Using Decision Trees for Predicting Academic Performance Based on Socio-Economic Factors

Segura-Morales, Marco; Loza-Aguirre, Edison

doi:10.1109/csci.2017.197

Cited by 5 publications

(10 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For independent group comparisons of the central tendencies, methods appropriate to the scale level were used (binomial test, Mann-Whitney U test, t-test). To obtain a broad overview of multiple factors predicting the utilization of web-assisted self-help for parents, a tree analysis using the CART (Classi cation and Regression Trees) method was conducted [44,45] within the statistical software R version 4.2.1 [46]. The method of decision tree analysis has recently become popular in medical research [47].…”

Section: Methodsmentioning

confidence: 99%

Acceptance and utilization of web-based self-help for caregivers of children with externalizing disorders

Wähnke,

Plück,

Bodden

et al. 2023

Preprint

View full text Add to dashboard Cite

Background. Web-based self-help interventions for parents of children with ADHD and other externalizing disorders have been proven to be effective. In order to recommend individualized and optimized interventions, a better understanding of the acceptance and utilization of this innovative treatment approach is needed. Previous research has frequently employed subjective reports of utilization, but the validity of these studies may be limited. Methods. Data from the German WASH study were used. Participants (n = 276) were randomly assigned to the intervention condition (a) web-based self-help or (b) web-based self-help with optional telephone-based support calls. Data collection took place at baseline (T1) and 12 weeks later (T2). Utilization data were tracked using a log file generated for each participant at T2. Prediction models were calculated using CART (Classification and Regression Trees), a method known mostly from the field of machine learning. Results. Acceptance of the intervention was very high and the average number of logins corresponded to recommendations. Predictors of acceptance and predictors of utilization were similar and included, e.g., child’s externalizing symptoms, parental psychopathology, and above all additional telephone-based support by counselors. Conclusions. Through a detailed identification of acceptance and utilization, and the predictors thereof, we were able to gain a better understanding of the acceptance and utilization of web-assisted self-help for a parent management intervention in the treatment of children with ADHD and ODD. These findings can be used to recommend web-based interventions to particularly suitable families. It should be noted that some form of support is required for an intensive engagement with the content of the program. Trial Registration: The protocol of the study (German Clinical Trials Register DRKS00013456 conducted on January 3rd, 2018) was approved by the Ethics Committee of the University Hospital, Cologne.

show abstract

Section: Methodsmentioning

confidence: 99%

Acceptance and utilization of web-based self-help for caregivers of children with externalizing disorders

Wähnke,

Plück,

Bodden

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…[1], [2], [30], [9], [21], [19], [8 ], [31], [26], [23], [34], [36], [25] , [38], [11], [29], [24], [6], [15] , [40] PF02 Age [19], [35], [25] , [23], [29], [24], [6] , [15], [40] PF03 Marital status [19], [26], [1], [15], [24], [6]…”

Section: Pf01 Gendermentioning

confidence: 99%

“…City of Origin [23], [1], [11], [29], [10] PF05 Date of birth [14] PF06 Scholarship [35], [29], [6] PF07 Health Status [24], [6], [40]…”

Section: Country Ormentioning

confidence: 99%

“…Exam Score [17], [23] AF02 Total Credits [18], [19], [23], [29], [10], [20] AF03 CGPA [21], [30], [18], [19], [35] , [8], [28] , [17], [23], [5], [1], [36], [11] , [4], [14], [29] , [25], [31], [32] , [39], [40] AF04 GPA High School [35], [1], [36], [6] AF05 Assistance [21], [28], [5], [25], [39] AF06 Faculty [30], [6] AF07 Participation [28], [5], [11], [25]…”

Section: Af01 Admissionmentioning

confidence: 99%

“…Article TP01 One-R [21] TP02 C4.5 [8], [28], [23], [1], [10], [16], [ 34] TP03 Naive Bayes (NB) [2], [21], [19], [8], [28], [25], [ 5] , [12], [23], [5], [31], [32], [1 ], [33], [10], [36], [40] TP04 Bayes Networks [19], [5] TP05 Decision trees [35], [17], [33], [24], [31] TP06 Neural Networks (MLPs) [2], [21], [8], [28], [17], [32], [5], [31], [15], [33], [36], [6], [40] TP07 Vector Support Machine (SVM) [2], [13], [12], [15],…”

Section: Code Techniquementioning

confidence: 99%

See 2 more Smart Citations

Predicting Academic Performance through Data Mining: A Systematic Literature

Daza¹,

Guerra²,

Cervera³

et al. 2022

TEM Journal

View full text Add to dashboard Cite

The main objective of this work is to make a systematic review of the literature on the prediction of the academic performance of university students by applying data mining techniques. For this purpose, an exhaustive search was carried out and after the analysis of the documentation collected, aspects such as: methodology, attributes, selection algorithms, techniques, tools, and metrics were considered, which served as the basis for the elaboration of this document. The results of the study showed that the most used methodology is KDD(database knowledge extraction), the most important attribute to achieve prediction is CGPA(academic performance), the most commonly used variable selection algorithm is InfoGain-AttributeEval, among the most efficient techniques are Naïve Bayes, Neural Networks (MLP) and Decision Tree (J48), the most used tools for the development of the models is the Weka software and finally the metrics necessary to determine the effectiveness of the model were Precision and Recall.

show abstract

Application of machine learning in higher education to assess student academic performance, at-risk, and attrition: A meta-analysis of literature

Fahd

Venkatraman²,

Miah

et al. 2021

Educ Inf Technol

View full text Add to dashboard Cite

Recently, the field of machine learning (ML) has evolved and finds its application in higher education (HE) for various data analysis. Studies have shown that such an emerging field in educational technology provides meaningful insights into several dimensions of educational quality. An in-depth analysis of the application of ML could have a positive impact on the HE sector. However, there is a scarcity of a systematic review of HE literature to gain from the overarching trends and patterns discovered using ML. This paper conducts a systematic review and meta-analyses of research studies that have reported on the application of ML in HE. The differentiating factors of this study are primarily vested in the meta-analyses including a specific focus on student academic performance, atrisk, and attrition in HE. Our detailed investigation adopts an evidence-based framework called PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) for reporting the findings of our systematic review and meta-analyses of literature on the use of ML models, algorithms, evaluation metrics, and other criteria including demographics for assessing student academic performance, at-risk and attrition in HE. After undergoing the PRISMA steps such as selection criteria and filtering, we arrive at a narrowed down dataset of 89 relevant studies published from 2010 to 2020 for an in-depth analysis. The results not only show the outcomes of the quantitative analysis of the application of ML types, models, evaluation metrics, and other related demographics but also provide quality insights of publication patterns and future trends towards predicting and monitoring student academic progress in HE.

show abstract

Using Decision Trees for Predicting Academic Performance Based on Socio-Economic Factors

Cited by 5 publications

References 23 publications

Acceptance and utilization of web-based self-help for caregivers of children with externalizing disorders

Acceptance and utilization of web-based self-help for caregivers of children with externalizing disorders

Predicting Academic Performance through Data Mining: A Systematic Literature

Application of machine learning in higher education to assess student academic performance, at-risk, and attrition: A meta-analysis of literature

Contact Info

Product

Resources

About