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Evolution and trends in intelligent tutoring systems research: a multidisciplinary and scientometric view

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Abstract

Intelligent tutoring systems (ITSs) are a promising integrated educational tool for customizing formal education using intelligent instruction or feedback. In recent decades, ITSs have transformed teaching and learning and associated research. This study examined the evolution and future trends of ITS research with scientometric methods. First, a dataset comprising 1173 relevant publications was compiled from the Web of Science Core Collection databases (including the Science Citation Index Expanded and the Social Science Citation Index). Then, the publication distributions by time, author, institution, country/region, and knowledge sources were analyzed to reveal the multidisciplinary integration paths. Dataset co-occurrence and co-citation analyses were conducted to identify the most popular research issues, the research chronology, and the emerging trends. It was found that: (a) ITS research has been growing in recent years. According to the Price literature exponential growth curve, this field is still in its initial stage while has high potential; (b) computer science, education, psychology, and engineering were the main ITS research knowledge sources, with ITS social science publications since 2007 being higher than ITS natural sciences publications; (c) interactive learning environments, student modeling, teaching/learning strategies, and machine learning have been the most popular research foci; and (d) the Coh-Metrix, problem-centered instruction, and STEM are the current research trends.

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References

  • Ahuja, N. J., & Sille, R. (2014). A critical review of development of intelligent tutoring systems: Retrospect, present and prospect. International Journal of Computer Science Issues (IJCSI), 10(4), 39.

    Google Scholar 

  • Akbulut, Y., & Cardak, C. S. (2012). Adaptive educational hypermedia accommodating learning styles: A content analysis of publications from 2000 to 2011. Computers and Education, 58(2), 835–842

    Article  Google Scholar 

  • Aleven, V., Mclaren, B. M., Roll, I., & Koedinger, K. R. (2006). Toward meta-cognitive tutoring: A model of help seeking with a cognitive tutor. International Journal of Artificial Intelligence in Education, 16(2), 101–128

    Google Scholar 

  • Aleven, V., Mclaren, B. M., Sewall, J., & Koedinger, K. R. (2009). A new paradigm for intelligent tutoring systems: Example-tracing tutors. International Journal of Artificial Intelligence in Education, 19(2), 105–154

    Google Scholar 

  • Almasri, A., Ahmed, A., Al-Masri, N., Abu Sultan, Y., Mahmoud, A. Y., Zaqout, I., Akkila, A. N., & Abu-Naser, S. S. (2019). Intelligent tutoring systems survey for the period 2000–2018. International Journal of Academic Engineering Research (IJAER), 3(5), 21–37

    Google Scholar 

  • Anderson, J. R. (1980). Cognitive psychology and its implications. San Francisco: Freeman.

  • Anderson, J. R. (1983). The architecture of cognition. Cambridge, Mass: Harvard University Press.

  • Anderson, J. R., Boyle, C. F., Corbett, A. T., & Lewis, M. W. (1990). Cognitive modeling and intelligent tutoring. Artificial Intelligence, 42(1), 7–49

    Article  Google Scholar 

  • Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive tutors: Lessons learned. Journal of the Learning Sciences, 4(2), 167–207

    Article  Google Scholar 

  • Anwar, M. A., Zhou, R., Asmi, F., Wang, D., & Hammad, A. (2019). Mapping the evolution of energy-growth nexus: Synergies and trade-offs. Journal of Economic Surveys, 33(3), 1–31

    Article  Google Scholar 

  • Arroyo, I., Cooper, D. G., Burleson, W., Woolf, B. P., Muldner, K., & Christopherson, R. (2009). Emotion sensors go to school. IOS Press.

    Google Scholar 

  • Azevedo, R., & Hadwin, A. F. (2005). Scaffolding self-regulated learning and metacognition–implications for the design of computer-based scaffolds. Instructional Science, 33(5–6), 367–379

    Article  Google Scholar 

  • Baker, R. S., D’Mello, S. K., Rodrigo, M. M. T., & Graesser, A. C. (2010). Better to be frustrated than bored: The incidence, persistence, and impact of learners’ cognitive–affective states during interactions with three different computer-based learning environments. International Journal of Human-Computer Studies, 68(4), 223–241

    Article  Google Scholar 

  • Baker, R. S., & Ryan, S. (2016). Stupid tutoring systems, intelligent humans. International Journal of Artificial Intelligence in Education, 26(2), 1–15

    Article  Google Scholar 

  • Baker, R. S. J. D., Corbett, A. T. & Aleven, V. (2008). More accurate student modeling through contextual estimation of slip and guess probabilities in bayesian knowledge tracing. International Conference on Intelligent Tutoring Systems. Springer-Verlag.

