Modeling the Effort and Learning Ability of Students in MOOCs

Abstract

With the popularity of MOOCs and other online learning platforms, Educational Data Mining (EDM) has been receiving tremendous attention from researchers due to its great significance. Modeling students' effort and learning ability is a very interesting but challenging research topic. It is beneficial for student profiling, personalization recommendation, etc. Thus, numerous attempts have been devoted to this study. However, most of the existing work treat the problem in a static scenario, but they ignore the dynamic characteristics in real word applications. To address this problem, we propose a novel model to describe students' effort and learning ability (ELA) from a generative perspective. The temporal variations of both effort and learning ability of students are taken into account. To evaluate the performance of the proposed model, some extensive experiments are carried out. The experimental results have demonstrated that the proposed model outperforms other competitive methods greatly.

Guaranteeing Correctness of Machine Learning Based Decision Making at Higher Educational Institutions

“…The results of the research were compared using the ROC curve to demonstrate outcomes. A novel model is proposed to address student's efforts and learnability from a generative perspective by exploiting temporal variations [46]. Francis and Babu [47] have proposed a hybrid predictive algorithm that uses both classification and clustering techniques to mine educational data sets to evaluate student performance in academia for higher educational institutes.…”

Os Avanços Da Mineração: De Dados Educacionais Processo, Tendências Temáticas E Técnicas De Mineração

“…Finalmente, quanto à quinta lacuna, é possível destacar que devido aos avanços tecnológicos muitos pesquisadores têm se dedicado a comparar novas técnicas de MDE com outras mais consolidadas, para verificação da eficácia de modelos. Nesse sentido, Gao et al (2019) propuseram um novo modelo para analisar o perfil de aprendizagem e o engajamento de alunos em MOOCs e para validar sua precisão os autores o compararam com algoritmos amplamente utilizados -Regressão Linear e Máquinas de Vetores de Suporte. Além desse exemplo, aponta-se a pesquisa de Waheed et al (2020), que tinha como objetivo prever o abandono em MOOCs, utilizando um Rede Neural Artificial Profunda, que foi comparada aos algoritmos de Regressão Logística e Máquinas de Vetores de Suporte.…”

Coordinated Cultivation of Innovative and Entrepreneurial Knowledge and Ability of College Students Based on Markov Modeling

“…Innovation and entrepreneurship education is crucial for developing students' innovative thinking, practical ability, and market sensitivity. It involves not only the learning of knowledge but also the cultivation of entrepreneurial spirit, the exercise of market analysis ability, and the enhancement of risk management and other aspects of ability [4][5]. Markov model is a statistical model used to predict the future state of a series of events.…”

“…Dan Yang, Sisi Zheng, Si Cheng and Qi Zhou. Applied Mathematics and Nonlinear Sciences, 9(1) (2024)[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] …”