Game Teaching Method in Preschool Education Based on Big Data Technology (PDF)

Hindawi Scientific Programming Volume 2021, Article ID 4751263, 11 pages https://doi.org/10.1155/2021/4751263 Research Article Game Teaching Method in Preschool Education Based on Big Data Technology Rui Bai School of Education, YuLin University, Yulin, China Correspo...

Hindawi Scientific Programming Volume 2021, Article ID 4751263, 11 pages https://doi.org/10.1155/2021/4751263 Research Article Game Teaching Method in Preschool Education Based on Big Data Technology Rui Bai School of Education, YuLin University, Yulin, China Correspondence should be addressed to Rui Bai; [email protected] Received 20 October 2021; Revised 3 November 2021; Accepted 30 November 2021; Published 16 December 2021 Academic Editor: Le Sun Copyright © 2021 Rui Bai. &is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. &e traditional preaching way of imparting knowledge can only stifle children’s imagination, creativity, and learning initiative a little bit, which is harmful to children’s healthy and happy growth. &is paper combines big data technology to evaluate the effect of game teaching method in preschool education, analyzes the teaching effect of game teaching method in preschool education, and combines big data technology to find problematic teaching points. Based on the collaborative filtering algorithm of preschool children, this paper estimates the current preschool children’s score for the game by referring to the scores of neighbor preschool children on the predicted game and constructs an intelligent model. Finally, this paper combines experimental research to verify the model proposed in this paper. From the experimental research, it can be seen that the method proposed in this paper has a certain effect. 1. Introduction how to construct a digital game curriculum suitable for the development of preschool children and implement it in real &e continuous development of information technology and preschool education activities. Compared with traditional network terminal technology has given mobile phones and outdoor games for children, the design and implementation of other life tools great entertainment. &e time required for digital games are easier to add elements that are conducive to games and the cost of money have been continuously reduced, the development of children’s cognitive development and and digital games have gradually entered various households. mathematical logic. It can be said that making good use of Whether it is young and middle-aged parents or the elderly digital games is of great benefit to the development of preschool taking care of children, they cannot avoid preschool children children. Here, how to take the essence of digital games and from coming into contact with games. &e magic of this kind of remove the dross of digital games is extremely important. game can make a crying child stop immediately, can make the &is paper combines big data technology to evaluate the child sit quietly in the corner and wait, and can be a variety of effect of game teaching method in preschool education and rewards for parents. However, this “game-style trick” can only analyzes the teaching effect of game teaching method in bring more problems such as more eyesight and distracted preschool education. Moreover, this paper combines big attention, and seriously, it also causes communication barriers data technology to discover problematic teaching points for preschoolers. While the digital age brings convenience to and, on this basis, further enhance the teaching effect of people’s lives, there are also risks that cannot be underestimated preschool education.. As practitioners of preschool education, when seeing children with lively, smart, and witty nature being increasingly 2. Related Work eroded by digital games, they deeply feel the crisis in preschool education and child development. It is the nature of children to Information multimedia technology is developing at a speed like to play games, and games are also the best way for children beyond people’s imagination. &e entire society is in a to learn. Games cannot be discarded in the growth of preschool critical period of transition from an industrialized society to children. Based on such concerns, they began to think about an informationized society. Informationization has become  5192, 2021, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1155/2021/4751263, Wiley Online Library on [31/12/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 2 Scientific Programming a common trend in the world’s economic and social de- kindergarten modern education environment, strengthen velopment. At present, many developed countries