Xu Wang
ABSTRACT
With the aim of better scaffolding discussion to improve learning in a MOOC context, this work investigates what kinds of discussion behaviors contribute to learning. We explored whether engaging in higher-order thinking behaviors results in more learning than paying general or focused attention to course materials. In order to evaluate whether to attribute the effect to engagement in the associated behaviors versus persistent characteristics of the students, we adopted two approaches. First, we used propensity score matching to pair students who exhibit a similar level of involvement in other course activities. Second, we explored individual variation in engagement in higher-order thinking behaviors across weeks. The results of both analyses support the attribution of the effect to the behavioral interpretation. A further analysis using LDA applied to course materials suggests that more social oriented topics triggered richer discussion than more biopsychology oriented topics.
- Allen, L. K., Snow, E. L., & McNamara, D. S. (2015). Are you reading my mind?: modeling students' reading comprehension skills with natural language processing techniques. In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge. ACM. DOI= http://dx.doi.org/10.1145/2723576.2723617. Google Scholar
Digital Library
- Anderson, A., Huttenlocher, D., Kleinberg, J., & Leskovec, J. (2014). Engaging with massive online courses. In Proceedings of the 23rd international conference on World wide web (pp. 687--698). International World Wide Web Conferences Steering Committee. DOI=http://dx.doi.org/10.1145/2566486.2568042 Google ScholarDigital Library
- Austin, P. C. (2011). An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate behavioral research, 46(3), 399--424. DOI=http://dx.doi.org/10.1080/00273171.2011.568786Google Scholar
- Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. the Journal of machine Learning research, 3, 993--1022. Google ScholarDigital Library
- Brinton, C. G., Chiang, M., Jain, S., Lam, H. K., Liu, Z., & Wong, F. M. F. (2014). Learning about social learning in MOOCs: From statistical analysis to generative model. Learning Technologies, IEEE Transactions on, 7(4), 346--359. DOI= http://dx.doi.org/10.1109/TLT.2014.2337900.Google ScholarCross Ref
- Brooks, C., Chavez, O., Tritz, J., & Teasley, S. (2015). Reducing selection bias in quasi-experimental educational studies. In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge (pp. 295--299). ACM. DOI= http://dx.doi.org/10.1145/2723576.2723614 Google ScholarDigital Library
- Chi, M. T. H., Siler, S., Jeong, H., Yamauchi, T., & Hausmann, R. G. (2001). Learning from human tutoring. Cognitive Science, 25, 471--533. DOI=http://dx.doi.org/10.1016/S0364-0213(01)00044-1Google ScholarCross Ref
- Chi, M. T., & Wylie, R. (2014). The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational Psychologist, 49(4), 219--243. DOI=http://dx.doi.org/10.1080/00461520.2014.965823Google ScholarCross Ref
- Cohen, E. G. (1994). Restructuring the classroom: Conditions for productive small groups. Review of Educational Research, 64, 1--35. DOI=http://dx.doi.org/10.3102/00346543064001001Google ScholarCross Ref
- Durik, A. M., & Harackiewicz, J. M. (2007). Different strokes for different folks: How individual interest moderates the effects of situational factors on task interest. Journal of Educational Psychology, 99(3), 597. DOI=http://dx.doi.org/10.1037/0022-0663.99.3.597Google ScholarCross Ref
- Ezen-Can, A., Boyer, K. E., Kellogg, S., & Booth, S. (2015). Unsupervised modeling for understanding MOOC discussion forums: a learning analytics approach. In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge (pp. 146--150). ACM. DOI=http://dx.doi.org/10.1145/2723576.2723589 Google ScholarDigital Library
- Ferguson, R., & Clow, D. (2015). Examining engagement: analysing learner subpopulations in massive open online courses (MOOCs). In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge. ACM. DOI= http://dx.doi.org/10.1145/2723576.2723606 Google ScholarDigital Library
- Fischer, F., Kollar, I., Stegmann, K., Wecker, C. & Zottmann, J. (2013). Collaboration scripts in computer-supported collaborative learning. The international handbook of collaborative learning, 403--419.Google Scholar
- Greer, J., & Mark, M. (2015). Evaluation Methods for Intelligent Tutoring Systems Revisited. International Journal of Artificial Intelligence in Education, 1--6.Google Scholar
- Hidi, S., & Renninger, K. A. (2006). The four-phase model of interest development. Educational psychologist, 41(2), 111--127. DOI=http://dx.doi.org/10.1207/s15326985ep4102_4Google Scholar
