Evaluation of Quality of Interaction in Online Learning Based on Representation Learning

Computer Science ›› 2021, Vol. 48 ›› Issue (2): 207-211.doi: 10.11896/jsjkx.201000042

• Artificial Intelligence • Previous Articles Next Articles

Evaluation of Quality of Interaction in Online Learning Based on Representation Learning

WANG Xue-cen, ZHANG Yu, LIU Ying-jie, YU Ge

School of Computer Science and Engineering,Northeastern University,Shenyang 110169,China

Received:2020-10-09 Revised:2020-11-04 Online:2021-02-15 Published:2021-02-04
About author:WANG Xue-cen,born in 1996,postgra-duate.Her main research interests include smart education and recommendation system.
ZHANG Yu,born in 1980,Ph.D,lectu-rer,is a member of China Computer Fe-deration.His main research interests include big data in education,social networks,etc.
Supported by:
The National Natural Science Foundation of China(U1811261) and Fundamental Research Funds for the Central Universities(N180716010).

Abstract

Abstract: The model of education today has undergone a very significant change,and education is developing in the direction of ubiquity,intelligence and individuation.Online education,represented by MOOCs,is gradually coming into the public field of vision,and the interactivity in online education has become the key to determine the quality of online learning.Some researches show that the interaction in the learning process provides efficient help and effective support for learners,and the feedback of learning process evaluation can effectively improve the interaction effect of learning.Modeling interactions between learners and learning resources is crucial in domains such as e-commerce.Representation learning presents a method to model the sequential interactions between learners and learning resources.Firstly,an interactive network of online learning is established.And then,the learners and learning resources can be embedded into a Euclidean space by using two recurrent neural networks.The evaluation index of the quality of interaction is proposed,which can judge whether the learner's learning effect is up to the expectation.The experiments on real datasets reveal the effectiveness of the proposed method.

Key words: Big data in education, Interactive evaluation, MOOCs, Representation learning

CLC Number:

TP311.13

WANG Xue-cen, ZHANG Yu, LIU Ying-jie, YU Ge. Evaluation of Quality of Interaction in Online Learning Based on Representation Learning[J].Computer Science, 2021, 48(2): 207-211.

References

[1] KEEGAN D.Reintegration of the teaching acts[J].Theoretical principles of distance education,1993:113-134.
[2] DOWMES S.Connectivism and connective knowledge:essays on meaning and learning networks[DB/OL].National Research Council Canada.http://www.downes.ca/files/books/Connective_Knowledge-19May2012.pdf.
[3] PARKER A,PARKER S.Interaction:The vital conversation in online instruction[OL].https://eric.ed.gov/?id=ED541876.
[4] ROSSI D,RENSBURG H,HARREVELD R,et al.Exploring a Cross-Institution research collaboration and innovation:deploying social software and web 2.0 technologies to investigate online learning designs and interactions in two Australian Universities[J].Dental & Medical Problems,2012,5(2):1-11.
[5] BIGGS J.Testing:to educate or to select?[M].Hongkong:Hong Kong Educational Publishing Company,1996.
[6] ALDERSON J C,WALL D.Does washback exist?[J].Applied Linguistics,1993,14(2):115-129.
[7] SIEMENS G.Orientation:sensemaking and wayfinding in complex distributed online information environments[D].Aberdeen:University of Aberdeen Doctoral Dissertation,2011.
[8] PRASHA S,SURAJ M,DUSTIN A,et al.Learning from dynamic user interaction graphs to forecast diverse social behavior[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM '19).2019:2033-2042.
[9] CAI R,BAI X,WANG Z,et al.Modeling sequential online interactive behaviors with temporal point process[C]//the 27th ACM International Conference.ACM,2018:873-882.
[10] FENG W,TANG J,LIU T X.Understanding Dropouts inMOOCs[J].Proceedings of the AAAI Conference on Artificial Intelligence,2019,33:517-524.
[11] MOORE M G.Three types of interaction[J].distance education new perspectives,1993,3(2):1-7.
[12] LAURILLARD D.Rethinking university teaching:a conversa-tional framework for the effective use of learning technologies[M].London:Routledge,2002.
[13] ROBLYER M D,WIENCKW W R.Design and use of a rubic to access and encourage interactive qualities in distance courses[J].The American Journal of Distance Education,2003,17(2):77-98.
[14] WEI Z,CHEN L,XI J H.Research on the evaluation index system of interactive quality of web-based course teaching[J].Open Education Research,2004(6):34-39.
[15] PEROZZI B,ALRFOU R,SKIENA S.DeepWalk:online learning of social representations[C]//ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.2014:701-710.
[16] GROVER A,LESKOVEC J.Node2vec:scalable feature learning for networks[C]//The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2016:855-864.
[17] RIBEIRO L F R,SAVARESE P H P,FIGUEIREDO D R.Struc2vec:learning node representations from structural identity[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2017.
[18] FU T Y,LEE W C,LEI Z.Hin2vec:explore meta-paths in heterogeneous information networks for representation learning[C]//Proceedings of the 2017 ACM on Conference on Information and Knowledge.ACM,2017.
[19] DAI H,WANG Y,TRIVEDI R,et al.Deep coevolutionary network:embedding user and item features for recommendation[J].arXiv:learning.arXiv preprint arXiv:1609.03675.2016.
[20] KUMAR S,ZHANG X,LESKOVEC J.Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks[C]//the 25th ACM SIGKDD International Conference.ACM,2019:1269-1278.

