A review of non-intrusive sensing based personalized resource recommendations for help-seekers in education

doi:10.3969/j.issn.1000-5641.2018.05.002

Abstract

Abstract: Mobile devices, data storage, and computing platforms of modern information technology have accelerated the integration of the information technology and education disciplines, promoted the "Education Informatization 2.0" Plan, and provided a solid technical foundation for academic help-seeking. With the help of new sensing mechanisms and techniques, non-intrusive sensing of help-seeking and personalized recommendation methods can now be used for teaching practices in academia. This study reviews the research progress of non-intrusive sensing based personalized resource recommendations, offers detailed analysis, and lists possible directions for research; potential future research topics include non-intrusive sensing for help-seeking, continuous association analysis and integration for multidimensional data, and personalized resource recommendations for help-seekers. This study also makes contributions to precision education and personalized education for the China Education Informatization 2.0 Plan by providing solutions for non-intrusive sensing based personalized resource recommendations for help-seekers.

Key words: help-seeking, non-interventional perception, education informatization 2.0, personalized education, precision education

CLC Number:

G202

TANG Lu-min, YU Ruo-nan, DONG Qi-wen, HONG Dao-cheng, FU Yun-bin. A review of non-intrusive sensing based personalized resource recommendations for help-seekers in education[J]. Journal of East China Normal University(Natural Sc, 2018, 2018(5): 17-29.

