Alexandron, G., Yoo, L. Y., Ruipérez-Valiente, J. A., Lee, S., & Pritchard, D. E. (2019). Are MOOC learning analytics results trustworthy? With fake learners, they might not be! International Journal of Artificial Intelligence in Education, 29(4), 484–506. https://doi.org/10.1007/s40593-019-00183-1
An, P., Holstein, K., D’Anjou, B., Eggen, B., & Bakker, S. (2020). The TA framework: Designing real-time teaching augmentation for K-12 classrooms. Conference on Human Factors in Computing Systems - Proceedings. https://doi.org/10.1145/3313831.3376277
Baig, M. I., Shuib, L., & Yadegaridehkordi, E. (2020). Big data in education: A state of the art, limitations, and future research directions. International Journal of Educational Technology in Higher Education, 17(1). https://doi.org/10.1186/s41239-020-00223-0
Barak, M. & Usher, M. (2020). Innovation in a MOOC: Project-based learning in the international context. In J. J. Mintzes & E. M. Walter (Eds.) Active Learning in College Science: The Case for Evidence Based Practice. Berlin: Springer Nature, pp. 639–653. https://doi.org/10.1007/978-3-030-33600-4_39
Barak, M., & Usher, M. (2022). The innovation level of engineering students’ team projects in hybrid and MOOC environments. European Journal of Engineering Education, 47(2), 299–313. https://doi.org/10.1080/03043797.2021.1920889
Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed). SAGE Publications, Inc.
Cui, L., Li, H., & Song, Q. (2014). Developing the ability for a deep approach to learning by students with the assistance of MOOCs. World Transactions on Engineering and Technology Education, 12(4), 685–689.
Dang, J., King, K. M., & Inzlicht, M. (2020). Why are self-report and behavioral measures weakly correlated? Trends in Cognitive Sciences, 24(4), 267–269.
Dillahunt, T., Wang, Z., & Teasley, S. D. (2014). Democratizing higher education: Exploring MOOC use among those who cannot afford a formal education. The International Review of Research in Open and Distance Learning, 15(5), 177–196. https://doi.org/10.19173/irrodl.v15i5.1841
Drachsler, H., & Kalz, M. (2016). The MOOC and learning analytics innovation cycle (MOLAC): A reflective summary of ongoing research and its challenges. Journal of Computer Assisted Learning, 32(3), 281–290. https://doi.org/10.1111/jcal.12135
Er, E., Gómez-Sánchez, E., Dimitriadis, Y., Bote-Lorenzo, M. L., Asensio-Pérez, J. I., & Álvarez-Álvarez, S. (2019). Aligning learning design and learning analytics through instructor involvement: A MOOC case study. Interactive Learning Environments, 27(5–6), 685–698. https://doi.org/10.1080/10494820.2019.1610455
Fang, J., Tang, L., Yang, J., & Peng, M. (2019). Social interaction in MOOCs: The mediating effects of immersive experience and psychological needs satisfaction. Telematics and Informatics, 39(August 2018), 75–91. https://doi.org/10.1016/j.tele.2019.01.006
Gašević, D., Dawson, S., Pardo, A., Gašević, D., Dawson, S., & Pardo, A. (2016). How do we start? State and directions of learning analytics adoption. 2016 ICDE Presidents’ Summit, December, 1–24. https://doi.org/10.13140/RG.2.2.10743.42401
Goodman, L. A. (1961). Snowball sampling. Annals of Mathematical Statistics, 32(1), 148–170. https://doi.org/10.1214/AOMS/1177705148
Green, J. L., Schmitt-Wilson, S., Versland, T., Kelting-Gibson, L., & Nollmeyer, G. E. (2016). Teachers and data literacy: A blueprint for professional development to foster data driven decision making. Journal of Continuing Education and Professional Development, January. https://doi.org/10.7726/jcepd.2016.1002
Herodotou, C., Hlosta, M., Boroowa, A., Rienties, B., Zdrahal, Z., & Mangafa, C. (2019). Empowering online teachers through predictive learning analytics. British Journal of Educational Technology, 50(6), 3064–3079. https://doi.org/10.1111/bjet.12853
