Some computerscience concepts, and programming in particular, are hard to learn. As CS is (re-)entering national school curricula throughout the world, qualified CS teachers need to be trained. In this PhD work we wi...
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ISBN:
(纸本)9781450383264
Some computerscience concepts, and programming in particular, are hard to learn. As CS is (re-)entering national school curricula throughout the world, qualified CS teachers need to be trained. In this PhD work we will propose a training that will help teachers teach those concepts effectively. Based on the educational framework of learning transfer and cognitive load theory, we will do this through evidence-based instructional strategies. These explicit programming strategies aim to decrease cognitive load and foster learning transfer. My PhD will advance the topic of CS teacher training by understanding how CS teachers apply those programming strategies in their teaching through qualitative studies and by designing a validated training that can be used with tutors and teacher trainers.
In the contemporary digital age, the integration of Artificial Intelligence (AI) into educational practices has garnered significant attention, particularly within the domain of programming education. AI-powered tools...
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ISBN:
(数字)9798331534592
ISBN:
(纸本)9798331534608
In the contemporary digital age, the integration of Artificial Intelligence (AI) into educational practices has garnered significant attention, particularly within the domain of programming education. AI-powered tools offer the potential to streamline the learning process, enabling students to acquire knowledge and skills more efficiently. By automating tasks such as code generation, debugging, and plagiarism detection, AI can alleviate the cognitive load associated with programming, thereby fostering a more engaging and productive learning experience. This study aims to investigate the specific impact of AI on students' programming knowledge and academic performance. Through an online survey administered to a diverse sample of Indonesian university students, this research explores the extent to which AI adoption influences factors such as motivation, self-efficacy, and perceived usefulness. The Information Adoption Model (IAM) serves as the theoretical framework for analysing the collected data. Structural Equation Modelling (SEM) is employed to assess the hypothesized relationships between AI usage and various academic outcomes. The findings of this study are expected to provide valuable insights into the efficacy of AI in enhancing programming education and inform the development of effective AI-integrated learning strategies.
Arkansas Tech University (ATU) has over 650 students (2020–2021) from STEM (science, Technology, Engineering, and Mathematics) majors taking programming sequence (PS) courses each semester. However, despite minor eff...
Arkansas Tech University (ATU) has over 650 students (2020–2021) from STEM (science, Technology, Engineering, and Mathematics) majors taking programming sequence (PS) courses each semester. However, despite minor efforts to increase retention, the Engineering and Computing sciences Department has had difficulty retaining students in the PS courses. The programming Sequence Improvement Program (PSIP) has been implemented to address this issue. Its goal is to improve retention and student success in three PS courses: Foundations of computerprogramming I (COMS 2104), Foundations of computerprogramming II (COMS 2203), and Data Structures (COMS 2213). The PSIP aims to reduce the rate of D, F, Withdraw, and Incomplete (DFWI) grades by 10% annually from the current average baseline of 32 % , 35%, and 27%, respectively (2015–2019). This program aims to revolutionize the programming Sequence (PS) curriculum within the initial two years of study, fostering increased knowledge and improving overall degree completion rates. To achieve this goal, the PS Improvement Program (PSIP) employs four distinct approaches: establishing a dedicated tutoring lab for students enrolled in programming courses, integrating effective communication and collaboration tools like Discord and WebEx, conducting research on contemporary pedagogies for programming instruction, and aligning the curriculum with prominent platforms such as GitHub, Hacker Rank, and LeetCode. By implementing these strategies, the PSIP creates a transformative learning environment that enhances students' programming skills and facilitates their success in the program.
In recent times, the field of computerprogramming has experienced a significant revolution, thanks to advancements in machine learning. Applications have emerged with the capability to generate source code from natur...
In recent times, the field of computerprogramming has experienced a significant revolution, thanks to advancements in machine learning. Applications have emerged with the capability to generate source code from natural language descriptions. These tools primarily utilize language models based on deep learning, which have been trained on a collection of programs and projects hosted in public repositories. One of these tools is Github Copilot, an artificial intelligence capable of generating source code that can be integrated as an extension into development environments. The objective of this study is to experimentally explore, analyze, and evaluate the suggestions made by the Github Copilot tool in programming topics related to the computerscience degree at the University of Bio-Bio. We propose five steps: (1) collecting natural language statements for both general and specific programming problems; (2) utilizing Github Copilot to generate programs; (3) evaluating its performance; (4) conducting an analysis; and (5) measuring code quality. This approach allows us to gain an initial understanding of its effectiveness, emphasizing its application for well-established problems and monitoring its use for problems with distinct objectives.
