Systematic review of research on artificial intelligence applications in higher educationwhere are the educators?

Referencias bibliográficas

• Acikkar, M., & Akay, M. F. (2009). Support vector machines for predicting the admission decision of a candidate to the School of Physical Education and Sports at Cukurova University. Expert Systems with Applications, 36(3 PART 2), 7228–7233. https://doi.org/10.1016/j.eswa.2008.09.007.
• Adamson, D., Dyke, G., Jang, H., & Rosé, C. P. (2014). Towards an agile approach to adapting dynamic collaboration support to student needs. International Journal of Artificial Intelligence in Education, 24(1), 92–124. https://doi.org/10.1007/s40593- 013-0012-6.
• Agaoglu, M. (2016). Predicting instructor performance using data mining techniques in higher education. IEEE Access, 4, 2379–2387. https://doi.org/10.1109/ACCESS.2016.2568756.
• Ahmad, H., & Rashid, T. (2016). Lecturer performance analysis using multiple classifiers. Journal of Computer Science, 12(5), 255–264. https://doi.org/10.3844/fjcssp.2016.255.264.
• Alfarsi, G. M. S., Omar, K. A. M., & Alsinani, M. J. (2017). A rule-based system for advising undergraduate students. Journal of Theoretical and Applied Information Technology, 95(11) Retrieved from http://www.jatit.org.
• Alkhasawneh, R., & Hargraves, R. H. (2014). Developing a hybrid model to predict student first year retention in STEM disciplines using machine learning techniques. Journal of STEM Education: Innovations & Research, 15(3), 35–42 https:// core.ac.uk/download/pdf/51289621.pdf.
• Aluko, R. O., Adenuga, O. A., Kukoyi, P. O., Soyingbe, A. A., & Oyedeji, J. O. (2016). Predicting the academic success of architecture students by pre-enrolment requirement: Using machine-learning techniques. Construction Economics and Building, 16(4), 86–98. https://doi.org/10.5130/AJCEB.v16i4.5184.
• Aluthman, E. S. (2016). The effect of using automated essay evaluation on ESL undergraduate students’ writing skill. International Journal of English Linguistics, 6(5), 54–67. https://doi.org/10.5539/ijel.v6n5p54.
• Amigud, A., Arnedo-Moreno, J., Daradoumis, T., & Guerrero-Roldan, A.-E. (2017). Using learning analytics for preserving academic integrity. International Review of Research in Open and Distance Learning, 18(5), 192–210. https://doi.org/10. 19173/irrodl.v18i5.3103.
• Andris, C., Cowen, D., & Wittenbach, J. (2013). Support vector machine for spatial variation. Transactions in GIS, 17(1), 41–61. https://doi.org/10.1111/j.1467-9671.2012.01354.x.
• Aparicio, F., Morales-Botello, M. L., Rubio, M., Hernando, A., Muñoz, R., López-Fernández, H., … de Buenaga, M. (2018). Perceptions of the use of intelligent information access systems in university level active learning activities among teachers of biomedical subjects. International Journal of Medical Informatics, 112(December 2017), 21–33. https://doi.org/ 10.1016/j.ijmedinf.2017.12.016.
• Babić, I. D. (2017). Machine learning methods in predicting the student academic motivation. Croatian Operational Research Review, 8(2), 443–461. https://doi.org/10.17535/crorr.2017.0028.
• Bahadır, E. (2016). Using neural network and logistic regression analysis to predict prospective mathematics teachers’ academic success upon entering graduate education. Kuram ve Uygulamada Egitim Bilimleri, 16(3), 943–964. https://doi. org/10.12738/estp.2016.3.0214.
• Bakeman, R., & Gottman, J. M. (1997). Observing interaction an introduction to sequential analysis. Cambridge: Cambridge University Press.
• Baker, R. S. (2016). Stupid Tutoring Systems, Intelligent Humans. International Journal of Artificial Intelligence in Education, 26(2), 600–614. https://doi.org/10.1007/s40593-016-0105-0.
• Baker, T., & Smith, L. (2019). Educ-AI-tion rebooted? Exploring the future of artificial intelligence in schools and colleges. Retrieved from Nesta Foundation website: https://media.nesta.org.uk/documents/Future_of_AI_and_education_v5_WEB.pdf
• Barker, T. (2010). An automated feedback system based on adaptive testing: Extending the model. International Journal of Emerging Technologies in Learning, 5(2), 11–14. https://doi.org/10.3991/ijet.v5i2.1235.
