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Comunicar Journal 75: Youth, gender identity and power in digital platforms (Vol. 31 - 2023)

The student self-assessment paradigm in MOOC: An example in Chinese higher education


Tingting Duan

Binghui Wu


Although scholars have proposed many types of self-assessment methods. There are still many teachers in China who consider that student self-assessment is “difficult to implement”. This paper aims to optimize the assessment of MOOC learning, and to establish an integrated student self-assessment paradigm with “student-centered, teacher, and peer auxiliary”. We started by selecting nine key factors that influence the implementation of self-assessment in MOOCs. Then, we clarified the relationship between the nine factors by using the interpretative structure model (ISM) and the MICMAC analysis, and a six-level paradigm of integrated student self-assessment was established. Moreover, we put forward the following suggestions to optimize student self-assessment in MOOC learning. First, it’s necessary to consider student self-assessment in MOOCs as a formative assessment method. Second, universities should enhance student awareness of self-assessment through publicity. Third, institutions of higher education could set up assessment courses to enhance the quality of assessment of students. Fourth, schools should optimize the environment of student self-assessment with the help of technology. This study is of great significance for students to make self-assessment become the basis of online learning and thus perfect the research on MOOC learning.


MOOC, MOOC learning, self-assessment, interpretative structure, lifelong learning, cognitive learning

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Abbas, H., Mehdi, M., Azad, I., & Frederico, G.F. (2022). Modelling the abstract knots in supply chains using interpretive structural modeling (ISM) approaches: A review-based comprehensive toolkit. Benchmarking: An International Journal, 29(10), 3251-3274. https://doi.org/10.1108/BIJ-08-2021-0459

Admiraal, W., Huisman, B., & Pilli, O. (2015). Assessment in massive open online courses. Electron. J. e Learn. 13(4), 207- 216.

Alonso-Tapia, J., & Panadero, E. (2010). Effects of Self-assessment Scripts on Self-regulation and Learning. Infancia y Aprendizaje, 33(3), 385-397. https://doi.org/10.1174/021037010792215145

Andrade, H.L., & Du, Y. (2007). Student responses to criteria referenced self-assessment. Assessment & Evaluation in Higher Education, 32(2), 159-181. https://doi.org/10.1080/02602930600801928

Ashton, S., & Davies, R.S. (2015). Using scaffolded rubrics to improve peer assessment in a MOOC writing course. Distance Education, 36(3), 312-334. https://doi.org/10.1080/01587919.2015.1081733

Barak, M., & Rafaeli, S. (2004). Online question-posing and peer-assessment as means for web-based knowledge sharing in learning. International Journal of Human-Computer Studies, 61(1), 84-103. https://doi.org/10.1016/j.ijhcs.2003.12.005

Bayne, S., & Ross, J. (2013). The pedagogy of the Massive Open Online Course: The UK view. The report, UK. https://bit.ly/3YdUFYd

Beg, A., Alhemeiri, M., & Beg, A. (2020). A tool for facilitating the automated assessment of engineering/science courses. The International Journal of Electrical Engineering & Education. https://doi.org/10.1177/0020720920953134

Boud, D., & Brew, A. (1995). Developing a typology for learner self-assessment practices. Research and Development in Higher Education, 18, 130-135. https://bit.ly/3uG0iRx

Boud, D., & Falchikov, N. (1989). Quantitative studies of student self-assessment in higher education: A critical analysis of findings. Higher Education, 18, 529-549. https://doi.org/10.1007/BF00138746

Brown, G.T.L., & Harris, L.R. (2014). The future of self-assessment in classroom practice: Reframing self-assessment as a core competency. Frontline Learning Research, 3(11), 22-30. https://doi.org/10.14786/flr.v2i1.24

Burns, J.M. (1996). Leadership. Harper & Row.

Capuano, N., & Caballé, S. (2018). Multi-criteria fuzzy ordinal peer assessment for MOOC. In F. Xhafa, L. Barolli, M. Greguš (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2018. Lecture Notes on Data Engineering and Communications Technologies (pp. 373-383). Springer. https://doi.org/10.1007/978-3-319-98557-2_34

Cho, Y.H., & Cho, K., (2011). Peer reviewers learn from giving comments. Instructional Science, 39(5), 629-643. https://doi.org/10.1007/s11251-010-9146-1

Chudowsky, N.P., & James, W. (2003). Large-scale assessment that supports learning: What will it take? Theory into Practice, 42(1), 75-83. https://doi.org/10.1207/s15430421tip4201_10

Chunwijitra, S., Khanti, P., Suntiwichaya, S., Krairaksa, K., Tummarattamamont, P., Buranarach, M., & Wutiwiwatchai, C. (2020). Development of MOOC service framework for life long learning: A case study of Thai MOOC. IEICE Transactions on Information and Systems, 5, 1078-1087. https://doi.org/10.1587/transinf.2019EDP7262

