An Intelligent Platform for Offline Learners Based on Model-Driven Crowdsensing Over Intermittent Networks

Despite the continuous growth of global Internet users, almost 4 billion people do not use the Internet. The offline population includes people who live in developing regions or rural aging communities. In this context, we propose a learning-support platform for learners without an easy, reliable, and affordable means to access digital learning environments on the Internet. Unlike existing systems that provide little support for efficient educational data collection from offline learners, our proposed platform combines delay-tolerant networking mechanisms and active learning-based model-driven crowdsensing techniques to deliver learning materials and collect educational data efficiently.

[1]  Driss Mammass,et al.  A machine learning algorithm framework for predicting students performance: A case study of baccalaureate students in Morocco , 2019, Education and Information Technologies.

[2]  Stephen Alstrup,et al.  High-School Dropout Prediction Using Machine Learning: A Danish Large-scale Study. , 2015 .

[3]  Dario Sansone.  Beyond Early Warning Indicators: High School Dropout and Machine Learning , 2019 .

[4]  Burr Settles,et al.  Active Learning Literature Survey , 2009 .

[5]  Murat Pojon,et al.  Using Machine Learning to Predict Student Performance , 2017 .

[6]  Khamisi Kalegele,et al.  A Survey of Machine Learning Approaches and Techniques for Student Dropout Prediction , 2019, Data Sci. J..

[7]  Boran Sekeroglu,et al.  Student Performance Prediction and Classification Using Machine Learning Algorithms , 2019, Proceedings of the 2019 8th International Conference on Educational and Information Technology.

[8]  Atsushi Shimada,et al.  Towards Supporting Multigenerational Co-creation and Social Activities: Extending Learning Analytics Platforms and Beyond , 2018, HCI.

[9]  George Karypis,et al.  Collaborative multi-regression models for predicting students' performance in course activities , 2015, LAK.

[10]  Mykola Pechenizkiy,et al.  Predicting Students Drop Out: A Case Study , 2009, EDM.

[11]  Jennifer G. Dy,et al.  Active Learning from Crowds , 2011, ICML.

[12]  M. Hilbert,et al.  Big Data for Development: A Review of Promises and Challenges , 2016 .

[13]  Mingjie Tan,et al.  Prediction of Student Dropout in E-Learning Program Through the Use of Machine Learning Method , 2015, Int. J. Emerg. Technol. Learn..

[14]  Martin Müller,et al.  Towards User‐Centered Active Learning Algorithms , 2018, Comput. Graph. Forum.

[15]  Matthew Lease,et al.  On Quality Control and Machine Learning in Crowdsourcing , 2011, Human Computation.

[16]  Matthew Kam,et al.  The Case for Technology in Developing Regions , 2005, Computer.

[17]  Sasu Tarkoma,et al.  Crowd Replication , 2019, ACM Trans. Spatial Algorithms Syst..

[18]  Abdul Rahim Ahmad,et al.  Tracking Student Performance in Introductory Programming by Means of Machine Learning , 2019, 2019 4th MEC International Conference on Big Data and Smart City (ICBDSC).

[19]  Jae Young Chung,et al.  The Machine Learning-Based Dropout Early Warning System for Improving the Performance of Dropout Prediction , 2019, Applied Sciences.

[20]  Bernardete Ribeiro,et al.  On using crowdsourcing and active learning to improve classification performance , 2011, 2011 11th International Conference on Intelligent Systems Design and Applications.

[21]  Niwat Thepvilojanapong,et al.  A Study of Cooperative Human Probes in Urban Sensing Environments , 2010, IEICE Trans. Commun..

[22]  Ji Won You,et al.  Identifying significant indicators using LMS data to predict course achievement in online learning , 2016, Internet High. Educ..

[23]  Gil Alterovitz,et al.  Deep Probabilistic Matrix Factorization Framework for Online Collaborative Filtering , 2019, IEEE Access.

[24]  S. Heath,et al.  A Comparison of RNA-Seq Results from Paired Formalin-Fixed Paraffin-Embedded and Fresh-Frozen Glioblastoma Tissue Samples , 2017, PloS one.

[25]  Chunyan Miao,et al.  Online Active Learning with Expert Advice , 2018, ACM Trans. Knowl. Discov. Data.

[26]  Dacheng Tao,et al.  Active Learning for Crowdsourcing Using Knowledge Transfer , 2014, AAAI.

[27]  L. Igual,et al.  Data-driven system to predict academic grades and dropout , 2017, PloS one.

[28]  Vikas Sindhwani,et al.  Data Quality from Crowdsourcing: A Study of Annotation Selection Criteria , 2009, HLT-NAACL 2009.

[29]  Ermiyas Birihanu Belachew,et al.  Student Performance Prediction Model using Machine Learning Approach: The Case of Wolkite University , 2017 .

[30]  Pietro Perona,et al.  Online crowdsourcing: Rating annotators and obtaining cost-effective labels , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

[31]  Amandeep Kaur,et al.  Machine Learning Approach to Predict Student Academic Performance , 2018 .

[32]  Yu Sun,et al.  An Intelligent Mobile Crowdsourcing Information Notification System for Developing Countries , 2016, MLICOM.

[33]  Lubna Mahmoud Abu Zohair.  Prediction of Student’s performance by modelling small dataset size , 2019 .

[34]  Edward Cutrell,et al.  mClerk: enabling mobile crowdsourcing in developing regions , 2012, CHI.

[35]  Dimitrios Kalles,et al.  ANALYZING STUDENT PERFORMANCE IN DISTANCE LEARNING WITH GENETIC ALGORITHMS AND DECISION TREES , 2006, Appl. Artif. Intell..

[36]  Waylon Brunette,et al.  Open Data Kit 2.0: A Services-Based Application Framework for Disconnected Data Management , 2017, MobiSys.

[37]  D. Y. Turdakov,et al.  Active learning and crowdsourcing: a survey of annotation optimization methods , 2018 .

[38]  Dongjiang Liu,et al.  An active learning algorithm for multi-class classification , 2018, Pattern Analysis and Applications.

[39]  Gita Reese Sukthankar,et al.  Incremental Relabeling for Active Learning with Noisy Crowdsourced Annotations , 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

[40]  Edward D. Lazowska,et al.  Designing an Architecture for Delivering Mobile Information Services to the Rural Developing World , 2006, Seventh IEEE Workshop on Mobile Computing Systems & Applications (WMCSA'06 Supplement).

[41]  Denis Turdakov,et al.  Active Learning and Crowdsourcing: A Survey of Optimization Methods for Data Labeling , 2018, Programming and Computer Software.

[42]  Rong Zheng,et al.  When data acquisition meets data analytics: A distributed active learning framework for optimal budgeted mobile crowdsensing , 2017, IEEE INFOCOM 2017 - IEEE Conference on Computer Communications.

[43]  Mathew Hillier,et al.  Bridging the digital divide with off-line e-learning , 2018, Expanding Horizons in Open and Distance Learning.

[44]  Tassos A. Mikropoulos,et al.  Predicting Secondary School Students' Performance Utilizing a Semi-supervised Learning Approach , 2019 .

[45]  Prageet Aeron,et al.  Online Education: Worldwide Status, Challenges, Trends, and Implications , 2018, Journal of Global Information Technology Management.

[46]  Baldoino Fonseca dos Santos Neto,et al.  A predictive model for identifying students with dropout profiles in online courses , 2015, EDM.