When the coding assignments step into more complicated and structured periods for individuals,how to cultivate the skills have become the urgent needs for people who are engaged in sophisticated coding development in ...
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When the coding assignments step into more complicated and structured periods for individuals,how to cultivate the skills have become the urgent needs for people who are engaged in sophisticated coding development in both academics as well as industries,especially those who have some basic knowledge in their undergraduate *** this paper,a state-of-art interactive tool:programming Decomposition Tool(PDT) is proposed to help people enhance their complicated coding skills and further to broaden their CS learning *** tools have two unique features:*** learning interface is adaptive and dynamic with respect to the detection of the knowledge depth of *** learning parts will be decomposed into several easier sessions for better acquisition.
Reliable and pedagogically sound automated feedback and grading systems are highly coveted by educators. Automatic grading systems are useful for ensuring equity of grading of student submissions to assignments and pr...
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ISBN:
(纸本)9781450389594
Reliable and pedagogically sound automated feedback and grading systems are highly coveted by educators. Automatic grading systems are useful for ensuring equity of grading of student submissions to assignments and providing timely feedback on the work. Many of these systems test submissions to assignments based on test cases and the outputs that they achieve, while others use unit tests to check the submissions. The approach presented in this paper checks submissions based on test cases but also analyses what the students actually wrote in their code. Assignment questions are constructed based around the concepts that the student are currently learning in lectures, and the patterns searched for in their submissions are based on these concepts. In this paper we show how to implement this approach effectively. We analyse the use of an automatic grading system within first year computerscienceprogramming modules and show that the system is straightforward to use and suited for novice programmers, while providing automatic grading and feedback. Evaluation received from students, demonstrators and lecturers show the system is extremely beneficial. The evaluation shows that such systems allow demonstrators more time to assist students during labs. Lecturers can also provide instant feedback to students while keeping track of their progress and identifying where the gaps in students' knowledge are.
LLM chatbot interfaces allow students to get instant, interactive assistance with homework, but doing so carelessly may not advance educational objectives. In this study, an interactive homework help system based on D...
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The concept of expectation is fundamental in computerscience education, especially for analyzing the time efficiency of algorithms. However, the gap between theoretical knowledge and practical application can make it...
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The subgoal learning framework has improved performance for novice programmers in higher education, but it has only started to be applied and studied in K-12 (primary/secondary). programming education in K-12 is growi...
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The subgoal learning framework has improved performance for novice programmers in higher education, but it has only started to be applied and studied in K-12 (primary/secondary). programming education in K-12 is growing, and many international initiatives are attempting to increase participation, including curricular initiatives like computerscience Principles and non-profit organizations like *** . Given that subgoal learning is designed to help students with no prior knowledge, we designed and implemented subgoals in the introduction to programming unit in ***'s computerscience Principles course. The redesigned unit includes subgoal-oriented instruction and subgoal-themed pre-written comments that students could add to their programming activities. To evaluate efficacy, we compared behaviors and performance of students who received the redesigned subgoal unit to those receiving the original unit. We found that students who learned with subgoals performed better on problem-solving questions but not knowledge-based questions and wrote more in open-ended response questions, including a practice Performance Task for the AP exam. Moreover, at least one-third of subgoal students continued to use the subgoal comments after the subgoal-oriented instruction had been faded, suggesting that they found them useful. Survey data from the teachers suggested that students who struggled with the concepts found the subgoals most useful. Implications for future designs are discussed.
Mathematical programming publishes original articles dealing with every aspect of mathematical optimization; that is, everything of direct or indirect use concerning the problem of optimizing a function of many variab...
