Smart educational tools and learning management systems: supportive framework

Fardinpour, A., Pedram, M.M., Burkle, M.: Intelligent learning management systems: definition, features and measurement of intelligence. Int. J. Distance Educ. Technol. 12(4), 19–31 (2014)

Anil, G. R., Moiz, S. A. Blockchain enabled smart learning environment framework. In: Satapathy, S.C., Raju, K. S., Shyamala, K., Krishna, D. R., Margarita N. Favorskaya, M. N. (eds.) Advances in Decision Sciences, Image Processing, Security and Computer Vision, pp. 728–740. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-24318-0_83

Srivastava, M., Saurabh, P., Verma, B.: IOT for capturing information and providing assessment framework for higher educational institutions—a framework for future learning. In: Das, K. N., Bansal, J. C., Deep, K., Nagar, A. K., Pathipooranam, P., Naidu, R. C. (eds.) Soft Computing for Problem Solving, pp. 249–261. Springer, Singapore (2020)

Uskov, V. L., Bakken, J. P., Heinemann, C., Rachakonda, R., Guduru, V. S., Thomas, A. B., Bodduluri, D. P.: Building smart learning analytics system for smart university. In: Uskov V., Howlett R., Jain L. (eds.) International Conference on Smart Education and Smart E-Learning, pp. 191–204. Springer, Cham (2017)

Hoel, T., Mason, J.: Standards for smart education–towards a development framework. Smart Learn. Environ. 5(1), 3 (2018)

Spector, J.M.: Conceptualizing the emerging field of smart learning environments. Smart Learn. Environ. 1(1), 2 (2014)

Uskov, V. L., Bakken, J. P., Gayke, K., Jose, D., Uskova, M. F., Devaguptapu, S. S.: Smart university: a validation of “smartness features—main components” matrix by real-world examples and best practices from universities worldwide. In: Uskov V., Howlett R., Jain L. (eds.) Smart Education and e-Learning 2019, pp. 3–17. Springer, Singapore (2019)

Ardashkin, I.B., Chmykhalo, A.Y., Makienko, M.A., Khaldeeva, M.A.: Smart-technologies in higher engineering education: modern application trends. Int. Conf. Res. Parad. Transform Soc. Sci. 50, 57–64 (2018)

IASLE: Background: Smart learning. (2018). http://iasle.net/about-us/background/. Accessed 2 June 2020

Zhu, Z.T., Yu, M.H., Riezebos, P.: A research framework of smart education. Smart Learn. Environ. 3(1), 4 (2016)

Alajmi, Q., Al-Sharafi, M.A., Abuali, A.: Smart learning gateways for Omani HEIs towards educational technology: benefits, challenges and solutions. Int. J. Inform. Technol.Lang. Stud. 4(1), 12–17 (2020)

Aker, M., Herrera, L. J. P.: Smart literacy learning in the twenty-first century: facilitating PBSL pedagogic collaborative clouds. In: Yu S., Ally M., Tsinakos A. (eds.) Emerging Technologies and Pedagogies in the Curriculum, pp. 429–445. Springer, Singapore (2020)

Budhrani, K., Ji, Y., Lim, J.H.: Unpacking conceptual elements of smart learning in the Korean scholarly discourse. Smart Learn. Environ. 5(1), 23 (2018)

Ţălu, Ş.: New perspectives in the implementation of smart-technologies in higher education. In: 2nd International Scientific and Practical Conference “Modern Management Trends and the Digital Economy: from Regional Development to Global Economic Growth”(MTDE 2020), pp. 253–257. Atlantis Press (2020)

Coccoli, M., Guercio, A., Maresca, P., Stanganelli, L.: Smarter universities: a vision for the fast changing digital era. J. Vis. Lang. Comput. 25(6), 1003–1011 (2014). https://doi.org/10.1016/j.jvlc.2014.09.007

Tikhomirov, V., Dneprovskaya, N.: Development of strategy for smart University. In: 2015 Open Education Global International Conference, Banff, Canada, pp. 22–24 (2015)

Uskov, V. L., Bakken, J. P., Pandey, A.: The ontology of next generation smart classrooms. In: L. Uskov V., Howlett R., Jain L. (eds.) Smart Education and Smart e-Learning, pp. 3–14. Springer, Cham (2015)

Dong, Z.Y., Zhang, Y., Yip, C., Swift, S., Beswick, K.: Smart campus: definition, framework, technologies, and services. IET Smart Cities 2(1), 43–54 (2020)

Arnold, K. E., Pistilli, M. D.: Course signals at Purdue: using learning analytics to increase student success. In: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, pp. 267–270 (2012). https://doi.org/10.1145/2330601.2330666

