Fostering Motivation and Improving Student Performance in an introductory programming course: An Integrated Teaching Approach

Pabón, Oswaldo Solarte; Machuca-Villegas, Liliana

doi:10.24050/reia.v16i31.1230

Cited by 8 publications

(9 citation statements)

References 18 publications

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“…However, they tend to follow syntax-oriented programming teaching approaches, without focusing on computational thinking, with few connections to mathematics and science [19]. Furthermore, in the work of [18], they indicate that the Python programming language is suitable for introductory programming courses because of its simple syntax and ease for code debugging, they also point out that it is necessary to consider other aspects such as pedagogical strategies that allow improving the programming teaching-learning process.…”

Section: Discussionmentioning

confidence: 99%

“…In [18], they present a teaching approach based on four components: The use of the Python programming language, project-oriented and problem-based learning methodologies, multimedia resources available on virtual platforms, and evaluation rubrics. The approach used improved the academic performance of the students, which is evidenced in the grades obtained, and the dropout rates were reduced.…”

Section: Related Workmentioning

confidence: 99%

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Incorporation of Computational Thinking Practices to Enhance Learning in a Programming Course

Laura-Ochoa¹,

Bedregal-Alpaca²

2022

IJACSA

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The development of computational thinking skills is essential for information management, problem-solving, and understanding human behavior. Thus, the aim of the experience described here was to incorporate computational thinking practices to improve learning in a first Python programming course using programming tools such as PSeInt, CodingBat, and the turtle graphic library. A quasi-experimental methodological design was used in which the experimental and control groups are in different academic semesters. Exploratory mixed research was carried out. The control and experimental group consisted of 41 and 36 students, respectively. The results show that with the use of support programming tools, such as PSeInt, CodingBat, Python turtle graphic library, and the incorporation of computational thinking practices, the experimental group students obtained better learning results. It is concluded that student performance and motivation in university programming courses can be improved by using proper tools that help the understanding of programming concepts and the skills development related to computational thinking, such as abstraction and algorithmic thinking.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Incorporation of Computational Thinking Practices to Enhance Learning in a Programming Course

Laura-Ochoa¹,

Bedregal-Alpaca²

2022

IJACSA

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“…Collaborative Learning (CSCL) approach integrates artificial intelligence (AI) to improve the learning process through collaboration and information and communication technology (ICT) (Kozma, 2000, Loizzo & Ertmer, 2016, Docq & Daele, 2001, Solarte-Pabón & Machuca-Villegas, 2019, Thomas et al, 2002. According to Magnisalis et al, 2011, the CSCL approach is based on the following processes: a)…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and Computer-Supported Collaborative Learning in Programming: A Systematic Mapping Study

Suarez

Bucheli

Ordóñez

2023

Tecnura

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Objective: The Computer-Supported Collaborative Learning (CSCL) approach integrates artificial intelligence (AI) to enhance the learning process through collaboration and information and communication technologies (ICTs). In this sense, innovative and effective strategies could be designed for learning computer programming. This paper presents a systematic mapping study from 2009 to 2021, which shows how the integration of CSCL and AI supports the learning process in programming courses. Methodology: This study was conducted by reviewing data from different bibliographic sources such as Scopus, Web of Science (WoS), ScienceDirect, and repositories of the GitHub platform. It employs a quantitative methodological approach, where the results are represented through technological maps that show the following aspects: i) the programming languages used for CSCL and AI software development; ii) CSCL software technology and the evolution of AI; and iii) the ACM classifications, research topics, artificial intelligence techniques, and CSCL strategies. Results: The results of this research help to understand the benefits and challenges of using the CSCL and AI approach for learning computer programming, identifying some strategies and tools to improve the process in programming courses (e.g., the implementation of the CSCL approach strategies used to form groups, others to evaluate, and others to provide feedback); as well as to control the process and measure student results, using virtual judges for automatic code evaluation, profile identification, code analysis, teacher simulation, active learning activities, and interactive environments, among others. However, for each process, there are still open research questions. Conclusions: This work discusses the integration of CSCL and AI to enhance learning in programming courses and how it supports students' education process. No model integrates the CSCL approach with AI techniques, which allows implementing learning activities and, at the same time, observing and analyzing the evolution of the system and how its users (students) improve their learning skills with regard to programming. In addition, the different tools found in this paper could be explored by professors and institutions, or new technologies could be developed from them.

