Predicting patterns of persistence at a South African university: a decision tree approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Vino Paideya ◽  
Annah V. Bengesai
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
South African ◽  
Large Scale ◽  
Classification Tree ◽  
First Year ◽  
Content Type ◽  
Factors Associated ◽  
Chemistry Major ◽  
Factors Influencing

PurposeThe emerging field of educational data mining provides an opportunity to process large-scale data emerging from higher education institutions (HEIs) into reliable knowledge. The purpose of this paper is to examine factors influencing persistence amongst students enrolled in a Chemistry major at a South African university using enrolment data.Design/methodology/approachThe sample consisted of 511 students registered for a Chemistry major beginning in 2012, 2013 and 2014. Descriptive statistics in counts and percentages and classification (decision) tree methods were used in the analysis.FindingsGraduation from the Chemistry major is likely to occur after 4 years, which is regulation time plus 1 year, whilst departure mainly occurs in the first year of study. Classification tree modelling demonstrated that first year accumulated credits (FYAC), gender, financial aid status and school quintile were the factors associated with persistence. FYAC was the most critical factor.Research limitations/implicationsAlthough this study has many strengths, significantly the use of data mining methods to classify students, some limitations might affect how the results are interpreted. First, the analysis focused on a one-degree major in one institution, which leads to the suspicion that the observed results are discipline or institution-specific. Thus, the findings cannot be generalised to other contexts or disciplines. Second, with so many potential factors influencing student persistence, the analysis presented in this paper, which was limited to the covariates obtained in the institutional dataset the authors used, is by no means exhaustive. There is the possibility that some factors, which are not included in the present analysis, might have more predictive power.Originality/valueGlobally, university administrators are interested in predicting student outcomes and understanding the intricate balance between enrolment and throughput. Thus, whilst the findings from this study have an institutional focus, they resonate with other HEIs and present an alternative and highly visual way of identifying specific combinations of factors associated with persistence. The results from a classification tree model can also classify students at risk and inform the development of interventions that will support them.

Predicting customer churn in mobile industry using data mining technology

2017 ◽  
Vol 117 (1) ◽  
pp. 90-109 ◽  
Author(s):  
Eui-Bang Lee ◽  
Jinwha Kim ◽  
Sang-Gun Lee
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Online News ◽  
Partial Least Square ◽  
Churn Prediction ◽  
Mining Technology ◽  
Content Type ◽  
Customer Churn ◽  
Mobile Industry ◽  
Using Data

Purpose The purpose of this paper is to identify the influence of the frequency of word exposure on online news based on the availability heuristic concept. So that this is different from most churn prediction studies that focus on subscriber data. Design/methodology/approach This study examined the churn prediction through words presented the previous studies and additionally identified words what churn generate using data mining technology in combination with logistic regression, decision tree graphing, neural network models, and a partial least square (PLS) model. Findings This study found prediction rates similar to those delivered by subscriber data-based analyses. In addition, because previous studies do not clearly suggest the effects of the factors, this study uses decision tree graphing and PLS modeling to identify which words deliver positive or negative influences. Originality/value These findings imply an expansion of churn prediction, advertising effect, and various psychological studies. It also proposes concrete ideas to advance the competitive advantage of companies, which not only helps corporate development, but also improves industry-wide efficiency.

Pathways through homelessness: the perceptions of homeless children in Pietermaritzburg, South Africa

2020 ◽  
Vol 15 (3) ◽  
pp. 141-160
Author(s):  
Anna Wilshire Jones Bornman ◽  
Carol Jean Mitchell
Keyword(s):  
South Africa ◽  
South African ◽  
Well Being ◽  
South African Context ◽  
Homeless Children ◽  
The South ◽  
Research Attention ◽  
Content Type ◽  
Factors Influencing ◽  
African Context

Purpose The purpose of this study was to explore children’s pathways through homelessness within the South African context, with particular attention paid to pathways out of homelessness. This study focusses on factors influencing children’s successful transitions out of homelessness. Design/methodology/approach A qualitative exploratory design was used, using interviews with nine children who had exited or were in the process of exiting homelessness. Interviews were conducted at a children’s shelter in Pietermaritzburg or in the children’s home environments. Interviews were analysed thematically. Findings An ecological framework was used to frame the factors influencing children’s pathways in, through and out of homelessness in the children’s narratives. These included institutions, relationships and intrapersonal strengths and resources. The study suggested that constructive relationships with shelter staff and parental figures, as well as intrapersonal strengths, were the most prominent factors in children successfully negotiating their way through their homelessness. The importance of a relationship with the paternal family within some African cultures was also a point of leverage. Research limitations/implications Implications for policy and practise include the need for systemic change, as well as greater support for shelters and shelter staff. The issue of rivalry in the shelter context and the role of the paternal family in the reintegration process require more research attention. The research is limited to homeless children in Pietermaritzburg, South Africa. Practical implications This study provided feedback to the shelter regarding their strategies for assisting homeless children off the streets. It further provided evidence for the importance of the work of the shelter, to strengthen advocacy efforts. This may be useful to others in similar circumstances. Social implications This study highlights the importance of macrosystemic interventions in the efforts to assist homeless children, while at the same time not ignoring the inter and intra, personal elements to enhancing their well-being. Originality/value This paper is singular in its exploration of factors influencing children’s successful transitions out of homelessness within the South African context.