• Barker, T. (2011). An automated individual feedback and marking system: An empirical study. Electronic Journal of E-Learning, 9(1), 1–14 https://www.learntechlib.org/p/52053/.
• Bartolomé, A., Castañeda, L., & Adell, J. (2018). Personalisation in educational technology: The absence of underlying pedagogies. International Journal of Educational Technology in Higher Education, 15(14). https://doi.org/10.1186/s41239-018-0095-0.
• Ben-Zvi, T. (2012). Measuring the perceived effectiveness of decision support systems and their impact on performance. Decision Support Systems, 54(1), 248–256. https://doi.org/10.1016/j.dss.2012.05.033.
• Biletska, O., Biletskiy, Y., Li, H., & Vovk, R. (2010). A semantic approach to expert system for e-assessment of credentials and competencies. Expert Systems with Applications, 37(10), 7003–7014. https://doi.org/10.1016/j.eswa.2010.03.018.
• Blikstein, P., Worsley, M., Piech, C., Sahami, M., Cooper, S., & Koller, D. (2014). Programming pluralism: Using learning analytics to detect patterns in the learning of computer programming. Journal of the Learning Sciences, 23(4), 561–599. https://doi. org/10.1080/10508406.2014.954750.
• Brunton, J., & Thomas, J. (2012). Information management in systematic reviews. In D. Gough, S. Oliver, & J. Thomas (Eds.), An introduction to systematic reviews, (pp. 83–106). London: SAGE.
• Calvo, R. A., O’Rourke, S. T., Jones, J., Yacef, K., & Reimann, P. (2011). Collaborative writing support tools on the cloud. IEEE Transactions on Learning Technologies, 4(1), 88–97 https://www.learntechlib.org/p/73461/.
• Camacho, D., & Moreno, M. D. R. (2007). Towards an automatic monitoring for higher education learning design. International Journal of Metadata, Semantics and Ontologies, 2(1), 1. https://doi.org/10.1504/ijmso.2007.015071.
• Casamayor, A., Amandi, A., & Campo, M. (2009). Intelligent assistance for teachers in collaborative e-learning environments. Computers & Education, 53(4), 1147–1154. https://doi.org/10.1016/j.compedu.2009.05.025.
• Castañeda, L., & Selwyn, N. (2018). More than tools? Making sense of he ongoing digitizations of higher education. International Journal of Educational Technology in Higher Education, 15(22). https://doi.org/10.1186/s41239-018-0109-y.
• Chaudhri, V. K., Cheng, B., Overtholtzer, A., Roschelle, J., Spaulding, A., Clark, P., … Gunning, D. (2013). Inquire biology: A textbook that answers questions. AI Magazine, 34(3), 55–55. https://doi.org/10.1609/aimag.v34i3.2486.
• Chen, J.-F., & Do, Q. H. (2014). Training neural networks to predict student academic performance: A comparison of cuckoo search and gravitational search algorithms. International Journal of Computational Intelligence and Applications, 13(1). https://doi.org/10.1142/S1469026814500059.
• Chi, M., VanLehn, K., Litman, D., & Jordan, P. (2011). Empirically evaluating the application of reinforcement learning to the induction of effective and adaptive pedagogical strategies. User Modeling and User-Adapted Interaction, 21(1), 137–180. https://doi.org/10.1007/s11257-010-9093-1.
• Chodorow, M., Gamon, M., & Tetreault, J. (2010). The utility of article and preposition error correction systems for English language learners: Feedback and assessment. Language Testing, 27(3), 419–436. https://doi.org/10.1177/ 0265532210364391.
• Chou, C.-Y., Huang, B.-H., & Lin, C.-J. (2011). Complementary machine intelligence and human intelligence in virtual teaching assistant for tutoring program tracing. Computers & Education, 57(4), 2303–2312 https://www.learntechlib.org/p/167322/.
• Cobos, C., Rodriguez, O., Rivera, J., Betancourt, J., Mendoza, M., León, E., & Herrera-Viedma, E. (2013). A hybrid system of pedagogical pattern recommendations based on singular value decomposition and variable data attributes. Information Processing and Management, 49(3), 607–625. https://doi.org/10.1016/j.ipm.2012.12.002.
• Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20, 37–46. https:// doi.org/10.1177/001316446002000104.
