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Akcaoglu, Mete; Rosenberg, Joshua M.; Hodges, Charles B.; Hilpert, Jonathan C. – Computers in the Schools, 2021
Computer programming is becoming an essential skill for young students regardless of their education or career goals. Therefore, for students to develop and for educators and researchers to accurately measure self-efficacy in and value for programming is important. Although student motivation in subject matter can be measured using self-report…
Descriptors: Middle School Students, Student Attitudes, Value Judgment, Self Efficacy
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Ginat, David – Informatics in Education, 2021
The notion of algorithm may be perceived in different levels of abstraction. In the lower levels it is an operational set of instructions. In higher levels it may be viewed as an object with properties, solving a problem with characteristics. Novices mostly relate to the lower levels. Yet, higher levels are very relevant for them as well. We…
Descriptors: Problem Solving, Computation, Comparative Analysis, Competence
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Yadav, Alok Kumar; Oyelere, Solomon Sunday – Education and Information Technologies, 2021
This paper intends to present an overview of a mobile game-based learning application, BaghLearn that develops and upskills programming and algorithmic knowledge by cross-curricular capabilities through a traditional world-based game. The focus of this research was to explore the learning effectiveness of BaghLearn on students. Mixed method…
Descriptors: Foreign Countries, Undergraduate Students, Handheld Devices, Educational Games
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Loksa, Dastyni; Margulieux, Lauren; Becker, Brett A.; Craig, Michelle; Denny, Paul; Pettit, Raymond; Prather, James – ACM Transactions on Computing Education, 2022
Metacognition and self-regulation are important skills for successful learning and have been discussed and researched extensively in the general education literature for several decades. More recently, there has been growing interest in understanding how metacognitive and self-regulatory skills contribute to student success in the context of…
Descriptors: Metacognition, Programming, Computer Science Education, Learning Processes
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Wang, Jianlan; Zhang, Yuanlin; Jones, Arthur; Eckel, Rory; Hawkins, Joshua; Musslewhite, Darrel – Journal of Computers in Mathematics and Science Teaching, 2022
Despite the importance of computer science education and computational thinking, there have been limited examples of computer science education at K-12 classrooms that authentically represents the work of computer scientists, especially programming. One reason is the lack of a measurable definition of computational thinking and a programming…
Descriptors: Teaching Methods, Computer Science Education, Programming, Thinking Skills
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Podworny, Susanne; Hüsing, Sven; Schulte, Carsten – Statistics Education Research Journal, 2022
Data science surrounds us in contexts as diverse as climate change, air pollution, route-finding, genomics, market manipulation, and movie recommendations. To open the "data-science-black-box" for lower secondary school students, we developed a data science teaching unit focusing on the analysis of environmental data, which we embedded…
Descriptors: Statistics Education, Programming, Programming Languages, Data Analysis
Prokopyev, Mikhail Semenovich; Vlasova, Elena Zotikovna; Tretyakova, Tatyana Vasilyevna; Sorochinsky, Maksim Anatolyevich; Solovyeva, Rimma Alekseyevna – Journal of Educational Psychology - Propositos y Representaciones, 2020
At present, teaching programming to future teachers is superficial or is excluded from computer science curricular. Only programming fundamentals are taught during computer studies, and skills acquired during the training are not applied in practice. The aims of this article are (1) to develop a programming course for teacher training disciplines…
Descriptors: Programming, Computer Science Education, Programming Languages, Teacher Education Programs
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Morrison, Briana B.; Margulieux, Lauren E.; Decker, Adrienne – Computer Science Education, 2020
Background and Context: Subgoal labeled worked examples have been extensively researched, but the research has been reported piecemeal. This paper aggregates data from three studies, including data previously unreported, to holistically examine the effect of subgoal labeled worked examples across three student populations and across different…
Descriptors: Computer Science Education, Instructional Materials, Instructional Effectiveness, Problem Solving
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Günbatar, Mustafa Serkan – International Journal of Computer Science Education in Schools, 2020
The purpose of this research is to examine Computational Thinking (CT) skills, Computer Programming Self-Efficacy (CPSE), and Computer Programming Attitude (CPA) of middle school students who took the Information Technologies & Software (IT&S) courses and those who did not, and make various analyses according to the relationships between…
Descriptors: Thinking Skills, Self Efficacy, Programming, Computer Science Education
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Bowman, Nicholas A.; Jarratt, Lindsay; Culver, K. C.; Segre, Alberto M. – Journal of Research on Educational Effectiveness, 2020
Pair programming is a form of collaborative learning in computer science that involves two students working together on a coding project. Previous research has identified mostly positive outcomes from this practice, such as course grades and the quality of the resulting code. Pair programming may also facilitate interactions that improve the…
Descriptors: Cooperative Learning, Programming, Computer Science, Academic Persistence
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Yildiz, Talha; Gündüz, Semseddin – Shanlax International Journal of Education, 2020
In this research, the effect of using the peer instruction method in programming teaching on the secondary school student's attitudes towards information technologies and software course and their perception of programming self-efficacy comparing to traditional teaching methods are examined. In the study, the "mixed method," in which…
Descriptors: Peer Teaching, Instructional Effectiveness, Programming, Computer Science Education
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Schwarzenberg, Pablo; Navon, Jaime; Pérez-Sanagustín, Mar – Journal of Computing in Higher Education, 2020
The flipped classroom gives students the flexibility to organize their learning, while teachers can monitor their progress analyzing their online activity. In massive courses where there are a variety of activities, automated analysis techniques are required in order to process the large volume of information that is generated, to help teachers…
Descriptors: Models, Blended Learning, Teaching Methods, Electronic Learning
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Hosseini, Roya; Akhuseyinoglu, Kamil; Brusilovsky, Peter; Malmi, Lauri; Pollari-Malmi, Kerttu; Schunn, Christian; Sirkiä, Teemu – International Journal of Artificial Intelligence in Education, 2020
This research is focused on how to support students' acquisition of program construction skills through worked examples. Although examples have been consistently proven to be valuable for student's learning, the learning technology for computer science education lacks program construction examples with interactive elements that could engage…
Descriptors: Programming, Computer Science Education, Problem Solving, Learner Engagement
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Rich, Kathryn M.; Spaepen, Elizabet; Strickland, Carla; Moran, Cheryl – Interactive Learning Environments, 2020
A key debate in computer science education is whether and how computational thinking (CT) is used within disciplines other than computer science. Broad definitions provide many avenues for developing integrated instruction, as practices within existing activities can simply be reframed in terms of CT. But such general use of the term CT may…
Descriptors: Computer Science Education, Mathematics Education, Mathematics Skills, Elementary School Students
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Liew, Tze Wei; Tan, Su-Mae; Tan, Teck Ming; Kew, Si Na – Information and Learning Sciences, 2020
Purpose: This study aims to examine the effects of voice enthusiasm (enthusiastic voice vs calm voice) on social ratings of the speaker, cognitive load and transfer performance in multimedia learning. Design/methodology/approach: Two laboratory experiments were conducted in which learners learned from a multimedia presentation about computer…
Descriptors: Cues, Cognitive Processes, Difficulty Level, Transfer of Training
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