Aleven, V., Mclaren, B., Roll, I., & Koedinger, K. (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., Roll, I., McLaren, B. M., & Koedinger, K. R. (2016). Help helps, but only so Much: Research on help seeking with intelligent tutoring systems. International Journal of Artificial Intelligence in Education, 26, 205–223.
Article
Google Scholar
Azevedo, R., Greene, J. A., & Moos, D. C. (2007). The effect of a human agent’s external regulation upon college students’ hypermedia learning. Metacognition and learning, 2(2–3), 67–87.
Article
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), 367–379.
Article
Google Scholar
Azevedo, R., Johnson, A., Chauncey, A., & Burkett, C. (2010). Self-regulated learning with MetaTutor: Advancing the science of learning with MetaCognitive tools. In: New science of learning (pp. 225–247). Springer, New York, NY.
Brusilovskiy, P. L. (1994). The construction and application of student models in intelligent tutoring systems. Journal of Computer and Systems Sciences International, 32(1), 70–89.
MATH
Google Scholar
Bull, S. (2004). Supporting Learning with Open Learner Models. In Proceedings of 4th Hellenic Conference with International Participation: Information and Communication Technologies in Education, Athens, Greece. Keynote.
Bull, S. (2016). Negotiated learner modelling to maintain today’s learner models. Research and Practice in Technology Enhanced Learning, 11(1), 1–29.
Article
MathSciNet
Google Scholar
Bull, S., Ginon, B., Boscolo, C., & Johnson, M. (2016). Introduction of learning visualisations and metacognitive support in a persuadable open learner model. In Proceedings of the sixth international conference on learning analytics & knowledge (pp. 30–39). ACM.
Bull, S., & Kay, J. (2013). Open learner models as drivers for metacognitive processes (pp. 349–365). International handbook of metacognition and learning technologies. New York: Springer.
Google Scholar
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(1), 293–331.
Article
Google Scholar
Bull, S., Johnson, M. D., Alotaibi, M., Byrne, W., & Cierniak, G. (2013). Visualising multiple data sources in an independent open learner model. In H. C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.), Artificial intelligence in education (pp. 199–208). Berlin Heidelberg: Springer.
Chapter
Google Scholar
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.
Article
Google Scholar
Bull, S., Quigley, S., & Mabbott, A. (2006). Computer-based formative assessment to promote reflection and learner autonomy. Engineering Education, 1(1), 8–18.
Article
Google Scholar
Burns, E. C., Martin, A. J., & Collie, R. J. (2018). Adaptability, personal best (PB) goals setting, and gains in students’ academic outcomes: A longitudinal examination from a social cognitive perspective. Contemporary Educational Psychology, 53, 57–72.
Article
Google Scholar
Butler, D. L., & Winne, P. H. (1995). Feedback and self-regulated learning: A theoretical synthesis. Review of Educational Research, 65(3), 245–281.
Article
Google Scholar
Chambers, J. M., Cleveland, W. S., Kleiner, B., & Tukey, P. A. (2018). Graphical methods for data analysis. New York: CRC Press.
Book
MATH
Google Scholar
Chen, C. M. (2009). Personalized E-learning system with self-regulated learning assisted mechanisms for promoting learning performance. Expert Systems with Applications, 36(5), 8816–8829.
Article
Google Scholar
Chen, Z. H., Lu, H. D., & Chou, C. Y. (2019). Using game-based negotiation mechanism to enhance students’ goal setting and regulation. Computers & Education, 129, 71–81.
Article
Google Scholar
Chou, C. Y., Chih, W. C., Tseng, S. F. & Chen, Z. H. (2019). Simulatable Open Learner Models of Core Competencies for Setting Goals for Course Performance. In Proceedings of the 27th International Conference on Computer in Education (ICCE 2019), pp. 93–95. Kenting, Taiwan.
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.
Article
Google Scholar
Chou, C. Y., Lai, K. R., Chao, P. Y., Lan, C. H., & Chen, T. H. (2015). Negotiation based adaptive learning sequences: Combining adaptivity and adaptability. Computers & Education, 88, 215–226.
Article
Google Scholar
Chou, C. Y., Lai, K. R., Chao, P. Y., Tseng, S. F., & Liao, T. Y. (2018). A negotiation-based adaptive learning system for regulating help-seeking behaviors. Computers & Education, 126, 115–128.
Article
Google Scholar
Chou, C. Y., & Sun, P. F. (2013). An educational tool for visualizing students’ program tracing processes. Computer Applications in Engineering Education, 21(3), 432–438.
