Advertisement

An Analysis of Open Learner Models for Supporting Learning Analytics

  • Stylianos Sergis
  • Demetrios Sampson
Chapter

Abstract

Teaching and learning are increasingly being offered in distributed, online digital environments, often openly and at large-scale, traversing spatial and temporal boundaries. Within such environments, Learning Analytics technologies aim to provide the means for tracking and making sense of the multitude of educational data that is being generated, in order to inform educational and pedagogical decision making of different actors, such as learners, teachers, school leaders and parents. However, at the heart of Learning Analytics technologies in such distributed and open learning environments lies the Open Learner Model (OLM), that informs the data collection, processing and sense-making capabilities of the analytics technology. In this context the contribution of this chapter is to present a generic educational data-driven layered Open Learner Modelling framework, which can be used as a blueprint for the analysis (and design) of OLM instances. Furthermore, capitalizing on this framework, the chapter also performs a critical analysis of existing research in OLM works, in order to draw conclusions on the current status of this emerging field.

Keywords

Open learner model Learning analytics Learner profile Educational data 

Notes

Acknowledgements

The work presented in this paper has been partially funded by (a) the European Commission in the context of the OSOS project (Grant Agreement no. 741572) under the Horizon 2020 Framework Programme, Science with and for Society: Open Schooling and Collaboration on Science Education (H2020-SwafS-15-2016), and (b) the Greek General Secretariat for Research and Technology, under the Matching Funds 2014–2016 for the EU project “Inspiring Science: Large Scale Experimentation Scenarios to Mainstream eLearning in Science, Mathematics and Technology in Primary and Secondary Schools” (Project Number: 325123). This document does not represent the opinion of neither the European Commission nor the Greek General Secretariat for Research and Technology, and the European Commission and the Greek General Secretariat for Research and Technology are not responsible for any use that might be made of its content.

References

  1. Abu Issa, A., Al-Jadaa, A., Ghanem, W., & Hussein, M. (2017). Enhancing the intelligence of web tutoring systems using a multi-entry based open learner model. In Proceedings of the ICC’2017.Google Scholar
  2. Agudo-Peregrina, Á. F., Iglesias-Pradas, S., Conde-González, M. Á., & Hernández-García, Á. (2014). Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning. Computers in Human Behavior, 31, 542–550.CrossRefGoogle Scholar
  3. Ahmad, N., & Bull, S. (2009). Learner trust in learner model externalisations. In Proceedings of the 2009 Conference on Artificial Intelligence in Education (pp. 617–619). Amsterdam: IOS Press.Google Scholar
  4. Ali, L., Hatala, M., Gašević, D., & Jovanović, J. (2012). A qualitative evaluation of evolution of a learning analytics tool. Computers & Education, 58(1), 470–489.CrossRefGoogle Scholar
  5. Al-Shamri, M. Y. H., & Bharadwaj, K. K. (2008). Fuzzy-genetic approach to recommender systems based on a novel hybrid user model. Expert Systems with Applications, 35(3), 1386–1399.CrossRefGoogle Scholar
  6. Arthi, K., & Tamilarasi, A. (2008). Prediction of autistic disorder using neuro fuzzy system by applying ANN technique. International Journal of Developmental Neuroscience, 26, 699–704.CrossRefGoogle Scholar
  7. Barua, D., Kay, J., Kummerfeld, B., & Paris, C. (2014). Modelling long term goals. In V. Dimitrova, T. Kuflik, D. Chin, F. Ricci, P. Dolog, & G. J. Houben (Eds.), User modeling, adaptation, and personalization (pp. 1–12). Cham: Springer International Publishing.Google Scholar
