VisOJ: real-time visual learning analytics dashboard for online programming judge

Fu, Qian; Bai, Xue; Zheng, Yafeng; Du, Rong; Wang, Dongqing; Zhang, Tianyi

doi:10.1007/s00371-022-02586-z

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…Fu et al [40] presented a visual learning analytics dashboard for the online judge system (VisOJ) that contains two kinds of user interface: student and teacher. The interface of teacher provided ranking trends and learning status that assisted teachers in monitoring and presenting feedback on students' online activities.…”

Section: Literature Surveymentioning

confidence: 99%

Learning analytics with correlation-based SAN-LSTM mechanism for formative evaluation and improved online learning

2024

IJATEE

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Learning analytics (LA) is the measuring, gathering, analyzing, and reporting of data on learners and their environments. This data is used to analyze and improve e-learning. LA has traditionally been used for a variety of purposes, including the prediction of student academic progress and more specifically, the identification of students who are in danger of failing a course or quitting their studies. However, the majority of the existing schemes have the issue of accurately predicting the students' performance in online courses. In this paper, correlation-based self-attention networklong short-term memory (SAN-LSTM) is proposed to predict students' outcomes, along with the effectiveness of the teaching experience, as well as the assessment methods. Initially, the data is collected from three datasets namely, WorldUC, Liru, and Junyi to evaluate the performance of the proposed approach. The min-max normalization is employed to improve the performance of the approach. The correlation-based feature selection (CFS) is employed to select appropriate features from the pre-processed data. Finally, the correlation-based SAN-LSTM is established to forecast the effectiveness of fine-grained learning. Three real-world datasets gathered from various e-learning empirically validated that the proposed model improves prediction outcomes and provides useful data for formative evaluation. The existing methods such as adaptive sparse self-attention network (AS-SAN), Bangor engagement metric (BEM), and deep belief network learning style (DBNLS) are used for comparison to justify the effectiveness of the correlation-based SAN-LSTM method. The proposed correlation-based SAN-LSTM achieves better results of 98% of accuracy and 93% of precision. The proposed method achieves 98% accuracy which is higher when compared to those of AS-SAN, BEM, and DBNLS.

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Section: Literature Surveymentioning

confidence: 99%

Learning analytics with correlation-based SAN-LSTM mechanism for formative evaluation and improved online learning

2024

IJATEE

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“…Qian et al visualized and analyzed learners' performance on OJ systems by using a dashboard that provides real-time insights into the distribution of learners' mistakes and their mastery of knowledge. This fine-grained analysis assists teachers in monitoring and providing feedback on learners' activities [6].…”

Section: Introductionmentioning

confidence: 99%

Design of an Online Programming Platform and a Study on Learners’ Testing Ability

Hsueh,

Lai,

Tseng

2023

Electronics

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Online judge (OJ) systems are essential in programming education as they efficiently evaluate learners’ programming skills and reduce instructor workload. However, these systems often overlook the importance of software-testing concepts. To address this gap, we developed a system called Pytutor that integrates software-testing concepts to assess learners’ programming abilities and proficiency by exploring students’ test cases and learning behaviors. Drawing on software engineering theory and practical techniques, test capabilities are evaluated by analyzing the code coverage and mutation testing of Defining Test Cases. Since our experiment is conducted in an online environment, we can collect students’ learning behaviors and further analyze the relationship between software engineering abilities and learning behaviors. We also analyzed the differences in programming and testing abilities between computer science majors and non-computer-science majors. Our findings suggest that better testing abilities may contribute to the improvement in programming abilities, whereas in the current Taiwanese education context, computer science majors do not necessarily have better testing abilities. This result provides suggestions for us to strengthen software-testing education no matter which type of students it is targeted at.

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A Systematic Review on Student-Facing Learning Analytics Dashboards: Reference Frames and Indicators

Kew,

Koh,

Choo

et al. 2024

2024 6th International Conference on Computer Science and Technologies in Education (CSTE)

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VisOJ: real-time visual learning analytics dashboard for online programming judge

Cited by 3 publications

References 28 publications

Learning analytics with correlation-based SAN-LSTM mechanism for formative evaluation and improved online learning

Learning analytics with correlation-based SAN-LSTM mechanism for formative evaluation and improved online learning

Design of an Online Programming Platform and a Study on Learners’ Testing Ability

A Systematic Review on Student-Facing Learning Analytics Dashboards: Reference Frames and Indicators

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