# Methods for Cognitive Diagnosis of Students' Abilities Based on Keystroke Features
## 6 Conclusions
### Exploration of the Laws of Programming Education Based on Clustering Results
#### 6.2.2 The Law of Learning Strategy Effectiveness  
The learning strategies applied in programming education typically cover several key aspects such as time management and problem-solving approach planning. There is a strong correlation between programming scores and the effectiveness of learning strategies. The learning difficulties of students of the \"Syntax-Familiar but Problem-Struggling\" type reveal this law. Although these students were somewhat familiar with programming syntax, they lacked effective learning strategies when facing complex programming problems. In the end, they lacked reasonable time allocation or clear problem-solving ideas, resulting in much time spent but achieving unsatisfactory results. Simple knowledge memorization or mechanical practice cannot help students overcome the hurdle of tackling complex problems. Nonlinear Growth Characteristics  
The development of programming ability showed a pronounced threshold effect. It could be seen from the clustering results that students of the \"Steady and Proficient\" type accounted for more than 65%. This proportion indicates that when students' basic knowledge accumulation reaches a certain critical point, their learning efficiency will undergo exponential growth. In the early stage of programming learning, students need to go through a long process of quantitative change. During this stage, the learning process mainly focuses on three aspects: constantly accumulating programming knowledge, improving programming skills, and cultivating programming thinking. As students continuously consolidate their foundations and reach the threshold, their programming abilities will make a qualitative leap, enabling them to independently solve complex programming problems and even carry out innovative programming. According to existing research, this characteristic of nonlinear growth is in line with the general laws of students' ability development in education [20]. The Theory of Process Quality Dominance  
Compared to superficial behavioral indicators like the keystroke speed, behavioral characteristics in the programming process, such as the frequency of code modification and the time spent on processing keywords, can predict learning outcomes more accurately. This reflects the importance of the quality of in-depth learning in programming study. Taking students of the \"Foundation-Lingering\" type as an example, while their average keystroke speed was at a moderate level, the proportion of code modification actions was relatively high, and the average time taken to type keywords was also relatively long. This reflected two aspects. First, the quality of their in-depth learning during the programming process was not high. Second, their understanding of programming knowledge was not profound enough. These factors led to frequent errors in programming practice and thus the need for continuous code modification. In contrast, students of the \"Steady and Proficient\" type had the lowest proportion of modification actions and the shortest average time taken to type keywords. This indicates that they had a deep understanding of the knowledge during the learning process. They could accurately apply programming syntax and keywords, thereby completing programming tasks efficiently. Through an in-depth analysis of students' programming keystroke data, this study effectively conducted a cognitive diagnosis of students' programming abilities. This study also revealed the differences among students in different categories. These differences were reflected in aspects such as programming learning abilities, styles, key factors influencing their grades, and the problems and challenges they faced during the learning process. In the present study, the data employed were limited to keystroke data. Moreover, only clusteranalysis was conducted within the context of programming course examinations at colleges and universities. For future research, the scope of data collection could be broadened.