  8. Baschera, G. M., & Gross, M. (2010). Poisson-based inference for perturbation models in adaptive spelling training. International Journal of Artificial Intelligence in Education, 20(4), 333–360.Google Scholar
  9. Blikstein, P., & Worsley, M. (2016). Multimodal learning analytics and education data mining: Using computational technologies to measure complex learning tasks. Journal of Learning Analytics, 3(2), 220–238.CrossRefGoogle Scholar
  10. Branch, R. M. (2010). Instructional design: The ADDIE approach. New York, NY: Springer.Google Scholar
  11. Bremgartner, V., Netto, J. M., & Menezes, C. (2014). Using agents and open learner model ontology for providing constructive adaptive techniques in virtual learning environments. In A. Bazzan & K. Pichara (Eds.), Advances in artificial intelligence (pp. 625–636). Cham: Springer International Publishing.Google Scholar
  12. Brusilovsky, P., Hsaio, I.-H., & Folajimi, Y. (2011). QuizMap: Open social student modeling and adaptive navigation support with treemaps. In C. D. Kloos, D. Gillet, R. M. Crespo Garcia, F. Wild, & M. Wolpers (Eds.), Proceedings of the 2011 EC-TEL (pp. 71–82). Berlin: Springer.Google Scholar
  13. Brusilovsky, P., & Millan, E. (2007). User models for adaptive hypermedia and adaptive educational systems. In The adaptive web (pp. 3–53). Berlin: Springer.CrossRefGoogle Scholar
  14. Brusilovsky, P., Somyurek, S., Guerra, J., Hosseini, R., Zadorozhny, V., & Durlach, P. (2016). Open social student modeling for personalized learning. IEEE Transactions on Emerging Topics in Computing, 4, 450.CrossRefGoogle Scholar
  15. Bull, S., & Al-Shanfari, L. (2015). Negotiating individual learner models in contexts of peer assessment and group learning. In Proceedings of Workshop on Intelligent Support for Learning in Groups, AIED.Google Scholar
  16. Bull, S., Gakhal, I., Grundy, D., Johnson, M., Mabbott, A., & Xu, J. (2010). Preferences in multiple-view open learner models. In Sustaining TEL: From innovation to learning and practice (pp. 476–481). Berlin: Springer.CrossRefGoogle Scholar
  17. Bull, S., Jackson, T. J., & Lancaster, M. J. (2010). Students’ interest in their misconceptions in first-year electrical circuits and mathematics courses. International Journal of Electrical Engineering Education, 47(3), 307–318.CrossRefGoogle Scholar
  18. Bull, S., Johnson, M., Masci, D., & Biel, C. (2015). Integrating and visualising diagnostic information for the benefit of learning. In P. Reimann, S. Bull, M. Kickmeier-Rust, R. K. Vatrapu, & B. Wasson (Eds.), Measuring and visualizing learning in the information-rich classroom. Routledge: Taylor & Francis. (Chapter 12).Google Scholar
  19. Bull, S., Johnson, M. D., Alotaibi, M., Byrne, W., & Cierniak, G. (2013). Visualising multiple data sources in an independent open learner model. In Artificial intelligence in education (pp. 199–208). Berlin: Springer.CrossRefGoogle Scholar
  20. Bull, S., & Kay, J. (2010). Open learner models. In R. Nkambou, R. Mizoguchi, & J. Bourdeau (Eds.), Advances in intelligent tutoring systems (pp. 301–322). Berlin: Springer.CrossRefGoogle Scholar
  21. Bull, S., & Kay, J. (2016). SMILI☺: A framework for interfaces to learning data in open learner models, learning analytics and related fields. International Journal of Artificial Intelligence in Education, 26, 293–331.CrossRefGoogle Scholar
  22. Bull, S., Mabbott, A., & Abu Issa, A. S. (2007). UMPTEEN: Named and anonymous learner model access for instructors and peers. International Journal of Artificial Intelligence in Education, 17(3), 227–253.Google Scholar
  23. Bull, S., & McKay, M. (2004). An open learner model for children and teachers: Inspecting knowledge level of individuals and peers. In J. C. Lester, R. M. Vicari, & F. Paraguaçu (Eds.), Intelligent tutoring systems (pp. 646–655). Berlin: Springer.CrossRefGoogle Scholar