This paper delves into the evolving relationship between humans and computers in the realm of programming. Historically, programming has been a dialogue where humans meticulously crafted communication to suit machine ...
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RWTH Aachen University in Germany offers a bridge course that introduces students of a variety of study programs to the basics of imperative programming. Due to the high number of students and limited availability of ...
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ISBN:
(纸本)9781665455374
RWTH Aachen University in Germany offers a bridge course that introduces students of a variety of study programs to the basics of imperative programming. Due to the high number of students and limited availability of tutors, it is hard to provide instant individual feedback to all students, to notice how difficult the tasks are for the students, and to reliably monitor their progression during the course. This motivated us to use an Automatic Program Assessment System (APAS) to provide instant formative feedback to students and to systematically assess the course's tasks. In this paper, we present our study in which we investigated (1) if the use of our APAS influences the students' perceived difficulty of the programming tasks, (2) whether the use of our APAS increases the students' progression speed, and (3) if the number of automated assessments triggered by the students can serve as an indicator of a task's perceived difficulty. The results did not allow us to identify any meaningful differences between the study and control group with regards to the perceived difficulty and the progression speed. We found that the number of automated assessments can serve as a rough indicator for the task's perceived difficulty. We also found initial indication that the use of the automated assessment helps to ensure that the students complete the tasks in full and as intended by the teachers and might improve code quality. This needs to be further investigated in future work.
This study examines the adaptation of the problem-solving studio to computerscience education by combining it with pair programming. Pair programming is a software engineering practice in industry, but has seen mixed...
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This paper supports the importance of teaching logic (and logic programming) in computerscience degrees and discusses several proposals that can be included in current curricula without the need to adapt the academic...
This paper supports the importance of teaching logic (and logic programming) in computerscience degrees and discusses several proposals that can be included in current curricula without the need to adapt the academic guides. In addition, some practical examples are described and the tools used for their subsequent application are related.
Brain activity analysis can be used to determine an individual's mental state while performing a certain task. This study examined the learners' brain activity changes, the correlation between their performanc...
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ISBN:
(纸本)9781665454803
Brain activity analysis can be used to determine an individual's mental state while performing a certain task. This study examined the learners' brain activity changes, the correlation between their performances in reading tasks and answering exercises, and regression analysis to determine the significant impact of mental load tasks on the performance score of the learners. This experiment involved 15 learners, performing eleven (11) tasks which included reading, exercise questions, and coding tasks at their own pace while wearing EmotivEpocX brainware technology. The EEG signals collected were processed to compute the absolute power spectra that served as input values for analysis. Findings in this study revealed that the learners exhibited a frequency band BETA classified as "high mental load," indicating that learners were actively engaged and alert in performing the tasks, and there were no significant differences based on Gamma, Beta, Alpha, and Theta. Moreover, the correlation analysis revealed that the mental load tasks were not statistically significant since the p-value of the variables in between was greater than the 0.05 level of significance. Likewise, regression analysis showed that the brain activity, having a "high mental load" had no significant impact or relationship on the learners' over-all performance score with an F-statistic of 0.9293. As all the p-value of the task coefficients were found to be greater than the significant level, the teaching-learning activities must be revisited to further improve the analytical and logical skills of the learners and serve as a strong basis for educational strategists to manage the activities and avoid information overload that clearly affects the learners' performance.
Predicting students' success prior to their admission to computer-related degree programs is challenging due to diverse educational backgrounds and varying skill requirements. Educational Data Mining (EDM) can be ...
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ISBN:
(数字)9798350389692
ISBN:
(纸本)9798350389708
Predicting students' success prior to their admission to computer-related degree programs is challenging due to diverse educational backgrounds and varying skill requirements. Educational Data Mining (EDM) can be used to optimize the college admission process by selecting only well-equipped students for the program. The attrition rate of the College of Computing Studies at Western Mindanao State University remains high despite the rigorous admission process for the computerscience and Information Technology programs. Many students fail programming courses, causing them to leave the program. This study developed a decision support system and ensemble models by combining Support Vector Machine, Decision Tree, and Neural Network models using learning techniques such as voting, bagging, and stacking. The goal is to automate the admission of pre-qualified students based on their likelihood of success in programming courses. Factors such as SHSGPA, CET score, class rank, and specific personality traits significantly influence academic success. The combined models, particularly using stacking techniques, emerged as the most reliable and effective model for predicting unseen data compared to standalone classification models. Students and faculty members evaluated the functionality, reliability, and usability of the decision-support system. The feedback indicated a positive reception, affirming its potential as a valuable tool in the college's admission process. Future enhancements may include incorporating multiple intelligence and grit tests and exploring additional ensemble methods to improve predictive capabilities.
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