  • Beal, C. R., Arroyo, I. M., Cohen, P. R., & Woolf, B. P. (2010). Evaluation of animal watch: An intelligent tutoring system for arithmetic and fractions. Journal of Interactive Online Learning, 9(1), 64–67

    Google Scholar 

  • Braam, R. R., Moed, H. F., & Van Raan, A. F. (1991). Mapping of science by combined co-citation and word analysis. Structural aspects. Journal of the American Society for Information Science, 42(4), 233–266

    Article  Google Scholar 

  • Cavanagh, P. (1963). THE AUTOTUTOR AND CLASSROOM INSTRUCTION 3 COMPARATIVE STUDIES INTRODUCTION. Occupational Psychology, 37, 44–48

    Google Scholar 

  • Cavanagh, P. (1964). THE AUTOTUTOR AND CLASSROOM INSTRUCTION 3 COMPARATIVE STUDIES2 THE ROYAL AIR-FORCE STUDY. Programmed Learning, 1(1), 26–31

    Article  Google Scholar 

  • Chang, M., D’Aniello, G., Gaeta, M., et al. (2020). Building ontology-driven tutoring models for intelligent tutoring systems using data mining. IEEE Access, 8(1), 48151–48162

    Article  Google Scholar 

  • Chang, M., D’Aniello, G., Gaeta, M., Orciuoli, F., Sampson, D., & Simonelli, C. (2020). Building ontology-driven tutoring models for intelligent tutoring systems using data mining. IEEE Access, 8, 48151–48162

    Article  Google Scholar 

  • Chassignol, M., Khoroshavin, A., Klimova, A., & Bilyatdinova, A. (2018). Artificial Intelligence trends in education: A narrative overview. Procedia Computer Science, 136, 16–24

    Article  Google Scholar 

  • Chen, C. (2005). The centrality of pivotal points in the evolution of scientific networks. Proceedings of the 10th International Conference on Intelligent User Interfaces, 98–105

  • Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359–377

    Article  Google Scholar 

  • Chen, C. (2012). Retrieved 29 Oct 2019 from http://blog.sciencenet.cn/u/ChaomeiChen

  • Chen, C. (2017a). Science mapping: A systematic review of the literature. Journal of Data and Information Science, 2(2), 1–39

    Article  Google Scholar 

  • Chen, C., Ibekwe-SanJuan, F., & Hou, J. (2010). The structure and dynamics of cocitation clusters: A multiple-perspective cocitation analysis. Journal of the American Society for information Science and Technology, 61(7), 1386–1409

    Article  Google Scholar 

  • Chen, C., Song, I. Y., Yuan, X., & Zhang, J. (2008). The thematic and citation landscape of data and knowledge engineering (1985–2007). Data and Knowledge Engineering, 67(2), 234–259

    Article  Google Scholar 

  • Chen, K., & Guan, J. (2011). A bibliometric investigation of research performance in emerging nanobiopharmaceuticals. Journal of Informetrics, 5(2011), 233–247

    Article  Google Scholar 

  • Chen, W., Chan, T. W., Wong, L. H., Looi, C. K., Liao, C. C. Y., Cheng, H. N. H., & Pi, Z. (2020). IDC theory: Habit and the habit loop. Research and Practice in Technology Enhanced Learning. https://doi.org/10.1186/s41039-020-00127-7

    Article  Google Scholar 

  • Chen, X., Li, J., Sun, X., & Wu, D. (2019). Early identification of intellectual structure based on co-word analysis from research grants. Scientometrics, 121(1), 349–369

    Article  Google Scholar 

  • Chen, X., Xie, H., Zou, D., & Hwang, G.-J. (2020). Application and theory gaps during the rise of Artificial Intelligence in education. Computers and Education: Artificial Intelligence, 1, 100002. https://doi.org/10.1016/j.caeai.2020.100002