  24. Bull, S., Pain, H., & Brna, P. (1995). Mr. Collins: A collaboratively constructed, inspectable student model for intelligent computer assisted language learning. Instructional Science, 23(1-3), 65–87.CrossRefGoogle Scholar
  25. Calvo, R., & D’Mello, S. (2010). Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Transactions on Affective Computing, 1(1), 18–37.CrossRefGoogle Scholar
  26. Carini, R. M., Kuh, G. D., & Klein, S. P. (2006). Student engagement and student learning: Testing the linkages. Research in Higher Education, 47(1), 1–32.CrossRefGoogle Scholar
  27. Carmona, C., & Conejo, R. (2004). A learner model in a distributed environment. In N. Wolfgang & P. De Bra (Eds.), Adaptive hypermedia and adaptive web-based systems (pp. 353–359). Berlin: Springer.CrossRefGoogle Scholar
  28. Chang, R. I., Hung, Y. H., & Lin, C. F. (2015). Survey of learning experiences and influence of learning style preferences on user intentions regarding MOOCs. British Journal of Educational Technology, 46(3), 528–541.CrossRefGoogle Scholar
  29. Chatti, M. A., Muslim, A., & Schroeder, U. (2017). Toward an open learning analytics ecosystem. In B. Daniel (Ed.), Big data and learning analytics in higher education (pp. 195–219). Cham: Springer International Publishing.CrossRefGoogle Scholar
  30. Cho, M.-H., & Kim, B. J. (2013). Students’ self-regulation for interaction with others in online learning environments. Internet and Higher Education, 17, 69–75.CrossRefGoogle Scholar
  31. Chrysafiadi, K., & Virvou, M. (2012). Evaluating the integration of fuzzy logic into the student model of a web-based learning environment. Expert Systems with Applications, 39(18), 13127–13134.CrossRefGoogle Scholar
  32. Chrysafiadi, K., & Virvou, M. (2013). Student modeling approaches: A literature review for the last decade. Expert Systems with Applications, 40(11), 4715–4729.CrossRefGoogle Scholar
  33. Chrysafiadi, K., & Virvou, M. (2015). Student modeling for personalized education: A review of the literature. In K. Chrysafiadi & M. Virvou (Eds.), Advances in personalized web-based education (pp. 1–24). Cham: Springer International Publishing.Google Scholar
  34. Clemente, J., Ramírez, J., & De Antonio, A. (2011). A proposal for student modeling based on ontologies and diagnosis rules. Expert Systems with Applications, 38(7), 8066–8078.CrossRefGoogle Scholar
  35. Conejo, R., Trella, M., Cruces, I., & Garcia, R. (2011). INGRID: A web service tool for hierarchical open learner model visualization. In L. Ardissono & T. Kuflik (Eds.), Advances in user modeling (pp. 406–409). Berlin: Springer.Google Scholar
  36. Cook, R., Kay, J., & Kummerfeld, B. (2015). MOOClm: User modelling for MOOCs. In S. Carberry, S. Weibelzahl, A. Micarelli, & G. Semeraro (Eds.), User modeling, adaptation and personalization (pp. 80–91). Cham: Springer International Publishing.CrossRefGoogle Scholar
  37. Cruz-Benito, J., Borrás-Gené, O., García-Peñalvo, F. J., Blanco, Á. F., & Therón, R. (2015). Extending MOOC ecosystems using web services and software architectures. In Proceedings of the XVI International Conference on Human Computer Interaction (pp. 438–444). New York, NY: ACM.Google Scholar
  38. D’Mello, S., & Graesser, A. (2012). Dynamics of affective states during complex learning. Learning and Instruction, 22(2), 145–157.CrossRefGoogle Scholar
  39. D’Mello, S., Lehman, B., Pekrun, R., & Graesser, A. (2014). Confusion can be beneficial for learning. Learning and Instruction, 29, 153–170.CrossRefGoogle Scholar
  40. D’Mello, S. K., Blanchard, N., Baker, R., Ocumpaugh, J., & Brawner, K. (2014). I feel your pain: A selective review of affect-sensitive instructional strategies. In R. Sottilare, A. Graesser, X. Hu, & B. Goldberg (Eds.), Design recommendations for adaptive intelligent tutoring systems: Volume 2 – Instructional management (pp. 35–48). Orlando, FL: U.S. Army Research Laboratory.Google Scholar