Mathematical programming publishes original articles dealing with every aspect of mathematical optimization; that is, everything of direct or indirect use concerning the problem of optimizing a function of many variables, often subject to a set of constraints. This involves theoretical and computational issues as well as application studies. Included, along with the standard topics of linear, nonlinear, integer, conic, stochastic and combinatorial optimization, are techniques for formulating and applying mathematical programming models, convex, nonsmooth and variational analysis, the theory of polyhedra, variational inequalities, and control and game theory viewed from the perspective of mathematical programming. The editorial boards are particularly interested in novel applications of mathematical programming and interfaces with engineering, economics, and computerscience. Articles primarily concerned with computational issues such as implementation and testing should in general be submitted to Mathematical programming Computation. Mathematical programming consists of two series. Series A publishes original research articles, expositions and surveys, and reports on computational experimentation and new or innovative practical applications as well as short communications dealing with the above. Issues of Series B each focus on a single subject of current interest to the mathematical programming community. Each issue of Series B has one or more guest editors, who need not be members of the editorial board. An issue may be a collection of original articles, a single research monograph or a selection of papers from a conference. The official journal of the Mathematical Optimization Society Publishes original articles dealing with every aspect of mathematical programming Consists of two series: one that presents original expositions and surveys, and reports on computational experimentation and new or innovative practical applications, and another that focuses on a sing
In this paper we report our approach and experiences concerning the introduction of Python programming language in programming-related academic curricula. Firstly we motivate our choice and approach regarding the use ...
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ISBN:
(纸本)9783030456962;9783030456979
In this paper we report our approach and experiences concerning the introduction of Python programming language in programming-related academic curricula. Firstly we motivate our choice and approach regarding the use of Python programming language. Then we discuss the results obtained in two courses that we taught to computerscience and engineering students, both with a strong focus on developing students' practical programming skills: Algorithm Design and Artificial Intelligence. We report our approach and findings, including identified difficulties and obtained results, as well as proposed future improvements.
The ability to program is the "visible" competency to acquire in an introductory unit in computerscience. However, before a student is able to write a program, he needs to understand the problem: before for...
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ISBN:
(纸本)9783030456962;9783030456979
The ability to program is the "visible" competency to acquire in an introductory unit in computerscience. However, before a student is able to write a program, he needs to understand the problem: before formalizing, the student must have to (be able) to think, (be able) to solve and (be able) to define. At an early stage of learning there are no significant differences between programming languages. The discussion of the early programming language continues: probably never will be a consensus among academics. The Association for Computing Machinery (ACM) and Institute of Electrical and Electronics Engineers (IEEE) computerscience curriculum recommendations haven't clearly defined which programming language to adopt: it is the course directors and teachers who must make this choice, consciously and not only following the trends. This article presents a set of items that should be considered when you make a programming language choice for the first programming unit in higher education computerscience courses.
After the rapid development of technology, challenges related to decreasing concentration in children and youth have become apparent, posing a serious issue in the education process. This paper explores how the implem...
After the rapid development of technology, challenges related to decreasing concentration in children and youth have become apparent, posing a serious issue in the education process. This paper explores how the implementation of artificial intelligence (AI) can significantly enhance education, with a specific focus on improving concentration and developing technical skills, particularly in the fields of programming and technical sciences. The initial analysis will present a hypothesis on how AI can serve as a powerful tool to overcome contemporary challenges and provide effective support for achieving results and answers more efficiently. The motivation behind this research lies in the necessity to develop innovative methods to support education through technological advancements. A review will be conducted on various studies that examine the impact of AI applications in an educational context.
Data science (DS) has emerged as a new academic discipline where students are introduced to data-centric thinking and generating data-driven insights through programming. Unlike traditional introductory computer Scien...
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ISBN:
(纸本)9798400704239
Data science (DS) has emerged as a new academic discipline where students are introduced to data-centric thinking and generating data-driven insights through programming. Unlike traditional introductory computerscience (CS) education, which focuses on program syntax and core CS topics (e.g., algorithms and data structures), introductory DS education emphasizes skills such as analyzing data to gain insights by making effective use of programming libraries (e.g., re, NumPy, pandas, scikit-learn). To better understand learners' needs and pain points when they are introduced to DS programming, we investigated a large online course on data manipulation designed for graduate students who do not have a CS or Statistics undergraduate degree. We qualitatively analyzed students' incorrect code submissions for computational notebookbased assignments in Python. We identified common mistakes and grouped them into the following themes: (1) programming language and environment misconceptions, (2) logical mistakes due to data or problem-statement misunderstanding or incorrectly dealing with missing values, (3) semantic mistakes due to incorrect use of DS libraries, and (4) suboptimal coding. Our work provides instructors insights to understand student needs in introductory DS courses and improve course pedagogy, and recommendations for developing assessment and feedback tools to support students in large courses.
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