have paid teacher information technology training, pay attention to attention to the cultivation of the information quality of the teaching research and practice under the information tech- next generation, and all countries in the world are accel- nology environment, and establish a comprehensive evalu- erating the informatization process of basic education. ation system for kindergarten information technology Educational informatization has a comprehensive impact on education. &e process evaluation, stage evaluation, and preschool education. It has changed the educational goals, comprehensive evaluation here have given me a lot of en- educational structure, educational content, educational lightenment and provided a reference for the monthly stage methods, and even teaching evaluation. evaluation standard selection and formulation. Literature &e United Kingdom no longer stacks computers but studies the informatization of environmental education and moves them into the class to make it a gaming area. integrates the element of information technology into envi- Computer education is called information technology ed- ronmental education, making environmental education with ucation in British kindergartens. Almost every kindergarten the characteristics of informatization. &e two are interactive class is equipped with a computer and learning software that and two-way integration. &is integration is not a simple matches the model. Computers can not only teach application of courseware for demonstration auxiliary children English, mathematics, and science but also teach teaching, but the integration of modern information tech- them to sing, draw, play chess, and walk through mazes. &e nology methods and courses, and it is no longer just a more entertaining software can greatly arouse children’s demonstration of media or tools. More abstract environ- interest in learning computer and through the intervention mental knowledge and problems are more visualized, pro- of multimedia, such as sound, image, text, animation, and so mote children’s environmental protection emotions, and on,, make children feel the endless joy of learning. &e trigger the use of information technology to present a ho- educational software associated with the teaching of various lographic learning environment for environmental education, subjects can play a role in assisting education. &rough the so that children can be in the information world and deeply method of entertaining and teaching, children can learn feel the application of information technology. easily and happily and increase their intelligence. &e method of computer education mainly adopts the game 3. Game Teaching Method in Preschool method, that is to say, computer education starts from the Education Based on Big Data game. &e game activity stimulates the children’s learning interest and thirst for knowledge, and the children never get Recommendation Algorithm tired of it. Japanese families can receive a set of video tapes, &e recommendation algorithm is one of the cores of the books, and magazines every month to encourage parents to recommendation system because it is directly related to the help their children play cartoon characters, text, and digital accuracy of the recommendation system and the satisfaction games, and open a hotline. In the United States, com- of preschoolers. A tag is a kind of keywords used to describe puters are now popularized in all kindergartens. Under the information without hierarchical structure. It can be used to guidance of full-time computer teachers, three- or four-year- describe the semantics of the game and the interests of old children “touch the future” in front of the keyboard and preschoolers, and to connect the two. As shown in Figure 1, mouse. In addition to playing computer games, the com- there are three main ways to source tags in the recom- munity also provides gamification and information tech- mendation system. (1) Preschoolers use labels to describe nology–teaching activities. In Canada, kindergartens their personal interests. (2) &e administrator uses tags to have opened “virtual schools” for teaching activities. Aus- describe the game features when creating the game. (3) tralia has a computer game group, New Zealand has a Preschool children use several tags to describe the game. computer game center, and France and Sweden have also Figure 1 also shows the relationship between preschoolers, incorporated computers and networks into their preschool tags, and games. When the tags used by the preschool education plans. It should be said that the computer entering children match the tags added by the game, to a certain kindergarten is another development trend of today’s