Prediction of Students’ Graduation Time Using a Two-Level Classification Algorithm

Tampakas, Vassilis; Livieris, Ioannis E.; Pintelas, Emmanuel G.; Karacapilidis, Nikos; Pintelas, Panayiotis E.

doi:10.1007/978-3-030-20954-4_42

Cited by 18 publications

(15 citation statements)

References 17 publications

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“…In this subsection, we highlight the differences between our work and previously published research. In Section 2, we provide multiple examples of research conducted by others involving the application of machine learning techniques in various educational settings [19][20][21][22][23]. These applications can be broadly classified into the following categories:…”

Section: Discussionmentioning

confidence: 99%

“…• predicting how likely an admission committee is to admit an applicant based on the information provided in their application file [19] (dataset size 588); • predicting student dropouts in an online program of study [20] (dataset size 189); and • predicting student progress and performance [21] (dataset size 288) [22,23] (dataset size 2260).…”

Section: Discussionmentioning

confidence: 99%

“…There are multiple authors who use data science techniques to describe and predict student progress and performance in educational settings. Tampakas et al [21] analyzed data from about 288 students and applied machine learning algorithms to classify students into one of two categories "Graduate" or "Fail". They then predicted which of the graduating students would take four, five or six years to graduate.…”

Section: Literature Reviewmentioning

confidence: 99%

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Predictive Models of Student College Commitment Decisions Using Machine Learning

Basu¹,

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Buckmire

et al. 2019

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Every year, academic institutions invest considerable effort and substantial resources to influence, predict and understand the decision-making choices of applicants who have been offered admission. In this study, we applied several supervised machine learning techniques to four years of data on 11,001 students, each with 35 associated features, admitted to a small liberal arts college in California to predict student college commitment decisions. By treating the question of whether a student offered admission will accept it as a binary classification problem, we implemented a number of different classifiers and then evaluated the performance of these algorithms using the metrics of accuracy, precision, recall, F-measure and area under the receiver operator curve. The results from this study indicate that the logistic regression classifier performed best in modeling the student college commitment decision problem, i.e., predicting whether a student will accept an admission offer, with an AUC score of 79.6%. The significance of this research is that it demonstrates that many institutions could use machine learning algorithms to improve the accuracy of their estimates of entering class sizes, thus allowing more optimal allocation of resources and better control over net tuition revenue.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Predictive Models of Student College Commitment Decisions Using Machine Learning

Basu¹,

Basu

Buckmire

et al. 2019

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“…In the sequel, we introduce our proposed two-level classification scheme for the prediction of hospitalized patients' LoS. It is worth mentioning, that two-level classification schemes are heuristic pattern recognition tools that are anticipated to yield better classification accuracy than single-level ones at the expense of a certain complication of the classification structure [20][21][22][23]. To the best of our knowledge, in the literature there has not been proposed any similar approach for the prediction of LoS while all proposed prediction models are single-level classifiers based on several classification algorithms (see [13][14][15][16][17]).…”

Section: Two-level Classifiermentioning

confidence: 99%

Decision Support Software for Forecasting Patient’s Length of Stay

et al. 2018

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Length of stay of hospitalized patients is generally considered to be a significant and critical factor for healthcare policy planning which consequently affects the hospital management plan and resources. Its reliable prediction in the preadmission stage could further assist in identifying abnormality or potential medical risks to trigger additional attention for individual cases. Recently, data mining and machine learning constitute significant tools in the healthcare domain. In this work, we introduce a new decision support software for the accurate prediction of hospitalized patients’ length of stay which incorporates a novel two-level classification algorithm. Our numerical experiments indicate that the proposed algorithm exhibits better classification performance than any examined single learning algorithm. The proposed software was developed to provide assistance to the hospital management and strengthen the service system by offering customized assistance according to patients’ predicted hospitalization time.

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“…A multilevel model allows one to estimate the degree programme's effect on the predicted probability of obtaining the degree. Machine learning and tree-based methods have been applied in the literature to model student dropout [24][25][26][27][28][29], but to the best of our knowledge, we are presenting the first time that a multilevel tree-based method has been applied to predict student dropout probability.…”

Section: Introductionmentioning

confidence: 99%

Performing Learning Analytics via Generalised Mixed-Effects Trees

Fontana

Masci

Ieva

et al. 2021

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Nowadays, the importance of educational data mining and learning analytics in higher education institutions is being recognised. The analysis of university careers and of student dropout prediction is one of the most studied topics in the area of learning analytics. From the perspective of estimating the likelihood of a student dropping out, we propose an innovative statistical method that is a generalisation of mixed-effects trees for a response variable in the exponential family: generalised mixed-effects trees (GMET). We performed a simulation study in order to validate the performance of our proposed method and to compare GMET to classical models. In the case study, we applied GMET to model undergraduate student dropout in different courses at Politecnico di Milano. The model was able to identify discriminating student characteristics and estimate the effect of each degree-based course on the probability of student dropout.

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Prediction of Students’ Graduation Time Using a Two-Level Classification Algorithm

Cited by 18 publications

References 17 publications

Predictive Models of Student College Commitment Decisions Using Machine Learning

Predictive Models of Student College Commitment Decisions Using Machine Learning

Decision Support Software for Forecasting Patient’s Length of Stay

Performing Learning Analytics via Generalised Mixed-Effects Trees

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