• Contact North. (2018). Ten facts about artificial intelligence in teaching and learning. Retrieved from https://teachonline.ca/ sites/default/files/tools-trends/downloads/ten_facts_about_artificial_intelligence.pdf
• Crown, S., Fuentes, A., Jones, R., Nambiar, R., & Crown, D. (2011). Anne G. Neering: Interactive chatbot to engage and motivate engineering students. Computers in Education Journal, 21(2), 24–34.
• DeCarlo, P., & Rizk, N. (2010). The design and development of an expert system prototype for enhancing exam quality. International Journal of Advanced Corporate Learning, 3(3), 10–13. https://doi.org/10.3991/ijac.v3i3.1356.
• Delen, D. (2010). A comparative analysis of machine learning techniques for student retention management. Decision Support Systems, 49(4), 498–506. https://doi.org/10.1016/j.dss.2010.06.003.
• Delen, D. (2011). Predicting student attrition with data mining methods. Journal of College Student Retention: Research, Theory and Practice, 13(1), 17–35. https://doi.org/10.2190/CS.13.1.b.
• Dikli, S. (2010). The nature of automated essay scoring feedback. CALICO Journal, 28(1), 99–134. https://doi.org/10.11139/cj.28.1.99-134. Dobre, I. (2014). Assessing the student′s knowledge in informatics discipline using the METEOR metric. Mediterranean Journal
• of Social Sciences, 5(19), 84–92. https://doi.org/10.5901/mjss.2014.v5n19p84. Dodigovic, M. (2007). Artificial intelligence and second language learning: An efficient approach to error remediation.
• Language Awareness, 16(2), 99–113. https://doi.org/10.2167/la416.0. Duarte, M., Butz, B., Miller, S., & Mahalingam, A. (2008). An intelligent universal virtual laboratory (UVL). IEEE Transactions on
• Education, 51(1), 2–9. https://doi.org/10.1109/SSST.2002.1027009. Duffy, M. C., & Azevedo, R. (2015). Motivation matters: Interactions between achievement goals and agent scaffolding for selfregulated learning within an intelligent tutoring system. Computers in Human Behavior, 52, 338–348. https://doi.org/10. 1016/j.chb.2015.05.041.
• Duzhin, F., & Gustafsson, A. (2018). Machine learning-based app for self-evaluation of teacher-specific instructional style and tools. Education Sciences, 8(1). https://doi.org/10.3390/educsci8010007.
• Easterday, M. W., Rees Lewis, D. G., & Gerber, E. M. (2018). The logic of design research. Learning: Research and Practice, 4(2), 131–160. https://doi.org/10.1080/23735082.2017.1286367.
• EDUCAUSE. (2018). Horizon report: 2018 higher education edition. Retrieved from EDUCAUSE Learning Initiative and The New Media Consortium website: https://library.educause.edu/~/media/files/library/2018/8/2018horizonreport.pdf
• EDUCAUSE. (2019). Horizon report: 2019 higher education edition. Retrieved from EDUCAUSE Learning Initiative and The New Media Consortium website: https://library.educause.edu/-/media/files/library/2019/4/2019horizonreport.pdf
• Feghali, T., Zbib, I., & Hallal, S. (2011). A web-based decision support tool for academic advising. Educational Technology and Society, 14(1), 82–94 https://www.learntechlib.org/p/52325/.
• Feng, S., Zhou, S., & Liu, Y. (2011). Research on data mining in university admissions decision-making. International Journal of Advancements in Computing Technology, 3(6), 176–186. https://doi.org/10.4156/ijact.vol3.issue6.21.
• Fleiss, J. L. (1981). Statistical methods for rates and proportions. New York: Wiley. Garcia-Gorrostieta, J. M., Lopez-Lopez, A., & Gonzalez-Lopez, S. (2018). Automatic argument assessment of final project reports
• of computer engineering students. Computer Applications in Engineering Education, 26(5), 1217–1226. https://doi.org/10. 1002/cae.21996
• Ge, C., & Xie, J. (2015). Application of grey forecasting model based on improved residual correction in the cost estimation of university education. International Journal of Emerging Technologies in Learning, 10(8), 30–33. https://doi.org/10.3991/ijet.v10i8.5215.
• Gierl, M., Latifi, S., Lai, H., Boulais, A., & Champlain, A. (2014). Automated essay scoring and the future of educational assessment in medical education. Medical Education, 48(10), 950–962. https://doi.org/10.1111/medu.12517.
