Learning Pattern Recognition and Performance Prediction Method Based on Learners'Behavior Evolution

Online learning provides learners with open and flexible learning opportunities,but suffers low learning engagement and unsatisfactory academic performance.Existing works on academic performance prediction mainly study how behaviors will impact performance from a static perspective,and neglect learners' behavior evolution over time and lack a deep understanding of learning patterns and learners' motivations,which are the key factors in learning performance.Therefore,a method of perfor-mance prediction based on learners' learning pattern and motivation is proposed to model the effects of learners' patterns and motivations on their performances.First,we quantify learning efficiency based on learners' efforts and gains and construct the dynamic evolution sequence of learning efficiency with time.Then,we cluster learners' behavior and identify four typical learning patterns combined with the actual learning scenarios.Based on this,learning pattern recognition and motivation prediction mode-ling are designed.The final performance prediction model is constructed by combining them with the bi-directional long-and short-term memory networks.Furthermore,we conduct a detailed and in-depth data analysis on each type of learning patterŃs efforts and gains in eight online courses.Comparative experiments show that the proposed model performs better on several metrics,with improvements ranging from 6.9% to 29.2%.Our work will help online learners,teachers,and platforms accurately understand learners' learning states and improve online learning performance.