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Learning management system and course influences on student actions and learning experiences

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Abstract

From massive open online courses (MOOC) to the smaller scale use of learning management systems, many students interact with online platforms that are meant to support learning. Investigations into the use of these systems have considered how well students learn when certain approaches are employed. However, we do not fully understand how course type or system design influence student actions and experiences, meaning prior findings cannot be properly interpreted and used because we do not understand how these factors influence those findings. Accordingly, we conducted a study to compare student experiences and behaviors across learning management systems and courses from a learning analytics perspective. Differences in student behaviors and experiences highlight how system design and the nature of the course interact: Students reported increased learning support when using a system that foregrounds student interaction through discussion forums, but this relationship did not hold across all course types. Similarly, students from the content-delivery focused system spent more time online while feeling less supported regardless of which type of course they were taking. This newly found evidence for the often-interrelated influence that the course and system have on student experiences and behaviors should therefore be considered when selecting a system to meet particular pedagogical goals.

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References

  • Akcayir, G., Chen, Z., Demmans Epp, C., et al. (2020). Two case studies of online discussion use in computer science education: deep vs. shallow integration and recommendations. In L. Wilton, C. Brett (Eds.) Handbook of Research on Online Discussion-Based Teaching Methods: IGI Global, (pp. 409–434).

  • Akyol, Z., & Garrison, D. R. (2008). The development of a community of inquiry over time in an online course: Understanding the progression and integration of social, cognitive and teaching presence. Journal of Asynchronous Learning Networks, 12, 3–22.

    Google Scholar 

  • Alhabeeb, A., & Rowley, J. (2018). E-learning critical success factors: Comparing perspectives from academic staff and students. Computers & Education, 127, 1–12. https://doi.org/10.1016/j.compedu.2018.08.007.

    Article  Google Scholar 

  • Back, D. A., Behringer, F., Haberstroh, N., et al. (2016). Learning management system and e-learning tools: an experience of medical students’ usage and expectations. Int J Med Educ, 7, 267–273. https://doi.org/10.5116/ijme.57a5.f0f5.

    Article  Google Scholar 

  • Baglione, S. L., & Nastanski, M. (2007). The Superiority of online discussion: Faculty perceptions. Quarterly Review of Distance Education, 8, 139–150.

    Google Scholar 

  • Baikadi, A., Demmans Epp, C., & Schunn, C. D. (2018). Participating by activity or by week in MOOCs. Information and Learning Science. https://doi.org/10.1108/ILS-04-2018-0033.

    Article  Google Scholar 

  • Baikadi, A., Schunn, C. D., Long, Y., & Demmans Epp, C. (2016). Redefining “What” in analyses of who does what in MOOCs. 9th International Conference on Educational Data Mining (EDM 2016) (pp. 569–570). USA: International Educational Data Mining Society (IEDMS), Raleigh, NC.

    Google Scholar 

  • Bakhshinategh, B., Zaiane, O. R., ElAtia, S., & Ipperciel, D. (2017). Educational data mining applications and tasks: A survey of the last 10 years. Education and Information Technologies, 23, 537–553. https://doi.org/10.1007/s10639-017-9616-z.

    Article  Google Scholar 

  • Barokas, J., Ketterl, M., Brooks, C., & Greer, J. (2010). Lecture capture: Student perceptions, expectations, and behaviors. In J. Sanchez & K. Zhang (Eds.), World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 424–431). Florida, USA: Orlando.

    Google Scholar 

  • Bateman, S., Farzan, R., Brusilovsky, P., McCalla, G. (2006). OATS: The open annotation and tagging system. In 3rd annual international scientific conference of the learning object repository research network. (p. 10). Montreal, Canada.

  • Blackburn, G. (2017). A university’s strategic adoption process of an PBL-aligned eLearning environment: An exploratory case study. Educational Technology Research and Development, 65, 147–176. https://doi.org/10.1007/s11423-016-9472-3.

    Article  Google Scholar 

  • Boroujeni, M. S., Dillenbourg, P. (2018). Discovery and temporal analysis of latent study patterns in MOOC interaction sequences. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge. (pp. 206–215). New York: ACM.

  • Brooks, C., Demmans Epp, C., Logan, G., & Greer, J. (2011). The who, what, when, and why of lecture capture. Learning Analytics and Knowledge (pp. 86–92). Banff, Canada: ACM Press.

