Massive open online course (MOOC) has been widely used as a supplementary education tool in higher education. However, MOOC efficacy was doubted for its low completion rate compared with traditional face-to-face courses. To explore the factors affecting MOOC learning in higher education, our study presents a constructivist grounded theory on the process of how college students learned MOOC. Sixteen participants from a college in China were interviewed. After analyzing the transcripts of interviews by open coding, axial coding, and selective coding, the findings indicate that college students studied MOOC because of intrinsic motivation, extrinsic motivation, and enhanced motivation. MOOC learners encountered barriers of learning devices and learning locations. MOOC learning behaviors had characteristics of fragmented, unplanned, and low priority. MOOC has birth defects in learning motivation and learning environment which lead to poor learning behavior. A conceptual model was built to reflect the relationships among motivation defect, environment defect, behavior, and college. Implications are provided for college instructors and administrators to improve MOOC teaching and learning effectiveness.
AgasistiT.AzzoneG.SoncinM. (2021). Assessing the effect of massive open online courses as remedial courses in higher education. Innovations in Education and Teaching International, 59(4), 462–471. https://doi.org/10.1080/14703297.2021.1886969
2.
AlamriA.SunZ.CristeaA. I.SenthilnathanG.ShiL.StewartC. (2020). Is MOOC learning different for dropouts? A visually-driven, multi-granularity explanatory ML approach. 12149 LNCS, 363.
3.
AldowahH.Al-SamarraieH.AlzahraniA. I.AlalwanN. (2020). Factors affecting student dropout in MOOCs: A cause and effect decision-making model. Journal of Computing in Higher Education, 32(2), 429–454. https://doi.org/10.1007/s12528-019-09241-y
4.
Al-HarthiA. S. A.AniW. T. A. (2022). Learner readiness for MOOCs in Omani higher education institutions: Disparities between projections and reality. Education and Information Technologies, 28(1), 303–319. https://doi.org/10.1007/s10639-022-11183-5
5.
Aznar-DíazI.Hinojo-LucenaF.-J.Cáceres-RecheM.-P.Romero-RodríguezJ.-M. (2020). Analysis of the determining factors of good teaching practices of mobile learning at the Spanish University. An explanatory model. Computers & Education, 159(159), 104007. https://doi.org/10.1016/j.compedu.2020.104007
6.
CharmazK. (2015). Grounded theory: Methodology and theory construction. In WrightJ. D. (Ed.), International encyclopedia of the social & behavioral sciences (pp. 402–407). Elsevier.
7.
CromptonH.BurkeD. (2018). The use of mobile learning in higher education: A systematic review. Computers & Education, 123(123), 53–64. https://doi.org/10.1016/j.compedu.2018.04.007
8.
DangA.KhanraS.KagziM. (2022). Barriers towards the continued usage of massive open online courses: A case study in India. The International Journal of Management Education, 20(1), 100562. https://doi.org/10.1016/j.ijme.2021.100562
9.
de BarbaP. G.MalekianD.OliveiraE. A.BaileyJ.RyanT.KennedyG. (2020). The importance and meaning of session behaviour in a MOOC. Computers & Education, 146(146), 103772. https://doi.org/10.1016/j.compedu.2019.103772
10.
Díaz-SainzG.PérezG.Gómez-ComaL.Ortiz-MartínezV. M.Domínguez-RamosA.IbañezR.RiveroM. J. (2021). Mobile learning in chemical engineering: An outlook based on case studies. Education for Chemical Engineers, 35(35), 132–145. https://doi.org/10.1016/j.ece.2021.01.013
11.
GalikyanI.AdmiraalW.KesterL. (2021). MOOC Discussion forums: The interplay of the cognitive and the social. Computers & Education, 165(165), 104133. https://doi.org/10.1016/j.compedu.2021.104133
12.
GamageD.FernandoS.PereraI. (2015). Factors leading to an effective MOOC from participiants perspective. Paper presented at the 8th International Conference on Ubi-Media Computing (UMEDIA).
13.
GangyaoZ.ZiyiL. (2018). Study on the attribution of high dropout rate of MOOCs based on qualitative analysis. Internet Education, 2018(1), 29–35. https://doi.org/10.13811/j.cnki.eer.2018.01.004
GoopioJ.CheungC. (2020). The MOOC dropout phenomenon and retention strategies. Journal of Teaching in Travel & Tourism, 21(2), 177–197. https://doi.org/10.1080/15313220.2020.1809050
16.
HsuR. L.-W. (2021). A grounded theory exploration of language massive open online courses (LMOOCs): Understanding students’ viewpoints. Sustainability, 13(5), 2577. https://doi.org/10.3390/su13052577
17.
JuliaK.PeterV. R.MarcoK. (2021). Educational scalability in MOOCs: Analysing instructional designs to find best practices. Computers & Education, 161(161), 104054. https://doi.org/10.1016/j.compedu.2020.104054
18.
KimD.JungE.YoonM.ChangY.ParkS.KimD.DemirF. (2021). Exploring the structural relationships between course design factors, learner commitment, self-directed learning, and intentions for further learning in a self-paced MOOC. Computers & Education, 166(166), 104171. https://doi.org/10.1016/j.compedu.2021.104171
19.
KoteraY.TaylorE.FidoD.WilliamsD.Tsuda-McCaieF. (2021). Motivation of UK graduate students in education: Self-compassion moderates pathway from extrinsic motivation to intrinsic motivation. Current Psychology, 42(12), 10163–10176. https://doi.org/10.1007/s12144-021-02301-6
20.
