An Online Engineering Practice Evaluation Framework Based on BiLSTM with Attention Mechanism
In a virtual learning environment,engineering practice courses usually collaborate online in groups to complete a practical project.However,existing online teaching platforms lack deep mining based on learning behavior data,making it difficult for teachers to perceive the learning status of learners like in offline practice,thus making it difficult to conduct very objective and fair evaluations.To this end,a bidirec-tional long short-term memory network based on attention mechanism is proposed as an online engineering practice evaluation framework.Based on online practice behavior data,a bidirectional long short-term memory network model with attention mechanism(GEP-BiLSTM)is constructed to predict whether students can pass future practical exams.Practice has shown that the proposed method can provide more target-ed assistance to students before they encounter academic warnings,and can improve the teaching effectiveness of engineering practice.
educational data mininglong short-term memory networkpractical evaluationattention mechanismengineering practice
国家科技期刊平台
NSTL
万方数据