  41. D’mello, S. K., & Kory, J. (2015). A review and meta-analysis of multimodal affect detection systems. ACM Computing Surveys, 47(3), 43-1–43-36.Google Scholar
  42. Darr, C. W. (2012). Measuring student engagement: The development of a scale for formative use. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 149–172). New York, NY: Springer.Google Scholar
  43. Davis, D., Jivet, I., Kizilcec, R. F., Chen, G., Hauff, C., & Houben, G. J. (2017). Follow the successful crowd: Raising MOOC completion rates through social comparison at scale. In Proceedings of the 7th International Conference on Learning Analytics and Knowledge (pp. 454–463). New York, NY: ACM.Google Scholar
  44. De Barba, P., Kennedy, G. E., & Ainley, M. D. (2016). The role of students’ motivation and participation in predicting performance in a MOOC. Journal of Computer Assisted Learning, 32(3), 218–231.CrossRefGoogle Scholar
  45. Desmarais, M. C., & Baker, R. S. J. D. (2012). 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.CrossRefGoogle Scholar
  46. Devedzic, V., & Jovanović, J. (2015). Developing open badges: A comprehensive approach. Educational Technology Research and Development, 63(4), 603–620.CrossRefGoogle Scholar
  47. Dimitrova, V. (2003). STyLE-OLM: Interactive open learner modelling. International Journal of Artificial Intelligence in Education, 13(1), 35–78.Google Scholar
  48. Eberle, J., Lund, K., Tchounikine, P., & Fischer, F. (Eds.). (2016). Grand challenge problems in technology-enhanced learning II: MOOCs and beyond: Perspectives for research, practice, and policy making. Cham: Springer International Publishing.Google Scholar
  49. Epp, C. D., & McCalla, G. (2011). ProTutor: Historic open learner models for pronunciation tutoring. In G. Biswas, S. Bull, J. Kay, & A. Mitrovic (Eds.), Artificial intelligence in education (pp. 441–443). Berlin: Springer.Google Scholar
  50. Gakhal, I., & Bull, S. (2008). An open learner model for trainee pilots. Research in Learning Technology, 16(2), 123–135.CrossRefGoogle Scholar
  51. Galan, F. C., & Beal, C. R. (2012). EEG estimates of engagement and cognitive workload predict math problem solving outcomes. In J. Masthoff, B. Mobasher, M. Desmarais, & R. Nkambou (Eds.), User modeling, adaptation, and personalization (pp. 51–62). Berlin: Springer.CrossRefGoogle Scholar
  52. Gaudioso, E., Montero, M., & Hernandez-Del-Olmo, F. (2012). Supporting teachers in adaptive educational systems through predictive models: A proof of concept. Expert Systems with Applications, 39(1), 621–625.CrossRefGoogle Scholar
  53. Gaudioso, E., Montero, M., Talavera, L., & Hernandez-del-Olmo, F. (2009). Supporting teachers in collaborative student modeling: A framework and an implementation. Expert Systems with Applications, 36(2), 2260–2265.CrossRefGoogle Scholar
  54. Georgopoulos, V. C., Malandraki, G. A., & Stylios, C. D. (2003). A fuzzy cognitive map approach to differential diagnosis of specific language impairment. Artificial Intelligence in Medicine, 29, 261–278.CrossRefGoogle Scholar
  55. Giannandrea, L., & Sansoni, M. (2013). A literature review on intelligent tutoring systems and on student profiling. Learning & Teaching with Media & Technology, 287.Google Scholar
  56. Girard, S., & Johnson, H. (2010). Designing affective computing learning companions with teachers as design partners. In Proceedings of the 3rd International Workshop on Affective Interaction in Natural Environments (pp. 49–54). New York, NY: ACM.Google Scholar
  57. Glushkova, T. (2008). Adaptive model for user knowledge in the e-learning system. In Proceedings of the International Conference on Computer Systems and Technologies (pp. 16-1–16-6). New York, NY: ACM.Google Scholar
  58. Grubisic, A., Stankov, S., & Žitko, B. (2013). Stereotype student model for an adaptive e-learning system. World Academy of Science, Engineering and Technology, 7, 16–23.Google Scholar