    Google Scholar 

  • Brooks, C., Erickson, G., Greer, J., & Gutwin, C. (2014). Modelling and quantifying the behaviours of students in lecture capture environments. Computers & Education, 75, 282–292. https://doi.org/10.1016/j.compedu.2014.03.002.

    Article  Google Scholar 

  • Brooks, C., Greer, J., & Gutwin, C. (2014). The Data-Assisted Approach to Building Intelligent Technology-Enhanced Learning Environments. In J. A. Larusson & B. White (Eds.), Learning Analytics (pp. 123–156). New York, New York, NY: Springer.

    Chapter  Google Scholar 

  • Brooks, C., Hansen, C., Greer, J. (2006). social awareness in the ihelp courses learning content management system. In workshop on social navigation and community-based adaptation technologies, fourth international conference on adaptive hypermedia and adaptive web-based systems (AH). Dublin, Ireland.

  • Buckingham Shum, S. (2018). Transitioning education’s knowledge infrastructure: Shaping design or shouting from the touchline?

  • Bull, S., Ginon, B., Boscolo, C., Johnson, M. (2016). Introduction of learning visualisations and metacognitive support in a persuadable open learner model. In Learning Analytics and Knowledge (pp. 30–39). ACM Press.

  • Bull, S., Greer, J. E., McCalla, G. I., Kettel, L. (2001). Help-seeking in an asynchronous help forum. In Proceedings of Workshop on Help Provision and Help Seeking in Interactive Learning Environments, International Conference on Artificial Intelligence in Education (AIED). San Antonio.

  • Chandrasekaran, M. K., Demmans Epp, C., Kan, M.-Y., & Litman, D. (2017). Using Discourse Signals for Robust Instructor Intervention Prediction. Thirty-First AAAI Conference on Artificial Intelligence (AAAI) (pp. 3415–3421). San Francisco: CA, USA.

    Google Scholar 

  • Chandrasekaran, M.K., Kan, M. -Y., Tan, B.C.Y., Ragupathi, K. (2015). Learning instructor intervention from MOOC forums: Early Results and Issues. In Educational Data Mining (EDM) (pp 218–225).

  • Charmaz, K. (2010). Constructing grounded theory. Thousand Oaks, Calif: Sage Publications, London.

    Google Scholar 

  • Chaw, L. Y., & Tang, C. M. (2018). What makes learning management systems effective for learning? Journal of Educational Technology Systems, 47, 152–169. https://doi.org/10.1177/0047239518795828.

    Article  Google Scholar 

  • Chen, Z., & Demmans Epp, C. (2020). CSCLRec: Personalized Recommendation of Forum Posts to Support Socio-collaborative Learning. In A. N. Rafferty, J. Whitehill, V. Cavalli-Sforza, & C. Romero (Eds.), Thirteenth International Conference on Educational Data Mining (EDM 2020) (pp. 364–373). Fully Virtual: International Educational Data Mining Society.

    Google Scholar 

  • Chipps, J., Kerr, J., Brysiewicz, P., & Walters, F. (2015). A survey of university students’ perceptions of learning management systems in a low-resource setting using a technology acceptance. Model: CIN, 33, 71–77. https://doi.org/10.1097/CIN.0000000000000123.

    Article  Google Scholar 

  • Cho, J., Demmans Epp, C. (2019). Improving the classroom community scale: Toward a short-form of the CCS. In American Educational Research Association (AERA) Annual Meeting. AERA, Toronto.

  • Coates, H., James, R., & Baldwin, G. (2005). A critical examination of the effects of learning management systems on university teaching and learning. Tertiary Education and Management, 11, 19–36.

    Article  Google Scholar 

  • Coopman, S. J. (2009). A critical examination of Blackboard’s e-learning environment. First Monday. https://doi.org/10.5210/fm.v14i6.2434.

    Article  Google Scholar 

  • Crossley, S., Paquette, L., Dascalu, M., et al. (2016). Combining click-stream data with NLP tools to better understand MOOC completion. In Learning Analytics & Knowledge (pp 6–14). ACM.

  • Dawson, S., McWilliam, E., & Tan, J. P.-L. (2008). Teaching smarter: how mining ICT data can inform and improve learning and teaching practice. Annual Conference of the Australasian Society for Computers in Learning in Tertiary Education (pp. 221–230). Australia: Melbourne.