    Article  Google Scholar 

  • Chi, M. T. H., Siler, S. A., Jeong, H., Yamauchi, T., & Hausmann, R. G. (2001). Learning from human tutoring. Cognitive Science, 25(4), 471–533

    Article  Google Scholar 

  • Choi, B. C. K., & Pak, A. W. P. (2006). Multidisciplinarity, interdisciplinarity and transdisciplinarity in health research, services education and policy: 1 Definitions, objectives, and evidence of effectiveness. Clinical and Investigative Medicine, 29, 351–364

    Google Scholar 

  • Corbett, A. T., & Anderson, J. R. (1994). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4(4), 253–278

    Article  Google Scholar 

  • Conati, C. (2009). Intelligent tutoring systems: New challenges and directions. Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence (IJCAI-09).

  • Conati, C., & Kardan, S. (2013). Student modeling: supporting personalized instruction, from problem solving to exploratory open-ended activities. Ai Magazine, 34(3), 13–26.

  • Conati, C., Gertner, A., & Vanlehn, K. (2002b). Using Bayesian networks to manage uncertainty in student modeling. User Modeling and User-Adapted Interaction, 12(4), 371–417

    Article  Google Scholar 

  • Conati, C., Gertner, A., & Vanlehn, K. (2002a). Using Bayesian networks to manage uncertainty in student modeling. User Modeling and User-Adapted Interaction, 12, 371–417

    Article  Google Scholar 

  • Conati, C., & Maclaren, H. (2009). Empirically building and evaluating a probabilistic model of user affect. User Modeling and User-Adapted Interaction, 19(3), 267–303

    Article  Google Scholar 

  • Cotton, K. (2001). Classroom questioning. School improvement research series (SIRS). Retrieved 25 Oct 2005 from http://www.nwrel.org/scpd/sirs/3/cu5.html

  • Craig, S., Graesser, A., Sullins, J., & Gholson, B. (2004a). Affect and learning: An exploratory look into the role of affect in learning with AutoTutor. Journal of Educational Media, 29(3), 241–250. https://doi.org/10.1080/1358165042000283101

    Article  Google Scholar 

  • Dargue, B., & Biddle, E. (2014). Just enough fidelity in student and expert modeling for ITS. International Conference on Augmented Cognition. Springer International Publishing.

  • Desmarais, M., & Baker, R. (2012a). A review of recent advances in learner and skill modeling in intelligent learning environments. User Modeling and User-Adapted Interaction, 22(1–2), 9–38

    Article  Google Scholar 

  • Desmarais, M., & Naceur, R. (2013). A Matrix factorization method for mapping items to skills and for enhancing expert-based Q-matrices. In H. C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.), Artificial intelligence in education. AIED 2013. Lecture notes in computer science.Springer.

    Google Scholar 

  • Desmarais, M. C., & d Baker, R. S. (2012). A review of recent advances in learner and skill modeling in intelligent learning environments. User Modeling and User-Adapted Interaction, 22(1), 9–38

    Article  Google Scholar 

  • Deyi, L. (2018). Introduction to artificial intelligence. China Science and Technology Press.

    Google Scholar 

  • D’Mello, S., & Graesser, A. (2011). The half-life of cognitive-affective states during complex. Cognition and Emotion. https://doi.org/10.1080/02699931.2011.613668

    Article  Google Scholar 

  • D’Mello, S., Graesser, A., & Picard, R. W. (2007). Toward an affect-sensitive autotutor. IEEE Intelligent Systems, 22(4), 53–61

    Article  Google Scholar 

  • Du Boulay, B., Rebolledo-Mendez, G., Luckin, R., Martínez-Mirón, E., & Harris, A. (2007). Motivationally intelligent systems: Diagnosis and feedback. In: AIEd. 563–565.

  • Egghe, L. (2005). Expansion of the field of informetrics: Origins and consequences. Information Processing and Management, 41(6), 1311–1316

    Article  Google Scholar 

  • Elham, M., Nahid, Z., Sharareh, R. N. K., Mahnaz, R., Leila, K. & Marjan, G. S. (2018). Intelligent tutoring systems: A systematic review of characteristics, applications, and evaluation methods. Interactive Learning Environments. https://doi.org/10.1080/10494820.2018.1558257.