kin- extent, the preschool children and the game have a set of dergarten curriculum. potential consumption relationships. &e tags used by Literature elaborated on the influence of multimedia preschoolers here include tags for preschoolers to describe computer-assisted teaching MCAI in children’s teaching and personal interests and tags for preschoolers to describe mentioned the combination of multimedia computer-assisted games. teaching and the use of game courseware to carry out &erefore, the process of tag-based recommendation mathematics education. Practice has proved that children algorithm is as follows: learn best in a game environment, and what they learn can be quickly applied to more abstract and formal situations. &e (1) &e algorithm calculates the common labels of each action thinking in the game can solve their more disciplinary preschooler, and the number of times the pre- problems in the future. &e formation of abilities, such as schoolers have used these labels. mind image and recording, lays a solid foundation. &erefore, (2) &e algorithm calculates the number of times each it is feasible to carry out research on gamification theme game has been hit by each tag. &e more times a teaching in kindergartens, and it has significant effects. &e game is described by a tag, the more relevant the main research content of the literature is how to build a game is to that tag.  5192, 2021, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1155/2021/4751263, Wiley Online Library on [31/12/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Scientific Programming 3 User-used tags User-used Describe tags Belong to personal interest Belong to Describe items Matching Matching User Express user Tags interest Items The tags added The tags of when the items items described are created by users Figure 1: &e relationship between preschoolers, tags, and games. (3) When recommending for a preschooler, the algo- preschoolers describe each game with tags, we use these two to rithm associates the preschoolers’ common labels punish popular tags and popular games. &e improved algo- with the most relevant games described by these rithm (TFIDF Tag Based) is shown in formula (2) : labels and recommends them to the preschoolers nu.t nt.i according to the correlation, as shown in formula (1) P(u, i) 􏽘 (n). (2) t∈T(u) log􏼐 1 + n t 􏼑 log􏼐 1 + n(u) i 􏼑 : Among them, n(n) t indicates how many different pre- schoolers have used the label t, and n(u) i indicates how many P(u, i) 􏽘 nu.t nt.i. (1) different preschoolers have used the label to describe the t∈T(u) game i. Among them, P(u, i) represents the degree of interest of In response to this problem, the solution used in this the preschooler u in the game i and T(u) is the set of tags paper is to extend the original label. &e original tags include used by the preschooler u. nu.t is the number of times the tags used by preschool children or tags that have been preschooler u has used the label t, and nt.i is the number of described in games, while the expanded tag set includes the times the label t is used to describe the game i. original tags and the tag set with higher similarity to the In practical applications, certain tags will be used many original tags. Measuring the similarity between two tags can times by preschoolers, and tags of certain popular games will be simplified to calculate the proportion of the number of also be used repeatedly by preschoolers when evaluating the games that have been described by the two tags at the same game. &e algorithm described in formula (1) will be overly time to the total number of games that have been described inclined to popular tags and popular games for preschool by the two tags. &e Jaccard formula can be used to calculate children in terms of results. &e main problem is that the al- the similarity between tags t1 and t2, as shown in formula (3) gorithm cannot distinguish which labels are popular labels and : 􏼌􏼌 􏼌 which labels are personalized labels for preschoolers. &erefore, 􏼌􏼌I t1 􏼁 ∩ I t2 􏼁􏼌􏼌􏼌 we borrow the idea of TF-IDF5. Considering how many dif- sim t1 , t2 􏼁 􏼌􏼌􏼌 􏼌􏼌. (3) 􏼌I t1 􏼁 ∪ I t2 􏼁􏼌􏼌 ferent preschoolers use each tag and how many different  5192, 2021, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1155/2021/4751263, Wiley Online Library on [31/12/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 4 Scientific Programming Among them, I(t) represents the set of games described Based on the game-based collaborative filtering algo- by the label r. In addition, the cosine similarity formula can rithm, we estimate the current preschool children’s score for also be used to calculate the similarity between tags t1 and t2: the game by referring to the score records of the preschool children’s games with the neighbors of the predicted game. 