  59. Guerra-Hollstein, J., Barria-Pineda, J., Schunn, C., Bull, S., & Brusilovsky, P. (2017). Fine-grained open learner models: Complexity versus support. In Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization (pp. 41–49). New York, NY: ACM.CrossRefGoogle Scholar
  60. Haya, P. A., Daems, O., Malzahn, N., Castellanos, J., & Hoppe, H. U. (2015). Analysing content and patterns of interaction for improving the learning design of networked learning environments. British Journal of Educational Technology, 46(2), 300–316.CrossRefGoogle Scholar
  61. Henrie, C. R., Halverson, L. R., & Graham, C. R. (2015). Measuring student engagement in technology-mediated learning: A review. Computers & Education, 90, 36–53.CrossRefGoogle Scholar
  62. Hew, K. F. (2015). Promoting engagement in online courses: What strategies can we learn from three highly rated MOOCS. British Journal of Educational Technology, 47(2), 320–341.CrossRefGoogle Scholar
  63. Hosseini, R., Hsiao, I. H., Guerra, J., & Brusilovsky, P. (2015). Off the beaten path: The impact of adaptive content sequencing on student navigation in an open social student modeling interface. In Artificial intelligence in education (pp. 624–628). Cham: Springer International Publishing.CrossRefGoogle Scholar
  64. Hsiao, I. H., Bakalov, F., Brusilovsky, P., & König-Ries, B. (2013). Progressor: Social navigation support through open social student modeling. New Review of Hypermedia and Multimedia, 19(2), 112–131.CrossRefGoogle Scholar
  65. Jain, K., Manghirmalani, P., Dongardive, J., & Abraham, S. (2009). Computational diagnosis of learning disability. International Journal of Recent Trends in Engineering, 2(3), 64–66.Google Scholar
  66. Johnson, M., & Bull, S. (2009). Belief exploration in a multiple-media open learner model for basic harmony. In Artificial intelligence in education: Building learning systems that care: From knowledge representation to affective modelling (pp. 299–306). New York, NY: ACM.Google Scholar
  67. Kay, J. (2000). Stereotypes, student models and scrutability. In G. Gauthier, C. Frasson, & K. Van Lehn (Eds.), Intelligent tutoring systems (pp. 19–30). Berlin: Springer.CrossRefGoogle Scholar
  68. Kay, J., & Bull, S. (2015). New opportunities with open learner models and visual learning analytics. In C. Conati, N. Heffernan, A. Mitrovic, & M. F. Verdejo (Eds.), Artificial intelligence in education (pp. 666–669). Cham: Springer International Publishing.CrossRefGoogle Scholar
  69. Kerly, A., Ellis, R., & Bull, S. (2007). CALMsystem: A conversational agent for learner modelling. In R. Ellis, T. Allen, & M. Petridis (Eds.), Applications and innovations in intelligent systems (Vol. XV, pp. 89–102). Berlin: Springer.Google Scholar
  70. Kohli, M., & Prasad, T. V. (2010). Identifying dyslexic students by using artificial neural networks. In Proceedings of the World Congress on Engineering (pp. 118–121).Google Scholar
  71. Kump, B., Seifert, C., Beham, G., Lindstaedt, S. N., & Ley, T. (2012). Seeing what the system thinks you know: Visualizing evidence in an open learner model. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 153–157).Google Scholar
  72. Kusurkar, R. A., Ten Cate, T. J., Vos, C. M. P., Westers, P., & Croiset, G. (2013). How motivation affects academic performance: A structural equation modelling analysis. Advances in Health Sciences Education, 18(1), 57–69.CrossRefGoogle Scholar
  73. Lam, S. F., Jimerson, S., Shin, H., Cefai, C., Veiga, F. H., Hatzichristou, C., … Basnett, J. (2016). Cultural universality and specificity of student engagement in school: The results of an international study from 12 countries. British Journal of Educational Psychology, 86, 137–153.CrossRefGoogle Scholar
  74. Lazarinis, F., & Retalis, S. (2007). Analyze me: Open learner model in an adaptive web testing system. International Journal of Artificial Intelligence in Education, 17(3), 255–271.Google Scholar
