从数据到证据:面向循证教学的学情诊断方法

From Data to Evidence:The Diagnosis Method of Learning Situation Oriented to Evidence-based Teaching

在循证教学领域,证据的可靠性是确保教学改进措施科学性和有效性的重要前提。文章针对循证教学中证据的可靠性问题,提出了面向循证教学的学情诊断方法,能提炼可靠证据并应用于循证教学。此方法基于大规模的学生学习数据,运用随机森林对数据的特征重要性进行评估,增强了数据选取的可信度;同时将随机森林与AdaBoost融合成随机森林-AdaBoost算法,用于学生期末综合评价成绩层级的学情诊断,提升了诊断结果的可靠性。为验证此方法的应用效果,文章通过对比实验进行了实证研究,结果显示:该方法不仅具有较高的预测准确度,而且表现出良好的稳定性和鲁棒性。文章从繁杂的学习数据中提炼出有效证据,提高了学情诊断的准确性,为实施个性化教学提供了依据,并有助于推动循证教学向数字化和智能化方向发展。

In the field of evidence-based teaching,the reliability of evidence is an important premise to ensure the scientificity and effectiveness of teaching improvement measures.Aiming at the reliability of evidence in evidence-based teaching,this paper put forward a diagnosis method of learning situation oriented to evidence-based teaching,which can extract reliable evidence and apply it to evidence-based teaching.This method was based on large-scale student learning data and used random forest to evaluate the importance of data features,enhancing the credibility of data selection.At the same time,random forest and AdaBoost were integrated into random forest-AdaBOOST algorithm,which was used for the diagnosis of students’learning situation at the level of final comprehensive evaluation performance,improving the reliability of the diagnostic results.In order to verify the application effect of this method,the paper conducted empirical research through comparative experiments,and the results showed that this method not only had high prediction accuracy but also exhibited good stability and robustness.This paper extracted effective evidence from complex learning data,which improved the accuracy of learning situation diagnosis,provided a basis for implementing personalized teaching,and helped promote the development of evidence-based teaching towards digitalization and intelligence.