• Gough, D., Oliver, S., & Thomas, J. (2017). An introduction to systematic reviews, (2nd ed., ). Los Angeles: SAGE. Gutierrez, G., Canul-Reich, J., Ochoa Zezzatti, A., Margain, L., & Ponce, J. (2018). Mining: Students comments about teacher
• performance assessment using machine learning algorithms. International Journal of Combinatorial Optimization Problems and Informatics, 9(3), 26–40 https://ijcopi.org/index.php/ojs/article/view/99.
• Hall Jr., O. P., & Ko, K. (2008). Customized content delivery for graduate management education: Application to business statistics. Journal of Statistics Education, 16(3). https://doi.org/10.1080/10691898.2008.11889571.
• Haugeland, J. (1985). Artificial intelligence: The very idea. Cambridge, Mass.: MIT Press Hew, K. F., Lan, M., Tang, Y., Jia, C., & Lo, C. K. (2019). Where is the “theory” within the field of educational technology
• research? British Journal of Educational Technology, 50(3), 956–971. https://doi.org/10.1111/bjet.12770. Hinojo-Lucena, F.-J., Aznar-Díaz, I., Cáceres-Reche, M.-P., & Romero-Rodríguez, J.-M. (2019). Artificial intelligence in higher
• education: A bibliometric study on its impact in the scientific literature. Education Sciences, 9(1), 51. https://doi.org/10. 3390/educsci9010051.
• Zawacki-Richter et al. International Journal of Educational Technology in Higher Education (2019) 16:39 Page 24 of 27
• Hoffait, A.-S., & Schyns, M. (2017). Early detection of university students with potential difficulties. Decision Support Systems, 101, 1–11. https://doi.org/10.1016/j.dss.2017.05.003.
• Hooshyar, D., Ahmad, R., Yousefi, M., Yusop, F., & Horng, S. (2015). A flowchart-based intelligent tutoring system for improving problem-solving skills of novice programmers. Journal of Computer Assisted Learning, 31(4), 345–361. https://doi.org/10. 1111/jcal.12099.
• Howard, C., Jordan, P., di Eugenio, B., & Katz, S. (2017). Shifting the load: A peer dialogue agent that encourages its human collaborator to contribute more to problem solving. International Journal of Artificial Intelligence in Education, 27(1), 101– 129. https://doi.org/10.1007/s40593-015-0071-y.
• Howard, E., Meehan, M., & Parnell, A. (2018). Contrasting prediction methods for early warning systems at undergraduate level. Internet and Higher Education, 37, 66–75. https://doi.org/10.1016/j.iheduc.2018.02.001.
• Huang, C.-J., Chen, C.-H., Luo, Y.-C., Chen, H.-X., & Chuang, Y.-T. (2008). Developing an intelligent diagnosis and assessment eLearning tool for introductory programming. Educational Technology & Society, 11(4), 139–157 https://www.jstor.org/ stable/jeductechsoci.11.4.139.
• Huang, J., & Chen, Z. (2016). The research and design of web-based intelligent tutoring system. International Journal of Multimedia and Ubiquitous Engineering, 11(6), 337–348. https://doi.org/10.14257/ijmue.2016.11.6.30.
• Huang, S. P. (2018). Effects of using artificial intelligence teaching system for environmental education on environmental knowledge and attitude. Eurasia Journal of Mathematics, Science and Technology Education, 14(7), 3277–3284. https://doi. org/10.29333/ejmste/91248.
• Hussain, M., Zhu, W., Zhang, W., & Abidi, S. M. R. (2018). Student engagement predictions in an e-Learning system and their impact on student course assessment scores. Computational Intelligence and Neuroscience. https://doi.org/10.1155/2018/6347186.
• Iglesias, A., Martinez, P., Aler, R., & Fernandez, F. (2009). Reinforcement learning of pedagogical policies in adaptive and intelligent educational systems. Knowledge-Based Systems, 22(4), 266–270 https://e-archivo.uc3m.es/bitstream/handle/1 0016/6502/reinforcement_aler_KBS_2009_ps.pdf?sequence=1&isAllowed=y.
• Jackson, M., & Cossitt, B. (2015). Is intelligent online tutoring software useful in refreshing financial accounting knowledge? Advances in Accounting Education: Teaching and Curriculum Innovations, 16, 1–19. https://doi.org/10.1108/S1085-462220150000016001.
