At present, the theoretical and experimental teaching parts of chemistry teaching are difficult to present through online teaching, and the teaching effect is difficult to effectively improve. In order to improve the effect of online teaching of chemistry courses and discover the problems in teaching in a timely and effective manner, this paper builds the corresponding teaching system, optimizes the performance of the system with key information search and big data cloud platform, improves the data mining ability and data analysis ability, and improves the system running speed. Moreover, this article constructs functional modules according to the actual needs of chemistry course network teaching and constructs sub-modules for functional modules. The constructed model can be displayed in the form of a webpage, and the model has the ability of data mining and problem analysis of chemistry course network teaching. In addition, in order to verify the effect of the model, this paper designs experiments to analyze its performance. The research results show that the system constructed in this paper has good performance.
LiA., LiK. and GeZ., Application of Data Mining in the Colleges’ in-Class Teaching Quality Evaluation System, Journal of Computers10(3) (2015), 166–175.
2.
AngellK. and TewellE., Teaching and Un-Teaching Source Evaluation: Questioning Authority in Information Literacy Instruction, Communications in Information Literacy11(1) (2017), 95–121.
3.
BrkovicM. and ChilesP., ‘Spector - the sustainability inspector’: Participatory teaching, learning and evaluation game for architects, architecture students and pupils, Facta Universitatis14(1) (2016), 1–20.
4.
ReisenwitzT.H., Student Evaluation of Teaching: An Investigation of Nonresponse Bias in an Online Context, Journal of Marketing Education38(4) (2015), 139–144.
5.
JiangY. and WangY., Evaluation of Teaching Quality of Public Physical Education in Colleges Based on the Fuzzy Evaluation Theory, Journal of Computational and Theoretical Nanoence13(12) (2016), 9848–9851.
6.
RoyalK., A Guide for Making Valid Interpretations of Student Evaluation of Teaching (SET) Results, Journal of Veterinary Medical Education44(2) (2016), 1–7.
7.
EcklerU., GreisbergerA., HöhneF., et al., Blended learning versus traditional teaching-learning-setting: Evaluation of cognitive and affective learning outcomes for the inter-professional field of occupational medicine and prevention / Blended Learning versus traditionelles Lehr-Lernsetting: Evaluierung von kognitiven und affektiven Lernergebnissen für das interprofessionelle Arbeitsfeld Arbeitsmedizin und Prävention, Nephron Clinical Practice4(2) (2017), 109–121.
8.
GarofaloF., Mota-MoyaP., MundayA., et al., Total Extraperitoneal Hernia Repair: Residency Teaching Program and Outcome Evaluation, World Journal of Surgery41(1) (2017), 1–6.
9.
GongG. and LiuS., Consideration of Evaluation of Teaching at Colleges, Open Journal of Social Sciences04(7) (2016), 82–84.
10.
ZhaoH., College Physics Teaching Model Design and Evaluation Research of Students’ Seriousness, Open Cybernetics & Systemics Journal9(1) (2015), 2017–2020.
11.
TranN.D., Reconceptualisation of approaches to teaching evaluation in higher education, Issues In Educational Research25(1) (2015), 50–61.
12.
DelgatyL., Twelve tips for academic role and institutional change in distance learning, Medical Teacher37(1) (2015), 41–46.
13.
StefanovicM., TadicD., NesticS., et al., An Assessment of Distance Learning Laboratory Objectives for Control Engineering Education, Computer Applications in Engineering Education23(2) (2015), 191–202.
14.
RemeshB.P., Developing Open and Distance Learning Programme in Labour and Development: Results of a Needs Assessment Study, Journal of Natural History196(29) (2015), 265–291.
15.
WillisE.A., Szabo-ReedA.N., PtomeyL.T., et al., Distance learning strategies for weight management utilizing social media: A comparison of phone conference call versus social media platform, Rationale and design for a randomized study, Contemporary Clinical Trials47 (2016), 282–288.
16.
YeH.J., ZhanD.C. and JiangY., Fast generalization rates for distance metric learning: Improved theoretical analysis for smooth strongly convex distance metric learning, Machine Learning108(2) (2019), 267–295.
17.
LuoY., WenY., LiuT., et al., Transferring Knowledge Fragments for Learning Distance Metric from a Heterogeneous Domain, IEEE Transactions on Pattern Analysis and Machine Intelligence41(4) (2019), 1013–1026.
18.
HsuC.C., KungC.H., JhengJ.J., et al., Unsupervised distance learning for extended self-organizing map and visualization of mixed-type data, Intelligent Data Analysis23(4) (2019), 799–823.
19.
NenovS., AntonovA. and TsvetkovT., Impulsive Models: Maximum Yield of Some Biological Systems, I, Dynamic Systems and Applications28 (2019), 317–328.
20.
LiH., The motivation and degree of reading promotion participation of University Library Based on data variable analysis, Dynamic Systems and Applications29(3) (2020), 507–521.
21.
MaoJ., LuK., LiG., et al., Profiling users with tag networks in diffusion-based personalized recommendation, Journal of Information Science42(5) (2016), 711–722.
22.
ZuoY., GongM., ZengJ., et al., Personalized Recommendation Based on Evolutionary Multi-Objective Optimization [Research Frontier], Computational Intelligence Magazine IEEE10(1) (2015), 52–62.
23.
CostaG. and OrtaleR., Model-Based Collaborative Personalized Recommendation on Signed Social Rating Networks, Acm Transactions on Internet Technology16(3) (2016), 1–21.
24.
GuanY., WeiQ. and ChenG., Deep learning based personalized recommendation with multi-view information integration, Decision Support Systems118(MAR.) (2019), 58–69.
25.
YangC., FanQ., WangT., et al., RepoLike: a multi-feature-based personalized recommendation approach for open-source repositories, Frontiers of Information Technology & Electronic Engineering20(02) (2019), 86–101.
26.
PaulA., JiangY.C., WangJ.F. and YangJ.F., Parallel reconfigurable computing-based mapping algorithm for motion estimation in advanced video coding, ACM Transactions on Embedded Computing Systems (TECS)11(S2) (2012), 1–18.
27.
RathoreM.M., PaulA., HongW.H., SeoH.C., AwanI. and SaeedS., Exploiting IoT and big data analytics: Defining smart digital city using real-time urban data, Sustainable Cities and Society40 (2018), 600–610.
28.
PaulA., Internet of Things: A primer’, R Jeyaraj Human Behavior and Emerging Technologies1(1) (2019), 37–47.
29.
PaulA., DanielA., AhmadA. and RhoS., Cooperative cognitive intelligence for internet of vehicles, IEEE Systems Journal11(3) (2017), 1249–1258.
30.
PaulA., AhmadA., Mazhar RathoreM. and JabbarS., Smartbuddy: defining human behaviors using big data analytics in social internet of things, IEEE Wireless Communications23(5) (2016), 68–74.