    Google Scholar 

  • Demmans Epp, C., & Bull, S. (2015). Uncertainty representation in visualizations of learning analytics for learners: Current approaches and opportunities. IEEE TLT, 8, 242–260. https://doi.org/10.1109/TLT.2015.2411604.

    Article  Google Scholar 

  • Demmans Epp, C., Mancilla, R., & Swigart, V. (2018). Language MOOCs: The relationship between student knowledge within and outside an English for specific purposes MOOC. In S. Link & J. Li (Eds.), Assessment Across Online Language Education (pp. 71–90). Bristol, CT, USA: Equinox.

    Google Scholar 

  • Demmans Epp, C., McEwen, R., Campigotto, R., & Moffatt, K. (2015). Information practices and user interfaces: Student use of an iOS application in special education. Education and Information Technologies, 21, 1–24. https://doi.org/10.1007/s10639-015-9392-6.

    Article  Google Scholar 

  • Demmans Epp, C., Phirangee, K., Despres-Bedward, A., Wang, L. (2017a). Resourceful instructors and students: Overcoming barriers to integrating mobile tools. In R. Power, M. Ally, D. Cristol, Palalas, Agnieszka (Ed.) IAmLearning: Mobilizing and Supporting Educator Practice. IAmLearn (p. E-book).

  • Demmans Epp, C., Phirangee, K., Hewitt, J. (2017b). Talk with me: Student behaviours and pronoun use as indicators of discourse health across facilitation methods. Journal of Learning Analytics, 4, 47–75, https://doi.org/10.18608/jla.2017.43.4.

  • Demmans Epp, C., Phirangee, K., Hewitt, J. (2017c) Student actions and community in online courses: The roles played by course length and facilitation method. Online Learning, 21, 53–77. https://doi.org/10.24059/olj.v21i4.1269.

  • Derks, D., Fischer, A. H., & Bos, A. E. R. (2008). The role of emotion in computer-mediated communication: A review. Computers in Human Behavior, 24, 766–785. https://doi.org/10.1016/j.chb.2007.04.004.

    Article  Google Scholar 

  • Devers, K. J., & Frankel, R. M. (2000). Study design in qualitative research–2: Sampling and data collection strategies. Education for Health: Change in Learning & Practice, 13, 263–271. https://doi.org/10.1080/13576280050074543.

    Article  Google Scholar 

  • Dix, A. (2016). Challenge and potential of fine grain, cross-institutional learning data. In Proceedings of the Third (2016) ACM Conference on Learning @ Scale (pp 261–264). New York: ACM.

  • Dowell, N., Lin, Y., Godfrey, A., Brooks, C. (2019). Promoting inclusivity through time-dynamic discourse analysis in digitally-mediated collaborative learning. In S. Isotani, E. Millán, A. Ogan, et al. (Ed.), Artificial Intelligence in Education (pp. 207–219). Springer International Publishing.

  • Dowell, N. M. M., Brooks, C., Kovanović, V., et al. (2017). The changing patterns of MOOC discourse. In Proceedings of the fourth ACM conference on learning @ scale (pp. 283–286). New York: ACM.

  • Dowell, N. M. M., & Graesser, A. C. (2014). Modeling learners’ cognitive, affective, and social processes through language and discourse. Journal of Learning Analytics, 1, 183–186.

    Article  Google Scholar 

  • Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39, 175–191. https://doi.org/10.3758/BF03193146.

    Article  Google Scholar 

  • Ferguson, R., Buckingham Shum, S. (2012). Towards a social learning space for open educational resources. In A. Okada, T. Connolly, P. J. Scott (Ed.), Collaborative learning 2.0: open educational resources (pp. 309–327). IGI Global.

  • Fowler, F. J. (2009). Survey research methods (4th ed.). Thousand Oaks: Sage Publications.

    Google Scholar 

  • Gao, F., Zhang, T., & Franklin, T. (2013). Designing asynchronous online discussion environments: Recent progress and possible future directions. British Journal of Educational Technology, 44, 469–483. https://doi.org/10.1111/j.1467-8535.2012.01330.x.

    Article  Google Scholar 

  • Garrison, D. R., Anderson, T., & Archer, W. (1999). Critical inquiry in a text-based environment: computer conferencing in higher education. The Internet and Higher Education, 2, 87–105. https://doi.org/10.1016/S1096-7516(00)00016-6.