􏽐i∈I(t1 ) ∩ I(t2 )nt1 ,i(u) nt2 ,i(u) &e neighbor game here refers to a game that is highly sim t1 , t2 􏼁 􏽱. (4) 􏽐i∈I(t1 ) ∩ I(t2 )n2t1 ,i n2t2 ,i similar to the predicted game. &e calculation of preschooler u’s prediction score for game i is shown in formula (6) : Among them, n(u) t,i is the number of preschool children 􏽐v∈Ni (i)sim(i, j)Rv,i who have described game i with the label t. P(u, i). (6) 􏽐v∈Ni (i)sim(i, j) &e accuracy of the tag-based recommendation algo- rithm has a lot to do with the quality of the tag itself. &e Among them, Nu (i) is the game neighbor set composed quality here refers to whether the label is descriptive, of K games with the highest similarity to game i among the whether it is distinguishable, whether it follows the standard games evaluated by preschooler u, and sim(i,j) represents the grammar, and so on. &e quality of the labels in the rec- similarity between preschooler i and preschooler j. ommendation system is mainly guaranteed through low- Commonly used similarity measurement methods in quality label cleaning and high-quality label collaborative filtering algorithms mainly include Cosine recommendation. Similarity and Pearson Correlation. &e cosine similarity &e entire cleaning process is shown in Figure 2. &e method is often used in game-based collaborative filtering nonreferenced tags are primarily screened by identifying algorithms. &is method represents objects as vectors and commonly used stop words and defining and expanding the obtains the similarity between objects by calculating the stop dictionary. After that, we can let preschoolers mark cosine angle between the vectors: useless labels through feedback from preschoolers. Tags with high-text content similarity can be identified and processed 􏽐u∈Ui,j Ru,i Ru,j through regular expressions and string edit distance sim(i, j) 􏽱. (7) 􏽐u∈Ui,j R2u,j 􏽐u∈Ui,j R2u,j algorithms. To describe the collaborative filtering algorithm, this Since this similarity measurement method does not paper introduces the following symbols: U represents the set consider the difference between preschool children’s scores of preschool children in the recommendation system; I and their average scores, we use Adjusted Cosine Similarity, represents the set of all recommended candidate games; R as shown in formula (8) : represents the set of score records in the system; a score record is a triple set of preschool children, games, and scores; 􏽐u∈Ui,j 􏼐Ru,i − Ru 􏼑􏼐Ru,j − Ru 􏼑 and S represents the range of scores (e.g. S {1,2,3,4,5}, S sim(i, j) 􏽱. (8) {interest, not interested). At the same time, we assume that 􏽐u∈Ui,j 􏼐Ru,i − Ru 􏼑􏽐u∈Ui,j 􏼐Ru,j − Ru 􏼑 any preschooler u ∈ U can only have at most one score for each game i ∈ I, and this score is recorded as Ru,i. Ui rep- Among them, Ru represents the average value of the sum resents the subset of preschoolers who have evaluated game of u scores of preschool children. It shows that adjusting the i, and Iu,v represents the subset of games evaluated by cosine similarity is more suitable for use in game-based preschooler u. Iu means preschooler u and preschooler v methods than the Pearson correlation coefficient. In con-. trast, the Pearson correlation coefficient has better results in &e intersection of the reviewed items is Iu,v Iu ∩ Iv , the method based on preschool children, which is shown in Ui,j represents the set of preschoolers who have reviewed formula (9) : both game i and game j, that is, Ui,j Ui ∩ Uj. 􏽐u∈Iiu,v 􏼐Ru,i − Ru 􏼑􏼐Rv,j − Ru 􏼑 &e collaborative filtering algorithm based on preschool sim(i, j) 􏽱. (9) children estimates the current preschool children’s score for 􏽐u∈Iu,v 􏼐Ru,i − Ru 􏼑􏽐u∈Iu,v 􏼐Rv,j − Ru 􏼑 the game by referring to the scores of neighbor preschool children on the predicted game. &e neighbor preschoolers In the actual scoring process, the evaluation criteria of each here refer to a collection of preschoolers with similar scoring preschool child are different. Some preschool children are more patterns to the current preschoolers. &e calculation of relaxed and tend to give most games 4 or even 5 points, and preschooler u’s prediction score for game ü is shown in some preschool children are stricter and more cautious and formula (5) : tend to give most games less than 3 points. In other words, if a 􏽐v∈Ni (u)sim(u, v)Rv,i score record is 4 points, it does not necessarily mean that P(u, i). (5) preschoolers like the game. For relaxed preschoolers, maybe 5 |sim(u, v)| points are really liked. However, for strict preschool children, a Among them, Ni (u) is the preschooler’s neighbor set score of 4 has already indicated the preschool children’s