  • Feng, F., Zhang, L., Du, Y., & Wang, W. (2015). Visualization and quantitative study in bibliographic databases: A case in the field of university–industry cooperation. Journal of Informetrics, 9(1), 118–134

    Article  Google Scholar 

  • Gardner, H. (1987). The mind’s new science: A history of the cognitive revolution.

  • Graesser, A., Chipman, P., & Leeming, F. (2009). Deep learning and emotion in serious games. Serious games. (pp. 105–124). Routledge.

    Google Scholar 

  • Graesser, A. C., Chipman, P., Haynes, B. C., & Olney, A. (2005a). Autotutor: An intelligent tutoring system with mixed-initiative dialogue. IEEE Transactions on Education, 48(4), 612–618

    Article  Google Scholar 

  • Graesser, A. C., Conley, M. W., & Olney, A. M. (2011). Intelligent tutoring systems. In S. Graham & K. Harris (Eds.), APA educational psychology handbook: Vol 3. Applications to learning and teaching.American Psychological Association.

    Google Scholar 

  • Graesser, A. C., & D’Mello, S. (2012). Emotions during the learning of difficult material. Psychology of Learning and Motivation, 57, 183–225

    Article  Google Scholar 

  • Graesser, A. C., McNamara, D. S., Cai, Z., Conley, M., Li, H., & Pennebaker, J. (2014). Coh-Metrix measures text characteristics at multiple levels of language and discourse. Elementary School Journal, 115, 211–229

    Article  Google Scholar 

  • Graesser, A. C., Mcnamara, D. S., & Kulikowich, J. M. (2011). Coh-metrix: Providing multilevel analyses of text characteristics. Educational Researcher, 40(5), 223–234

    Article  Google Scholar 

  • Graesser, A. C., Mcnamara, D. S., Louwerse, M. M., & Cai, Z. (2004). Coh-metrix: Analysis of text on cohesion and language. Behavior Research Methods Instruments and Computers, 36(2), 193

    Article  Google Scholar 

  • Graesser, A.C., Moreno, K., Marineau, J., Adcock, A., Olney, A., Person, N., & The Tutoring Research Group (2003). AutoTutor improves deep learning of computer literacy: Is it the dialogue or the talking head? In U. Hoppe, F. Verdejo, & J. Kay (Eds.), Proceedings of artificial intelligence in education (pp. 47–54). Amsterdam: IOS Press.

  • Graham, S., Hebert, M., & Harris, K. R. (2015). Assessment and writing formative: A meta-analysis. The Elementary School Journal, 4(115), 523–547

    Article  Google Scholar 

  • Griffith, B. C., Small, H. G., Stonehill, J. A., & Dey, S. (1974). The structure of scientific literatures II: Toward a macro-and microstructure for science. Science Studies, 4(4), 339–365

    Article  Google Scholar 

  • Hinton, G. E., Osindero, S., & Teh, Y. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18, 1527–1554

    Article  Google Scholar 

  • Hood, W. W., & Wilson, C. S. (2001). The literature of bibliometrics, scientometrics, and informetrics. Scientometrics, 52, 291

    Article  Google Scholar 

  • Hou, J., Yang, X., & Chen, C. (2018). Emerging trends and new developments in information science: A document co-citation analysis (2009–2016). Scientometrics, 115, 869–892

    Article  Google Scholar 

  • Hu, Z., & Zhou, T. (2017). New media industries from frontiers artificial intelligence and virtual reality. Social Sciences Academic Press (China), 1, 2–3

    Google Scholar 

  • Hu, X., Liu, F., & Bu, C. (2020). Research advances on knowledge tracing models in educational big data. Journal of Computer Research and Development, 57(12), 2523–2546

  • Isotani, S., & Mizoguchi, R. (2008). Theory-driven group formation through ontologies. Intelligent Tutoring Systems, 9th International Conference, ITS 2008, Montreal, Canada, June 23–27, 2008, Proceedings. DBLP.

  • Johnson, W. L., & Rickel, J. W. (2000). Animated pedagogical agents face-to-face interaction in interactive learning environments. International Journal of Artificial Intelligence in Education, 11, 47–78

    Google Scholar 

  • Khasseh, A. A., Soheili, F., Moghaddam, H. S., & Chelak, A. M. (2017). Intellectual structure of knowledge in iMetrics: A co-word analysis. Information Processing and Management, 53(3), 705–720

    Article  Google Scholar 

  • Koedinger, K. R., & Corbett, A. T. (2006). Cognitive tutors: Technology bringing learning science to the classroom. In K. Sawyer (Ed.), The Cambridge handbook of the learning sciences. (pp. 61–78). Cambridge University Press.

