Predicting High School Students’ Interest in Majoring in a STEM Field: Insight into High School Students’ Postsecondary Plans

Lichtenberger, Eric J.; George-Jackson, Casey E.

doi:10.21061/jcte.v28i1.571

Cited by 71 publications

(82 citation statements)

References 35 publications

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“…showing am increasing trend in scientific research, an area of inquiry, termed as Educational Data Mining (EDM) [1]. EDM aims towards discovering useful information from large amounts of electronic data collected by educational systems.…”

Section: Machine Learning Has In the Recent Years Found Larger And Wimentioning

confidence: 99%

“…Preparing an educated workforce to enter Science, Technology, Engineering and Mathematics (STEM) careers is important for scientific innovations and technological advancements, as well as economic development and competitiveness [1]. In addition to expanding the nation's workforce capacity in STEM, broadening participation and success in STEM is also imperative for women given their historical underrepresentation and the occupational opportunities associated with these fields.…”

Section: Introductionmentioning

confidence: 99%

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Improving Student Enrollment Prediction Using Ensemble Classifiers

Wanjau¹,

Muketha²

2018

IJCATR

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Abstract:In the recent years, data mining has been utilized in education settings for extracting and manipulating data, and for establishing patterns in order to produce useful information for decision making. There is a growing need for higher education institutions to be more informed and knowledgeable about their students, and for them to understand some of the reasons behind students' choice to enroll and pursue careers. One of the ways in which this can be done is for such institutions to obtain information and knowledge about their students by mining, processing and analyzing the data they accumulate about them. In this paper, we propose a general framework for mining student data enrolled in Science, Technology, Engineering and Mathematics (STEM) using performance weighted ensemble classifiers. We train an ensemble of classification models from enrollment data streams to improve the quality of student data by eliminating noisy instances, and hence improving predictive accuracy. We empirically compare our technique with single model based techniques and show that using ensemble models not only gives better predictive accuracies on student enrollment in STEM, but also provides better rules for understanding the factors that influence student enrollment in STEM disciplines.

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Section: Machine Learning Has In the Recent Years Found Larger And Wimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving Student Enrollment Prediction Using Ensemble Classifiers

Wanjau¹,

Muketha²

2018

IJCATR

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“…Situational interest predicts depth of cognitive processing of information (Linnenbrink-Garcia et al 2013), degree of positive affect (Ely et al 2013), and effort regulation . Personal interest predicts continued mathematics and mathematicsintensive course taking (Lichtenberger and George-Jackson 2013), mathematics achievement in the long term (Murayama et al 2013), and perhaps most importantly, mathematics identity (Mangu et al 2015)-that aspect of ourselves that integrates our self-concept and cultural role with our conception of mathematics and what makes one mathematically adept (Boaler and Greeno 2000).…”

Section: Key Individual Motivational Factorsmentioning

confidence: 99%

Attitudes, Beliefs, Motivation, and Identity in Mathematics Education

Hannula

Martino

Pantziara

et al. 2016

ICME-13 Topical Surveys

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The purpose of this publication is to record the current state of the art in research on mathematics-related affect. Research on mathematics-related affect is varied in theories and concepts. Rather than trying to address all perspectives in one chapter, we have identified significant strands of research and invited colleagues from these strands to each write a short section summarizing the state of the art in that strand.The concepts and theories pertaining to the affective domain can be mapped along three dimensions (Hannula 2012). The first dimension identifies three broad categories of affect: motivation, emotions, and beliefs. In this Topical Survey, motivation is covered in Sect. 2.5 (Middleton, Jansen, and Goldin), which also discusses how emotions and beliefs relate to motivation; Sects. 2.2 (Pantziara) and 1.2.3 (Zhang and Morselli) are on beliefs; and Sect. 2.1 (Di Martino) on attitude more or less cross-cuts through all these categories. The second dimension is movement from rapidly fluctuating state to more stable trait. All of the sections in this chapter focus on trait-type affect while only Sect. 2.5 (by Middleton, Jansen, and Goldin) discusses both of these dimensions (referred to as "in the moment" and "long term"). The last dimension covers the theorizing level, which has three main levels in mathematics-related affect: physiological (embodied), psychological (individual), and social. Mathematics-related affect has mainly been studied using psychological theories and consequently most sections discuss only such research. The so-called social turn (Lerman 2000) in mathematics education is in this Topical Survey mainly reflected in Sect. 2.4 (Heyd-Metzuyanim, Lutovac, and Kaasila) on identity, but Sect. 2.5 (Middleton, Jansen, and Goldin) on motivation also has both a section which discusses the social level and how it interplays with the individual level and a section on self-efficacy which highlights the emerging research on the collective efficacy of collaborative groups. The physiological level

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“…This is because, preparing an educated workforce to enter Science, Technology, Engineering and Mathematics (STEM) occupations is important for scientific innovations and technological advancements, as well as economic development and competitiveness [7]. In addition, the growth in the number of University students enrolling in STEM fields of study does not keep pace with the STEM labour market demand and on the other hand, there is indication that high school graduates' interest in and readiness for STEM fields of study have been in the decline [8], [9]. Given the growing need to attract more high school graduates into these specific postsecondary areas of study, research devoted to understanding the influences on students' academic choices in regard to postsecondary STEM majors is becoming essential [8].…”

Section: Introductionmentioning

confidence: 99%

“…Lichtenberger and George-Jackson [9] study identified several factors that impact high school students' interest in STEM fields which may be categorized into three main themes: students' interests and motivations, academic qualification, and educational contexts. These three themes largely correspond with two of the factors included in Perna and Thomas' model; namely internal and school-level contexts [10].…”

Section: Introductionmentioning

confidence: 99%

Data Mining Model for Predicting Student Enrolment in STEM Courses in Higher Education Institutions

Wanjau¹,

Okeyo²,

Rimiru³

2016

IJCATR

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Educational data mining is the process of applying data mining tools and techniques to analyze data at educational institutions. In this paper, educational data mining was used to predict enrollment of students in Science, Technology, Engineering and Mathematics (STEM) courses in higher educational institutions. The study examined the extent to which individual, sociodemographic and school-level contextual factors help in pre-identifying successful and unsuccessful students in enrollment in STEM disciplines in Higher Education Institutions in Kenya. The Cross Industry Standard Process for Data Mining framework was applied to a dataset drawn from the first, second and third year undergraduate female students enrolled in STEM disciplines in one University in Kenya to model student enrollment. Feature selection was used to rank the predictor variables by their importance for further analysis. Various predictive algorithms were evaluated in predicting enrollment of students in STEM courses. Empirical results showed the following: (i) the most important factors separating successful from unsuccessful students are: High School final grade, teacher inspiration, career flexibility, pre-university awareness and mathematics grade. (ii) among classification algorithms for prediction, decision tree (CART) was the most successful classifier with an overall percentage of correct classification of 85.2%. This paper showcases the importance of Prediction and Classification based data mining algorithms in the field of education and also presents some promising future lines.

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Predicting High School Students’ Interest in Majoring in a STEM Field: Insight into High School Students’ Postsecondary Plans

Abstract: ABSTRACT

Cited by 71 publications

References 35 publications

Improving Student Enrollment Prediction Using Ensemble Classifiers

Improving Student Enrollment Prediction Using Ensemble Classifiers

Attitudes, Beliefs, Motivation, and Identity in Mathematics Education

Data Mining Model for Predicting Student Enrolment in STEM Courses in Higher Education Institutions

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