A review of non-intrusive sensing based personalized resource recommendations for help-seekers in education
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摘要: 现代信息技术提供的强大移动终端、数据存储和计算平台,极大地促进了信息技术和教育学科的深度融合,有利地推动了"教育信息化2.0行动计划"的实施,也为研究学业求助提供了坚实的技术保障.借助多种新型的感知机理和实现技术,建立日常教学实践活动中非干预式的学业求助行为感知和分类,使实现自适应个性化的学业求助资源推荐成为可能.本文针对非干预式感知的个性化学业求助资源推荐研究状况,展开具体分析,并针对未来可能研究进行了展望:学业求助非干预式感知、学业求助多源异构数据分析、以及学业求助资源个性化推荐方法.以上研究内容充分利用和发挥了现代信息技术的优势,探索其在学业求助应用场景下切实可行的途径和方法.有利于实现对学习者学业求助需求的精准定位并提供自适应个性化的资源推荐,贯彻了我国教育信息化2.0建设中的精准教育理念,具有理论和实际的双重意义.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.
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表 1 非干预式感知技术优点及缺陷
Tab. 1 Advantages and disadvantages of non-interventional perception technology
非干预式感知技术 优点 缺陷 hline辐射扫描技术 成本低, 环保, 耗能少 对人体有害 压电技术 灵敏度高, 可靠 易受干扰, 成本高 多媒体感知技术 集成度高, 交互方便, 易扩展 成本高, 实时传输要求高, 数据冗余 光纤感知技术 灵敏度高, 适应恶劣条件, 耗能少 成本高, 过于敏感 无线电信号技术 成本低, 普及度高, 穿透性强, 信号稳定性稍差 感知范围大, 适应恶劣条件 表 2 主流推荐算法的优势和局限
Tab. 2 Advantages and limitations of mainstream recommendation methods
方法 优势 局限 没有冷启动问题 项目特征提取困难 基于内容的推荐方法 不需要惯用数据 无法发掘用户潜在兴趣 可解释性较高 新用户无法推荐 协同过滤推荐方法 不需要用户和项目 稀疏性问题 大部分应用场景推荐质量高 冷启动问题 缓解了冷启动问题 需要大量的工作才能得到正确的平衡 混合方法 缓解了稀疏性问题 可发掘用户潜在兴趣 -
[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. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=QKC20172017051200193002 [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. doi: 10.3969/j.issn.1001-4918.2001.02.001 [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. http://qikan.cqvip.com/article/detail.aspx?id=669665330 [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. http://d.old.wanfangdata.com.cn/Periodical/jxygl-llb201211004 [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. doi: 10.1016/j.appdev.2015.12.002 [6] GALL S N L. Help-seeking:An understudied problem-solving skill in children[J]. Developmental Review, 1981, 1(3):224-246. doi: 10.1016/0273-2297(81)90019-8 [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. doi: 10.1037/0022-0663.83.2.221 [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. http://www.sciencedirect.com/science/article/pii/S0959475210000526 [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. doi: 10.1006/drev.1999.0502 [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. doi: 10.1037/0033-2909.127.1.87 [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. https://www.researchgate.net/publication/302919923_Help-Seeking_Attitudes_of_United_Arab_Emirates_Students_Examining_Loss_of_Face_Stigma_and_Self-Disclosure [13] PATACCHINI E, ZENOU Y. Racial identity and education in social networks[J]. Social Networks, 2016, 44:85-94. doi: 10.1016/j.socnet.2015.06.001 [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. http://d.old.wanfangdata.com.cn/Periodical/txsj201409060 [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. doi: 10.11896/j.issn.1005-023X.2017.017.018 [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. http://d.old.wanfangdata.com.cn/Periodical/hdsfdxxb-jykxb201703013 [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. http://d.old.wanfangdata.com.cn/Periodical/hdsfdxxb-jykxb201705010 [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. doi: 10.3969/j.issn.1006-2610.2011.02.021 [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. doi: 10.3969/j.issn.1002-137X.2009.11.037 [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. http://d.old.wanfangdata.com.cn/Periodical/zjdxxbc-e201701004 [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. doi: 10.1016/j.is.2015.09.004 [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. http://d.old.wanfangdata.com.cn/Periodical/zjdxxb-gx201704002 [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. http://d.old.wanfangdata.com.cn/Periodical/dqkxjz-e201404008 [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. doi: 10.1287/isre.2014.0546 [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. http://d.old.wanfangdata.com.cn/Periodical/jsjxb200901001 [28] CUNHA J V D. A dramaturgical model of the production of performance data[J]. MIS Quarterly, 2013, 37(3):723-748. doi: 10.25300/MISQ [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. doi: 10.1109/TKDE.2015.2499199 [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. http://d.old.wanfangdata.com.cn/Periodical/jsjxb201404020 [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. doi: 10.1109/TKDE.2005.99 [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. doi: 10.1109/TLT.2012.11 [36] BALABANOVIC M, SHOHAM Y. Fab:Content-based, collaborative recommendation[J]. Communications of the ACM, 1997, 40(3):66-72. doi: 10.1145/245108.245124 [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. http://youxian.cnki.com.cn/yxdetail.aspx?filename=JSJX20171115001&dbname=CAPJ2015 [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. doi: 10.1109/ACCESS.2017.2655150 [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.