    Google Scholar 

  • Koedinger, K. R., Corbett, A. T., & Perfetti, C. (2012). The knowledge-learning-instruction framework: Bridging the science-practice chasm to enhance robust student learning. Cognitive Science, 36(5), 757–798

    Article  Google Scholar 

  • Kolodner, J. (2002). Facilitating the learning of design practices: Lessons learned from an inquiry into science education. Journal of Industrial Teacher Education, 39(3), 9–40

    Google Scholar 

  • Kort, B., Reilly, R. & Picard, R.W. (2002). An affective model of interplay between emotions and learning: reengineering educational pedagogy-building a learning companion. Proceedings IEEE International Conference on Advanced Learning Technologies. IEEE.

  • Kulik, J. A. (2015). Effectiveness of intelligent tutoring systems: A meta-analytic review. Review of Educational Research, 86(1), 42–78

    Article  Google Scholar 

  • Kurshan, B. (2016). The future of artificial intelligence in education. Forbes Magazine.

    Google Scholar 

  • Landauer, T. K., McNamara, D. S., Dennis, S., & Kintsch, W. (2007). Handbook of latent semantic analysis. Lawrence Erlbaum Associates.

    Book  Google Scholar 

  • Lester, J. C., Ha, E. Y., Lee, S. Y., Mott, B. W., Rowe, J. P., & Sabourin, J. (2013). Serious games get smart: Intelligent game-based learning environments. AI Magazine, 34(4), 31–45

    Article  Google Scholar 

  • Leydesdorff, L., & Rafols, I. (2012). Interactive overlays: A new method for generating global journal maps from Web-of-Science data. Journal of Informetrics, 6(2), 318–332

    Article  Google Scholar 

  • Li, J., & Chen, C. (2016). CiteSpace: Text mining and visualization in scientific literature. Capital University of Economics and Business Press.

    Google Scholar 

  • Lighthill, J. (1973). Artificial Intelligence: A general survey. Artificial Intelligence: a paper symposium, Science Research Council.

  • Liu, K., & Jing, Hu. (2018). The theory framework of AIED: The symmetric hypothesis between learner and educational resources—An interview with ITS expert professor Xiangen Hu[J]. Open Education Research, 24(06), 4–11

    Google Scholar 

  • Liu, Q., Chen, E. H., Huang, Z. Y., Wu, R. Z., Su, Y., & Hu, G. P. (2008). Research on educational data mining technology for online intelligent learning. Pattern Recognition and Artificial Intelligence, 31(01), 77–90

    Google Scholar 

  • Liu, Y. (2003). Modern educational technology and intelligent computer-aided teaching. Journal of Natural Sciences of Harbin Normal University, 2003(5), 59–61

    Google Scholar 

  • Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence Unleashed. An argument for AI in education. Pearson.

    Google Scholar 

  • Ma, W., Adesope, O. O., Nesbit, J. C., & Liu, Q. (2014). Intelligent tutoring systems and learning outcomes: A meta-analysis. Journal of Educational Psychology, 106(4), 901–918

    Article  Google Scholar 

  • Madani, F., & Weber, C. (2016). The evolution of patent mining: Applying bibliometrics analysis and keyword network analysis. World Patent Information, 46, 32–48

    Article  Google Scholar 

  • Matz, M. (1981). Towards a generative theory of high school algebra errors. Intelligent tutoring systems: An overview. (p. 7). Academic Press.

    Google Scholar 

  • McCoy, J., Treanor, M., Samuel, B., Wardrip-Fruin, N., & Mateas, M. (2011). Comme Il Faut: A system for authoring playable social models. In Proceedings of the Seventh International Conference on Artificial Intelligence and Interactive Digital Entertainment, 158–163. Palo Alto, CA: AAAI Press.

  • McNamara, D. S., Graesser, A. C., McCarthy, P. M., & Cai, Z. (2014). Automated evaluation of text and discourse with Coh-Metrix. Cambridge University Press.