  75. LeBlanc, V. R., McConnell, M. M., & Monteiro, S. D. (2015). Predictable chaos: A review of the effects of emotions on attention, memory and decision making. Advances in Health Sciences Education, 20(1), 265–282.CrossRefGoogle Scholar
  76. Lee, S. J., & Bull, S. (2008). An open learner model to help parents help their children. Technology Instruction Cognition and Learning, 6(1), 29–51.Google Scholar
  77. Literat, I. (2015). Implications of massive open online courses for higher education: Mitigating or reifying educational inequities? Higher Education Research & Development, 34(6), 1164–1177.CrossRefGoogle Scholar
  78. Long, Y., & Aleven, V. (2013). Supporting students’ self-regulated learning with an open learner model in a linear equation tutor. In H. C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.), Artificial intelligence in education (pp. 219–228). Berlin: Springer.CrossRefGoogle Scholar
  79. Long, Y., & Aleven, V. (2017). Enhancing learning outcomes through self-regulated learning support with an open learner model. User Modeling and User-Adapted Interaction, 27, 55–88.CrossRefGoogle Scholar
  80. Mabbott, A., & Bull, S. (2006). Student preferences for editing, persuading, and negotiating the open learner model. In M. Ikeda, K. D. Ashley, & T.-W. Chan (Eds.), Intelligent tutoring systems (pp. 481–490). Berlin: Springer.CrossRefGoogle Scholar
  81. Martins, C., Faria, L., De Carvalho, C. V., & Carrapatoso, E. (2008). User modeling in adaptive hypermedia educational systems. Educational Technology & Society, 11(1), 194–207.Google Scholar
  82. Mathews, M., Mitrovic, A., Lin, B., Holland, J., & Churcher, N. (2012). Do your eyes give it away? Using eye-tracking data to understand students’ attitudes towards open student model representations. In S. A. Cerri, W. J. Clancey, G. Papadourakis, & K. Panourgia (Eds.), Intelligent tutoring systems (pp. 422–427). Berlin: Springer.CrossRefGoogle Scholar
  83. Mazzola, L., & Mazza, R. (2010). GVIS: A facility for adaptively mashing up and representing open learner models. In M. Wolpers, P. A. Kirschner, M. Scheffel, S. Lindstaedt, & V. Dimitrova (Eds.), Sustaining TEL: From innovation to learning and practice (pp. 554–559). Berlin: Springer.CrossRefGoogle Scholar
  84. Millan, E., Loboda, T., & Pιrez-de-la-Cruz, J. L. (2010). Bayesian networks for student model engineering. Computers and Education, 55(4), 1663–1683.CrossRefGoogle Scholar
  85. Mitrovic, A., & Martin, B. (2007). Evaluating the effect of open student models on self-assessment. International Journal of Artificial Intelligence in Education, 17(2), 121–144.Google Scholar
  86. Muldner, K., & Burleson, W. (2015). Utilizing sensor data to model students’ creativity in a digital environment. Computers in Human Behavior, 42, 127–137.CrossRefGoogle Scholar
  87. Muldner, K., Burleson, W., & VanLehn, K. (2010). “Yes!”: Using tutor and sensor data to predict moments of delight during instructional activities. In P. De Bra, A. Kobsa, & D. Chin (Eds.), User modeling, adaptation, and personalization (pp. 159–170). Berlin: Springer.CrossRefGoogle Scholar
  88. Nakic, J., Granic, A., & Glavinic, V. (2015). Anatomy of student models in adaptive learning systems: A systematic literature review of individual differences from 2001 to 2013. Journal of Educational Computing Research, 51(4), 459–489.CrossRefGoogle Scholar
  89. Nguyen, C. D., Vo, K. D., Bui, D. B., & Nguyen, D. T. (2011). An ontology-based IT student model in an educational social network. In Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Services (pp. 379–382). New York, NY: ACM.Google Scholar
  90. Nunn, S., Avella, J. T., Kanai, T., & Kebritchi, M. (2016). Learning analytics methods, benefits, and challenges in higher education: A systematic literature review. Online Learning, 20(2), 13.CrossRefGoogle Scholar