Related Articles 15

[1]	SONG Jie, LIANG Mei-yu, XUE Zhe, DU Jun-ping, KOU Fei-fei. Scientific Paper Heterogeneous Graph Node Representation Learning Method Based onUnsupervised Clustering Level [J]. Computer Science, 2022, 49(9): 64-69.
[2]	HUANG Li, ZHU Yan, LI Chun-ping. Author’s Academic Behavior Prediction Based on Heterogeneous Network Representation Learning [J]. Computer Science, 2022, 49(9): 76-82.
[3]	XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai. Temporal Knowledge Graph Representation Learning [J]. Computer Science, 2022, 49(9): 162-171.
[4]	LI Zong-min, ZHANG Yu-peng, LIU Yu-jie, LI Hua. Deformable Graph Convolutional Networks Based Point Cloud Representation Learning [J]. Computer Science, 2022, 49(8): 273-278.
[5]	HUANG Pu, DU Xu-ran, SHEN Yang-yang, YANG Zhang-jing. Face Recognition Based on Locality Regularized Double Linear Reconstruction Representation [J]. Computer Science, 2022, 49(6A): 407-411.
[6]	JIANG Zong-li, FAN Ke, ZHANG Jin-li. Generative Adversarial Network and Meta-path Based Heterogeneous Network Representation Learning [J]. Computer Science, 2022, 49(1): 133-139.
[7]	WANG Ying-li, JIANG Cong-cong, FENG Xiao-nian, QIAN Tie-yun. Time Aware Point-of-interest Recommendation [J]. Computer Science, 2021, 48(9): 43-49.
[8]	ZHAO Jin-long, ZHAO Zhong-ying. Recommendation Algorithm Based on Heterogeneous Information Network Embedding and Attention Neural Network [J]. Computer Science, 2021, 48(8): 72-79.
[9]	YE Hong-liang, ZHU Wan-ning, HONG Lei. Music Style Transfer Method with Human Voice Based on CQT and Mel-spectrum [J]. Computer Science, 2021, 48(6A): 326-330.
[10]	YANG Ru-han, DAI Yi-ru, WANG Jian, DONG Jin. Humans-Cyber-Physical Ontology Fusion of Industry Based on Representation Learning [J]. Computer Science, 2021, 48(5): 190-196.
[11]	QIAN Sheng-sheng, ZHANG Tian-zhu, XU Chang-sheng. Survey of Multimedia Social Events Analysis [J]. Computer Science, 2021, 48(3): 97-112.
[12]	WANG Xing , KANG Zhao. Smooth Representation-based Semi-supervised Classification [J]. Computer Science, 2021, 48(3): 124-129.
[13]	LI Xin-chao, LI Pei-feng, ZHU Qiao-ming. Directed Network Representation Method Based on Hierarchical Structure Information [J]. Computer Science, 2021, 48(2): 100-104.
[14]	FU Kun, ZHAO Xiao-meng, FU Zi-tong, GAO Jin-hui, MA Hao-ran. Deep Network Representation Learning Method on Incomplete Information Networks [J]. Computer Science, 2021, 48(12): 212-218.
[15]	KANG Yan, KOU Yong-qi, XIE Si-yu, WANG Fei, ZHANG Lan, WU Zhi-wei, LI Hao. Deep Clustering Model Based on Fusion Variational Graph Attention Self-encoder [J]. Computer Science, 2021, 48(11A): 81-87.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!