Griffith University. https://intranet.secure.griffith.edu.au/computing/using-learning-at-griffith/students/communication-and-collaboration/jpoll. Accessed 10 April 2020

Teaching with smartboard. University of Sussex. http://www.sussex.ac.uk/tel/learningtechnologies/iwb. Accessed 10 April 2020

Peking University. China’s ‘Harvard’ Installs Facial Recognition System to Track People Who Come on Campus. https://nextshark.com/chinese-university-facial-recognition-system/. Accessed 10 April 2020]

Hamdan Bin Mohammed Smart University. University shows its smarts with cloud-based lighting controls and indoor positioning. https://www.ledsmagazine.com/smart-lighting-iot/article/16701517/university-shows-its-smarts-with-cloudbased-lighting-controls-and-indoor-positioning. Accessed 10 April 2020

Bahadur, M. D., Sakamoto, K., Sogabe, T.: Multi-agent based energy balancing management algorithm for smart grid system. Bull. Network. Comput. Syst. Softw. 9(1), 58–62 (2020). http://w.bncss.org/index.php/bncss/article/view/138

Mantha, B.R., Menassa, C.C., Kamat, V.R., D’Souza, C.R.: Evaluation of preference-and constraint-sensitive path planning for assisted navigation in indoor building environments. J. Comput. Civ. Eng. 34(1), 04019050 (2020)

Rosato, R., Di Cuonzo, D., Ritorto, G., Fanchini, L., Bustreo, S., Racca, P., Pagano, E.: Tailoring chemotherapy supply according to patients’ preferences: a quantitative method in colorectal cancer care. Curr. Med. Res. Opin. 36(1), 73–81 (2020)

Ramirez-Mendoza, R. A., Morales-Menendez, R., Iqbal, H., Parra-Saldivar, R.: Engineering education 4.0:—proposal for a new curricula. In: 2018 IEEE Global Engineering Education Conference (EDUCON), pp. 1273–1282. IEEE (2018)

Hwang, G.J., Tsai, C.C., Yang, S.J.: Criteria, strategies and research issues of context-aware ubiquitous learning. J. Educ. Technol. Soc. 11(2), 81–91 (2008)

Hwang, G.J.: Definition, framework and research issues of smart learning environments-a context-aware ubiquitous learning perspective. Smart Learn. Environ. 1(1), 4 (2014). https://doi.org/10.1186/s40561-014-0004-5

Deshpande, S. B., Mngalwede, S. R., Dandannavar, P.: Factors affecting a mobile learning system: a case study. In: EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing, pp. 121–129. Springer, Cham (2020)

Huang, Y.M., Lin, Y.T., Cheng, S.C.: Effectiveness of a mobile plant learning system in a science curriculum in Taiwanese elementary education. Comput. Educ. 54(1), 47–58 (2010)

Nielit, S. G., Thanuskodi, S.: E-discovery components of E-teaching and M-learning: AN overview. In: Mobile Devices in Education: Breakthroughs in Research and Practice, pp. 928–936. IGI Global (2020)

Gros, B.: The design of smart educational environments. Smart Learn. Environ. 3(1), 15 (2016)

Abdel-Basset, M., Manogaran, G., Mohamed, M., Rushdy, E.: Internet of things in smart education environment: supportive framework in the decision-making process. Concurr. Comput. Pract. Exp. 31(10), e4515 (2019). https://doi.org/10.1002/cpe.4515

Sundmaeker, H., Guillemin, P., Friess, P., Woelfflé, S.: Vision and challenges for realising the internet of things. Clust. Eur. Res. Proj. Internet Things Eur. Comm. 3(3), 34–36 (2010)

Vermesan, O., Friess, P. (eds.): Internet of Things: Converging Technologies for Smart Environments and Integrated Ecosystems. River Publishers, Gistrup (2013)

Xiang, Z., Tussyadiah, I.: Information and communication technologies in tourism 2014. In: eProceedings of the ENTER 2014 PhD Workshop, Springer International Publishing, Cham, pp. 1–146 (2014)

Lakshmanaprabu, S.K., Shankar, K., Ilayaraja, M., Nasir, A.W., Vijayakumar, V., Chilamkurti, N.: Random forest for big data classification in the internet of things using optimal features. Int. J. Mach. Learn. Cybernet. 10(10), 2609–2618 (2019)

Vyas, R., Srinivasan, K., Cherukuri, A. K., Jodha, K. S.: Assessing the performance of container technologies for the internet of things based application. In: Toward Social Internet of Things (SIoT): Enabling Technologies, Architectures and Applications, pp. 211–233. Springer, Cham (2020)