Article
Google Scholar
Chou, C. Y., Tseng, S. F., Chih, W. C., Chen, Z. H., Chao, P. Y., Lai, K. R., et al. (2017). Open student models of core competencies at the curriculum level: Using learning analytics for student reflection. IEEE Transactions on Emerging Topics in Computing, 5(1), 32–44.
Article
Google Scholar
Clark, R. C., & Mayer, R. E. (2008). Who's in Control? Guidelines for e-Learning Navigation. In E-learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning (pp. 309–338). San Francisco: Pfeiffer.
Conati, C., & Kardan, S. (2013). Student modeling: Supporting personalized instruction, from problem solving to exploratory open ended activities. AI Magazine, 34(3), 13–26.
Article
Google Scholar
Demmans, E. C., & Bull, S. (2015). Uncertainty representation in visualizations of learning analytics for learners: Current approaches and opportunities. IEEE Transactions on Learning Technologies., 8(3), 242–260.
Article
Google Scholar
Desmarais, M. C., & 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(1–2), 9–38.
Article
Google Scholar
Devolder, A., van Braak, J., & Tondeur, J. (2012). Supporting self-regulated learning in computer-based learning environments: Systematic review of effects of scaffolding in the domain of science education. Journal of Computer Assisted Learning, 28(6), 557–573.
Article
Google Scholar
Dunning, D., Heath, C., & Suls, J. M. (2004). Flawed self-assessment implications for health, education, and the workplace. Psychological Science in the Public Interest, 5(3), 69–106.
Article
Google Scholar
Garcia, R., Falkner, K., & Vivian, R. (2018). Systematic literature review: Self-regulated learning strategies using e-learning tools for computer science. Computers & Education, 123, 150–163.
Article
Google Scholar
Griffin, T. D., Wiley, J., & Salas, C. R. (2013). Supporting effective self-regulated learning: The critical role of monitoring. In International handbook of metacognition and learning technologies (pp. 19–34). Springer, New York, NY.
Hadwin, A. F., Järvelä, S., & Miller, M. (2011). Self-regulated, co-regulated, and socially shared regulation of learning. In Handbook of self-regulation of learning and performance (pp. 65–84).
Harley, J. M., Taub, M., Azevedo, R., & Bouchet, F. (2017). Let’s set up some subgoals: Understanding human-pedagogical agent collaborations and their implications for learning and prompt and feedback compliance. IEEE Transactions on Learning Technologies, 11(1), 54–66.
Article
Google Scholar
Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112.
Article
Google Scholar
Holt, P., Dubs, S., Jones, M., & Greer, J. (1994). The state of student modeling. In Student Modeling: the Key to Individualized Knowledge-Based Instruction. (Greer, J. & McCalla, G. I. Eds.) (pp. 3–35), Springer, Berlin.
Hwang, G. J. (2003). A conceptual map model for developing intelligent tutoring systems. Computers & Education, 40(3), 217–235.
Article
Google Scholar
Jansen, R. S., van Leeuwen, A., Janssen, J., Conijn, R., & Kester, L. (2020). Supporting learners’ self-regulated learning in Massive Open Online Courses. Computers & Education, 146, 103771.
Article
Google Scholar
Järvelä, S., Kirschner, P. A., Panadero, E., Malmberg, J., Phielix, C., Jaspers, J., et al. (2015). Enhancing socially shared regulation in collaborative learning groups: Designing for CSCL regulation tools. Educational Technology Research and Development, 63(1), 125–142.
Article
Google Scholar
Karabenick, S. A. (2011). Methodological and assessment issues in research on help seeking. In Handbook of Self-regulation of Learning and Performance, pp. 267–281.
Lai, C.-L., & Hwang, G.-J. (2016). A self-regulated flipped classroom approach to improving students’ learning performance in a mathematics course. Computers & Education, 100, 126–140.
Article
Google Scholar
Lajoie, S. P. (1993). Computer environments as cognitive tools for enhancing learning. In Computers as cognitive tools, pp. 261–288.
Lee, D., Watson, S. L., & Watson, W. R. (2019). Systematic literature review on self-regulated learning in massive open online courses. Australasian Journal of Educational Technology, 35(1), 28–41.
Google Scholar
Lin, J. W., Lai, Y. C., Lai, Y. C., & Chang, L. C. (2016). Fostering self-regulated learning in a blended environment using group awareness and peer assistance as external scaffolds. Journal of Computer Assisted Learning, 32(1), 77–93.