Cristianti, M., Utomo, C.B., & Murwatiningsi, M. (2020). The analysis of reflective learning toward the development of students’ attitude. Educational Management, 9(2), 191-199. https://bit.ly/3FlroCm

Deng, R., Benckendorff, P., & Gannaway, D. (2020). Linking learner factors, teaching context, and engagement patterns with MOOC learning outcomes. Journal of computer-assisted learning, 36(5), 688-708. https://doi.org/10.1111/jcal.12437

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. https://doi.org/10.1111/j.1529-1006.2004.00018.x

Earl, L., & Torrance, N. (2000). Embedding accountability and improvement into large-scale assessment: What difference does it make? Peabody Journal of Education, 75(4), 114-41. https://doi.org/10.1207/S15327930PJE7504_6

Earl, L.M. (2003). Assessment as learning: Using classroom assessment to maximize student learning. Corwin Press, Inc. https://bit.ly/3USuEdY

Eschenbrenner, B., & Nah, F. (2007). Mobile technology in education: Uses and benefits. International Journal of Mobile Learning and Organisation, 1(2), 159-183. https://doi.org/10.1504/IJMLO.2007.012676

Falchikov, N. (2004). Involving students in assessment. Psychology Learning & Teaching, 3(2), 102-108. https://doi.org/10.2304/plat.2003.3.2.102

Hew, K.F., & Cheung, W.S., (2014). Students and instructors’ use of massive open online courses (MOOC): Motivations and challenges. Educational Research Review, 12, 45-58. https://doi.org/10.1016/j.edurev.2014.05.001

Ivaniushin, D.A., Lyamin, A.V., & Kopylov, D.S. (2016). Assessment of outcomes in collaborative project based learning in online courses. In R.J. Howlett & C.J. Lakhmi (Eds.), Smart innovation, systems and technologies. Springer. https://doi.org/10.1007/978-3-319-39690 3_31

Kitsantas, A., Reiser, R.A., & Doster, J. (2004). Developing self-regulated learners: Goal setting, self-evaluation, and organizational signals during the acquisition of procedural skills. The Journal of Experimental Education, 12(4), 269-287. https://doi.org/10.3200/JEXE.72.4.269-287

Kulkarni, C., Wei, K.P., Le, H., Chia, D., Papadopoulos, K., Cheng, J., Koller, D., & R. Klemmer, S. (2013). Peer and self-assessment in massive online classes. ACM Transactions on Computer-Human Interaction, 20(6). https://doi.org/10.1145/2505057

Lepp, M., Luik, P., Palts, T., Papli, K., Suviste, R., Säde, M., Hollo, A., Vaherpuu, V., & Tõnisson, E. (2017). Self and automated assessment in programming MOOC. Communications in Computer and Information Science. Springer. https://doi.org/10.1007/978-3-319-57744-9_7

Li, Y.L. (2017). Literature review oil chinese students’self-evaluation over the past decade. Educational Perspective, 3, 41-47.

Liyanagunawardena, T.R., Adams, A.A., & Williams, S.A. (2013). MOOCs: A systematic study of the published literature 2008–2012. The International Review of Open and Distance Learning, 14(3), 202-227. https://doi.org/10.19173/irrodl.v14i3.1455

Motycka, C. A., Rose, R.L., Ried, L.D., & Brazeau, G.(2010). Self-assessment in pharmacy and health science education and professional practice. American Journal of Pharmaceutical Education, 74(5), 1-7. https://doi.org/10.5688/aj740585

Olivares, S.L., Hernández, R.I.E., & Corolla, M.L.T. (2021). MOOC learning assessment in clinical settings: Analysis from quality dimensions. Medical Science Educator, 31, 447-455. https://doi.org/10.1007/s40670-020-01178-7

Panadero, E., Alonso-Tapia, J., & Reche, E. (2013). Rubrics vs. self-assessment scripts affect self-regulation? performance and self-efficacy in pre-service teachers. Studies in Educational Assessment, 39(3), 125-132. https://doi.org/10.1016/j.stueduc.2013.04.001

Papathoma-Köhle, M., Zischg, A., Fuchs, S., Glade, T., & Keiler, M. (2015). Loss estimation for landslides in mountain areas-an integrated toolbox for vulnerability assessment and damage documentation. Environ Model Softw, 62, 156-169. https://doi.org/10.1016/j.envsoft.2014.10.003

Pfohl, H.C., Gallus, P., & Thomas, D. (2011). Interpretive structural modeling of supply chain risks. Int. J. Phys. Distrib. Logist. Manag, 41(9), 839-859. https://doi.org/10.1108/09600031111175816

Pieterse, V. (2013). Automated assessment of programming assignments. In M. van-Eekelen, E. Barendsen, P. Sloep, G. van-der-Veer (Eds.), Proceedings of the 3rd Computer Science Education Research Conference on Computer Science Education Research (pp. 45-56). CSERC. https://bit.ly/3uFnhw2

Ravi, V., & Shankar, R. (2005). Analysis of interactions among the barriers of reverse logistics. Technol. Forecast. Soc. Chang, 72(8), 1011-1029. https://doi.org/10.1016/j.techfore.2004.07.002

Reinholz, D. (2016). The assessment cycle: A model for learning through peer assessment. Assessment & Evaluation in Higher Education, 41(2), 301-315. https://doi.org/10.1080/02602938.2015.1008982

Rolheiser, C., & Ross, J. (2000). Student self-evaluation: What do we know. Orbit, 30(4), 33-36.