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“…In addition, learning programming may result in resistance to learn among non-information or non-computer majors when their learning experiences and performances are worse than those of computer science majors [ 25 ]. It is reported that programming courses typically have a large number of students who fail or drop out, whether computer science majors or not [ 104 ]. Many students regard programming courses as difficult and lowly motivating [ 2 , 3 , 14 , 59 , 104 ].…”

Section: Introductionmentioning

confidence: 99%

“…It is reported that programming courses typically have a large number of students who fail or drop out, whether computer science majors or not [ 104 ]. Many students regard programming courses as difficult and lowly motivating [ 2 , 3 , 14 , 59 , 104 ]. It is mentioned that the two key reasons for students’ high failure rates in programming courses are teaching methods and curriculum organization [ 106 ].…”

Section: Introductionmentioning

confidence: 99%

Integrating online meta-cognitive learning strategy and team regulation to develop students’ programming skills, academic motivation, and refusal self-efficacy of Internet use in a cloud classroom

Tsai

Lee

Cheng

et al. 2022

Univ Access Inf Soc

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With the development of technology and demand for online courses, there have been considerable quantities of online, blended, or flipped courses designed and provided. However, in the technology-enhanced learning environments, which are also full of social networking websites, shopping websites, and free online games, it is challenging to focus students’ attention and help them achieve satisfactory learning performance. In addition, the instruction of programming courses constantly challenges both teachers and students, particularly in online learning environments. To overcome and solve these problems and to facilitate students’ learning, the researchers in this study integrated two teaching approaches, using meta-cognitive learning strategy (MCLS) and team regulation (TR), to develop students’ regular learning habits and further contribute to their programming skills, academic motivation, and refusal self-efficacy of Internet use, in a cloud classroom. In this research, a quasi-experiment was conducted to investigate the effects of MCLS and TR adopting the experimental design of a 2 (MCLS vs. non-MCLS) × 2 (TR vs. non-TR) factorial pre-test/post-test. In this research, the participants consisted of four classes of university students from non-information or computer departments enrolled in programming design, a required course. The experimental groups comprised three of the classes, labelled as G1, G2, and G3. G1 concurrently received both the online MCLS and TR intervention, while G2 only received the online MCLS intervention, and G3 only received the online TR intervention. Serving as the control group, the fourth class (G4) received traditional teaching. This study investigated the effects of MCLS, TR, and their combination, on improving students’ programming skills, academic motivation, and refusal self-efficacy of Internet use in an online computing course. According to the results, students who received online TR significantly enhanced their programming design skills and their refusal self-efficacy of Internet use a cloud classroom. However, the expected effects of MCLS on developing students’ programming skills, academic motivation, and refusal self-efficacy of Internet use were not found in this study. The teaching strategy of integrating MCLS and TR in an online programming course in this study can serve as a reference for educators when conducting online, blended, or flipped courses during the COVID-19 pandemic.

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Fostering Motivation and Improving Student Performance in an introductory programming course: An Integrated Teaching Approach

Cited by 8 publications

References 18 publications

Incorporation of Computational Thinking Practices to Enhance Learning in a Programming Course

Incorporation of Computational Thinking Practices to Enhance Learning in a Programming Course

Artificial Intelligence and Computer-Supported Collaborative Learning in Programming: A Systematic Mapping Study

Integrating online meta-cognitive learning strategy and team regulation to develop students’ programming skills, academic motivation, and refusal self-efficacy of Internet use in a cloud classroom

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