• Jain, G. P., Gurupur, V. P., Schroeder, J. L., & Faulkenberry, E. D. (2014). Artificial intelligence-based student learning evaluation: A concept map-based approach for analyzing a student’s understanding of a topic. IEEE Transactions on Learning Technologies, 7(3), 267–279. https://doi.org/10.1109/TLT.2014.2330297.
• Jeschike, M., Jeschke, S., Pfeiffer, O., Reinhard, R., & Richter, T. (2007). Equipping virtual laboratories with intelligent training scenarios. AACE Journal, 15(4), 413–436 https://www.learntechlib.org/primary/p/23636/.
• Jia, J. (2009). An AI framework to teach English as a foreign language: CSIEC. AI Magazine, 30(2), 59–59. https://doi.org/10. 1609/aimag.v30i2.2232.
• Jonassen, D., Davidson, M., Collins, M., Campbell, J., & Haag, B. B. (1995). Constructivism and computer-mediated communication in distance education. American Journal of Distance Education, 9(2), 7–25. https://doi.org/10.1080/ 08923649509526885.
• Kalz, M., van Bruggen, J., Giesbers, B., Waterink, W., Eshuis, J., & Koper, R. (2008). A model for new linkages for prior learning assessment. Campus-Wide Information Systems, 25(4), 233–243. https://doi.org/10.1108/10650740810900676.
• Kao, Chen, & Sun (2010). Using an e-Learning system with integrated concept maps to improve conceptual understanding. International Journal of Instructional Media, 37(2), 151–151.
• Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15–25. https://doi.org/10.1016/j.bushor.2018.08.004.
• Kardan, A. A., & Sadeghi, H. (2013). A decision support system for course offering in online higher education institutes. International Journal of Computational Intelligence Systems, 6(5), 928–942. https://doi.org/10.1080/18756891.2013.808428.
• Kardan, A. A., Sadeghi, H., Ghidary, S. S., & Sani, M. R. F. (2013). Prediction of student course selection in online higher education institutes using neural network. Computers and Education, 65, 1–11. https://doi.org/10.1016/j.compedu. 2013.01.015.
• Kose, U., & Arslan, A. (2016). Intelligent e-Learning system for improving students’ academic achievements in computer programming courses. International Journal of Engineering Education, 32(1, A), 185–198.
• Li, X. (2007). Intelligent agent-supported online education. Decision Sciences Journal of Innovative Education, 5(2), 311–331. https://doi.org/10.1111/j.1540-4609.2007.00143.x.
• Lo, J. J., Chan, Y. C., & Yeh, S. W. (2012). Designing an adaptive web-based learning system based on students’ cognitive styles identified online. Computers and Education, 58(1), 209–222. https://doi.org/10.1016/j.compedu.2011.08.018.
• Lodhi, P., Mishra, O., Jain, S., & Bajaj, V. (2018). StuA: An intelligent student assistant. International Journal of Interactive Multimedia and Artificial Intelligence, 5(2), 17–25. https://doi.org/10.9781/ijimai.2018.02.008.
• Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed an argument for AI in education. Retrieved from http://discovery.ucl.ac.uk/1475756/
• Ma, H., & Slater, T. (2015). Using the developmental path of cause to bridge the gap between AWE scores and writing teachers’ evaluations. Writing & Pedagogy, 7(2), 395–422. https://doi.org/10.1558/wap.v7i2-3.26376.
• McNamara, D. S., Crossley, S. A., Roscoe, R. D., Allen, L. K., & Dai, J. (2015). A hierarchical classification approach to automated essay scoring. Assessing Writing, 23, 35–59. https://doi.org/10.1016/j.asw.2014.09.002.
• Misiejuk, K., & Wasson, B. (2017). State of the field report on learning analytics. SLATE report 2017–2. Bergen: Centre for the Science of Learning & Technology (SLATE) Retrieved from http://bora.uib.no/handle/1956/17740.
• Miwa, K., Terai, H., Kanzaki, N., & Nakaike, R. (2014). An intelligent tutoring system with variable levels of instructional support for instructing natural deduction. Transactions of the Japanese Society for Artificial Intelligence, 29(1), 148–156. https://doi. org/10.1527/tjsai.29.148.
• Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. BMJ, 339, b2535. https://doi.org/10.1136/bmj.b2535 Clinical Research Ed.