Sadler, P.M., & Good, E. (2006). The impact of self and peer grading on student learning. Educational Assessment?11(1), 1-31. https://doi.org/10.1207/s15326977ea1101_1

Sánchez-Vera, M. M., & Prendes-Espinosa, M. (2015). Beyond objective testing and peer assessment: alternative ways of assessment in MOOCs. Revista de Universidad y Sociedad del Conocimiento, 12(1), 119-129. https://doi.org/10.7238/rusc.v12i1.2262

Sandeen, S.K. (2021). A typology of disclosure. Akron Law Review, 27, 31. https://bit.ly/3HDP5bJ

Shahabadkar, P. (2012). Deployment of interpretive structural modelling methodology in supply chain management—An overview. Int. J. Ind. Eng. Prod. Res, 23, 195-205.

Shen, L.Y., Song, X.N., Wu, Y., Liao, S.J., & Zhang, X.L. (2016). Interpretive structural modeling based factor analysis on the implementation of emission trading system in the Chinese building sector. Journal of Cleaner Production, 127, 214-227. https://doi.org/10.1016/j.jclepro.2016.03.151

Shrader, S., Wu, M., Owens, D., & Ana, K. (2016). Massive open online courses (MOOCs): Participant activity, demographics, and satisfaction. Online Learning, 20(2), 199-216. https://doi.org/10.24059/olj.v20i2.596

Stan?i?, M. (2020). Peer assessment as a learning and self-assessment tool: A look inside the black box. Assessment & Assessment in Higher Education, 1-13. https://doi.org/10.1080/02602938.2020.1828267

Tapia, J.A., & Panadero, E. (2010). Effect of self-assessment scripts on self-regulation and learning. Journal for the Study of Education and Development, 33(3), 385-397. https://doi.org/10.1174/021037010792215145

Taras, M. (2016). Situating power potentials and dynamics of learners and tutors within self-assessment models. Journal of Further and Higher Education, 40(6), 846-863. https://doi.org/10.1080/0309877X.2014.1000283

Tauber, T. (2013). The dirty little secret of online learning: Students are bored and dropping out [EB/OL]. https://bit.ly/3G0ohS1

Valdivia-Vázquez, J.A., Ramirez-Montoya, M.S., & Valenzuela-González J.R. (2021). Psychometric assessment of a tool to evaluate motivation and knowledge of an energy-related topic MOOC. Educational Media International, 58(3), 280-295. https://doi.org/10.1080/09523987.2021.1976827

Wang, M., Yuan, B., & Kirschner, P.A. (2018). Reflective learning with complex problems in a visualization-based learning environment with expert support. Computers in Human Behavior, 87, 406-415. https://doi.org/10.1016/j.chb.2018.01.025

Wang, Y.F., & Sun, S.Y. (2002). Students’ self-identification and self-assessment. Subject Education, 3, 45-49.

Watson, S.L., Watson, W., Yu, J.H., Alamri, H., & Mueller, C.(2017). Learner profiles of attitudinal learning in a MOOC: An explanatory sequential mixed methods study. Computers & Education, 114, 274-285. https://doi.org/10.1016/j.compedu.2017.07.005

Wilkowski, J., Russell, D.M., & Deutsch, A. (2014). Self-evaluation in advanced power searching and mapping with google MOOC. In M. Sahami, A. Fox, M.A. Hearst, M.T.H. Chi (Eds.), L@S '14: Proceedings of the first ACM Conference on Learning (pp. 109-116). ACM. https://doi.org/10.1145/2556325.2566241

Wong, B.T.M. (2016). Factors leading to effective teaching of MOOCs. Asian Association of Open Universities Journal, 11(1), 105-118. https://doi.org/10.1108/AAOUJ-07-2016-0023

Zeng, W. J. (2017). On the philosophy of learning: Research on the deepening path of the construction of learning society. People's Education Press, 231-232.

Zhao, C., Bhalla, S., Halliday, L., Travaglia, J., & Kennedy, J. (2017). Exploring the role of assessment in developing learners’ critical thinking in massive open online courses. In C. Delgado-Kloos, P. Jermann, M. Pérez-Sanagustín, D. Seaton, & S. White (Eds), Digital education: Out to the world and back to the campus. EMOOCs 2017 (pp. 280-289). Springer. https://doi.org/10.1007/978-3-319-59044-8_33