A Research On The Effect Of Feedback Mode In Educational Games On Deep Learning

In recent years,with the continuous development of virtual simulation technology and Internet technology,educational games have gradually become a research hotspot for technology to promote teaching in education.Educational games have realized the vision of students playing in learning and learning in playing.However,there are still some problems in the promotion of educational games in the education field.The main problem is that the value of educational games by students,parents,and teachers is not clear enough.At present,the research on the value of games is relatively discrete,and educational games lack the more systematic research on value orientation.Through the analysis of the value of various current educational games,it is found that most of the educational games can promote students' deep learning to a large extent;as a new requirement of education and teaching,deep learning has become the intrinsic value pursuit of educational games.Previous studies have shown that appropriate feedback methods in the learning process can effectively promote students' deep learning.Therefore,studying the impact of feedback methods on deep learning in educational games can provide a systematic value orientation for educational games and can also be used for the design of educational games.Provide new development ideas.Based on above research background,this study takes constructivist learning theory,situational cognition theory and immersion theory as the theoretical basis,and uses literature analysis method,experimental method and questionnaire survey method.Firstly,the feedback classification method of this study was determined according to the relevant literature of feedback type.According to the detailed level of feedback,this study divided the feedback into result feedback,correct response feedback,and detailed feedback.The designed educational game is then applied to the actual classroom for experiments.Research shows that when learners use the same technology to learn different types of knowledge(declarative and procedural),different results will be produced.Therefore,this study divides the experiment into two rounds according to the type of knowledge.The first round of experimental content is a declarative knowledge educational game.The second round of experimental content is a procedural knowledge educational game.The participants were 123 students from three classes of grade five in a primary school.After the experiment,the learning effect was measured through relevant questionnaires and deep learning measurement questions,and the data was analyzed by the one-way analysis of variance.The following conclusions were reached.In the declarative knowledge game,there are significant differences in the impact of different feedback methods on students' deep learning.In the three layers of deep learning,the results of feedback learning are significantly lower than the effects of correct response feedback and detailed feedback learning.There is no significant difference in the effects of feedback and detailed feedback on the effectiveness of deep learning.In the procedural knowledge game,there are significant differences in the impact of different feedback methods on students' deep learning.In the first level of cognitive understanding of deep learning,the effect of result feedback learning is significantly lower than that of correct response and detailed feedback,there is no significant difference between correct response feedback and detailed feedback.In the second-level internal correlation transfer and the third-level external expansion transfer of deep learning,the effect of result feedback and correct response feedback learning is significantly lower than that of detailed feedback learning.There is no significant difference in learning effect between result feedback and correct response feedback.This research applies feedback theory to the design of educational games,and provides new ideas for the design and development of educational games.Meanwhile,by studying the impact of feedback methods in educational games on deep learning,it provides a more systematic value orientation for educational games.