• Nehm, R. H., Ha, M., & Mayfield, E. (2012). Transforming biology assessment with machine learning: Automated scoring of written evolutionary explanations. Journal of Science Education and Technology, 21(1), 183–196. https://doi.org/10.1007/ s10956-011-9300-9.
• Neumann, W. L. (2007). Social research methods: Qualitative and quantitative approaches. Boston: Pearson.
• Ng, S. C., Wong, C. K., Lee, T. S., & Lee, F. Y. (2011). Design of an agent-based academic information system for effective education management. Information Technology Journal, 10(9), 1784–1788. https://doi.org/10.3923/itj.2011.1784.1788.
• Nguyen, J., Sánchez-Hernández, G., Armisen, A., Agell, N., Rovira, X., & Angulo, C. (2018). A linguistic multi-criteria decisionaiding system to support university career services. Applied Soft Computing Journal, 67, 933–940. https://doi.org/10.1016/j. asoc.2017.06.052.
• Nicholas, D., Watkinson, A., Jamali, H. R., Herman, E., Tenopir, C., Volentine, R., … Levine, K. (2015). Peer review: still king in the digital age. Learned Publishing, 28(1), 15–21. https://doi.org/10.1087/20150104.
• Oztekin, A. (2016). A hybrid data analytic approach to predict college graduation status and its determinative factors. Industrial Management and Data Systems, 116(8), 1678–1699. https://doi.org/10.1108/IMDS-09-2015-0363.
• Ozturk, Z. K., Cicek, Z. I. E., & Ergul, Z. (2017). Sentiment analysis: An application to Anadolu University. Acta Physica Polonica A, 132(3), 753–755. https://doi.org/10.12693/APhysPolA.132.753.
• Palocsay, S. W., & Stevens, S. P. (2008). A study of the effectiveness of web-based homework in teaching undergraduate business statistics. Decision Sciences Journal of Innovative Education, 6(2), 213–232. https://doi.org/10.1111/j.1540-4609.2008.00167.x.
• Paquette, L., Lebeau, J. F., Beaulieu, G., & Mayers, A. (2015). Designing a knowledge representation approach for the generation of pedagogical interventions by MTTs. International Journal of Artificial Intelligence in Education, 25(1), 118–156 https://www.learntechlib.org/p/168275/.
• Payne, V. L., Medvedeva, O., Legowski, E., Castine, M., Tseytlin, E., Jukic, D., & Crowley, R. S. (2009). Effect of a limitedenforcement intelligent tutoring system in dermatopathology on student errors, goals and solution paths. Artificial Intelligence in Medicine, 47(3), 175–197. https://doi.org/10.1016/j.artmed.2009.07.002.
• Pedró, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development. Paris: UNESCO.
• Perez, S., Massey-Allard, J., Butler, D., Ives, J., Bonn, D., Yee, N., & Roll, I. (2017). Identifying productive inquiry in virtual labs using sequence mining. In E. André, R. Baker, X. Hu, M. M. T. Rodrigo, & B. du Boulay (Eds.), Artificial intelligence in education, (vol. 10,331, pp. 287–298). https://doi.org/10.1007/978-3-319-61425-0_24.
• Perin, D., & Lauterbach, M. (2018). Assessing text-based writing of low-skilled college students. International Journal of Artificial Intelligence in Education, 28(1), 56–78. https://doi.org/10.1007/s40593-016-0122-z.
• Petticrew, M., & Roberts, H. (2006). Systematic reviews in the social sciences: A practical guide. Malden; Oxford: Blackwell Pub. Phani Krishna, K. V., Mani Kumar, M., & Aruna Sri, P. S. G. (2018). Student information system and performance retrieval
• through dashboard. International Journal of Engineering and Technology (UAE), 7, 682–685. https://doi.org/10.14419/ijet. v7i2.7.10922.
• Popenici, S., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning. https://doi.org/10.1186/s41039-017-0062-8.
• Prinsloo, P. (2017). Fleeing from Frankenstein’s monster and meeting Kafka on the way: Algorithmic decision-making in higher education. E-Learning and Digital Media, 14(3), 138–163. https://doi.org/10.1177/2042753017731355.
• Quixal, M., & Meurers, D. (2016). How can writing tasks be characterized in a way serving pedagogical goals and automatic analysis needs? Calico Journal, 33(1), 19–48. https://doi.org/10.1558/cj.v33i1.26543.
