On modeling the quality of concept mapping toward more intelligent online learning feedback: a fuzzy logic-based approach

Dias, Sofia B.; Dolianiti, Foteini S.; Hadjileontiadou, Sofia J.; Diniz, José Alves; Hadjileontiadis, Leontios J.

doi:10.1007/s10209-019-00656-z

Cited by 6 publications

(2 citation statements)

References 15 publications

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“…The research results revealed the effectiveness of the principles on learner motivation and satisfaction. Using a fuzzy logic-based approach, Dias et al [51] developed a fuzzy inference system concept mapping model to shape intelligent online learning feedback that encompassed information that helped students to correct errors, and it was found to positively contribute to learners' deeper learning.…”

Section: Learning Feedback In Online Learning Environmentsmentioning

confidence: 99%

Learning Performance Prediction-Based Personalized Feedback in Online Learning via Machine Learning

Wang

Zhang

2022

Sustainability

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Online learning has become a vital option for ensuring daily instruction in response to the emergence of the COVID-19 epidemic. However, different from conventional massive online learning, inadequate available data bring challenges for instructors to identify underachieving students in school-based online learning, which may obstruct timely guidance and impede learning performance. Exploring small-sample-supported learning performance prediction and personalized feedback methods is an urgent need to mitigate these shortcomings. Consequently, considering the problem of insufficient data, this study proposes a machine learning model for learning performance prediction with additional pre-training and fine-tuning phases, and constructs a personalized feedback generation method to improve the online learning effect. With a quasi-experiment involving 62 participants (33 in experimental group and 29 in control group), the validity of the prediction model and personalized feedback generation, and the impact of the personalized feedback on learning performance and cognitive load, were evaluated. The results revealed that the proposed model reached a relatively high level of accuracy compared to the baseline models. Additionally, the students who learned with personalized feedback performed significantly better in terms of learning performance and showed a lower cognitive load.

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Section: Learning Feedback In Online Learning Environmentsmentioning

confidence: 99%

Learning Performance Prediction-Based Personalized Feedback in Online Learning via Machine Learning

Wang

Zhang

2022

Sustainability

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“…Literature [15] built a multimedia teaching platform based on Excel VBA to construct the Cost Accounting course, and through the observation and analysis of practical teaching, it was found that the teaching effect of Cost Accounting was significantly improved. Literature [16] envisioned a new model, i.e., fuzzy inference system (FIS) concept mapping (fismap), to improve the intelligent feedback of online learning and then optimize and satisfy the needs of students' online learning. Teaching experiments have confirmed that the model can achieve the expected purpose of teaching feedback.…”

Section: Introductionmentioning

confidence: 99%

Research on Intelligent Development and Practice of Accounting Laboratory Courses in Colleges and Universities in the Context of Interdisciplinary Cooperation

2024

Applied Mathematics and Nonlinear Sciences

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In this paper, an innovative solution is proposed to address the problems of single content design, lack of relevance and specialized training in traditional accounting laboratory courses. In interdisciplinary cooperation, an efficient intelligent assistance model for accounting laboratory courses is constructed by combining the collaborative filtering recommendation model and K-Means clustering algorithm. The model significantly improves the recommendation accuracy and computational efficiency by optimizing the cosine similarity calculation and introducing the similarity-weighted average, combined with the enhanced dichotomous K-means algorithm. After dataset testing and practical application verification in a university, the model effectively improves the quality of personalized recommendation of teaching resources. In particular, students’ mastery of knowledge points K2 and K3 is more adequate through the model recommendation, while their mastery of K8 is significantly improved. In addition, the model successfully identifies the similarity between students, which lays the foundation for providing more personalized learning resources. Overall, the model solves the traditional problem of accounting laboratory courses and provides a practical methodology for intelligent recommendation of teaching resources in universities.

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Extended Kalman filter and Takagi-Sugeno fuzzy observer for a strip winding system

Szedlak-Stînean¹,

Precup²,

Petriu³

et al. 2022

Expert Systems with Applications

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On modeling the quality of concept mapping toward more intelligent online learning feedback: a fuzzy logic-based approach

Cited by 6 publications

References 15 publications

Learning Performance Prediction-Based Personalized Feedback in Online Learning via Machine Learning

Learning Performance Prediction-Based Personalized Feedback in Online Learning via Machine Learning

Research on Intelligent Development and Practice of Accounting Laboratory Courses in Colleges and Universities in the Context of Interdisciplinary Cooperation

Extended Kalman filter and Takagi-Sugeno fuzzy observer for a strip winding system

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