Model Of Student Attrition Research Articles

Dropout intent of students with disabilities

We examine the mechanisms explaining the dropout intentions of students with disabilities by integrating Tinto’s model of student integration, the student attrition model, the composite persistence model, and insights from social stratification research. The resulting theoretical model posits that not only students’ academic and social integration, but also their private resources (financial, home learning, and personal resources) are crucial for academic success. Analysing data from a 2020 Germany-wide student survey, we find that students with disabilities are substantially more likely to intend to drop out of higher education than students without disabilities. Linear regressions and Kitagawa-Oaxaca-Blinder decompositions show that their lower academic integration and fewer personal resources are most relevant for explaining this difference, while their lower social integration, home learning, and financial resources play subordinate roles. Further analyses reveal that dropout intent is highest among students with psychic disabilities, followed by students with learning disabilities and students with physical disabilities. Regarding all three disability groups, less academic integration and fewer personal resources are most relevant for explaining their higher dropout intent (compared to students without disabilities). However, the disability groups differ regarding the importance of the different explanatory factors. Overall, our results highlight the importance of considering both students’ integration into higher education and their private resources for understanding student-group-specific dropout intent.

Back out or hang on? An event history analysis of withdrawal from doctoral education in Germany

Despite the benefits of a PhD for degree-holders as well for society as a whole, doctoral student attrition is a common phenomenon. Unfortunately, the empirical literature on dropout from doctoral education is scant, especially for non-US countries—an omission we address in the current study. Building on Tinto’s model of student attrition and rational choice theory, the study empirically assesses the association of different individual, institutional, and external factors with the propensity to leave doctoral studies. Unlike most studies in the field, it draws on longitudinal data using event history modelling, observing doctoral students in multiple disciplines and a wide range of universities. The key results can be summarized as follows: In Germany, women are more likely to dropout than men. The probability of dropping out strongly depends on the discipline and the availability of a scholarship. A close contact with the supervisor and exchange with other PhDs are associated with a lower dropout probability. Moreover, having children increases dropout rates. The study findings provide first empirical guidance for interventions that can possibly help reducing dropout.

Prediction of student attrition risk using machine learning

PurposeThe prediction of student attrition is critical to facilitate retention mechanisms. This study aims to focus on implementing a method to predict student attrition in the upper years of a physiotherapy program.Design/methodology/approachMachine learning is a computer tool that can recognize patterns and generate predictive models. Using a quantitative research methodology, a database of 336 university students in their upper-year courses was accessed. The participant's data were collected from the Financial Academic Management and Administration System and a platform of Universidad Autónoma de Chile. Five quantitative and 11 qualitative variables were chosen, associated with university student attrition. With this database, 23 classifiers were tested based on supervised machine learning.FindingsAbout 23.58% of males and 17.39% of females were among the attrition student group. The mean accuracy of the classifiers increased based on the number of variables used for the training. The best accuracy level was obtained using the “Subspace KNN” algorithm (86.3%). The classifier “RUSboosted trees” yielded the lowest number of false negatives and the higher sensitivity of the algorithms used (78%) as well as a specificity of 86%.Practical implicationsThis predictive method identifies attrition students in the university program and could be used to improve student retention in higher grades.Originality/valueThe study has developed a novel predictive model of student attrition from upper-year courses, useful for unbalanced databases with a lower number of attrition students.

An Examination of Student Retention at a 2-Year College Through Structural Equation Modeling

The purpose of this study was to develop a retention model specific to the 2-year college environment using factors not typically combined with the study of student retention. The study operationalized factors representing the basic psychological needs of autonomy, competence, and relatedness combined with elements of Bean and Metzner’s nontraditional student attrition model. The data were analyzed using structural equation modeling techniques. The results indicated acceptable model fit and small to moderate effects were noted for competence and autonomy with grade point average (GPA). GPA exhibited the sole direct effect on student retention. The results further indicated the student characteristics of full-time enrollment and completion of 30 or more college-level credits combined with GPA explained a higher variance on student retention than did the factors of autonomy, competence, relatedness, external pull, or external support combined.