  91. Ohlsson, S. (2015). Constraint-based modeling: From cognitive theory to computer tutoring–And back again. International Journal of Artificial Intelligence in Education, 26, 1–17.Google Scholar
  92. Panagiotopoulos, I., Kalou, A., Pierrakeas, C., & Kameas, A. (2012). An ontology-based model for student representation in intelligent tutoring systems for distance learning. In L. Iliadis, I. Maglogiannis, & H. Papadopoulos (Eds.), Artificial intelligence applications and innovations (pp. 296–305). Berlin: Springer.CrossRefGoogle Scholar
  93. Papamitsiou, Z. K., & Economides, A. A. (2014). Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence. Educational Technology & Society, 17(4), 49–64.Google Scholar
  94. Papanikolaou, K. A. (2015). Constructing interpretative views of learners’ interaction behavior in an open learner model. IEEE Transactions on Learning Technologies, 8(2), 201–214.CrossRefGoogle Scholar
  95. Pohl, A., Bry, F., Schwarz, J., & Gottstein, M. (2012). Sensing the classroom: Improving awareness and self-awareness of students in Backstage. In 15th International Conference on Interactive Collaborative Learning (pp. 1–8). Washington, DC: IEEE.Google Scholar
  96. Powell, G. (1997). On being a culturally sensitive instructional designer and educator. Educational Technology, 37(2), 6–14.Google Scholar
  97. Reeve, J. (2012). A self-determination theory perspective on student engagement. In S. L. Christenson, A. L. Reschly, & C. Wylie (Eds.), Handbook of research on student engagement (pp. 149–172). New York, NY: Springer.CrossRefGoogle Scholar
  98. Rodríguez-Triana, M. J., Martínez-Monés, A., Asensio-Pérez, J. I., & Dimitriadis, Y. (2015). Scripting and monitoring meet each other: Aligning learning analytics and learning design to support teachers in orchestrating CSCL situations. British Journal of Educational Technology, 46(2), 330–343.CrossRefGoogle Scholar
  99. Sampson, D. (2017). Teaching and learning analytics to support teacher inquiry. In IEEE Global Engineering Education Conference (EDUCON2017). Washington, DC: IEEE.Google Scholar
  100. Schiaffino, S., & Amandi, A. (2009). Intelligent user profiling. In M. Bramer (Ed.), Artificial intelligence an international perspective (pp. 193–216). Berlin: Springer.CrossRefGoogle Scholar
  101. Schwendimann, B. A., Rodriguez-Triana, M. J., Vozniuk, A., Prieto, L. P., Boroujeni, M. S., Holzer, A., … Dillenbourg, P. (2017). Perceiving learning at a glance: A systematic literature review of learning dashboard research. IEEE Transactions on Learning Technologies, 10(1), 30–41.CrossRefGoogle Scholar
  102. Sergis, S., & Sampson, D. (2016). School analytics: A framework for supporting systemic school leadership. In J. M. Spector, D. Ifenthaler, D. Sampson, & P. Isaias (Eds.), Competencies in teaching, learning and educational leadership in the digital age (pp. 79–122). New York, NY: Springer.CrossRefGoogle Scholar
  103. Sergis, S., & Sampson, D. (2017). Teaching and learning analytics to support teacher inquiry: A systematic literature review. In A. Ayala (Ed.), Learning analytics: Fundaments, applications, and trends: A view of the current state of the art (pp. 25–63). Cham: Springer International Publishing.Google Scholar
  104. Sergis, S., Sampson, D. G., & Pelliccione, L. (2017). Educational design for MOOCs: Design considerations for technology-supported learning at large scale. In Open education: From OERs to MOOCs (pp. 39–71). Berlin: Springer.CrossRefGoogle Scholar
  105. Tempelaar, D. T., Rienties, B., & Giesbers, B. (2015). In search for the most informative data for feedback generation: Learning analytics in a data-rich context. Computers in Human Behavior, 47, 157–167.CrossRefGoogle Scholar
  106. Ting, C. Y., & Phon-Amnuaisuk, S. (2012). Properties of Bayesian student model for INQPRO. Applied Intelligence, 36(2), 391–406.CrossRefGoogle Scholar
