# Progressive Teaching Improvement For Small Scale Learning: A Case Study in China
## 1 Introduction  
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. However, there are rare studies on small-scale learning data analysis, especially for on-site teaching and learning. Recently in China, the usage of internet-driven learning platforms has an exponential increment due to the corona virus outbreak. However, the teachers and students just make use of instant chatting services to continue their teaching and learning online, essentially the same as on-site form which illustrates the high significance of small-scale learning data analysis. Although 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]. Currently in China, with the purpose of improving curriculum teaching quality, students in most universities are required to make evaluations for all courses of this semester with very limited time available at the end of each term. Students tend to finish the evaluation arbitrarily and casually due tothe boring repeated procedure, which results in inaccurate feedback from students and difficulties in helping courses improvement. However, Data Mining (DM) approaches for MOOCs are incapable to address small-scale problems. Thus, this paper focuses on progressive learning feedback and analysis on a case study in China, i.e., a Data Structure course during the 2019 Summer semester from School of Educational Science and Technology, Nanjing University of Posts and Telecommunications. In this study, students could give satisfaction feedback instantly after each class with a convenient and time-saving manner. Consequently, the objective of this study is to address the problems as follows: (1) How could instructors have direct knowledge of learners via their feedback data and make relatively necessary interventions? (2) What kind of algorithm could perform well in small-scale data processing and how to implement it? (3) What positive influences will this study bring for future education development? The conceptual graph of this study is demonstrated in Figure 1.  
Compared with small-scale learning such as on-site education, the data of massive online learners is relatively convenient to collect from the virtual learning environments (VLEs) and learning management systems. Nevertheless, the collection of such dataset has also several inevitable limitations. For instance, scientists have no access to some dataset due to privacy issues, e.g. online-course platforms rejected to publish users' data due to confidentiality and privacy issues in the work done by [PERSON] et al. [3]. [PERSON] et al. [4] also proved that promising absolute privacy, confidentiality, and anonymity are impractical. For small scale on-site education, however, the aforementioned limitations are mostly nonexistent because learning data are collected by teachers or universities only for the purpose of course improvement. In this paper, an innovative approach is proposed for students to submit their feedback after each class instantly and conveniently. Inspired by the evaluation mechanism of electronic business websites, where consumers are allowed to give a piece of evaluation to elaborate their feedback of the shopping experience after every deal, the proposed method develop a WeChat mini program with a novel user interface to gather students' feedback after each class.