Revisiting Student Attrition Studies: A New Conceptual Perspective

This article focuses on the overview of the student attrition model as well as related theory that are pertinent to student attrition studies. It comprises an investigation into the prominent student attrition models, such as the Undergraduate Dropout Process Model presented by Spady, and Tinto’s Model of Institutional Departure. Then, the focus is on the work by Bean that resulted in the Student Attrition Model, and the Pascarella produced the Conceptual Model for Research on Student-Faculty Informal Contact, as well as an exploration of attrition studies in Malaysian Higher Education. In addition, an initial conceptual research model derived from existing prominent student attrition models was proposed by the researchers for investigation in Malaysian Technical and Vocational Education and Training (TVET) student attrition studies, as well as for academic student attrition studies.

Keeping Students in by Sending Them out: Retention and Service-Learning

<p>This review of recent literature examines the research on the impact of service-learning on student retention. The theoretical framework of the review draws on both Tinto’s model of student attrition and Knowles’s theory of adult learning, which together suggest that academic and social integration, active participation and engagement in learning, and application and relevancy of the subject-matter under study are key factors in student success. The role of these factors has been confirmed in a growing body of research around learning experiences in general and, as this review shows, particularly in service-learning experiences. Suggestions are made for how future research might expand and critically deepen this evidence and offers some implications for service-learning as a means of improving student retention.</p>

Persist/Dropout Differences In Pre-matriculation Attitudes Of Freshman Towards College Attrition: A Longitudinal Multiple Group Structural Equations Model

This study proposes a structural equations model of college student attrition based on pre-matriculation freshman attitudes. The study uses a survey administered annually from 1995 to 1999 and tracks persistence and dropout behavior for five years until the last cohort graduated. It discusses seven constructs of pre-matriculation freshman attitudes that resemble the Cabrera, Nora, and Castaeda (1993) model of College Persistence that was based on attitudes of first-to-second year enrolled college students. We applied our survey to a Northeastern sectarian private college. Our model includes external factors such as financial attitude, and endogenous variables such as academic reputation, social integration, institutional commitment, goal commitment, academic performance, and intent to persist. In addition to the variables related to the Cabrera model, we added two measures of social integration, political interests and concern-for the disadvantaged because these were suitable to the type of college which hosted the study. Results provide a multiple group comparative and predictive model of student attrition for annual use by the Dean of Student Affairs in designing proactive plans and implementing intervention strategies to enhance student retention.

The effect of the fit between secondary and university education on first‐year student achievement

Central in this study is the role of student variables and the fit between secondary education and university education in the explanation of first‐year student achievement. The fit between two levels of education can be defined in different ways. In this study the focus is on four fit‐aspects: appropriateness of expectations, and the degree to which there is a continuance with respect to teaching approaches, knowledge and workload. The conceptual model, a synthesis of the Model of Student Attrition, the Educational Productivity Model and the Transition Model, is tested with structural equation modelling, based on maximum likelihood estimation. The results indicate that the fit between secondary and university education, in terms of resemblance between the learning environments and experience of some adjustment, as well as social integration, student employment and motivation, affect first‐year achievement indirectly through time investment. The theoretical and practical implications of these results are discussed.

Configural Invariance of a Model of Student Attrition

The model presented used available data to predict whether or not a student will drop out at some time during his or her college career. The model successfully identified students who would or would not drop out approximately 80% of the time. Logistic regression analysis was employed to predict chances of attrition for matriculating freshmen soon after freshman orientation. The dependent variable was the probability a student would, at some time in their college experience, drop out of college. The independent or predictor variables included demographics, high school experiences and factor scores based on a survey of attitudes, opinions and values. A two-stage procedure consisting of factor analysis in the first stage and logistic regression in the second stage provided model parameter estimates for each cohort of matriculating freshman in years 2000 to 2003. Empirical tests revealed the model was fully configural invariant in the first stage and partially configural invariant in the second stage.