Large-Scale Data Mining and Distributed Processing in Big Data Internet

2014 ◽  
Vol 989-994 ◽  
pp. 4594-4597
Author(s):  
Chun Zhi Xing
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Decision Tree ◽  
Large Scale ◽  
Distributed Processing ◽  
Processing Method ◽  
Decision Tree Algorithm ◽  
Data Query ◽  
Large Scale Data ◽  
Scale Data

With the development of Internet, various Internet-based large-scale data are facing increasing competition. With the hope of satisfying the need of data query, it is necessary to use data mining and distributed processing. As a consequence, this paper proposes a large-scale data mining and distributed processing method based on decision tree algorithm.

scholarly journals Implementasi Metode Decision Tree Klasifikasi Data Mining Untuk Prediksi Peminatan Jurusan Robotika oleh Mahasiswa

2019 ◽  
Vol 5 (2) ◽  
pp. 161-166
Author(s):  
Mugi Raharjo ◽  
Ridwan Ridwan ◽  
Jordy Lasmana Putra ◽  
Tommi Alfian Armawan Sandi
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Computer Technology ◽  
Computer Network ◽  
Classification Tree ◽  
Technology Study ◽  
Study Program ◽  
Final Project ◽  
Decision Method ◽  
The Past

Specialization of majors in a study program becomes something important must be an option for a student, for that they must think carefully before choosing the majors. Because later this thing can determine the success or failure of a student to understand what they learned to apply to during the final project. In the past few years there has been a question about the problem of electing majors in the Computer Technology Study Program. Because almost every year the majority of interest voters in majors are interested in computer network majors rather than robotics majors. majoring in majors, so the authors analyzed and retrieved data from 145 student samples in the electronic practicum course and chose 7 attributes in this study because this course was very influential on the interest in the robotics department in the Computer Technology study program. The author uses the classification tree Decision method to predict interest in students. Therefore, with this research, the authors hope that in the future with the results of this analysis can be found a solution to the problem of why students are more inclined to choose the interests of departments other than robotics, whether due to factors or other factors. Keywords: Computer Technology, Analysis, Classification

The relationship between university teachers' self-evaluation of online teaching and their background: based on the survey of 334 Chinese universities

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Wei Wu ◽  
Rui Yao ◽  
Zuoxu Xie
Keyword(s):  
Evaluation System ◽  
Online Teaching ◽  
Large Scale ◽  
The Self ◽  
Three Dimensions ◽  
Content Type ◽  
University Teachers ◽  
Self Evaluation ◽  
Background Levels ◽  
Factors Influencing

PurposeThis paper aims to take Chinese university teachers as the research objects to examine their self-evaluation of online teaching and analyze the main factors influencing their evaluation during COVID-19.Design/methodology/approachAccording to the theory of educational ecology, the factors influencing teachers' self-evaluation of online teaching in this paper include university background, courses background and teachers' personal background from the macro- to micro-levels. Through exploratory factor analysis, independent sample T-test and one-way analysis of variance (ANOVA), the self-evaluation of online teaching of 13,997 teachers from 334 universities and their relationship with teachers' background have been subject to data statistics and analysis.FindingsTeachers' self-evaluation of online teaching mainly includes three dimensions: online teaching methods, online teacher–student interaction and online teaching techniques. There are significant differences in these three dimensions among teachers with different background characteristics, including regions, the types of universities, the nature of universities in macro background levels, the types and numbers of online courses in meso background levels, and the gender, years of teaching, professional titles and disciplines in micro background levels.Practical implicationsTo improve teachers' self-evaluation of online teaching, it is suggested to build an online teaching self-evaluation system for teachers, strengthen university support and guarantee, strengthen online teaching training and improve the information accomplishments of teachers.Originality/valueThis large-scale empirical survey of online teaching evaluation of Chinese teachers can provide scholars with a deeper understanding of the implementation of online teaching in China and the self-evaluation of online teaching by teachers.

scholarly journals The Analysis of Tropical Cyclone Tracks in the Western North Pacific through Data Mining. Part II: Tropical Cyclone Landfall

2013 ◽  
Vol 52 (6) ◽  
pp. 1417-1432 ◽  
Author(s):  
Wei Zhang ◽  
Yee Leung ◽  
Johnny C. L. Chan
Keyword(s):  
Data Mining ◽  
Tropical Cyclone ◽  
North Pacific ◽  
Western North Pacific ◽  
Large Scale ◽  
North Pacific Ocean ◽  
Classification Tree ◽  
Monsoon Index ◽  
Data Mining Approach ◽  
Research Findings

