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Lucy D'Agostino McGowan; Travis Gerke; Malcolm Barrett – Journal of Statistics and Data Science Education, 2024
This article introduces a collection of four datasets, similar to Anscombe's quartet, that aim to highlight the challenges involved when estimating causal effects. Each of the four datasets is generated based on a distinct causal mechanism: the first involves a collider, the second involves a confounder, the third involves a mediator, and the…
Descriptors: Statistics Education, Programming Languages, Statistical Inference, Causal Models
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Austin M. Shin; Ayaan M. Kazerouni – ACM Transactions on Computing Education, 2024
Background and Context: Students' programming projects are often assessed on the basis of their tests as well as their implementations, most commonly using test adequacy criteria like branch coverage, or, in some cases, mutation analysis. As a result, students are implicitly encouraged to use these tools during their development process (i.e., so…
Descriptors: Feedback (Response), Programming, Student Projects, Computer Software
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Marcus Messer; Neil C. C. Brown; Michael Kölling; Miaojing Shi – ACM Transactions on Computing Education, 2024
We conducted a systematic literature review on automated grading and feedback tools for programming education. We analysed 121 research papers from 2017 to 2021 inclusive and categorised them based on skills assessed, approach, language paradigm, degree of automation, and evaluation techniques. Most papers assess the correctness of assignments in…
Descriptors: Automation, Grading, Feedback (Response), Programming
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Zengqing Wu; Huizhong Liu; Chuan Xiao – IEEE Transactions on Education, 2024
Contribution: This research illuminates information entropy's efficacy as a pivotal educational tool in programming, enabling the precise quantification of algorithmic complexity and student abstraction levels for solving problems. This approach can provide students quantitative, comparative insights into the differences between optimal and…
Descriptors: Information Theory, Student Evaluation, Thinking Skills, Algorithms
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Elena Novak; Chia-Ling Kuo; Janet Lynne Tassell; Grace Morris – TechTrends: Linking Research and Practice to Improve Learning, 2024
Recently, creativity has been acknowledged for its importance in improving computer science learning and attitudes. However, very little is known about how to integrate creativity in preservice teacher computing education. Toward this end, we developed a creativity-enhancement intervention in an online Creative Computing instructional module to…
Descriptors: Preservice Teachers, Elementary School Teachers, Creativity, Intervention
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Pavlos Toukiloglou; Stelios Xinogalos – Education and Information Technologies, 2024
Hour of Code is a widely recognized global event that aims to introduce programming to novice users and integrate computer science into education. This paper presents an analysis of the effectiveness of the support system and user interface of Minecraft Adventurer, a serious game designed for the Hour of Code global event. Although previous…
Descriptors: Novices, Programming, Coding, Computer Science Education
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Maria Llambrich; Pau Satorra; Eudald Correig; Josep Gumà; Jesús Brezmes; Cristian Tebé; Raquel Cumeras – Research Synthesis Methods, 2024
Meta-analysis is a useful tool in clinical research, as it combines the results of multiple clinical studies to improve precision when answering a particular scientific question. While there has been a substantial increase in publications using meta-analysis in various clinical research topics, the number of published meta-analyses in metabolomics…
Descriptors: Meta Analysis, Programming Languages, Information Technology, Computer Oriented Programs
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Oscar Karnalim; Hapnes Toba; Meliana Christianti Johan – Education and Information Technologies, 2024
Artificial Intelligence (AI) can foster education but can also be misused to breach academic integrity. Large language models like ChatGPT are able to generate solutions for individual assessments that are expected to be completed independently. There are a number of automated detectors for AI assisted work. However, most of them are not dedicated…
Descriptors: Artificial Intelligence, Academic Achievement, Integrity, Introductory Courses
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Paul Biberstein; Thomas Castleman; Luming Chen; Shriram Krishnamurthi – Informatics in Education, 2024
CODAP is a widely-used programming environment for secondary school data science. Its direct-manipulation-based design offers many advantages to learners, especially younger students. Unfortunately, these same advantages can become a liability when it comes to repeating operations consistently, replaying operations (for reproducibility), and also…
Descriptors: Data Science, Secondary School Students, Programming, Open Source Technology
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Maya B. Mathur – Research Synthesis Methods, 2024
As traditionally conceived, publication bias arises from selection operating on a collection of individually unbiased estimates. A canonical form of such selection across studies (SAS) is the preferential publication of affirmative studies (i.e., those with significant, positive estimates) versus nonaffirmative studies (i.e., those with…
Descriptors: Meta Analysis, Research Reports, Research Methodology, Research Problems
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Zhong, Baichang; Xia, Liying; Su, Siyu – Education and Information Technologies, 2022
One of the aspects of programming that novices often struggle with is the understanding of abstract concepts, such as variables, loops, expressions, and especially Boolean operations. This paper aims to explore the effects of programming tools with different degrees of embodiment on learning Boolean operations in elementary school. To this end, 67…
Descriptors: Programming Languages, Programming, Novices, Elementary Education
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Menon, Pratibha – Journal of Information Systems Education, 2023
This paper introduces a teaching process to develop students' problem-solving and programming efficacy in an introductory computer programming course. The proposed teaching practice provides step-by-step guidelines on using worked-out examples of code to demonstrate the applications of programming concepts. These coding demonstrations explicitly…
Descriptors: Introductory Courses, Programming, Computer Science Education, Feedback (Response)
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Hoq, Muntasir; Brusilovsky, Peter; Akram, Bita – International Educational Data Mining Society, 2023
Prediction of student performance in introductory programming courses can assist struggling students and improve their persistence. On the other hand, it is important for the prediction to be transparent for the instructor and students to effectively utilize the results of this prediction. Explainable Machine Learning models can effectively help…
Descriptors: Academic Achievement, Prediction, Models, Introductory Courses
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Shen, Guohua; Yang, Sien; Huang, Zhiqiu; Yu, Yaoshen; Li, Xin – Education and Information Technologies, 2023
Due to the growing demand for information technology skills, programming education has received increasing attention. Predicting students' programming performance helps teachers realize their teaching effect and students' learning status in time to provide support for students. However, few of the existing researches have taken the code that…
Descriptors: Prediction, Programming, Student Characteristics, Profiles
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Silva, Leonardo; Mendes, Antonio Jose; Gomes, Anabela; Fortes, Gabriel – IEEE Transactions on Education, 2023
Contribution: Students' problem-understanding abilities and their relationship with programming learning were investigated using a methodology little explored in the existing literature. Background: Problem comprehension is an ability used during software development. Current research points to conflicting results on students' ability to interpret…
Descriptors: Programming, Comprehension, Computer Software, Electronic Learning
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