Hilliger, I., Ortiz-Rojas, M., Pesántez-Cabrera, P., Scheihing, E., Tsai, Y. S., Muñoz-Merino, P. J., Broos, T., Whitelock-Wainwright, A., Gašević, D., & Pérez-Sanagustín, M. (2020). Towards learning analytics adoption: A mixed methods study of data-related practices and policies in Latin American universities. British Journal of Educational Technology, 51(4), 915–937. https://doi.org/10.1111/bjet.12933
Holstein, K., McLaren, B. M., & Aleven, V. (2019). Co-designing a real-time classroom orchestration tool to support teacher–AI complementarity. Journal of Learning Analytics, 6(2), 27–52. https://doi.org/10.18608/jla.2019.62.3
Jordan, K. (2015). Massive open online course completion rates revisited: Assessment, length and attrition. International Review of Research in Open and Distance Learning, 16(3), 3451–358. https://doi.org/10.19173/irrodl.v16i3.2112
Kim, D., Park, Y., Yoon, M., & Jo, I. H. (2016). Toward evidence-based learning analytics: Using proxy variables to improve asynchronous online discussion environments. Internet and Higher Education, 30, 30–43. https://doi.org/10.1016/j.iheduc.2016.03.002
Kizilcec, R. F., & Brooks, C. (2017). Diverse big data and randomized field experiments in MOOCs. Handbook of Learning Analytics, 211–222. https://doi.org/10.18608/hla17.018
Klein, C., Lester, J., Rangwala, H., & Johri, A. (2019). Technological barriers and incentives to learning analytics adoption in higher education: Insights from users. Journal of Computing in Higher Education, 31(3), 604–625. https://doi.org/10.1007/s12528-019-09210-5
Knight, D. B., Brozina, C., & Novoselich, B. (2016). An investigation of first-year engineering student and instructor perspectives of learning analytics approaches. Journal of Learning Analytics, 3(3), 215–238. https://doi.org/10.18608/jla.2016.33.11
Kuh, G. D. (2002). The National Survey of Student Engagement: Conceptual framework and overview of psychometric properties. Framework & Psychometric Properties, 1(1), 1–26. https://doi.org/10.5861/ijrse.2012.v1i1.19
Kulkarni, C., Wei, K. P., Le, H., Chia, D., Papadopoulos, K., Cheng, J., Koller, D., & Klemmer, S. R. (2013). Peer and self assessment in massive online classes. ACM Transactions on Computer-Human Interaction, 20(6). https://doi.org/10.1145/2505057
Larrabee Sønderlund, A., Hughes, E., & Smith, J. (2019). The efficacy of learning analytics interventions in higher education: A systematic review. British Journal of Educational Technology, 50(5), 2594–2618. https://doi.org/10.1111/bjet.12720
Leitner, P., Khalil, M., & Ebner, M. (2017). Learning analytics: Fundaments, applications, and trends. Learning Analytics: Fundaments, Applications, and Trends, Studies in Systems, Decision and Control, 94(February), 1–23. https://doi.org/10.1007/978-3-319-52977-6
Lorenz, A. (2016). The MOOC production fellowship: Reviewing the first German MOOC funding program. In M. Khalil, M. Ebner, M. Kopp, A. Lorenz, & M. Kalz (Eds.), The European Stakeholder Summit on Experiences and Best Practices in and around MOOCs (pp. 185–196).
Lu, O. H. T., Huang, J. C. H., Huang, A. Y. Q., & Yang, S. J. H. (2017). Applying learning analytics for improving students engagement and learning outcomes in an MOOCs enabled collaborative programming course. Interactive Learning Environments, 25(2), 220–234. https://doi.org/10.1080/10494820.2016.1278391
Maisarah, N., Khuzairi, S., & Cob, Z. C. (2021). A preliminary model of learning analytics to explore data visualization on educator’s satisfaction and academic performance in higher education. Springer International Publishing. https://doi.org/10.1007/978-3-030-90235-3
Mandinach, E. B., & Gummer, E. S. (2013). A systemic view of implementing data literacy in educator preparation. Educational Researcher, 42(1), 30–37. https://doi.org/10.3102/0013189X12459803
Margaryan, A., Bianco, M., & Littlejohn, A. (2015). Instructional quality of massive open online courses (MOOCs). Computers & Education, 80, 77–83.
Mcauley, A. A., Stewart, B., Siemens, G., & Cormier, D. (2010). The MOOC model for digital practice.