AbstractThis is the second paper of a two-part series of papers on the analysis of tropical cyclone (TC) tracks in the western North Pacific Ocean. In this paper, TC landfalls in the South China Sea and western North Pacific basins are investigated through the data-mining approach. On the basis of historical TC archives, the C4.5 algorithm, a classic tree algorithm for classification, has been employed to quantitatively discover rules governing TC landfall. A classification tree, with 14 leaf nodes, has been built. The path from the root node to each leaf node forms a rule. Fourteen rules governing TC landfall across the Chinese coast have been unraveled with respect to the selected attributes having potential influence on TC landfall. The rules are derived by the attributes and splitting values. From the classification tree, split values, such as 27°N latitude, 130°E longitude, 141°E in the west extension index, and 0.289 in the monsoon index have been shown to be useful for TC forecasting. The rules have been justified from the perspective of meteorology and knowledge of TC movement and recurvature (e.g., deep-layer mean winds and large-scale circulation). The research findings are also consistent with existing results concerning TC movement and landfall. Both the unraveled rules and the associated splitting values can provide useful references for the prediction of TC landfall over China.

Delhi’s Africa policy will have Indian Ocean emphasis

2019 ◽  
Keyword(s):  
Indian Ocean ◽  
South African ◽  
Prime Minister ◽  
First Year ◽  
Local Resistance ◽  
Content Type ◽  
The World ◽  
Military Facilities

Subject Outlook for India-Africa relations. Significance Delhi is devoting increasing attention to its relations with Africa, especially as it vies with Beijing for greater influence across the continent. Earlier this year, South African President Cyril Ramaphosa took the salute at India’s Republic Day parade. Impacts If re-elected, Indian Prime Minister Narendra Modi will likely undertake more trips to Africa in the first year of his second term. Delhi’s plans for military facilities in Mauritius and the Seychelles are likely to encounter further local resistance. India will aim to deepen ‘South-South’ connections in other parts of the world.

scholarly journals Phenotypic Screens Identify Parasite Genetic Factors Associated with Malarial Fever Response in Plasmodium falciparumpiggyBac Mutants

mSphere ◽  
2016 ◽  
Vol 1 (5) ◽  
Author(s):  
Phaedra Thomas ◽  
Jennifer Sedillo ◽  
Jenna Oberstaller ◽  
Suzanne Li ◽  
Min Zhang ◽  
...  
Keyword(s):  
Gene Function ◽  
Functional Annotation ◽  
Drug Targets ◽  
Large Scale ◽  
New Approach ◽  
Content Type ◽  
Factors Associated ◽  
The Face ◽  
Parasite Biology ◽  
Fever Response

ABSTRACT Though the P. falciparum genome sequence has been available for many years, ~40% of its genes do not have informative annotations, as they show no detectable homology to those of studied organisms. More still have not been evaluated via genetic methods. Scalable forward-genetic approaches that allow interrogation of gene function without any pre-existing knowledge are needed to hasten understanding of parasite biology, which will expedite the identification of drug targets and the development of future interventions in the face of spreading resistance to existing frontline drugs. In this work, we describe a new approach to pursue forward-genetic phenotypic screens for P. falciparum to identify factors associated with virulence. Future large-scale phenotypic screens developed to probe other such interesting phenomena, when considered in parallel, will prove a powerful tool for functional annotation of the P. falciparum genome, where so much remains undiscovered. Malaria remains one of the most devastating parasitic diseases worldwide, with 90% of the malaria deaths in Africa in 2013 attributable to Plasmodium falciparum. The clinical symptoms of malaria include cycles of fever, corresponding to parasite rupture from red blood cells every 48 h. Parasite pathways involved in the parasite’s ability to survive the host fever response, and indeed, the functions of ~40% of P. falciparum genes as a whole, are still largely unknown. Here, we evaluated the potential of scalable forward-genetic screening methods to identify genes involved in the host fever response. We performed a phenotypic screen for genes linked to the parasite response to febrile temperatures by utilizing a selection of single-disruption P. falciparum mutants generated via random piggyBac transposon mutagenesis in a previous study. We identified several mutants presenting significant phenotypes in febrile response screens compared to the wild type, indicating possible roles for the disrupted genes in this process. We present these initial studies as proof that forward genetics can be used to gain insight into critical factors associated with parasite biology. IMPORTANCE Though the P. falciparum genome sequence has been available for many years, ~40% of its genes do not have informative annotations, as they show no detectable homology to those of studied organisms. More still have not been evaluated via genetic methods. Scalable forward-genetic approaches that allow interrogation of gene function without any pre-existing knowledge are needed to hasten understanding of parasite biology, which will expedite the identification of drug targets and the development of future interventions in the face of spreading resistance to existing frontline drugs. In this work, we describe a new approach to pursue forward-genetic phenotypic screens for P. falciparum to identify factors associated with virulence. Future large-scale phenotypic screens developed to probe other such interesting phenomena, when considered in parallel, will prove a powerful tool for functional annotation of the P. falciparum genome, where so much remains undiscovered.

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