References

[1] 佚名. 中国教育学会发布调查报告显示2016年我国中小学课外辅导"吸金"超八千亿[J].教育发展研究, 2017(4):63.
[2] 李晓东, 林崇德. 个人目标取向、课堂目标结构及文化因素与学业求助策略的关系研究[J]. 心理发展与教育, 2001, 17(2):1-6.
[3] 伏干. 流动儿童学业求助的影响因素研究——社会认同的两类假说[J]. 天津师范大学学报(基础教育版), 2016, 17(3):15-18.
[4] 冯喜珍, 吴雪雷. 我国学业求助研究的现状与展望[J]. 教学与管理, 2012(33):13-15.
[5] KIEFER S M, SHIM S S. Academic help seeking from peers during adolescence:The role of social goals[J]. Journal of Applied Developmental Psychology, 2016, 42:80-88.
[6] GALL S N L. Help-seeking:An understudied problem-solving skill in children[J]. Developmental Review, 1981, 1(3):224-246.
[7] KARABENICK S A. Relationship of academic help seeking to the use of learning strategies and other instrumental achievement behavior in college students[J]. Journal of Educational Psychology, 1991, 83(2):221-230.
[8] RYAN A M, SHIN H. Help-seeking tendencies during early adolescence:An examination of motivational correlates and consequences for achievement[J]. Learning & Instruction, 2011, 21(2):247-256.
[9] KARABENICK S A, NEWMAN R S. Help Seeking in Academic Settings:Goals, Groups, and Contexts[M]. Mahwah, NJ, USA:Lawrence Erlbaum Associates, 2006.
[10] NEWMAN R S. Social influences on the development of children's adaptive help seeking:The role of parents, teachers, and peers[J]. Developmental Review, 2000, 20(3):350-404.
[11] COMPAS B E, CONNORSMITH J K, SALTZMAN H, et al. Coping with stress during childhood and adolescence:Problems, progress, and potential in theory and research[J]. Psychological Bulletin, 2001, 127(1):87-127.
[12] HEATH P J, VOGEL D L, AL-DARMAKI F R. Help-seeking attitudes of United Arab emirates students:Examining loss of face, stigma, and self-disclosure[J]. Counseling Psychologist, 2016, 44(3):331-352.
[13] PATACCHINI E, ZENOU Y. Racial identity and education in social networks[J]. Social Networks, 2016, 44:85-94.
[14] 宁辉政. 主动式电磁扫描检测技术及其信号处理方法[J]. 通讯世界, 2014(9):108-109.
[15] 薛子凡, 邢志国, 王海斗, 等. 面向结构健康监测的压电传感器综述[J]. 材料导报, 2017, 31(17):122-132.
[16] 郑太年, 仝玉婷. 课堂视频分析:理论进路、方法与应用[J]. 华东师范大学学报(教育科学版), 2017, 35(3):126-133.
[17] 布因克曼, 勒德尔, 陈红燕. 教育视频的现象学分析:课堂中的指示、注意和交互关注[J]. 华东师范大学学报(教育科学版), 2017, 35(5):30-45.
[18] 魏芳, 桑猛, 郭萍. 分布式光纤传感器灵敏度试验研究[J]. 西北水电, 2011(2):76-80.
[19] 李建国, 汤庸, 姚良超, 等. 社交网络中感知技术的研究与应用[J]. 计算机科学, 2009, 36(11):152-156.
[20] ZAFARANI R, LIU H. Connecting users across social media sites:A behavioral-modeling approach[C]//ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM, 2013:41-49.
[21] PENG Y X, ZHU W W, ZHAO Y, et al. Cross-media analysis and reasoning:Advances and directions[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1):44-57.
[22] WEILER A, GROSSNIKLAUS M, SCHOLL M H. Situation monitoring of urban areas using social media data streams[J]. Information Systems, 2016, 57:129-141.
[23] 鲁勇, 吕绍和, 王晓东, 等. 基于WiFi信号的人体行为感知技术研究综述[J/OL].[2018-03-06]. 计算机学报,, 2018:1-22. http://kns.cnki.net/kcms/detail/11.1826.TP. 20180303.1407.018.html.
[24] 王钰翔, 李晟洁, 王皓, 等. 基于Wi-Fi的非接触式行为识别研究综述[J]. 浙江大学学报(工学版), 2017, 51(4):648-654.
[25] BAHL P, PADMANABHAN V N. RADAR:An in-building RF-based user location and tracking system[J]. Proc IEEE Infocom, 2000, 2:775-784.
[26] AGARWAL R, DHAR V. Editorial-big data, data science, and analytics:The opportunity and challenge for IS research[J]. Informs, 2014, 25(3):443-448.
[27] 周傲英, 金澈清, 王国仁, 等. 不确定性数据管理技术研究综述[J]. 计算机学报, 2009, 32(1):1-16.
[28] CUNHA J V D. A dramaturgical model of the production of performance data[J]. MIS Quarterly, 2013, 37(3):723-748.
[29] ZHANG Y S, ZHOU X, ZHANG Y, et al. Virtual denormalization via array index reference for main memory OLAP[J]. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(4):1061-107.
[30] ZHU T, WANG D H, HU H Q, et al. Interactive transaction processing for in-memory database system[C]//International Conference on Database Systems for Advanced Applications. Berlin:Springer, 2018:228-246.
[31] 高明, 金澈清, 钱卫宁, 等. 面向微博系统的实时个性化推荐[J]. 计算机学报, 2014(4):963-975.
[32] ADOMAVICIUS G, TUZHILIN A. Toward the next generation of recommender systems:A survey of the stateof-the-art and possible extensions[J]. IEEE Transactions on Knowledge and Data Engineering, 2005, 17(6):734-749.
[33] WANG H, WANG N Y, YEUNG D Y. Collaborative deep learning for recommender systems[C]//Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM, 2015:1235-1244.
[34] YU A X, MENG Q Z, ZHOU X, et al. Query optimization on hybrid storage[C]//Proceedings of the 22nd International Conference Database Systems for Advanced Applications, 2017:361-375.
[35] VERBERT K, MANOUSELIS N, OCHOA X, et al. Context-aware recommender systems for learning:A survey and future challenges[J]. IEEE Transactions on Learning Technologies, 2012, 5(4):318-335.
[36] BALABANOVIC M, SHOHAM Y. Fab:Content-based, collaborative recommendation[J]. Communications of the ACM, 1997, 40(3):66-72.
[37] SARWAR B, KARYPIS G, KONSTAN J, et al. Item-based collaborative filtering recommendation algorithms[C]//Proceedings of the 10th International Conference on World Wide Web. New York:ACM Press, 2001:285-295.
[38] DONG X, YU L, WU Z, et al. A hybrid collaborative filtering model with deep structure for recommender systems[C]//Proceedings of the 31st AAAI Conference on Artificial Intelligence. Palo Alto, CA, USA:AAAI, 2017:1309-1315.
[39] WANG H, WANG N Y, YEUNG D Y. Collaborative deep learning for recommender systems[C]//Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM, 2015:1235-1244.