- Hsiao, I. H., & Awasthi, P. (2015). Topic facet modeling: semantic visual analytics for online discussion forums. In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge (pp. 231--235). ACM. DOI= http://dx.doi.org/10.1145/2723576.2723613 Google ScholarDigital Library
- Kizilcec, R. F., Piech, C., & Schneider, E. (2013). Deconstructing disengagement: analyzing learner subpopulations in massive open online courses. In Proceedings of the third international conference on learning analytics and knowledge (pp. 170--179). ACM. DOI= http://dx.doi.org/10.1145/2460296.2460330. Google ScholarDigital Library
- Klusener, M., & Fortenbacher, A. (2015). Predicting students' success based on forum activities in MOOCs. In Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, 2015. IEEE. DOI=http://dx.doi.org/10.1109/IDAACS.2015.7341439Google ScholarDigital Library
- Koedinger, K. R., Kim, J., Jia, J. Z., McLaughlin, E. A., & Bier, N. L. (2015, March). Learning is Not a Spectator Sport: Doing is Better than Watching for Learning from a MOOC. In Proceedings of the Second (2015) ACM Conference on Learning @ Scale (pp. 111--120). ACM. DOI=http://dx.doi.org/10.1145/2724660.2724681 Google ScholarDigital Library
- Koedinger, K. R., McLaughlin, E. A., Jia, J. Z., & Bier, N. L. (2016). Is the Doer Effect a Causal Relationship? How Can We Tell and Why It's Important. In Proceedings of the 6th International Conference on Learning, Analytics and Knowledge. Google ScholarDigital Library
- McKendree, J., Stenning, K., Mayes, T., Lee, J., & Cox, R. (1998). Why observing a dialogue may benefit learning. Journal of Computer Assisted Learning, 14(1), 110--119. DOI= http://dx.doi.org/10.1046/j.1365-2729.1998.1420110.xGoogle ScholarCross Ref
- McNamara, D. S., Kintsch, E., Songer, N. B., & Kintsch, W. (1996). Are good texts always better? Interactions of text coherence, background knowledge, and levels of understanding in learning from text. Cognition and instruction, 14(1), 1--43. DOI=http://dx.doi.org/10.1207/s1532690xci1401_1Google Scholar
- Robinson, A. (2015). Exploring Class Discussions from a Massive Open Online Course (MOOC) on Cartography. In J. Brus et al. (eds.), Modern Trends in Cartography, Lecture Notes in Geoinformation and Cartography. DOI=http://dx.doi.org/10.1007/978-3-319-07926-4_14Google ScholarCross Ref
- Rosé, C. P., Carlson, R., Yang, D., Wen, M., Resnick, L., Goldman, P., & Sherer, J. (2014, March). Social factors that contribute to attrition in moocs. In Proceedings of the first ACM conference on Learning @ scale conference. ACM. DOI= http://dx.doi.org/10.1145/2556325.2567879 Google ScholarDigital Library
- Rosenbaum, P. R., & Rubin, D. B. (1985). Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. The American Statistician, 39(1), 33--38. DOI=http://dx.doi.org/10.1080/00031305.1985.10479383Google ScholarCross Ref
- Wang, X., Yang, D., Wen, M., Koedinger, K., & Rosé, C. P. (2015). Investigating how student's cognitive behaviors in MOOC discussion forums affect learning gains. In Proceedings of the 8th International Conference on Educational Data Mining.Google Scholar
- Zhu, E. (2006). Interaction and cognitive engagement: An analysis of four asynchronous online discussions. Instructional Science, 34(6), 451--480.Google ScholarCross Ref
Index Terms
- Towards triggering higher-order thinking behaviors in MOOCs
Recommendations
Using learning analytics to explore help-seeking learner profiles in MOOCs
LAK '17: Proceedings of the Seventh International Learning Analytics & Knowledge ConferenceIn online learning environments, learners are often required to be more autonomous in their approach to learning. In scaled online learning environments, like Massive Open Online Courses (MOOCs), there are differences in the ability of learners to ...
Crowd-sourced learning in MOOCs: learning analytics meets measurement theory
LAK '15: Proceedings of the Fifth International Conference on Learning Analytics And KnowledgeThis paper illustrated the promise of the combination of measurement theory and learning analytics for understanding effective MOOC learning. It reports findings from a study of whether and how MOOC log file data can assist in understanding how MOOC ...
Moving Through MOOCS: Pedagogy, Learning Design and Patterns of Engagement
Design for Teaching and Learning in a Networked WorldAbstractMassive open online courses (MOOCs) are part of the lifelong learning experience of people worldwide. Many of these learners participate fully. However, the high levels of dropout on most of these courses are a cause for concern. Previous studies ...
Comments