  107. Tongchai, N. (2016). Impact of self-regulation and open learner model on learning achievement in blended learning environment. International Journal of Information and Education Technology, 6(5), 343.CrossRefGoogle Scholar
  108. Trowler, V. (2010). Student engagement literature review. Report for the Higher Education Academy. Retrieved from http://tinyurl.com/ztz2q2eGoogle Scholar
  109. Upton, K., & Kay, J. (2009). Narcissus: Group and individual models to support small group work. In User modeling, adaptation, and personalization (pp. 54–65). Berlin: Springer.CrossRefGoogle Scholar
  110. Van Labeke, N., Brna, P., & Morales, R. (2007). Opening up the interpretation process in an open learner model. International Journal of Artificial Intelligence in Education, 17(3), 305–338.Google Scholar
  111. Vélez, J., Fabregat, R., Bull, S., & Hueva, D. (2009). The potential for open learner models in adaptive virtual learning environments. In AIED 2009: 14th International Conference on Artificial Intelligence in Education Workshops Proceedings (p. 11).Google Scholar
  112. Verbert, K., Duval, E., Klerkx, J., Govaerts, S., & Santos, J. L. (2013). Learning analytics dashboard applications. American Behavioral Scientist, 57(10), 1500–1509.CrossRefGoogle Scholar
  113. Verginis, I., Gouli, E., Gogoulou, A., & Grigoriadou, M. (2011). Guiding learners into reengagement through the SCALE environment: An empirical study. IEEE Transactions on Learning Technologies, 4(3), 275–290.CrossRefGoogle Scholar
  114. Weber, G., & Brusilovsky, P. (2001). ELM-ART: An adaptive versatile system for web-based instruction. International Journal of Artificial Intelligence in Education (IJAIED), 12, 351–384.Google Scholar
  115. Wedelin, D., Adawi, T., Jahan, T., & Andersson, S. (2015). Investigating and developing engineering students’ mathematical modelling and problem-solving skills. European Journal of Engineering Education, 40(5), 557–572.CrossRefGoogle Scholar
  116. Wetzel, J., VanLehn, K., Butler, D., Chaudhari, P., Desai, A., Feng, J., … Samala, R. (2017). The design and development of the Dragoon intelligent tutoring system for model construction: Lessons learned. Interactive Learning Environments, 25(3), 361–381.CrossRefGoogle Scholar
  117. Woolf, B. P. (2010). Student modeling. In R. Nkambou, R. Mizoguchi, & J. Bourdeau (Eds.), Advances in intelligent tutoring systems (pp. 267–279). Berlin: Springer.CrossRefGoogle Scholar
  118. Woolf, B. P., Arroyo, I., Muldner, K., Burleson, W., Cooper, D. G., Dolan, R., & Christopherson, R. M. (2010). The effect of motivational learning companions on low achieving students and students with disabilities. In Intelligent tutoring systems (pp. 327–337). Berlin: Springer.CrossRefGoogle Scholar
  119. Xu, J., & Bull, S. (2010). Encouraging advanced second language speakers to recognise their language difficulties: A personalised computer-based approach. Computer Assisted Language Learning, 23(2), 111–127.CrossRefGoogle Scholar
  120. Yacef, K. (2005). The logic-ITA in the classroom: A medium scale experiment. International Journal of Artificial Intelligence in Education, 15(1), 41–62.Google Scholar
  121. Zakharov, K., Mitrovic, A., & Ohlsson, S. (2005). Feedback micro-engineering in EER-tutor. In Proceedings of the 2005 Conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology (pp. 718–725). New York, NY: ACM.Google Scholar
  122. Zapata-Rivera, D., Hansen, E., Shute, V. J., Underwood, J. S., & Bauer, M. (2007). Evidence-based approach to interacting with open student models. International Journal of Artificial Intelligence in Education, 17(3), 273–303.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Stylianos Sergis
    • 1
  • Demetrios Sampson
    • 1
    • 2
  1. 1.Department of Digital SystemsUniversity of PiraeusPiraeusGreece
  2. 2.School of EducationCurtin UniversityPerthAustralia

Personalised recommendations