Article
Google Scholar
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(1), 55–88.
Article
Google Scholar
Manlove, S., Lazonder, A. W., & de Jong, T. (2007). Software scaffolds to promote regulation during scientific inquiry learning. Metacognition and Learning, 2(2–3), 141–155.
Article
Google Scholar
Matcha, W., Gasevic, D., & Pardo, A. (2020). A systematic review of empirical studies on learning analytics dashboards: A self-regulated learning perspective. IEEE Transactions on Learning Technologies, 13(2), 226–245.
Article
Google Scholar
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
Müller, N. M., & Seufert, T. (2018). Effects of self-regulation prompts in hypermedia learning on learning performance and self-efficacy. Learning and Instruction, 58, 1–11.
Article
Google Scholar
Musso, M. F., Boekaerts, M., Segers, M., & Cascallar, E. C. (2019). Individual differences in basic cognitive processes and self-regulated learning: Their interaction effects on math performance. Learning and Individual Differences, 71, 58–70.
Article
Google Scholar
Nicol, D. J., & Macfarlane-Dick, D. (2006). Formative assessment and self-regulated learning: A model and seven principles of good feedback practice. Studies in Higher Education, 31(2), 199–218.
Article
Google Scholar
Nota, L., Soresi, S., & Zimmerman, B. J. (2004). Self-regulation and academic achievement and resilience: A longitudinal study. International Journal of Educational Research, 41(3), 198–215.
Article
Google Scholar
Nussbaumer, A., Hillemann, E. C., Gütl, C., & Albert, D. (2015). A competence-based service for supporting self-regulated learning in virtual environments. Journal of Learning Analytics, 2(1), 101–133.
Article
Google Scholar
Pakdaman-Savoji, A., Nesbit, J., & Gajdamaschko, N. (2019). The conceptualisation of cognitive tools in learning and teachnology: A review. Australasian Journal of Educational Technology, 35(2), 1–24.
Article
Google Scholar
Panadero, E. (2017). A review of self-regulated learning: Six models and four directions for research. Frontiers in Psychology, 8, 422.
Article
Google Scholar
Panadero, E., Broadbent, J., Boud, D., & Lodge, J. M. (2019). Using formative assessment to influence self-and co-regulated learning: The role of evaluative judgement. European Journal of Psychology of Education, 34(3), 535–557.
Article
Google Scholar
Panadero, E., Klug, J., & Järvelä, S. (2016). Third wave of measurement in the self-regulated learning field: When measurement and intervention come hand in hand. Scandinavian Journal of Educational Research, 60(6), 723–735.
Article
Google Scholar
Pérez-Álvarez, R., Maldonado-Mahauad, J., & Pérez-Sanagustín, M. (2018, September). Tools to support self-regulated learning in online environments: literature review. In European Conference on Technology Enhanced Learning (pp. 16–30). Springer, Cham.
Perkins, D. N., Hancock, C., Hobbs, R., Martin, F., & Simmons, R. (1986). Conditions of learning in novice programmers. Journal of Educational Computing Research, 2(1), 37–55.
Article
Google Scholar
Pintrich, P. R., Smith, D. A., Garcia, T., & McKeachie, W. J. (1993). Reliability and predictive validity of the Motivated Strategies for Learning Questionnaire (MSLQ). Educational and Psychological Measurement, 53(3), 801–813.
Article
Google Scholar
Roll, I., Aleven, V., McLaren, B. M., & Koedinger, K. R. (2011a). Metacognitive practice makes perfect: Improving students’ self-assessment skills with an intelligent tutoring system. In International Conference on Artificial Intelligence in Education (pp. 288–295). Springer, Berlin, Heidelberg.
Roll, I., Aleven, V., McLaren, B. M., & Koedinger, K. R. (2011). Improving students’ help-seeking skills using metacognitive feedback in an intelligent tutoring system. Learning and Instruction, 21(2), 267–280.
Article
Google Scholar
Roll, I., Wiese, E. S., Long, Y., Aleven, V., & Koedinger, K. R. (2014). Tutoring self-and co-regulation with intelligent tutoring systems to help students acquire better learning skills. Design Recommendations for Intelligent Tutoring Systems, 2, 169–182.
Google Scholar
Rovers, S. F., Clarebout, G., Savelberg, H. H., de Bruin, A. B., & van Merriënboer, J. J. (2019). Granularity matters: Comparing different ways of measuring self-regulated learning. Metacognition and Learning, 14(1), 1–19.
