatch & Garcia, 2017).…”

Section: Intention To Dropout and Dissatisfaction With The University Experiencementioning

confidence: 85%

“…A previous review of the literature on school dropout (De Witte et al, 2013) underlined the complex interaction of factors contributing to academic failure. Thus, it is important for any research on academic failure to consider multiple protective and risk factors, such as individual, psychological, organizational, and relational factors (Nicoletti, 2019).…”

Section: Intention To Dropout and Dissatisfaction With The University Experiencementioning

confidence: 99%

Academic Failure: Individual, Organizational, and Social Factors

Morelli

Chirumbolo

Baiocco

et al. 2021

Psicología Educativa

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Due to the high percentage of university dropouts in Europe, it is important to better understand which factors lead students to leave university prior to completion (Vossensteyn et al., 2015). With the Europe 2020 strategy, the European Committee seeks to promote academic success and increase the number of young people with a tertiary education (Vossensteyn et al., 2015).Italy has one of the lowest percentages of graduates in Europe, with only 27.8% of persons aged 30-34 years with a tertiary education (only Romania ranks worse, 24.6%). Italy is also among the European Union countries with more dropouts after Spain, Malta, and Romania (Eurostat, 2020;Istat, 2019). In recent years, the Italian university system has launched programs aimed at reducing university dropout rates (Belloc et al., 2010); however, scientific research is needed to support such programs, specifically by identifying students at high risk of dropping out, in order to inform the development of effective preventive and intervention measures to promote student retention.If in other countries attention to factors underlying academic success and failure has been accompanied by numerous studies in this area (e.g., Ballo et al., 2019;Fernández et al., 2017;Han et al., 2020), in the Italian context, research on university dropouts is scant (Clerici et al., 2015); despite this, in recent years, some studies have investigated factors that promote university retention. These studies have largely investigated the phenomenon of academic dropout and university success from sociological and economic perspectives

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“…Institutional characteristics include classroom environments such as cohort-based learning [21,22]. Tinto's model does not provide guidance on which variables best operationalize the concept of student attributes [23]; however, previous studies have considered age, sex, and ethnicity [17] and proximity to campus [20,24].…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Cohort-Based Education and Other Factors Related to Student Peer Relationships: A Mixed Methods Social Network Analysis

et al. 2022

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Graduate students face a variety of stressors that relate to poor academic performance, lower completion rates, and high rates of depressive symptoms, anxiety, and stress. Student peers provide supports for coping with these stressors and succeeding academically. However, graduate students may find it difficult to form relationships with their peers. This mixed method social network analysis (MMSNA) examines graduate student perceptions of their peer relationships and the factors associated with peer friendships in a program that placed students in cohorts for the first semester of a two-year Master of Social Work program. Findings from three student focus groups include four main themes related to the cohort system and the tendency for students to gravitate to others who were similar to them (i.e., homophily). Focus group findings informed model specification for a curved exponential family model of student friendships at the end of the third semester of the program (N = 70) that identified correlates of student friendships including statistically significant direct and homophily effects for age, gender, and race/ethnicity. First-semester relationships and shared classes in subsequent semesters also increased the likelihood of friendships in the third semester. Findings emphasize the value of cohorts and a diverse student body for peer relationships.

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Revisiting the Tinto's Theoretical Dropout Model

Cited by 37 publications

References 24 publications

A Machine Learning-Based Computational System Proposal Aiming at Higher Education Dropout Prediction

A Machine Learning-Based Computational System Proposal Aiming at Higher Education Dropout Prediction

Academic Failure: Individual, Organizational, and Social Factors

Cohort-Based Education and Other Factors Related to Student Peer Relationships: A Mixed Methods Social Network Analysis

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