    Article  Google Scholar 

  • Gillani, B. B. (2000). Using the Web to Create Student Centred Curriculum. In R. A. Cole (Ed.), Issues in Web-based pedagogy: a critical primer (pp. 161–182). Westport, Conn: Greenwood Press.

    Google Scholar 

  • Greer, J., & Mark, M. (2015). Evaluation methods for intelligent tutoring systems revisited. International Journal of Artificial Intelligence in Education, 26, 387–392. https://doi.org/10.1007/s40593-015-0043-2.

    Article  Google Scholar 

  • Guo, C., Chen, X., Hou, Y. (2019). A case study of students’ participation and knowledge construction in two online discussion settings. In Proceedings of the 2019 4th International Conference on Distance Education and Learning - ICDEL 2019 (pp 45–49). Shanghai: ACM Press.

  • Hara, N., Bonk, C. J., & Angeli, C. (2000). Content analysis of online discussion in an applied educational psychology course. Instructional Science, 28, 115–152. https://doi.org/10.1023/A:1003764722829.

    Article  Google Scholar 

  • Haythornthwaite, C., Kazmer, M. M., Robins, J., & Shoemaker, S. (2006). Community development among distance learners: temporal and technological dimensions. Journal of Computer-Mediated Communication, https://doi.org/10.1111/j.1083-6101.2000.tb00114.x.

    Article  Google Scholar 

  • Hew, K. F. (2014). Student perceptions of peer versus instructor facilitation of asynchronous online discussions: Further findings from three cases. Instructional Science, 43, 19–38. https://doi.org/10.1007/s11251-014-9329-2.

    Article  Google Scholar 

  • Hewitt, J. (2001). Beyond threaded discourse. International Journal of Educational Telecommunications, 7, 207–221.

    Google Scholar 

  • Hewitt, J. (2005). Toward an understanding of how threads die in asynchronous computer conferences. The Journal of the Learning Sciences, 14, 567–589.

    Article  Google Scholar 

  • IMS Global Learning Consortium. (2016). Learning tools interoperability (LTI). Version, 1, 3.

    Google Scholar 

  • Ishola, O. M., McCalla, G. (2017). Predicting prospective peer helpers to provide just-in-time help to users in question and answer forums. International Educational Data Mining Society.

  • Islam, A. K. M. N., & Azad, N. (2015). Satisfaction and continuance with a learning management system: Comparing perceptions of educators and students. International Journal of Information and Learning Technology, 32, 109–123. https://doi.org/10.1108/IJILT-09-2014-0020.

    Article  Google Scholar 

  • Kacewicz, E., Pennebaker, J. W., Davis, M., et al. (2014). Pronoun Use Reflects Standings In Social Hierarchies. Journal of Language and Social Psychology, 33, 125–143. https://doi.org/10.1177/0261927X13502654.

    Article  Google Scholar 

  • Koedinger, K.R., Kim. J., Jia, J.Z., et al. (2015). Learning is not a spectator sport: Doing is better than watching for learning from a MOOC. In Learning @ Scale (pp. 111–120). ACM Press.

  • Larmuseau, C., Elen, J., Depaepe, F. (2018). The influence of students’ cognitive and motivational characteristics on students’ use of a 4C/ID-based online learning environment and their learning gain. In Proceedings of the 8th International Conference on Learning Analytics and Knowledge (pp. 171–180). New York: ACM.

  • Larson, B. E., & Keiper, T. A. (2002). Classroom discussion and threaded electronic discussion: Learning in two arenas. Contemporary Issues in Technology and Teacher Education, 2, 45–62.

    Google Scholar 

  • Lebis, A., Lefevre, M., Luengo, V., Guin, N. (2018). Capitalisation of analysis processes: enabling reproducibility, openness and adaptability thanks to narration. In Proceedings of the 8th international conference on learning analytics and knowledge (pp. 245–254). New York: ACM.

  • Lin, Y., Dowell, N., Godfrey, A., et al. (2019). Modeling gender dynamics in intra and interpersonal interactions during online collaborative learning. In Proceedings of the 9th international conference on learning analytics & knowledge. (pp. 431–435). New York: ACM.

  • MacFadden, R. J. (2005). Souls on ice: Incorporating emotion in web-based education. Journal of Technology in Human Services, 23, 79–98. https://doi.org/10.1300/J017v23n01_06.