An Examination of the Influence of Institutional Context on Student Persistence at 4-Year Colleges and Universities: A Multilevel Approach

Using constructs from Bean's (1990) [In: D. Hossler and J. P. Bean (Eds.), The strategic management of college enrollments. San Francisco:Jossey-Bass] student attrition model and the Berger and Milem (2000) [In: J. C. Smart (Ed.), Higher education: Handbook of theory and research (Vol. XV, pp. 268–338). New York: Agathon Press] college impact model, national survey data, and multilevel modeling techniques, this study examines the extent to which persistence is influenced by the institutional context. The analytic sample is limited to first-time, full-time, degree-seeking undergraduates attending 4-year colleges and universities nationwide. This study finds that selectivity, as measured by the average student academic ability at an institution, has a contextual effect on college student persistence that reflects a positive increment to the chance of persistence that accrues to a student beyond student-level predictors of persistence. Although the implications of the findings are mainly for theory and methods, policy implications are discussed.

Toward a Theoretical Model of Employee Turnover: A Human Resource Development Perspective

This article sets forth the Organizational Model of Employee Persistence, influenced by traditional turnover models and a student attrition model. The model was developed to clarify the impact of organizational practices on employee turnover from a human resource development (HRD) perspective and provide a theoretical foundation for research on that topic. What is unique about this unified longitudinal model in which organizational factors that impact human resources are the focal point is that these organizational factors are hypothesized to be symbiotically and reciprocally linked to key individual variables—intention, goals, commitment, and satisfaction. Implications include evaluating HRD practices on the basis of reduced costs of turnover, thus enhancing the strategic role of HRD in organizations.

In search of higher persistence rates in distance education online programs

Tinto's [Rev. Educ. Res. 45 (1975) 89; Tinto, V. (1987). Leaving college. Chicago: The University of Chicago Press; Tinto, V. (1993). Leaving college: rethinking the causes and cures of student attrition. Chicago: The University of Chicago Press] student integration model and Bean and Metzner's [Rev. Educ. Res. 55 (1985) 485] student attrition model have been influential in explaining persistence and attrition in higher education programs. However, these models were developed with on-campus programs in mind and, although they are broadly relevant to distance education programs, their ability to explain the persistence of online students is limited. Distance education students have characteristics and needs that differ from traditional learners and the virtual learning environment differs in important ways from an on-campus environment. This article draws chiefly from Tinto's and Bean and Metzner's models and the results of research into the needs of online distance education students in order to synthesize a composite model to better explain persistence and attrition among the largely nontraditional students that enroll in online courses.

Why do HE Students Drop Out? A test of Tinto's model

This study examined Tinto's (1975) model of student attrition, which suggests that student's dropout decisions are social and academic worlds. The model was tested with path analysis using LISREL8 software (Jöreskog & Sörbom, 1993a) using maximum likelihood estimation. Findings were that the model does not provide an acceptable description of the data. The findings were based on a global test of the model as a whole, as well as tests of individual paths within the model. Prior investigations into the model have been largely supportive, and reasons are considered for the disparity between previous and current findings. It is suggested that Tinto's perspective may not be the most appropriate for attrition research. Instead it is contended that interactionist and ethnographic approaches may result in a more appropriate theoretical framework.