ten- composed of K preschoolers who have evaluated game i and dency to be interested or like it. &erefore, the average score of have the highest similarity with preschooler u, sim(u,v) preschool children is introduced here to measure whether a represents the similarity between preschooler u and pre- certain preschool child’s score record is a positive or negative schooler v. tendency score, as shown in formula :  5192, 2021, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1155/2021/4751263, Wiley Online Library on [31/12/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Scientific Programming 5 Mark the Recalculate the tags low-quality Update the database- weight for tags through related records low quality tags user feedback Database Match with the stop tags and filte Groups of...... Grouped by similar tags the text High-quality Clean up the similarity of Several representative tags the tags tags are...... selected in each group Stop the Groups of term library similar tags Figure 2: Label cleaning process. 􏽐v∈Ni (u)sim(u, v)􏼐Rv,i − Rv 􏼑 Similarly, the improved game similarity is shown in P(u, i) Ru +. (10) formula : 􏽐v∈Ni (u)sim(u, v) 􏼌􏼌 􏼌􏼌 min􏽮􏼌􏼌Iu,v 􏼌􏼌, c􏽯 Game-based methods can also be similarly processed by sim′ (u, v) × sim(u, v). (14) c introducing average game scores, as shown in formula : &is method penalizes the similarity calculated involving 􏽐v∈Nu (i)sim(i, j)􏼐Ru,j − Rj 􏼑 P(u, i) Rt +. (11) the number of ratings less than the specified number y. &e y 􏽐v∈Nu (i)sim(i, j) value varies according to the data set, and cross-validation is required to determine the best y value. In the practical application of the recommender system, &is paper proposes another solution to the data sparsity the total number of score records is much smaller than the problem, which increases the number of available scores by product of preschool children and the number of games. &e improving the similarity calculation process. Carefully an- score matrix for preschool children and games contains a alyze the calculation process of the similarity of preschool large number of zero-value elements (indicating that pre- children. No matter which similarity calculation formula is school children have not rated the game or preschool used, the accuracy bottleneck lies in the size of the |Iu,v |. In children have not purchased the game). Such a scoring the traditional similarity calculation method, the calculation matrix has the problem of data sparsity. &e definition of the of Iu,v is done by exact matching, that is, only those games sparsity of the scoring matrix is shown in formula : that match exactly in the game set evaluated by preschooler u |R| and preschooler v will be used to calculate the similarity Sparsity MR 􏼁. (12) |U||I| between the two spend. Now, we consider the following situation: preschooler u rated game i with 5 points, pre- When calculating the similarity of preschoolers based on schooler v rated game j with 5 points, preschooler u did not a sparse scoring matrix, it is likely that only a few scores are rate j, and preschooler v did not rate game i. &ere is no score involved. When these scores are exactly similar or even intersection between u and preschooler v. It is known that equal, this group of preschool children will be considered the similarity between game i and game j is 0.9 (very similar). completely similar (the similarity is close to 1). In fact, According to the aforementioned description, because the because the number of common scores is too small, this exact match result of the game is an empty set, the traditional phenomenon may be just a coincidence, but it will cause similarity calculation method cannot calculate the similarity dissimilar preschoolers to have too high recommendation between the preschooler u and the preschooler v. weights in the recommendation calculation process, and &e set of high-scoring games for preschool children u is ultimately unreliable recommendation results. the User Favorite Item Set (User Favorite Item Set). By Aiming at the data sparsity problem in the collaborative accumulating the similarity between each group of suc- filtering algorithm, a feasible solution strategy is to reduce cessfully matched game pairs and taking the average, the the similarity obtained by only a small number of scores. &e result is the set similarity between I+ u and I+ v. To get closer improved similarity of preschool children is shown in for- to the similarity of the real game set, try to avoid the situation mula : where the same game is matched multiple times (as shown in 􏼌􏼌 􏼌􏼌 Figure 3). Especially, when the game similarity is calculated min􏽮􏼌􏼌Iu,v 􏼌􏼌, c􏽯 sim (u, v) × sim(u, v). (13) based on the collaborative filtering similarity