    Article  Google Scholar 

  • Makos, A., Zingaro, D., Oztok, M., Hewitt, J. (2014). Examining the qualities of liked notes versus non-liked notes in a collaborative online learning environment. In American education research association annual (AERA) annual conference Philadelphia.

  • Marbouti, F., & Wise, A. F. (2016). Starburst: a new graphical interface to support purposeful attention to others’ posts in online discussions. Educational Technology Research and Development, 64, 87–113. https://doi.org/10.1007/s11423-015-9400-y.

    Article  Google Scholar 

  • Marshall, F., Jiang, W., Dennen, V. (2015). An examination of the usefulness of a learning community system and blackboard in both online and face-to-face courses. In E-Learn: World conference on e-learning in corporate, government, healthcare, and higher education (pp. 1136-1141).

  • Mash, R. J., Marais, D., Van Der Walt, S., et al. (2005). Assessment of the quality of interaction in distance learning programmes utilising the Internet (WebCT) or interactive television (ITV). Medical Education, 39, 1093–1100. https://doi.org/10.1111/j.1365-2929.2005.02315.x.

    Article  Google Scholar 

  • Nelson, K. (2015). Using K-means clustering to model Students’ LMS participation in traditional courses. Issues in Information Systems, 16, 102–110.

    Google Scholar 

  • Nguyen, Q., Huptych, M., Rienties, B. (2018). Linking students’ timing of engagement to learning design and academic performance. In Proceedings of the 8th international conference on learning analytics and knowledge (pp. 141–150). New York: ACM.

  • Norman, D. A. (2002). The design of everyday things, 1st Basic paperback. New York: Basic Books.

    Google Scholar 

  • Nulty, D. D. (2008). The adequacy of response rates to online and paper surveys: What can be done? Assessment & Evaluation in Higher Education, 33, 301–314. https://doi.org/10.1080/02602930701293231.

    Article  Google Scholar 

  • Oblinger, D., Oblinger, J. L., & Lippincott, J. K. (Eds.). (2005). Educating the net generation. Boulder CO: EDUCAUSE.

    Google Scholar 

  • Ocheja, P., Flanagan, B., Ogata, H. (2018) Connecting decentralized learning records: A blockchain based learning analytics platform. In Proceedings of the 8th international conference on learning analytics and knowledge (pp. 265–269). New York: ACM.

  • Ouyang, J.R., Stanley, N. (2014). Theories and research in educational technology and distance learning instruction through blackboard. Universal Journal of Educational Research, 2, 161–172. https://doi.org/10.13189/ujer.2014.020208.

  • Paramythis, A., Weibelzahl, S., & Masthoff, J. (2010). Layered evaluation of interactive adaptive systems: Framework and formative methods. User Modeling and User-Adapted Interaction (UMUAI), 20, 383–453. https://doi.org/10.1007/s11257-010-9082-4.

    Article  Google Scholar 

  • Phirangee, K., Demmans Epp, C., Hewitt. J. (2016). Exploring the relationships between facilitation methods, students’ sense of community and their online behaviours. Special Issue on Online Learning Analytics Online Learning Journal, 20, 134–154. https://doi.org/10.24059/olj.v20i2.775.

  • Phirangee, K., & Hewitt, J. (2016). Loving this dialogue!!!!: Expressing emotion through the strategic manipulation of limited non-verbal cues in online learning environments. In S. Y. Tettegah & M. P. McCreery (Eds.), Emotions, technology, and learning (pp. 69–85). New York: Elsevier.

    Chapter  Google Scholar 

  • Poquet, O., Lim, L., Mirriahi, N., Dawson, S. (2018). Video and learning: A systematic review (2007–2017). In Proceedings of the 8th international conference on learning analytics and knowledge (pp. 151–160). New York: ACM.

  • Poulova, P., Simonova, I., & Manenova, M. (2015). Which one, or another? Comparative analysis of selected LMS. Procedia - Social and Behavioral Sciences, 186, 1302–1308. https://doi.org/10.1016/j.sbspro.2015.04.052.

    Article  Google Scholar 

  • Rafi, A., Samsudin, K., Hanafi, H. F. (2015). Differences in perceived benefit, use, and learner satisfaction between open source LMS and proprietary LMS. In B. Gradinarova (Ed.) E-Learning - Instructional Design, Organizational Strategy and Management InTech.

