Contributions of Student’s Assessment of Reflections on the Prediction of Learning Performance

机译：学生对学习表现预测的思考评估的贡献

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The effectiveness of learner's reflections on their learning activity was evaluated by predicting final exam scores using indices of participant's reflections, metrics of learning activity and student's characteristics. Some variables of participant's reflections were significant for the regression model of the final exam scores when a step-wise variable selection procedure was introduced. The fitness of the model which introduced reflection indices is higher than the fitness of models which are formulated without reflection. As the effectiveness of variables of participant's reflections is significant for both linear and logistic regression models, the contribution of factors of reflection on the prediction of learning performance was confirmed.

机译：通过预测使用参与者的反思，学习活动指标，学习活动和学生特征的指标预测最终考试评分，评估了学习者对学习活动的思考的有效性。当介绍逐步的可变选择程序时，参与者的反射的一些变量对于最终考试评分的回归模型非常重要。引入反射指数的模型的适应性高于在没有反射的情况下配制的模型的适应性。随着参与者的变量的有效性对于线性和逻辑回归模型具有重要意义，确认了对学习表现预测的反思因素的贡献。

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《International Conference on Information Technology Based Higher Education and Training》|2018年|303p|共4页
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Minoru Nakayama; Kouichi Mutsuura; Hiroh Yamamoto;
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中图分类信息处理技术;
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Predictive models; Atmospheric measurements; Particle measurements; Logistics; Input variables; Analytical models;

机译：预测模型;大气测量;粒子测量;物流;输入变量;分析模型;

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2. Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores [J] . Mushtaq Hussain, Wenhao Zhu, Wu Zhang, Computational intelligence and neuroscience . 2018,第3期

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3. Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores [J] . Hussain Mushtaq, Zhu Wenhao, Zhang Wu, Computational intelligence and neuroscience . 2018,第Pta3期

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4. Contributions of Student’s Assessment of Reflections on the Prediction of Learning Performance [C] . Minoru Nakayama, Kouichi Mutsuura, Hiroh Yamamoto International Conference on Information Technology Based Higher Education and Training . 2018

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5. An empirical assessment of attitude toward computers, motivation, perceived satisfaction from the e-learning system, and previous academic performance and their contribution to persistence of college student athletes enrolled in e-learning courses. [D] . Nichols, Anthony J. 2008

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6. The Prediction of Students’ Academic Performance With FluidIntelligence in Giving Special Consideration to the Contribution ofLearning [O] . Xuezhu Ren, Karl Schweizer, Tengfei Wang, 2015

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Contributions of Student’s Assessment of Reflections on the Prediction of Learning Performance

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