Article
Google Scholar
Saary, M. J. (2008). Radar plots: a useful way for presenting multivariate health care data. Journal of Clinical Epidemiology, 61(4), 311–317.
Article
Google Scholar
Scheiter, K., & Gerjets, P. (2007). Learner control in hypermedia environments. Educational Psychology Review, 19(3), 285–307.
Article
Google Scholar
Schraw, G. (2007). The use of computer-based environments for understanding and improving self-regulation. Metacognition and Learning, 2(2), 169–176.
Article
Google Scholar
Self, J. (1988). Bypassing the intractable problem of student modeling. In International Conference of Intelligent Tutoring Systems, Montreal, Canada, pp. 18–24.
Shyr, W. J., & Chen, C. H. (2018). Designing a technology-enhanced flipped learning system to facilitate students’ self-regulation and performance. Journal of Computer Assisted Learning, 34(1), 53–62.
Article
Google Scholar
Stone, N. J. (2000). Exploring the relationship between calibration and self-regulated learning. Educational Psychology Review, 12(4), 437–475.
Article
Google Scholar
Su, J. M. (2020). A rule-based self-regulated learning assistance scheme to facilitate personalized learning with adaptive scaffoldings: A case study for learning computer software. Computer Applications in Engineering Education., 28(3), 536–555.
Article
Google Scholar
Vainio, V., & Sajaniemi, J. (2007). Factors in novice programmers’ poor tracing skills. ACM SIGCSE Bulletin, 39(3), 236–240.
Article
Google Scholar
Vandewaetere, M., & Clarebout, G. (2011). Can instruction as such affect learning? The case of learner control. Computers & Education, 57, 2322–2332.
Article
Google Scholar
Winne, P. H. (1996). A metacognitive view of individual differences in self-regulated learning. Learning and Individual Differences, 8(4), 327–353.
Article
Google Scholar
Winne, P. H. (2010). Improving measurements of self-regulated learning. Educational Psychologist, 45(4), 267–276.
Article
Google Scholar
Winne, P. H. (2011). A cognitive and metacognitive analysis of self-regulated learning. In Handbook of self-regulation of learning and performance (pp. 15–32).
Winne, P. H., & Hadwin, A. F. (2013). nStudy: Tracing and supporting self-regulated learning in the Internet. In International handbook of metacognition and learning technologies (pp. 293–308). Springer, New York, NY.
Winne, P. H., & Nesbit, J. C. (2009). Supporting Self-Regulated Learning with Cognitive Tools. In Handbook of metacognition in education, p. 259.
Winne, P. H., & Perry, N. E. (2000). Measuring self-regulated learning. In Handbook of self-regulation (pp. 531–566). Academic Press.
Wong, J., Baars, M., Davis, D., Van Der Zee, T., Houben, G. J., & Paas, F. (2019). Supporting self-regulated learning in online learning environments and MOOCs: A systematic review. International Journal of Human-Computer Interaction, 35(4–5), 356–373.
Article
Google Scholar
Woolf, B. P. (2008). Building Intelligent Interactive Tutors: Student-centered Strategies for Revolutionizing e-learning. Boston: Morgan Kaufmann Publishers.
Google Scholar
Young, J. D. (1996). The effect of self-regulated learning strategies on performance in learner controlled computer-based instruction. Educational Technology Research and Development, 44(2), 17–27.
Article
Google Scholar
Zhou, M. (2012). From “Self-Tested” to “Self-Testing”: a review of self-assessment systems for learning. In S. Graf, F. Lin, & R. McGreal (Eds.), Intelligent and adaptivelearning systems: Technology enhanced support for learners and teachers (pp. 119–132). Hershey, PA: Information Science Reference.
Chapter
Google Scholar
Zimmerman, B. J. (1990). Self-regulated learning and academic achievement: An overview. Educational Psychologist, 25(1), 3–17.
Article
MathSciNet
Google Scholar
Zimmerman, B. J. (2001). Theories of self-regulated learning and academic achievement: An overview and analysis. Self-Regulated Learning and Academic Achievement: Theoretical Perspectives, 2, 1–37.
Google Scholar
Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into Practice, 41(2), 64–70.
Article
Google Scholar
Zimmerman, B. J., Bonner, S., & Kovach, R. (1996). Developing self-regulated learners: Beyond achievement to self-efficacy. New York: American Psychological Association.
Book
Google Scholar
Zimmerman, B. J., & Schunk, D. H. (1989). Self-regulated learning and academic achievement: Theory, research, and practice. New York: Springer.
Book
Google Scholar