algorithm, the ′ c similarity between popular games and other games is  5192, 2021, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1155/2021/4751263, Wiley Online Library on [31/12/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 6 Scientific Programming 4. Analysis of Game Teaching Method in Preschool Education Based on Big Five points Five points Data Technology Users can obtain information based on their existing knowledge, perception, and thinking through the intuitive interactive interface provided by the machine and react through the interactive interface. &e machine processes 0.6 the received information and then transmits it to the user through the man-machine interface or makes other forms 0.4 0.5 0.8 0.9 of feedback. &e human–computer interaction process can 0.7 be summarized as consisting of four basic functions: in- formation receiving function, information storage func- tion, information processing and decision-making function, and execution function, as shown in the following Figure 4: &e memory storage model is the three-level memory Five points model of memory. &is model divides the process of Five points Four points memory into three stages according to the time sequence of memory. Sensory memory is the initial stage, followed by short-term memory, and finally long-term memory. &e model can be shown in Figure 5: It can be seen from the model in Figure 5 that people first Figure 3: An example where a game is matched multiple times in obtain information from the environment through sensory the calculation of set similarity. memory, such as vision and hearing. Some information will be lost in this process. &en, when the information gets attention, the human brain begins to perform the next stage generally high, which will cause the calculated set similarity of memory, which is short-term memory. When performing to not well reflect the preschool children’s degree of simi- short-term memory, the human brain processes and reor- larity between interests. ganizes information and responds. To achieve this process, &erefore, the weight reduction penalty should be im- the human brain also needs to call up the knowledge in long- posed on games that have been matched more than once. term memory. When the information is retold and Considering that the result range of cosine similarity or strengthened, the information can be stored in long-term Pearson’s correlation coefficient is [-1,1], different penalty memory. &e arrows on the way indicate the flow of mechanisms are adopted for positive and negative similarity, information. although in theory it is difficult for games with negative &e human–computer interaction function diagram correlation to be the best match. &e penalty strategy is to clearly describes the flow of information: information input, reduce all similarities related to the game when calculating reception, processing, storage and output, and so on can the similarity according to the number of times the game has know the goal and structure of the information, but does not been matched. reflect the roles of the three modules of the user’s memory. In addition, for each game i, the game i∗ that is most In the process of using handheld mobile devices, in addition similar to game i in the set of scored games for each pre- to the user’s memory, the three modules affect all aspects of schooler u can be calculated, that is, the information circulation process, the interactive design of 􏽮i∗ ∈ Iu , sim(i, i∗ ) maxi∈Iu sim(i, i∗ )􏽯. &e collaborative the device, the difficulty of game tasks, and the user’s in- filtering method based on preschool children can be ex- formation cognitive ability also affect the effect of infor- tended with formula : mation transmission. Based on the mutual influence of the 􏽐v∈Ni (i)sim(u, v)sim(i, j)Rv,j aforementioned elements, a design model of instructional P(u, i). (15) games supported by mobile devices has been researched, as 􏽐v∈Ni (i)|sim(u, v)||sim(i, j)| shown in Figure 6. &e advantage of using this expansion formula to cal- &e multisensory and multidimensional interactive culate is that for different prediction games, even if the virtual reality environment composed of sensors-control- number of reliable neighbors for preschoolers is insufficient, lers (chips)-virtual worlds (computers) will have a better we can expand enough neighbors and scores by looking for immersive effect than virtual reality where computers are approximate games, thereby solving data sparseness to a solely used as visual and auditory output. &is research will certain extent. Improve the reliability of prediction results. combine the multisensory and multidimensional  5192, 2021, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1155/2021/4751263, Wiley