Rivera, J., Meulen, R. V. D.: Gartner Says the internet of things installed base will grow to 26 billion units by 2020. Gartner, 2013 (2014). http://www.gartner.com/newsroom/id/2636073

McNeill, D., Davenport, T.H. (eds.): Analytics in Healthcare and the Life Sciences: Strategies, Implementation Methods, and Best Practices. Pearson Education, London (2014)

Sauter, V.L.: Decision Support Systems for Business Intelligence. Wiley, Hoboken (2014)

Al Shobaki, M. J.: The role of decision support systems in re-engineering the academic and administrative systems within universities: a field study applied on Palestinian Universities in Gaza Strip (Doctoral dissertation) (2019)

Mazo, C., Kearns, C., Mooney, C., Gallagher, W.M.: Clinical decision support systems in breast cancer: a systematic review. Cancers 12(2), 369 (2020)

Maldonado, U., Feroz Khan, G., Moon, J., Jeung Rho, J.: E-learning motivation and educational portal acceptance in developing countries. Online Inf. Rev. 35(1), 66–85 (2011)

Vershitskaya, E.R., Mikhaylova, A.V., Gilmanshina, S.I., Dorozhkin, E.M., Epaneshnikov, V.V.: Present-day management of universities in Russia: prospects and challenges of e-learning. Educ. Inf. Technol. 25(1), 611–621 (2020)

Cruz-Jesus, F., Vicente, M.R., Bacao, F., Oliveira, T.: The education-related digital divide: an analysis for the EU-28. Comput. Hum. Behav. 56, 72–82 (2016)

Horrigan, J. B.: Lifelong learning and technology. Pew Res Center, 22: 68 (2016). http://hdl.voced.edu.au/10707/400486

Volles, N.: Lifelong learning in the EU: changing conceptualisations, actors, and policies. Stud. High. Educ. 41(2), 343–363 (2016)

Yoon, J., Yang, E., Lee, J., Hwang, S. J.: Lifelong learning with dynamically expandable networks (2017). arXiv preprint arXiv:1708.01547

Arinto, P.B.: Issues and challenges in open and distance e-learning: perspectives from the Philippines. Int. Rev. Res. Open Distrib. Learn. 17(2), 162–180 (2016)

Wicaksono, A., Florentinus, T.S., Ahmadi, F.: Development of E-learning in web programming subjects for moodle based vocational students. Innov. J. Curric. Educ. Technol. 9, 1–9 (2020)

Muniasamy, A., Alasiry, A.: Deep learning: the impact on future elearning. Int. J. Emerg. Technol. Learn. 15(01), 188–199 (2020)

Voloshinov, S., Kruglyk, V., Osadchyi, V., Osadcha, K., Symonenko, S.: Realities and prospects of distance learning at higher education institutions of Ukraine. Ukr. J. Educ. Stud. Inf. Technol. 8(1), 1–16 (2020)

Arafat, S., Aljohani, N., Abbasi, R., Hussain, A., Lytras, M.: Connections between e-learning, web science, cognitive computation and social sensing, and their relevance to learning analytics: a preliminary study. Comput. Hum. Behav. 92, 478–486 (2019)

Gubiani, D., Cristea, I., Urbančič, T.: Introducing e-learning to a traditional university: a case-study. In: Qualitative and Quantitative Models in Socio-Economic Systems and Social Work, pp. 225–241. Springer, Cham (2020)

Hayashi, A., Chen, C., Ryan, T., Wu, J.: The role of social presence and moderating role of computer self efficacy in predicting the continuance usage of e-learning systems. J. Inf. Syst. Educ. 15(2), 5 (2020)

Ameen, N., Willis, R., Abdullah, M.N., Shah, M.: Towards the successful integration of e-learning systems in higher education in Iraq: a student perspective. Br. J. Edu. Technol. 50(3), 1434–1446 (2019)

Naidu, V. R., Singh, B., Al Farei, K., Al Suqri, N. Machine learning for flipped teaching in higher education—a reflection. In: Sustainable Development and Social Responsibility—Volume 2, pp. 129–132. Springer, Cham (2020)

Ozdamli, F., Asiksoy, G.: Flipped classroom approach. World J. Educ. Technol. Curr. Issues 8(2), 98–105 (2016)

Bond, M.: Facilitating student engagement through the flipped classroom approach in K-12: a systematic review. Comput. Educ. 151, 103819 (2020)

Delen, D.: A comparative analysis of machine learning techniques for student retention management. Decis. Support Syst. 49(4), 498–506 (2010)