  • Rahmani Hanzaki, M., & Demmans Epp, C. (2018). The Effect of Personality and Course Attributes on Academic Performance in MOOCs. In V. Pammer-Schindler, M. Pérez-Sanagustín, H. Drachsler, et al. (Eds.), Lifelong Technology-Enhanced Learning (pp. 497–509). Cham: Springer International Publishing.

    Chapter  Google Scholar 

  • Richardson, J. C., Maeda, Y., Lv, J., & Caskurlu, S. (2017). Social presence in relation to students’ satisfaction and learning in the online environment: A meta-analysis. Computers in Human Behavior, 71, 402–417. https://doi.org/10.1016/j.chb.2017.02.001.

    Article  Google Scholar 

  • Roll, I., Russell, D. M., & Gašević, D. (2018). Learning at scale. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-018-0170-7.

    Article  Google Scholar 

  • Rosé, C. P., & Ferschke, O. (2016). Technology support for discussion based learning: from computer supported collaborative learning to the future of massive open online courses. International Journal of Artificial Intelligence in Education, 26, 660–678. https://doi.org/10.1007/s40593-016-0107-y.

    Article  Google Scholar 

  • Rourke, L., Anderson, T., Garrison, D. R., & Archer, W. (1999). Assessing social presence in asynchronous text­based computer conferencing. International Journal of E-Learning and Distance Education, 14, 50–71.

    Google Scholar 

  • Rovai, A. P. (2002). Development of an instrument to measure classroom community. The Internet and Higher Education, 5, 197–211. https://doi.org/10.1016/S1096-7516(02)00102-1.

    Article  Google Scholar 

  • Rovai, A. P., & Wighting, M. J. (2005). Feelings of alienation and community among higher education students in a virtual classroom. The Internet and Higher Education, 8, 97–110. https://doi.org/10.1016/j.iheduc.2005.03.001.

    Article  Google Scholar 

  • Sadera, W. A., Robertson, J., Song, L., & Midon, M. N. (2009). The Role of community in online learning success. Journal of Online Learning and Teaching, 5, 277–284.

    Google Scholar 

  • Savolainen, R. (2009). Information use and information processing: Comparison of conceptualizations. Journal of Documentation, 65, 187–207. https://doi.org/10.1108/00220410910937570.

    Article  Google Scholar 

  • Scardamalia, M., & Bereiter, C. (2008). Pedagogical biases in educational technologies. Educational Technology, 48, 3–11.

    Google Scholar 

  • Scardamalia, M., Bereiter, C. (2010). A brief history of knowledge building. Canadian Journal of Learning and Technology, 36.

  • Sentz, J., Stefaniak, J., Baaki, J., & Eckhoff, A. (2019). How do instructional designers manage learners’ cognitive load? An examination of awareness and application of strategies. Educational Technology Research and Development, 67, 199–245. https://doi.org/10.1007/s11423-018-09640-5.

    Article  Google Scholar 

  • Shackelford, J. L., & Maxwell, M. (2012). Contribution of learner-instructor interaction to sense of community in graduate online education. Journal of Online Learning and Teaching, 8, 248–260.

    Google Scholar 

  • Srba, I., Savic, M., Bielikova, M., et al. (2019). Employing community question answering for online discussions in university courses: Students’ perspective. Computers & Education, 135, 75–90. https://doi.org/10.1016/j.compedu.2019.02.017.

    Article  Google Scholar 

  • Suthers, D. D., Vatrapu, R., Medina, R., et al. (2008). Beyond threaded discussion: Representational guidance in asynchronous collaborative learning environments. Computers & Education, 50, 1103–1127. https://doi.org/10.1016/j.compedu.2006.10.007.

    Article  Google Scholar 

  • Swan, K. (2003). Learning effectiveness: What the research tells us. In J. Bourne, Sloan Consortium (Ed.) Elements of quality online education: practice and direction. Sloan Consortium (pp. 13–45). Needham, Mass.

  • Tan, Y., & Quintana, R. M. (2019). What can we learn about learner interaction when one course is hosted on two MOOC platforms? Companion Proceedings to the International Conference on Learning Analytics and Knowledge (LAK) (pp. 155–156). Tempe, Arizona: SoLAR.

    Google Scholar 

  • Teplovs, C. (2008). The knowledge space visualizer: A tool for visualizing online discourse. In Common Framework for CSCL Interaction Analysis Workshop at the International Conference of the Learning SciCces (ICLS). Utrecht: International Society of the Learning Sciences.