    Book  Google Scholar 

  • McNamara, D. S., Louwerse, M. M., McCarthy, P. M., & Graesser, A. C. (2010). Coh-Metrix: Capturing linguistic features of cohesion. Discourse Processes, 47(4), 292–330

    Article  Google Scholar 

  • Miranda, R., & Garcia-Carpintero, E. (2018). Overcitation and overrepresentation of review papers in the most cited papers. Journal of Informetrics, 12(4), 1015–1030

    Article  Google Scholar 

  • Murray, T. (1999). Authoring intelligent tutoring systems: An analysis of the state of the art. International Journal of Artificial Intelligence in Education, 10, 98–129

    Google Scholar 

  • Nalimov, V. V., & Mulchenko, Z. M. (1971). Measurement of science. Study of the development of science as an information process.

  • Núñez, R., Allen, M., Gao, R., Rigoli, C. M., Relaford-Doyle, J. & Semenuks, A. (2019). What happened to cognitive science? Nature Human Behaviour, 3, 782–791.

  • Nye, B. D., Graesser, A. C., & Hu, X. (2014). AutoTutor and family: A review of 17 years of natural language tutoring. International Journal of Artificial Intelligence in Education, 24, 427–469

    Article  Google Scholar 

  • Pane, J. F., Griffin, B. A., McCaffrey, D. F., & Karam, R. (2014). Effectiveness of cognitive tutor algebra I at scale. Educational Evaluation and Policy Analysis, 36(2), 127–144

    Article  Google Scholar 

  • Person, N. K., Graesser, A. C., Bautista, L., Mathews, E. C., & The Tutoring Research Group. (2001). Evaluating student learning gains in two versions of AutoTutor. In J. D. Moore, C. L. Redfield, & W. L. Johnson (Eds.), Artificial intelligence in education: AI-ED in the wired and wireless future. (pp. 286–293). Amsterdam: IOS Press.

    Google Scholar 

  • Piech, C., Spencer, J., Huang, J., Ganguli, S., Sahami, M., Guibas, L., et al. (2015). Deep knowledge tracing. Computer Science, 3(3), 19–23

    Google Scholar 

  • Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536

    Article  Google Scholar 

  • Santhanam, R., Liu, D., & Shen, W. C. (2016). Research note gamification of technology-mediated training: Not all competitions are the same. Information Systems Research, 27(2), 453–465

    Article  Google Scholar 

  • Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117.

  • Sharma, S., Ghorpade, S., Sahni, A., & Saluja, N. (2014). Survey of intelligent tutoring systems: A review on the development of expert/intelligent tutoring systems, various teaching strategies and expert tutoring system design suggestions. International Journal of Engineering Research and Technology, 3(11), 37–42

    Google Scholar 

  • Shneider, A. M. (2009). Four stages of a scientific discipline; four types of scientist. Trends in Biochemical Sciences, 34(5), 217–223

    Article  Google Scholar 

  • Shute, V. J., & Psotka, J. (1996). Intelligent tutoring systems: Past, present, and future. In D. H. Jonassen (Ed.), Handbook of research for educational communications and technology. (pp. 570–600). Macmillan.

    Google Scholar 

  • Sleeman, D. H., & Brown, J. S. (1982). Intelligent tutoring systems: An overview. (pp. 1–11). New York: Academic Press.

    Google Scholar 

  • Small, H., & Griffith, B. C. (1974). The structure of scientific literatures I: Identifying and graphing specialties. Science Studies, 4, 17–40

    Article  Google Scholar 

  • Spector, J., Merrill, M., & David, M. (2014). Handbook of research on educational communications and technology. Springer.

    Book  Google Scholar 

  • 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

    Article  Google Scholar 

  • Steenbergen-Hu, S., & Cooper, H. (2013). A meta-analysis of the effectiveness of intelligent tutoring systems on K–12 students’ mathematical learning. Journal of Educational Psychology, 105(4), 970

    Article  Google Scholar 

  • Tague, J., Beheshti, J., Rees-Potter L. (1981). The law of exponential growth: Evidence, implications and forecasts. LIBRARY TRENDS.125–149.

