Automatic Evaluation of Programming Assignments Using Information Retrieval Techniques

Rahaman, Md. Afzalur; Hoque, Abu Sayed Md. Latiful

doi:10.1007/978-981-13-6459-4_6

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…The scoring results are similar to those of human teachers. Rahaman et al 12 proposed an evaluation model that automatically evaluates C programming assignments using the TF-IDF(Term Frequency-Inverse Document Frequency) algorithm. By constructing TF-IDF score vectors of student programs and teacher programs, the cosine similarity between the vectors is calculated to give a score.…”

Section: Related Workmentioning

confidence: 99%

Graph semantic similarity-based automatic assessment for programming exercises

Xiang,

Wang,

Zhou

et al. 2024

Sci Rep

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This paper proposes an algorithm for the automatic assessment of programming exercises. The algorithm assigns assessment scores based on the program dependency graph structure and the program semantic similarity, but does not actually need to run the student’s program. By calculating the node similarity between the student’s program and the teacher’s reference programs in terms of structure and program semantics, a similarity matrix is generated and the optimal similarity node path of this matrix is identified. The proposed algorithm achieves improved computational efficiency, with a time complexity of $$O(n^2)$$ O ( n 2 ) for a graph with n nodes. The experimental results show that the assessment algorithm proposed in this paper is more reliable and accurate than several comparison algorithms, and can be used for scoring programming exercises in C/C++, Java, Python, and other languages.

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Section: Related Workmentioning

confidence: 99%

Graph semantic similarity-based automatic assessment for programming exercises

Xiang,

Wang,

Zhou

et al. 2024

Sci Rep

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show abstract

“…Another study conducted by [14,15,16] reported promising results regarding students' attention, relevance, confidence, and satisfaction. Continuous automatic evaluation of the students increases the learning process's quality metrics [17,18,19]. Various researchers have incorporated different teaching approaches and methods to improve programming learning using games [20], visualization [21], active learning [5,6,22], and projectbased styles [23].…”

Section: Related Workmentioning

confidence: 99%

Adoption of ICT-Based Teaching in Engineering: An Extended Technology Acceptance Model Perspective

Gupta

Stachowiak

2021

IEEE Access

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Passive knowledge procurement in face-to-face learning is no longer the way education is imparted in higher education these days. Instead, new and developing Information and Communication Technologies (ICT) are emerging as a promising paradigm for creating a profound change in digitizing education. It facilitates active learning with the enhancement in its delivery and how students grasp it by representing the shift from traditional desktop-as-a-platform towards the network-as-a-platform model. This paper proposes an extended Technology Acceptance Model (TAM) with ICT-based teaching and learning platform. TAM is the fundamental model to understand user perceptions and behavior towards potential acceptance or rejection of the technology. A mixed research methodology is adopted, comprising a series of survey questionnaires, evaluation of results, and the undergraduate student's performance in the collection of quantitative and qualitative data to measure the acceptance of the proposed research model. For the practical implication of the proposed research model, four groups of 300 students each or two programming courses: C++ and Java, were analyzed in two consecutive academic sessions in a controlled environment for lecture and laboratory classes. Series of statistical result analyses were performed and examined with hypothesized relationships to measure the desired outcome. It measures the impact on the following constructs: student interest, motivation, understanding, satisfaction, class attention and interaction, attendance, improve grades, and perception of pedagogical effect. Results show positive effects for identified research questions and hypotheses, helping students with the knowledge, understanding, and deep learning programming and debugging skills. The novelty of this research lies in extending the findings using real-time monitoring facilities to track student activities and progress, including frequency use and intention to use a technologically driven learning style. This study also addresses two main issues in existing literature (i) analyses and compares perspectives of both students and instructors (ii) determines and measures the impact and its acceptance of user perception and intention for the entire semester, not for a single point in time.INDEX TERMS higher education; programming language; sustainability; learning system; internet-based learning; information and communication technologies; technology acceptance model.

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“…For this reason, computer programming MOOCs usually provide some kind of automatic evaluation system. To this purpose, MOOC systems use automatic graders to automatically assess all the programs (usually thousands of them) submitted by the students [25,28,34,41,42,46,46]. The automatic grader system is a key element of any MOOC framework.…”

Section: Introductionmentioning

confidence: 99%

Semiautomatic generation and assessment of Java exercises in engineering education

Insa

Pérez

Silva

et al. 2020

Comp Applic In Engineering

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Continuous assessment is essential in education. It should be an integral part of education that provides immediate feedback to students. Unfortunately, the assessment of programming source code is still a hand‐operated and error‐prone task, and can take weeks before the student gets feedback. This study presents a semiautomatic code assessment method able to automatically apply black‐box assessment (which relies on the comparison of input‐output pairs) and white‐box assessment (which relies on checking different source code properties). The method proposed is a general‐purpose assessment system that was originally designated to be used in engineering education, but that can be used in other educational contexts to assist the assessment of any Java programming assignments or exams. The main advantage of this system is that the assessment made is quicker, exhaustive, and objective; and it does not produce false positives. After the application of this method over two years in several real university courses, we have released a public and free implementation. An empirical evaluation of this system estimates that the amount of assessment work automatically done by the tool is over 48%. Additionally, the system has been used to measure the average subjective influence (i.e., assessment errors) introduced by teachers when they assess exams manually.

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Automatic Evaluation of Programming Assignments Using Information Retrieval Techniques

Cited by 4 publications

References 5 publications

Graph semantic similarity-based automatic assessment for programming exercises

Graph semantic similarity-based automatic assessment for programming exercises

Adoption of ICT-Based Teaching in Engineering: An Extended Technology Acceptance Model Perspective

Semiautomatic generation and assessment of Java exercises in engineering education

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