LiJ.CheW. (2022). Challenges and coping strategies of online learning for college students in the context of COVID-19: A survey of Chinese universities. Sustainable Cities and Society, 83(83), 103958. https://doi.org/10.1016/j.scs.2022.103958
21.
LiuM.Oga-BaldwinW. L. Q. (2022). Motivational profiles of learners of multiple foreign languages: A self-determination theory perspective. System, 106(106), 102762. https://doi.org/10.1016/j.system.2022.102762
22.
LiyanagunawardenaT. R.AdamsA. A.WilliamsS. A. (2013). MOOCs: A systematic study of the published literature 2008-2012. International Review of Research in Open and Distributed Learning, 14(3), 227. https://doi.org/10.19173/irrodl.v14i3.1455
23.
MeetR. K.KalaD.Al-AdwanA. S. (2022). Exploring factors affecting the adoption of MOOC in Generation Z using extended UTAUT2 model. Education and Information Technologies, 27(7), 10261–10283. https://doi.org/10.1007/s10639-022-11052-1
24.
MooreR. L.BlackmonS. J. (2022). From the learner’s perspective: A systematic review of MOOC learner experiences (2008–2021). Computers & Education, 190(190), 104596. https://doi.org/10.1016/j.compedu.2022.104596
25.
Palacios HidalgoF. J.Huertas AbrilC. A.Gómez ParraM. E. (2020). MOOCs: Origins, concept and didactic applications: A systematic review of the literature (2012–2019). Technology, Knowledge and Learning, 25(4), 853–879. https://doi.org/10.1007/s10758-019-09433-6
26.
PattersonR. W. (2018). Can behavioral tools improve online student outcomes? Experimental evidence from a massive open online course. Journal of Economic Behavior & Organization, 153(153), 293–321. https://doi.org/10.1016/j.jebo.2018.06.017
27.
Pérez-SanagustínM.Sapunar-OpazoD.Pérez-ÁlvarezR.HilligerI.BeyA.Maldonado-MahauadJ.BaierJ. (2020). A MOOC-based flipped experience: Scaffolding SRL strategies improves learners’ time management and engagement. Computer Applications in Engineering Education, 29(4), 750–768. https://doi.org/10.1002/cae.22337
28.
RenningerD.KelsoA.ReimersA. K.MarziI.BeckF.EngelsE. S.StandageM.DemetriouY. (2022). Motivation and active travel in adolescent girls and boys in Germany – Findings from the ARRIVE study. Transportation Research Part F: Traffic Psychology and Behaviour, 90(90), 425–437. https://doi.org/10.1016/j.trf.2022.09.015
29.
ReparazC.Aznárez-SanadoM.MendozaG. (2020). Self-regulation of learning and MOOC retention. Computers in Human Behavior, 111(111), 106423. https://doi.org/10.1016/j.chb.2020.106423
30.
RyanR. M.DeciE. L. (2020). Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemporary Educational Psychology, 61(61), 101860. https://doi.org/10.1016/j.cedpsych.2020.101860
31.
SenevirathneM.AmaratungaD.HaighR.KumerD.KaklauskasA. (2022). A common framework for MOOC curricular development in climate change education - Findings and adaptations under the BECK project for higher education institutions in Europe and Asia. Progress in Disaster Science, 14(14), 100222. https://doi.org/10.1016/j.pdisas.2022.100222
32.
StoughL. M.LeeS. (2021). Grounded theory approaches used in educational research journals. International Journal of Qualitative Methods, 20(20), 160940692110522. https://doi.org/10.1177/16094069211052203
33.
SwansonB. A.ValdoisA. (2022). Acceptance of online education in China: A reassessment in light of changed circumstances due to the COVID-19 pandemic. International Journal of Educational Research Open, 3(3), 100214. https://doi.org/10.1016/j.ijedro.2022.100214
34.
TuliF. (2010). The basis of distinction between qualitative and quantitative research in social science: Reflection on ontological, epistemological and methodological perspectives. Ethiopian Journal of Education and Sciences, 6(1), 97–108. https://doi.org/10.4314/ejesc.v6i1.65384
35.
TurnerC.AstinF. (2021). Grounded theory: What makes a grounded theory study?European Journal of Cardiovascular Nursing, 20(3), 285–289. https://doi.org/10.1093/eurjcn/zvaa034
36.
WangY.DongJ.WangX.LiuQ.LiX. (2021). Grounded theory research on the factors that lead to doctor moral hazard. Saúde e Sociedade, 30(1), 1–11. https://doi.org/10.1590/s0104-12902021190995
37.
WelesilassieM. W.NikolovM. (2022). Relationships between motivation and anxiety in adult EFL learners at an Ethiopian university. Ampersand, 9(9), 100089. https://doi.org/10.1016/j.amper.2022.100089
38.
WieseA.BennettD. (2022). Orientation of medical trainees to a new clinical environment (the ready-steady-go model): A constructivist grounded theory study. BMC Medical Education, 22(1), 37. https://doi.org/10.1186/s12909-022-03105-3
39.
ZhangJ.GaoM.ZhangJ. (2021). The learning behaviours of dropouts in MOOCs: A collective attention network perspective. Computers & Education, 167(167), 104189. https://doi.org/10.1016/j.compedu.2021.104189
40.
ZhengY.ShaoZ.DengM.GaoZ.FuQ. (2022). MOOC Dropout prediction using a fusion deep model based on behaviour features. Computers and Electrical Engineering, 104(104), 108409. https://doi.org/10.1016/j.compeleceng.2022.108409