Online Library on [31/12/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Scientific Programming 7 Information Information Information processing and Execute Information receiving input decision-making function output function function Information storage function Figure 4: Basic functions of human-computer interaction. Feel Short Long Environment memory memory memory Information Information lost lost Figure 5: &ree-storage model of memory. Hand touch (button, Human- Feel touch screen computer memory sliding), sound interaction control, gesture Information acceptance Teaching content, objectives, game External Difficulty of the Short operation environment game memory requirements, barrier levels... Information processing, decision- making, execution Influencing factor: Cognition of the growth information Long environment, used memory cultural level, age and gender Figure 6: Teaching game design model supported by mobile devices. interaction concept of sensor-controller (chip)-virtual After constructing the aforementioned model, the per- world (computer) to design a virtual reality psychological formance of the model is verified. &e model built in this relaxation game suitable for preschool students, as shown paper is mainly used in preschool education, and it uses big in Figure 7. data recommendation algorithms to recommend  5192, 2021, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1155/2021/4751263, Wiley Online Library on [31/12/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 8 Scientific Programming Now Expansion Visual, Visual / auditory + touch / Person auditory Person flavor / + interaction Smart devices (the Smart devices (the (recipient) (recipient) model is unexplored sender) sender) Players Game world Players Game world Reverse Reverse and expansion Look at the Visual / auditory + touch / Person screen, touch the Person flavor / + interaction keyboard, and Smart devices (the (recipient) Smart devices (the (recipient) model is unexplored speak on the sender) sender) microphone Players Game world Players Game world Figure 7: Game teaching mode in preschool education. Table 1: Preschool education data mining and game recommendation effects. Number Data mining Game recommendation 1 92.6 80.0 2 85.5 85.7 3 93.9 77.6 4 89.2 90.0 5 82.3 84.5 6 85.1 90.0 7 84.9 87.3 8 81.7 88.4 9 88.3 89.5 10 91.3 88.9 11 89.6 78.2 12 89.5 84.9 13 82.7 76.8 14 86.2 88.7 15 90.9 76.3 16 86.8 90.8 17 88.1 82.0 18 89.2 82.8 19 84.2 89.2 20 83.0 87.5 21 93.1 76.9 22 92.3 87.9 23 82.1 87.4 24 90.5 81.9 25 90.8 88.1 26 87.1 76.9 27 89.3 83.1 28 82.5 84.5  5192, 2021, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1155/2021/4751263, Wiley Online Library on [31/12/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Scientific Programming 9 Table 1: Continued. Number Data mining Game recommendation 29 89.7 87.9 30 81.9 79.3 31 89.2 90.8 32 88.1 88.8 33 85.8 89.1 34 82.7 88.4 35 93.9 88.1 36 90.6 85.0 37 87.0 81.4 38 93.2 76.5 39 90.0 76.1 40 92.7 87.9 41 89.5 76.4 42 87.3 82.3 43 86.6 88.0 44 84.0 76.4 45 89.9 78.9 46 82.7 90.2 Table 2: Evaluation of the teaching effect. Number Teaching effect 1 91.3 2 89.8 3 88.2 4 87.1 5 80.8 6 85.9 7 90.1 8 80.9 9 88.5 10 81.5 11 85.3 12 80.9 13 92.0 14 88.6 15 87.4 16 81.6 17 85.1 18 86.7 19 81.9 20 82.5 21 80.7 22 89.7 23 81.2 24 88.4 25 88.9 26 86.9 27 91.6 28 81.6 29 86.4 30 88.2 31 89.2 32 86.7 33 82.6 34 84.1 35 82.2 36 92.3 37 85.7  5192, 2021, 1, Downloaded from https://onlinelibrary.wiley.com/doi/10.1155/2021/4751263, Wiley Online Library on [31/12/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 10 Scientific Programming Table 2: Continued. Number Teaching effect 38 86.5 39 84.3 40 85.8 41 89.3 42 88.8 43 92.1 44 89.3 45 87.2 46 90.1 appropriate games and uses data mining algorithms to mine Data Availability students’ learning conditions and improve real-time teaching. &e labeled dataset used to support the findings of this study &erefore, this paper first designs experiments to con- are available from the corresponding author upon request. duct preschool education data mining and game recom- mendation effect verification and obtain relevant Conflicts of Interest experimental data through multiple sets of simulation data. &e results are shown in Table 1 below. &e author declares no competing interests. From the experimental results in Table 1, the game teaching method in preschool education based on big data Acknowledgments technology proposed in this paper can effectively conduct &is work was supported by YuLin University. preschool education data mining and can recommend suitable games for preschool education. 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