Ties That Bind

A Social Network Approach to Understanding Student Integration and Persistence Introduction Astin's (1993, P. 398) claim that peers are single most potent source of influence in the lives of college students comes after decades of theoretical and empirical research examining the integrative nature of student experiences on the college campus. Review of the classic work of Newcomb (1943, 1962) and Newcomb and Wilson (1966), or the more contemporary observations of Horowitz (1987) or Moffatt (1989) demonstrates why student peer culture plays such a central contextual role in the understanding of a variety of college outcomes (e.g., Antrobus, Dobbelaer, & Salzinger, 1988; Chickering, 1974; Husband, 1976; Nora & Rendon, 1990; Stage, 1989; Terenzini & Pascarella, 1977). One outcome theorized to be dramatically affected by student peer culture is persistence. Much of the student persistence literature places great emphasis on student integration into campus subcultures (Spady, 1971; Tinto, 1993). Two major comprehensive conceptual models of student persistence have emerged from the various theoretical perspectives on this phenomenon, Bean's Student Attrition Model (1980, 1982, 1983, 1990) and Tinto's Student Integration Model (1975, 1987, 1993). Although Tinto's integration model places a greater emphasis on the role of within-institution peer culture than Bean's organizational model, which emphasizes the role that external forces play in the persistence process, considerable overlap exists between the two, especially in terms of the role of organizational factors and commitment to the institution (Cabrera, Casteneda, Nora, & Hengstler, 1992). In both models, student commitment to the institution is theorized to be affected by peers' attitudes and pressures. Bean theorizes that among other potential modifiers, the encouragement of close friends may enhance a sense of commitment to the institution (institutional fit and quality). Tinto postulates a similar relationship, namely that the higher the level of social integration the greater will be the commitment to the institution. As integration is the central feature of the Tinto model it has therefore been carefully elaborated both conceptually and empirically. This should not discount the theoretical role of social structure in Bean's model, for friendships, or social ties, are presumed to impact the extent of students' shared group values, support structure, and affinity for the institution in both models--simply in different ways. As many have pointed out, the operational measurement of the manifold dimensions of peer culture is often elusive. Adequate measurement of the effects of these important influences requires knowledge of students' individual group identifications as well as their desires for group affiliation, membership, and, ultimately, acceptance (Astin, 1993; Kuh, 1995; Newcomb, 1943, 1962; Newcomb & Wilson, 1966). The purpose of this study is to begin to explore such subcultural effects by assessing the role of student social structure in the persistence process. Specifically, I explore the effects of social integration from a social network perspective: a perspective that enables determination of subgroup membership and of the characteristics of relationships to and within those subgroups. My concern in this article is to take a first step toward a more appropriate empirical elaboration of the integration constructs central to these models. Accordingly, this study is exploratory in nature and is designed to examine the effects of structural integration on factors theorized to be central in the persistence process. Of the two major theories of student persistence, Tinto's Student Integration Model (1993) will be used as the framework for the development of a method that will enable the specification of a model incorporating a number of social network characteristics. Although similarities exist between Bean's and Tinto's theories, especially in terms of the posited relationships between integration and persistence, the Tinto model was chosen as a framework due to its particular theoretical underpinning and the large volume of work explicating is theory. …

Desire To Finish College: An Empirical Link between Motivation and Persistence.

A strong desire for achievement may be seen asan important component of student motivation to completecollege. This study examined the structuralrelationships among four constructs: (1) motivational factors, (2) student background factors, (3)academic performance, and (4) persistence. Motivationand background factors were examined to determine theirdirect and indirect effects on academic performance and persistence for minorities andnonminorities. The study supported utilization offactors external to the students' college experienceidentified in the retention literature based on Bean's(1980) Student Attrition Model. While it was found thatmotivation failed to impact academic performance foreither racial subgroup, a significant motivationaleffect on persistence was found for minorities but notfor nonminorities in this study. Minority studentswith high levels of motivation tended to persist totheir second year. Policy implications arediscussed.

An approach/avoidance behavioral model of college student attrition

We used approach/avoidance theory to develop a conceptual model of student attrition that we estimated using 262 first- and second-year students at a major midwestern research university. We found that we could use psychological theories to enhance our understanding of the retention process; that psychological and sociological factors accounted for 37 percent of the variance in student attrition; and that institutions can enhance retention by developing programs that increase appropriate academic and social approach behaviors and reduce avoidant behaviors.

Adult Student Drop-Out at Post-secondary Institutions

The age distribution of student populations at post-secondary institutions has become more heterogeneous, complicating assessments of students at risk for dropping out. Vincent Tinto’s (1975) model of student attrition is tested for age-related sensitivity.

