Abstract
Learning data feedback and analysis have been widely investigated in all aspects of education, especially for large scale remote learning scenario like Massive Open Online Courses (MOOCs) data analysis. On-site teaching and learning still remains the mainstream form for most teachers and students, and learning data analysis for such small scale scenario is rarely studied. In this work, we first develop a novel user interface to progressively collect students’ feedback after each class of a course with WeChat mini program inspired by the evaluation mechanism of most popular shopping website. Collected data are then visualized to teachers and pre-processed. We also propose a novel artificial neural network model to conduct a progressive study performance prediction. These prediction results are reported to teachers for next-class and further teaching improvement. Experimental results show that the proposed neural network model outperforms other state-of-the-art machine learning methods and reaches a precision value of 74.05% on a 3-class classifying task at the end of the term.
Highlights
One of the targets of educational scientists is to develop a high-quality education that is intimately linked with sustainable development goals
An artificial neural network (ANN) model is adopted to predict the performance of every student for further teaching improvements
We propose a novel approach to perform progressive class feedback, qualitative visualization, and student performance prediction, especially for small scale learning
Summary
One of the targets of educational scientists is to develop a high-quality education that is intimately linked with sustainable development goals. By virtue of high dropout and low academic success rate in education, learning data analysis has received significant attention in recent years, especially for large scale remote learning scenarios like Massive Open Online Courses (MOOCs). Those researches tend to focus on education resource prediction [1], aiming to keep a track of students’ learning activities to make predictions and recommendations for online platforms. The dropout rate of traditional classes is 10–20% lower than online courses, the analysis of small-scale learning data for on-site education institutions and organizations should not be ignored [2]
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