  • Tsay, C. H., Kofinas, A. K., & Luo, J. (2018). Enhancing student learning experience with technology-mediated gamification: An empirical study. Computers in Education, 121, 1–17

    Article  Google Scholar 

  • Van Eck, N., & Waltman, L. (2009). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538

    Google Scholar 

  • Vandewaetere, M., Desmet, P., & Clarebout, G. (2011). The contribution of learner characteristics in the development of computer-based adaptive learning environments. Computers in Human Behavior, 27, 118–130

    Article  Google Scholar 

  • VanLehn, K. (2006). The behavior of tutoring systems. International Journal of Artificial Intelligence in Education, 16, 227–265

    Google Scholar 

  • VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197–221

    Article  Google Scholar 

  • VanLehn, K., Graesser, A. C., Jackson, G. T., Jordan, P., Olney, A., & Rose, C. P. (2007). When are tutorial dialogues more effective than reading? Cognitive Science, 31, 3–62

    Article  Google Scholar 

  • Wang, H. (1997). SQL Tutor+: A co-operative ITS with repository support. Information and Software Technology, 5(39), 343–350

    Article  Google Scholar 

  • Wei, R. (2011). VOSviewer. Retrieved 8 Oct 2019 from http://blog.sciencenet.cn/blog-113146-451966.html

  • Wenger, E. (1987). Artificial intelligence and tutoring systems. San Francisco CA Morgan Kaufmann Publish.

  • Wescourt, K. T., Beard, M., GOUld, L., & Barr, A. (1977). Knowledge-Based CAI: CINs for Individualized Curriculum Sequencing (No. TR-290). STANFORD UNIV CALIF INST FOR MATHEMATICAL STUDIES IN THE SOCIAL SCIENCES.

  • Woolf, B. P. (2009). Building intelligent interactive tutors. Morgan Kaufman.

    Google Scholar 

  • Woolf, B. P., Chad Lane, H., Chaudhri, V. K., & Kolodner, J. L. (2013). AI grand challenges for education. Special issue on intelligent learning technologies. AI Magazine, 10, 66–84

    Article  Google Scholar 

  • Woolf, B. P., Lane, H. C., Chaudhri, V. K., & Kolodner, J. L. (2013). AI grand challenges for education. AI magazine, 34(4), 66–84

    Article  Google Scholar 

  • Wu, D., Xie, Y., Dai, Q., & Li, J. (2016). A systematic overview of operations research/management science research in Mainland China: Bibliometric analysis of the period 2001–2013. Asia-Pacifc Journal of Operational Research, 33(06), 1650044

    Article  Google Scholar 

  • Xie, P. (2015). Study of international anticancer research trends via co-word and document co-citation visualization analysis. Scientometrics, 105, 611–622

    Article  Google Scholar 

  • Xu, G., Zeng, W., & Huang, C. (2009). Research on intelligent teaching system. Research on Computer Applications, 2009(11), 4019–4022

    Google Scholar 

  • Yu, H., & Riedl, M. O. (2012). A Sequential recommendation approach for interactive personalized story generation. In Proceedings of the Eleventh International Conference on Autonomous Agents and Multiagent Systems, (pp. 71–78). Richland, SC: International Foundsation for Autonomous Agents and Multiagent Systems.

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  1. Lu Guo and Dong Wang have contributed equally in this work.

Authors and Affiliations

  1. School of Humanities and Social Sciences, University of Science and Technology of China, Hefei, 230026, China

    Lu Guo, Dong Wang, Fei Gu, Yezhu Wang & Rongting Zhou

  2. School of Humanities, Hefei Normal University, Hefei, 230061, China

    Lu Guo

  3. School of Journalism and Communication, Anhui University, Hefei, 230601, China

    Yazheng Li

  4. Key Laboratory of Immersive Media Technology (Wanxin Media), Ministry of Culture, Hefei, 230051, China

    Lu Guo & Rongting Zhou

Authors
  1. Lu Guo
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  2. Dong Wang
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  3. Fei Gu
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  4. Yazheng Li
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  5. Yezhu Wang
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  6. Rongting Zhou
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Correspondence to Dong Wang or Rongting Zhou.

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Guo, L., Wang, D., Gu, F. et al. Evolution and trends in intelligent tutoring systems research: a multidisciplinary and scientometric view. Asia Pacific Educ. Rev. 22, 441–461 (2021). https://doi.org/10.1007/s12564-021-09697-7

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  • DOI: https://doi.org/10.1007/s12564-021-09697-7

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