Shearer, C.: The CRISP-DM model: the new blueprint for data mining. J. Data Wareh 5(4), 13–22 (2000)

Gray, C.C., Perkins, D.: Utilizing early engagement and machine learning to predict student outcomes. Comput. Educ. 131, 22–32 (2019)

Li, I. W., Carroll, D.: Factors influencing university student satisfaction, dropout and academic performance: an Australian higher education equity perspective. Perth, Western Australia: National Centre for Student Equity in Higher Education, Curtin University. https://apo.org.au/node/74433. Accessed 31 May 2010 (2017)

Pham, Q. V., Fang, F., Ha, V. N., Le, M., Ding, Z., Le, L. B., Hwang, W. J. A survey of multi-access edge computing in 5G and beyond: Fundamentals, technology integration, and state-of-the-art (2019). arXiv preprint arXiv:1906.08452

Nguyen, D.C., Pathirana, P.N., Ding, M., Seneviratne, A.: Blockchain for 5 g and beyond networks: a state of the art survey. J. Netw. Comput. Appl. 166, 102693 (2020)

Chen, M., Challita, U., Saad, W., Yin, C., Debbah, M.: Machine learning for wireless networks with artificial intelligence: a tutorial on neural networks (2017). arXiv preprint arXiv:1710.02913

Kibria, M.G., Nguyen, K., Villardi, G.P., Zhao, O., Ishizu, K., Kojima, F.: Big data analytics, machine learning, and artificial intelligence in next-generation wireless networks. IEEE Access 6, 32328–32338 (2018)

Dey, N., Hassanien, A.E., Bhatt, C., Ashour, A., Satapathy, S.C. (eds.): Internet of Things and Big Data Analytics Toward Next-Generation Intelligence, pp. 3–549. Springer, Berlin (2018)

Kim, S., Kwon, Y., Cho, S.: A survey of scalability solutions on blockchain. In: 2018 International Conference on Information and Communication Technology Convergence (ICTC), pp. 1204–1207. IEEE (2018)

Ever, Y. K., Rajan, A. V.: The role of 5G networks in the field of medical sciences education. In: 2018 IEEE 43rd Conference on Local Computer Networks Workshops (LCN Workshops), pp. 59–63. IEEE (2018)

Lee, J., Kim, D.: A study on innovation in university education: focusing on 5G mobile communication. In: 2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC), pp. 1–4. IEEE (2020)

Liang, J.M., Su, W.C., Chen, Y.L., Wu, S.L., Chen, J.J.: Smart interactive education system based on wearable devices. Sensors 19(15), 3260 (2019)

Khan, D., Ullah, S., Ahmad, W., Cheng, Z., Jabeen, G., Kato, H.: A low-cost interactive writing board for primary education using distinct augmented reality markers. Sustainability 11(20), 5720 (2019)

Coca, D.M., Sliško, J.: Software Socrative and smartphones as tools for implementation of basic processes of active physics learning in classroom: an initial feasibility study with prospective teachers. Eur. J. Phys. Educ. 4(2), 17–24 (2017)

Doré, R., Pailhes, J., Fischer, X., Nadeau, J.P.: Identification of sensory variables towards the integration of user requirements into preliminary design. Int. J. Ind. Ergon. 37(1), 1–11 (2007)

Dore, R., Pailhes, J., Fischer, X., Nadeau, J.P.: Identification of design variables and criterion variables towards the integration of user requirements into preliminary design. Int. J. Prod. Dev. 4(5), 508–529 (2007)

Fischer, X., Nadeau, J. P.: Interactive design: then and now. In: Research in interactive design, vol. 3, pp. 1–5. Springer, Paris (2011)

Nadeau, J.P., Fischer, X. (eds.): Research in Interactive Design: Virtual, Interactive and Integrated Product Design and Manufacturing for Industrial Innovation, vol. 3. Springer, Berlin (2011)

Sébastian, P., Chenouard, R., Nadeau, J.P., Fischer, X.: The embodiment design constraint satisfaction problem of the BOOTSTRAP facing interval analysis and genetic algorithm based decision support tools. Int. J. Interact. Des. Manuf. 1(2), 99–106 (2007)

Fischer, X., Daidie, A., Eynard, B., Paredes, M. (eds.): Research in Interactive Design: Mechanics, Design Engineering and Advanced Manufacturing, vol. 4. Springer, Berlin (2016)

Mejía-Gutiérrez, R., Fischer, X.: A multi-agent platform to support knowledge based modelling in engineering design. In: Nature-inspired methods for metaheuristics optimization, pp. 245–263. Springer, Cham (2020)