Meek, S. E. M., Blakemore, L., & Marks, L. (2017). Is peer review an appropriate form of assessment in a MOOC? Student participation and performance in formative peer review. Assessment and Evaluation in Higher Education, 42(6), 1000–1013. https://doi.org/10.1080/02602938.2016.1221052
Muljana, P. S., & Luo, T. (2020). Utilizing learning analytics in course design: Voices from instructional designers in higher education. Journal of Computing in Higher Education. https://doi.org/10.1007/s12528-020-09262-y
Murphy, M. P. A. (2020). COVID-19 and emergency eLearning: Consequences of the securitization of higher education for post-pandemic pedagogy. Contemporary Security Policy, 41(3), 492–505. https://doi.org/10.1080/13523260.2020.1761749
OECD. (2018). The future of education and skills: Education 2030. https://www.oecd.org/education/2030/E2030 Position Paper (05.04.2018).pdf
Picciano, A. G. (2012). The evolution of big data and learning analytics in American higher education. Journal of Asynchronous Learning Network, 16(3), 9–20. https://doi.org/10.24059/olj.v16i3.267
Prinsloo, P., & Slade, S. (2014). Educational triage in open distance learning: Walking a moral tightrope. International Review of Research in Open and Distance Learning, 15(4), 306–331. https://doi.org/10.19173/irrodl.v15i4.1881
Raffaghelli, J. E., & Stewart, B. (2020). Centering complexity in ‘educators’ data literacy’ to support future practices in faculty development: A systematic review of the literature. Teaching in Higher Education, 25(4), 435–455. https://doi.org/10.1080/13562517.2019.1696301
Rizvi, S., Rienties, B., Rogaten, J., & Kizilcec, R. F. (2020). Investigating variation in learning processes in a FutureLearn MOOC. Journal of Computing in Higher Education, 32(1), 162–181. https://doi.org/10.1007/s12528-019-09231-0
Romero, C., & Ventura, S. (2017). Educational data science in massive open online courses. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 7(1). https://doi.org/10.1002/widm.1187
Ruipérez-Valiente, J. A., Muñoz-Merino, P. J., Pijeira Díaz, H. J., Ruiz, J. S., & Kloos, C. D. (2017). Evaluation of a learning analytics application for open edX platform. Computer Science and Information Systems, 14(1), 51–73. https://doi.org/10.2298/CSIS160331043R
Sakala, L. C., & Chigona, W. (2020). How lecturers neutralize resistance to the implementation of learning management systems in higher education. Journal of Computing in Higher Education, 32(2), 365–388. https://doi.org/10.1007/s12528-019-09238-7
Shah, D. (2020). By the numbers: MOOCs in 2020. Class-Central. https://www.classcentral.com/report/mooc-stats-2020/
Shibani, A., Knight, S., & Buckingham Shum, S. (2020). Educator perspectives on learning analytics in classroom practice. Internet and Higher Education, 46(February), 100730. https://doi.org/10.1016/j.iheduc.2020.100730
Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380–1400. https://doi.org/10.1177/0002764213498851
Tsai, Y., & hsun, Lin, C. hung, Hong, J. chao, & Tai, K. hsin. (2018). The effects of metacognition on online learning interest and continuance to learn with MOOCs. Computers and Education, 121, 18–29. https://doi.org/10.1016/j.compedu.2018.02.011
Usher, M., Barak, M., & Haick, H. (2021a). Online vs. on-campus higher education: Exploring innovation in students' self-reports and students' learning products. Thinking Skills and Creativity, 42, 100965. https://doi.org/10.1016/j.tsc.2021.100965
Usher, M., Hershkovitz, A., & Forkosh‐Baruch, A. (2021b). From data to actions: Instructors' decision making based on learners' data in online emergency remote teaching. British Journal of Educational Technology, 52(4), 1338–1356. https://doi.org/10.1111/bjet.13108
Vanlommel, K., Van Gasse, R., Vanhoof, J., & Van Petegem, P. (2017). Teachers’ decision-making: Data based or intuition driven? International Journal of Educational Research, 83(March 1994), 75–83. https://doi.org/10.1016/j.ijer.2017.02.013
Vigentini, L., Clayphan, A., & Chitsaz, M. (2017). Dynamic dashboard for educators and students in FutureLearn MOOCs: Experiences and insights. CEUR Workshop Proceedings, 1967(March 2017), 20–35.
Wang, Q., & Woo, H. L. (2007). Comparing asynchronous online discussions and face-to-face discussions in a classroom setting. British Journal of Educational Technology, 38(2), 272–286. https://doi.org/10.1111/j.1467-8535.2006.00621.x
Watted, A., & Barak, M. (2018). Motivating factors of MOOC completers: Comparing between university-affiliated students and general participants. Internet and Higher Education, 37(June 2017), 11–20. https://doi.org/10.1016/j.iheduc.2017.12.001
Yulina, I. K., Permanasari, A., & Hernani, h., & Setiawan, W. (2019). Analytical thinking skill profile and perception of pre service chemistry teachers in analytical chemistry learning. Journal of Physics: Conference Series. https://doi.org/10.1088/1742-6596/1157/4/042046
Zhu, M., Sari, A. R., & Lee, M. M. (2020). A comprehensive systematic review of MOOC research: Research techniques, topics, and trends from 2009 to 2019. Educational Technology Research and Development, 68(4), 1685–1710. https://doi.org/10.1007/s11423-020-09798-x
Zimmerman, C., Dreisiebner, D., & Hofler, E. (2017). Designing a MOOC to foster critical thinking and its application in business education. International Journal for Business Education, 157(1). https://doi.org/10.30707/IJBE157.1.1648132890.935577
Zhu, M., & Bonk, C. J. (2019). Designing MOOCs to facilitate participant self-monitoring for self-directed learning. Online Learning, 23(4), 106–134. https://doi.org/10.24059/olj.v23i4.2037