Predicting Students’ Performance in Introductory Programming Courses: A Literature Review

Sobral, Sónia Rolland; Oliveira, Catarina Félix de

doi:10.21125/inted.2021.1485

Cited by 9 publications

(12 citation statements)

References 27 publications

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“…There are some articles in the literature that review literature related to this topic. Some address only one area of education, such as programming [12] or introductory programming courses [13]; others are quite old [14,15]; others relate only to some dimensions [16]; and others are not just dedicated to higher education [17]. That is why we feel the need for this article to exist: it is comprehensive, up-to-date, and aimed at higher education.…”

Section: Introductionmentioning

confidence: 99%

How Does Learning Analytics Contribute to Prevent Students’ Dropout in Higher Education: A Systematic Literature Review

Oliveira

Sobral

Ferreira

et al. 2021

BDCC

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Retention and dropout of higher education students is a subject that must be analysed carefully. Learning analytics can be used to help prevent failure cases. The purpose of this paper is to analyse the scientific production in this area in higher education in journals indexed in Clarivate Analytics’ Web of Science and Elsevier’s Scopus. We use a bibliometric and systematic study to obtain deep knowledge of the referred scientific production. The information gathered allows us to perceive where, how, and in what ways learning analytics has been used in the latest years. By analysing studies performed all over the world, we identify what kinds of data and techniques are used to approach the subject. We propose a feature classification into several categories and subcategories, regarding student and external features. Student features can be seen as personal or academic data, while external factors include information about the university, environment, and support offered to the students. To approach the problems, authors successfully use data mining applied to the identified educational data. We also identify some other concerns, such as privacy issues, that need to be considered in the studies.

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Section: Introductionmentioning

confidence: 99%

How Does Learning Analytics Contribute to Prevent Students’ Dropout in Higher Education: A Systematic Literature Review

Oliveira

Sobral

Ferreira

et al. 2021

BDCC

Self Cite

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“…In the context of learning programming, computer science instructors can use the malleability of grit to prepare students for the potential difficulties and challenges associated with perseverance and focus through computer programming activities. Related research has shown that grit is positively linked to academic outcomes such as achievement (Mahatanankoon & Sikolia, 2017), enjoyment, and willingness to communicate their coding as proposed solutions in problem-solving situations (Wolf & Jia, 2015). Therefore, any research regarding grit can give educators and scholars information about a positive correlation with the growth mindset, which in turn can predict students' learning outcomes.…”

Section: Grit In Programming Coursesmentioning

confidence: 99%

Assessing Computational Thinking, Motivation, and Grit of Undergraduate Students Using Educational Robots

Pellas

2023

Journal of Educational Computing Research

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Educational technologists and practitioners have made substantial strides in developing affordable digital and tangible resources to support both formal and informal computer science instruction. However, there is a lack of research on practice-based assignments, such as Internet of Things (IoT) projects, that allow undergraduate students to design and demonstrate educational robots using digital or physical assistance, especially when it comes to computational thinking (CT) and programming skills development in association with their psycho-emotional experience. This study compares the impact of Scratch and LEGO® WeDo robotic kits on students' CT and programming skills development. A quasi-experimental approach was conducted, involving two hundred forty-six participants ( n = 246), who were equally divided between Scratch and LEGO® WeDo groups. Results indicate that the LEGO® WeDo group showed greater improvement in CT and programming skills development, while designing and presenting IoT projects. Nevertheless, no significant association between motivation, grit, and CT skills was observed. The findings highlight the potential of tangible robotics in facilitating students’ hands-on learning and enhancing motivation to foster CT and programming skills. This study provides a wide range of implications for instructional designers on how to use tangible robotics to support hands-on IoT projects in computer science courses.

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“…Researchers have examined the connection between programming talent development and several individual characteristics. Programming skill acquisition and other measures of individual differences have been the subject of several studies on, for example, background in mathematics, problem-solving ability, and cognitive skills [56,57].…”

Section: Spatial Testmentioning

confidence: 99%

Analysis of Machine Learning Classification Approaches for Predicting Students’ Programming Aptitude

Çetinkaya,

Baykan,

Kırgız

2023

Sustainability

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With the increasing prevalence and significance of computer programming, a crucial challenge that lies ahead of teachers and parents is to identify students adept at computer programming and direct them to relevant programming fields. As most studies on students’ coding abilities focus on elementary, high school, and university students in developed countries, we aimed to determine the coding abilities of middle school students in Turkey. We first administered a three-part spatial test to 600 secondary school students, of whom 400 completed the survey and the 20-level Classic Maze course on Code.org. We then employed four machine learning (ML) algorithms, namely, support vector machine (SVM), decision tree, k-nearest neighbor, and quadratic discriminant to classify the coding abilities of these students using spatial test and Code.org platform data. SVM yielded the most accurate results and can thus be considered a suitable ML technique to determine the coding abilities of participants. This article promotes quality education and coding skills for workforce development and sustainable industrialization, aligned with the United Nations Sustainable Development Goals.

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Predicting Students’ Performance in Introductory Programming Courses: A Literature Review

Cited by 9 publications

References 27 publications

How Does Learning Analytics Contribute to Prevent Students’ Dropout in Higher Education: A Systematic Literature Review

How Does Learning Analytics Contribute to Prevent Students’ Dropout in Higher Education: A Systematic Literature Review

Assessing Computational Thinking, Motivation, and Grit of Undergraduate Students Using Educational Robots

Analysis of Machine Learning Classification Approaches for Predicting Students’ Programming Aptitude

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