[40] 黄立威, 江碧涛, 吕守业, 等. 基于深度学习的推荐系统研究综述[J]. 计算机学报,, 2018:1-30.
[41] CHENG H T, KOC L, HARMSEN J, et al. Wide & deep learning for recommender systems[C]//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. New York:ACM, 2016:7-10.
[42] ALASHKAR T, JIANG S Y, WANG S Y, et al. Examples-rules guided deep neural network for makeup recommendation[C]//Proceedings of the 31st AAAI Conference on Artificial Intelligence. Palo Alto, CA, USA:AAAI. 2017:941-947.
[43] EBESU T, FANG Y. Neural citation network for context-aware citation recommendation[C]//Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York:ACM, 2017.
[44] HUANG W Y, WU Z H, CHEN L, et al. A neural probabilistic model for context based citation recommendation[C]//Proceedings of the 29th AAAI Conference on Artificial Intelligence. Palo Alto, CA, USA:AAAI, 2015:2404-2410.
[45] OUYANG Y X, LIU W Q, et al. Autoencoder-based collaborative filtering[C]//International Conference on Neural Information Processing. Berlin:Springer, 2014:284-291.
[46] SEDHAIN S, MENON A K, SANNER S, et al. Autorec:Autoencoders meet collaborative filtering[C]//Proceedings of the 24th International Conference on World Wide Web. New York:ACM, 2015:111-112.
[47] STRUB F, GAUDEL R, MARY J. Hybrid recommender system based on autoencoders[C]//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. New York:ACM, 2016:11-16.
[48] STRUB F, MARY J. Collaborative filtering with stacked denoising autoencoders and sparse inputs[C]//NIPS Workshop on Machine Learning for e-Commerce, 2015.
[49] GONG Y Y, ZHANG Q. Hashtag recommendation using attention-based convolutional neural network[C]//Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Palo Alto, CA, USA:AAAI, 2016:2782-2788.
[50] NGUYEN H T H, WISTUBA M, GRABOCKA J, et al. Personalized Deep Learning for Tag Recommendation[M]//Kim J, Shim K, Cao L, et al. Advances in Knowledge Discovery and Data Mining, PAKDD 2017. Berlin:Springer, Cham, 2017:186-197.
[51] WANG X J, YU L T, REN K, et al. Dynamic attention deep model for article recommendation by learning human editors demonstration[C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York:ACM, 2017.
[52] WEN J Q, LI X P, SHE J, et al. Visual background recommendation for dance performances using dancershared images[C]//2016 IEEE International Conference on Internet of Things and IEEE Green Computing and Communications and IEEE Cyber, Physical and Social Computing and IEEE Smart Data. IEEE, 2016:521-527.
[53] BANSAL T, BELANGER D, MCCALLUM A. Ask the gru:Multi-task learning for deep text recommendations[C]//Proceedings of the 10th ACM Conference on Recommender Systems. New York:ACM, 2016:107-114.
[54] DAI H J, WANG Y C, TRIVEDI R, et al. Deep coevolutionary network:Embedding user and item features for recommendation[C]//Proceedings of ACM Conference, Halifax, Canada. New York:ACM, 2017.
[55] KO Y J, MAYSTRE L, GROSSGLAUSER M. Collaborative recurrent neural networks for dynamic recommender systems[C]//Proceedings the 8th Asian Conference on Machine Learning. 2016:366-381.
[56] SMIRNOVA E, VASILE F. Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks[C]//Proceedings of ACM Recommender Sytem Conference. New York:ACM, 2017.
[57] ELKAHKY A M, SONG Y, HE X D. A multi-view deep learning approach for cross domain user modeling in recommendation systems[C]//Proceedings of the 24th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee, 2015:278-288.
[58] CHEN C, MENG X W, XU Z H, et al. Location-aware personalized news recommendation with deep semantic analysis[J]. IEEE Access 2017, 5:1624-1638.
[59] XU Z H, CHEN C, LUKASIEWICZ O, et al. Tag-aware personalized recommendation using a deep-semantic similarity model with negative sampling[C]//Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. New York:ACM, 2016:1921-1924.
[60] SALAKHUTDINOV R, MNIH A. Restricted Boltzmann machines for collaborative filtering[C]//Proceedings of the 24th International Conference on Machine Learning. New York:ACM, 2007:791-798.
[61] GEORGIEV K, NAKOV P. A non-ⅡD framework for collaborative filtering with restricted Boltzmann machines[C]//Proceedings of the 30th International Conference on Machine Learning. 2013:1148-1156.
[62] LIU X M, OUYANG Y X, RONG W G, et al. Item category aware conditional restricted Boltzmann machine based recommendation[C]//Proceedings of International Conference on Neural Information Processing. New York:Springer, 2015:609-616.
[63] XIE W Z, OUYANG Y X, OUYANG J S, et al. User occupation aware conditional restricted Boltzmann machine based recommendation[C]//2016 IEEE International Conference on Internet of Things and IEEE Green Computing and Communications and IEEE Cyber, Physical and Social Computing and IEEE Smart Data. IEEE, 2016:454-461.
[64] ZHENG Y, TANG B S, DING W K, et al. A neural autoregressive approach to collaborative filtering[C]//Proceedings of the 33rd International Conference on International Conference on Machine Learning. 2016:764-773.
[65] ZHENG Y, LIU C L, TANG B S, et al. Neural autoregressive collaborative filtering for implicit feedback[C]//Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. New York:ACM, 2016:2-6.
[66] WANG J, YU L T, ZHANG W N, et al. IRGAN:A minimax game for unifying generative and discriminative information retrieval models[C]//Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York:ACM, 2017.