  • Thomas, M. J. W. (2002). Learning within incoherent structures: The space of online discussion forums. Journal of Computer Assisted Learning, 18, 351–366. https://doi.org/10.1046/j.0266-4909.2002.03800.x.

    Article  Google Scholar 

  • Vassileva, J., & Sun, L. (2008). Evolving a social visualization design aimed at increasing participation in a class-based online community. International Journal of Cooperative Information Systems, 17, 443–466. https://doi.org/10.1142/S0218843008001932.

    Article  Google Scholar 

  • Vrasidas, C., & Zembylas, M. (2003). The nature of technology-mediated interaction in globalized distance education. International Journal of Training and Development, 7, 271–286.

    Article  Google Scholar 

  • Vygotsky, L. S. (1978). Mind in society: the development of higher psychological processes. Cambridge, MA, USA: Harvard University Press.

    Google Scholar 

  • Walker, D. S., Lindner, J. R., Murphrey, T. P., & Dooley, K. (2016). Learning management system usage: perspectives from university instructors. Quarterly Review of Distance Education, 12, 41–50.

    Google Scholar 

  • Wang, X., Yan, D., Wen, M., et al. (2015). Investigating how student’s cognitive behavior in MOOC discussion forums affect learning gains. In 8th international conference on educational data mining (EDM) (pp. 226–233).

  • Whitmer, J., Nuñez, N., Harfield, T., Forteza, D. (2016). Patterns in blackboard learn tool use: Five course design archetypes. Blackboard.

  • Wilcox, D., Thall, J., & Griffin, O. (2016). One Canvas, Two Audiences: How Faculty and Students use a Newly Adopted Learning Management System. Society for Information Technology & Teacher Education International Conference (pp. 1163–1168). USA: Association for the Advancement of Computing in Education (AACE), Chesapeake, VA.

    Google Scholar 

  • Williams, K. M., Stafford, R. E., Corliss, S. B., & Reilly, E. D. (2018). Examining student characteristics, goals, and engagement in massive open online courses. Computers & Education, 126, 433–442. https://doi.org/10.1016/j.compedu.2018.08.014.

    Article  Google Scholar 

  • Wise, A. F., & Cui, Y. (2018). Learning communities in the crowd: Characteristics of content related interactions and social relationships in MOOC discussion forums. Computers & Education, 122, 221–242. https://doi.org/10.1016/j.compedu.2018.03.021.

    Article  Google Scholar 

  • Wise, A. F., Hausknecht, S. N., & Zhao, Y. (2014). Attending to others’ posts in asynchronous discussions: Learners’ online “listening” and its relationship to speaking. International Journal of Computer-Supported Collaborative Learning, 9, 185–209. https://doi.org/10.1007/s11412-014-9192-9.

    Article  Google Scholar 

  • Wise, A. F., Hsiao, Y.-T., Marbouti, F., & Zhao, Y. (2012). Tracing Ideas and Participation in an Asynchronous Online Discussion across Individual and Group Levels over Time - Raw research notes and article annotations. International Conference of the Learning Sciences (ICLS) (pp. 431–435). Sydney, Australia: International Society of the Learning Sciences.

    Google Scholar 

  • Xing, W., & Gao, F. (2018). Exploring the relationship between online discourse and commitment in Twitter professional learning communities. Computers & Education, 126, 388–398. https://doi.org/10.1016/j.compedu.2018.08.010.

    Article  Google Scholar 

  • Young, S., & Bruce MAl,. (2011). Classroom community and student engagement in online courses. Journal of Online Learning and Teaching, 7, 219–230.

    Google Scholar 

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Acknowledgements

Part of this research was supported by the University of Pittsburgh through a joint project of the Learning Research and Development Center and the University Center for Teaching and Learning. The first author held Ontario Graduate Scholarships, A W. Garfield Weston Fellowship, and Walter C. Sumner Memorial Fellowships during data collection. During the analysis stage, this work received financial support from the Social Sciences and Humanities Research Council of Canada.

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Demmans Epp, C., Phirangee, K., Hewitt, J. et al. Learning management system and course influences on student actions and learning experiences. Education Tech Research Dev 68, 3263–3297 (2020). https://doi.org/10.1007/s11423-020-09821-1

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