• Raju, D., & Schumacker, R. (2015). Exploring student characteristics of retention that lead to graduation in higher education using data mining models. Journal of College Student Retention: Research, Theory and Practice, 16(4), 563–591. https://doi. org/10.2190/CS.16.4.e.
• Ramírez, J., Rico, M., Riofrío-Luzcando, D., Berrocal-Lobo, M., & Antonio, A. (2018). Students’ evaluation of a virtual world for procedural training in a tertiary-education course. Journal of Educational Computing Research, 56(1), 23–47. https://doi. org/10.1177/0735633117706047.
• Ray, R. D., & Belden, N. (2007). Teaching college level content and reading comprehension skills simultaneously via an artificially intelligent adaptive computerized instructional system. Psychological Record, 57(2), 201–218 https://opensiuc.lib. siu.edu/cgi/viewcontent.cgi?referer=https://www.google.com/&httpsredir=1&article=1103&context=tpr.
• Reid, J. (1995). Managing learner support. In F. Lockwood (Ed.), Open and distance learning today, (pp. 265–275). London: Routledge.
• Rovira, S., Puertas, E., & Igual, L. (2017). Data-driven system to predict academic grades and dropout. PLoS One, 12(2), 1–21. https://doi.org/10.1371/journal.pone.0171207.
• Russel, S., & Norvig, P. (2010). Artificial intelligence a modern approach. New Jersey: Pearson Education.
• Salmon, G. (2000). E-moderating the key to teaching and learning online, (1st ed., ). London: Routledge.
• Samarakou, M., Fylladitakis, E. D., Früh, W. G., Hatziapostolou, A., & Gelegenis, J. J. (2015). An advanced eLearning environment developed for engineering learners. International Journal of Emerging Technologies in Learning, 10(3), 22–33. https://doi. org/10.3991/ijet.v10i3.4484.
• Sanchez, E. L., Santos-Olmo, A., Alvarez, E., Huerta, M., Camacho, S., & Fernandez-Medina, E. (2016). Development of an expert system for the evaluation of students’ curricula on the basis of competencies. Future Internet, 8(2). https://doi.org/10. 3390/fi8020022.
• Schiaffino, S., Garcia, P., & Amandi, A. (2008). eTeacher: Providing personalized assistance to e-learning students. Computers & Education, 51(4), 1744–1754. https://doi.org/10.1016/j.compedu.2008.05.008.
• Sebastian, J., & Richards, D. (2017). Changing stigmatizing attitudes to mental health via education and contact with embodied conversational agents. Computers in Human Behavior, 73, 479–488. https://doi.org/10.1016/j.chb.2017.03.071.
• Selwyn, N. (2016). Is technology good for education? Cambridge, UK: Malden, MA : Polity Press.
• Shen, V. R. L., & Yang, C.-Y. (2011). Intelligent multiagent tutoring system in artificial intelligence. International Journal of Engineering Education, 27(2), 248–256.
• Å imundić, A.-M. (2009). Measures of diagnostic accuracy: Basic definitions. Journal of the International Federation of Clinical Chemistry and Laboratory Medicine, 19(4), 203–2011 https://www.ncbi.nlm.nih.gov/pubmed/27683318.
• Smith, R. (2006). Peer review: a flawed process at the heart of science and journals. Journal of the Royal Society of Medicine, 99, 178–182. https://doi.org/10.1258/jrsm.99.4.178.
• Spikol, D., Ruffaldi, E., Dabisias, G., & Cukurova, M. (2018). Supervised machine learning in multimodal learning analytics for estimating success in project-based learning. Journal of Computer Assisted Learning, 34(4), 366–377. https://doi.org/10. 1111/jcal.12263.
• Sreenivasa Rao, K., Swapna, N., & Praveen Kumar, P. (2018). Educational data mining for student placement prediction using machine learning algorithms. International Journal of Engineering and Technology (UAE), 7(1.2), 43–46. https://doi.org/10. 14419/ijet.v7i1.2.8988.
• Steenbergen-Hu, S., & Cooper, H. (2014). A meta-analysis of the effectiveness of intelligent tutoring systems on college students’ academic learning. Journal of Educational Psychology, 106(2), 331–347. https://doi.org/10.1037/a0034752.
• Sultana, S., Khan, S., & Abbas, M. (2017). Predicting performance of electrical engineering students using cognitive and noncognitive features for identification of potential dropouts. International Journal of Electrical Engineering Education, 54(2), 105–118. https://doi.org/10.1177/0020720916688484.