College Persistence

Although several theories have been advanced to explain the college persistence process |6, 44, 45, 50, 52~, only two theories have provided a comprehensive framework on college departure decisions. These two theoretical frameworks are Tinto's |50, 52~ Student Integration Model and Bean's |7~ Student Attrition Model. A review of the literature indicates that the Student Integration Model, for instance, has prompted a steady line of research expanding over a decade |see, for example, 37, 42, 30, 35, 24, 46, 18~. This research has validated Tinto's model across different types of institutions with differing student populations. In turn, the Student Attrition Model |4, 5, 6, 7, 10~ has also been proven to be valid in explaining student persistence behavior at traditional institutions |3, 4, 6, 7, 10, 18~, while modifications to the model have been incorporated to explain the persistence process among nontraditional students |9, 26~. Insofar as the two theories have attempted to explain the same phenomenon, no efforts have been made to examine the extent to which the two models can be merged to enhance our understanding of the process that affects students' decisions to remain in college. However, Cabrera, Castaneda, Nora, and Hengstler |18~ have provided evidence that there is considerable overlap between the two theoretical frameworks. Taking these findings one step further, this study attempts to document the extent to which these two theories can be merged in explaining students' persistence decisions by simultaneously testing all non-overlapping propositions underlying both conceptual frameworks. Theoretical Framework Student Integration Theory Building on Spady's |44, 45~ work, Tinto |50, 51, 52~ advanced a model of student departure that explains the process that motivates individuals to leave colleges and universities before graduating. Tinto's theory attributes attrition to the lack of congruency between students and institutions. Tinto's theory basically asserts that the matching between the student's motivation and academic ability and the institution's academic and social characteristics help shape two underlying commitments: commitment to an educational goal and commitment to remain with the institution. Accordingly, the higher the goal of college completion and/or the level of institutional commitment, the greater is the probability of persisting in college. The Student Integration Model has been subjected to considerable testing, and research findings have largely supported the predictive validity of the model as far as the role of precollege variables is concerned |37, 38~. Results are mixed, however, when the structural relations that the theory presumes to exist among academic integration, social integration, and institutional and goal commitments are subjected to empirical testing |14, 28, 30, 33, 39, 40, 36, 41, 46, 47~. Although contradictory findings have been attributed to type of institution, gender, ethnicity, and inconsistencies on the measurement of the constructs |19, 35, 31~, these mixed results can also be attributed to the lack of control for variables external to the institution. A major gap in Tinto's theory and allied research has been the role of external factors in shaping perceptions, commitments, and preferences |7~. This topic is particularly relevant from both a policy analysis and an institutional perspective, given the different social and institutional programs aimed at stimulating enrollment and preventing attrition by addressing variables other than institutional ones (that is, ability to pay, parental support). In spite of this limitation, researchers have found that the Student Integration Model is useful in exploring the role of such external factors as significant other's influence |30, 35, 19~ and finances |15, 19, 24, 30~. Student Attrition Model Over the years, Bean |3, 4, 5, 6, 7, 8~ has advanced an alternative model to explain the college persistence process. …

The influence of student problems on student withdrawal decisions: An autopsy on ?autopsy? studies

The influence of student problems, which are often cited by students as reasons for withdrawal, is compared with the influence of constructs derived from Tinto's student attrition model. The findings suggest that data from post-hoc attrition studies should be used cautiously.

Factors related to progress towards completion of correspondence courses in a baccalaureate nursing programme.

This study focuses on the instructional technique of correspondence instruction--the learner, the setting, and the problem of course non-completion. The purpose of this study was to develop a model of completion of correspondence courses which identifies factors related to progress towards course completion. The objectives were to estimate the model and identify factors associated with course completion. The causal model for this study was adapted from Bean's synthetic model of student attrition from institutions of higher education. Data to test the model were obtained from a questionnaire, transcripts, and progress records of students enrolled in independent study programme courses and declaring a nursing major. Eleven variables in the model estimated in this study accounted for 44% of variance in course progress. Students making progress intended completing the course in 3 months, submitted the first lesson within 40 days, had high SAT scores and GPAs, completed (and did not drop) other correspondence courses, perceived family support but not employer support, had high goals for completing the programme and obtaining the BSN, lived closer to the instructor, and entered the nursing programme with college preparation (as a registered nurse). The model can be used for understanding and explaining progress toward course completion as well as the distance education setting of correspondence instruction. The intent variable explains most variance and can be measured while students are enrolled in the course. The lesson submission variable is theoretically justified.