• Tai, D. W. S., Wu, H. J., & Li, P. H. (2008). Effective e-learning recommendation system based on self-organizing maps and association mining. Electronic Library, 26(3), 329–344. https://doi.org/10.1108/02640470810879482.
• Tegmark, M. (2018). Life 3.0: Being human in the age of artificial intelligence. London: Penguin Books.
• Teshnizi, S. H., & Ayatollahi, S. M. T. (2015). A comparison of logistic regression model and artificial neural networks in predicting of student’s academic failure. Acta Informatica Medica, 23(5), 296–300. https://doi.org/10.5455/aim.2015.23. 296–300
• Thatcher, S. J. (2014). The use of artificial intelligence in the learning of flight crew situation awareness in an undergraduate aviation programme. World Transactions on Engineering and Technology Education, 12(4), 764–768 https://www.semanticscholar.org/paper/ The-use-of-artificial-intelligence-in-the-learning-Thatcher/758d3053051511cde2f28fc6b2181b8e227f8ea2.
• Torres-Díaz, J. C., Infante Moro, A., & Valdiviezo Díaz, P. (2014). Los MOOC y la masificación personalizada. Profesorado, 18(1), 63–72 http://www.redalyc.org/articulo.oa?id=56730662005.
• Umarani, S. D., Raviram, P., & Wahidabanu, R. S. D. (2011). Speech based question recognition of interactive ubiquitous teaching robot using supervised classifier. International Journal of Engineering and Technology, 3(3), 239–243 http://www. enggjournals.com/ijet/docs/IJET11-03-03-35.pdf.
• Umer, R., Susnjak, T., Mathrani, A., & Suriadi, S. (2017). On predicting academic performance with process mining in learning analytics. Journal of Research in Innovative Teaching, 10(2), 160–176. https://doi.org/10.1108/JRIT-09-2017-0022.
• Vlugter, P., Knott, A., McDonald, J., & Hall, C. (2009). Dialogue-based CALL: A case study on teaching pronouns. Computer Assisted Language Learning, 22(2), 115–131. https://doi.org/10.1080/09588220902778260.
• Walsh, K., Tamjidul, H., & Williams, K. (2017). Human machine learning symbiosis. Journal of Learning in Higher Education, 13(1), 55–62 http://cs.uno.edu/~tamjid/pub/2017/JLHE.pdf.
• Welham, D. (2008). AI in training (1980–2000): Foundation for the future or misplaced optimism? British Journal of Educational Technology, 39(2), 287–303. https://doi.org/10.1111/j.1467-8535.2008.00818.x.
• Weston-Sementelli, J. L., Allen, L. K., & McNamara, D. S. (2018). Comprehension and writing strategy training improves performance on content-specific source-based writing tasks. International Journal of Artificial Intelligence in Education, 28(1), 106–137. https://doi.org/10.1007/s40593-016-0127-7.
• Wickham, H., & Grolemund, G. (2016). R for data science: Import, tidy, transform, visualize, and model data, (1st ed., ). Sebastopol: O’Reilly.
• Yang, F., Wang, M., Shen, R., & Han, P. (2007). Community-organizing agent: An artificial intelligent system for building learning communities among large numbers of learners. Computers & Education, 49(2), 131–147. https://doi.org/10.1016/j. compedu.2005.04.019.
• Yang, Y. F., Wong, W. K., & Yeh, H. C. (2009). Investigating readers’ mental maps of references in an online system. Computers and Education, 53(3), 799–808. https://doi.org/10.1016/j.compedu.2009.04.016.
• Yoo, J., & Kim, J. (2014). Can online discussion participation predict group project performance? Investigating the roles of linguistic features and participation patterns. International Journal of Artificial Intelligence in Education, 24(1), 8–32 https:// www.learntechlib.org/p/155243/.
• Yuanyuan, J., & Yajuan, L. (2014). Development of an intelligent teaching system based on 3D technology in the course of digital animation production. International Journal of Emerging Technologies in Learning, 9(9), 81–86. https://doi.org/10. 3991/ijet.v11i09.6116.
• Zhu, W., Marquez, A., & Yoo, J. (2015). “Engineering economics jeopardy!” Mobile app for university students. Engineering Economist, 60(4), 291–306. https://doi.org/10.1080/0013791X.2015.1067343.