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Typology of motivation and learning intentions of users in MOOCs: the MOOCKNOWLEDGE study

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Participants in massive open online courses show a wide variety of motivations. This has been studied with the elaboration of classifications of the users according to their behavior throughout the course. In this study, we aimed to classify the participants in the MOOCs according to the initial motivations and intentions, before long interaction with the online device. Using a survey of 1768 participants in 6 MOOCs, we classify the participants according to: internal motives, external motives and intention of persistence. Three profiles of involvement in the course were identified: poorly motivated (16.7%), self referential (28.8%) and highly committed (54.5%). All three profiles showed significant differences in self-reported learning experiences at the end of the course. The intensity of the initial motivation was positively related to the satisfaction and perceived quality of the training experience. According to our analysis, identifying motivational profiles before starting the course allows to diagnose in advance the educational use and the diversity of individual training itineraries.

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  1. Some of the most well-known platforms emerge at the university level, such as Coursera <> and Udacity <> at Stanford University, and edX <> at MIT and Harvard University. Subsequently, they have extended their services to other universities.

  2. Literature has distinguished between xMOOC, which gives priority to student-content interaction, and cMOOC, which promotes student–student interaction. The xMOOCs focus on content transmission and often resort to video lessons followed by brief exams. The cMOOCs are based on the active role of the students in the learning process and emphasize the autonomy, creativity, and participation of learners, who deploy their capacity to generate new content.

  3. In some cases, it has been observed that the payment of an enrollment fee in order to obtain a certificate attesting the completion of the course may function as a protective element of abandonment (Alario-Hoyos et al. 2017).

  4. The combination with face-to-face study groups seems to promote a sense of community and the exchange of social support, contributes to participant motivation, and reduces dropout rates (Bulger et al. 2015; Xing et al. 2015; de Freitas et al. 2015; Liu et al. 2015).

  5. Although the cluster analysis technique has been used previously to classify learners in MOOCS (Cabedo Gallén and Tovar Caro 2018), the innovation that we propose with our study consists of the classification according to the learning intentions of the participants.

  6. It is the only profile with more men than women, against the gender distribution of the sample. Specifically, more than half are men, while for the whole sample it does not reach 46%. However, for the group of participants, no statistically significant differences were observed with respect to gender (Chi square = 4.892, p = .087).

  7. This observation corresponds to ten different comparisons of means, in all cases with a significance level of ANOVA of p < .0001, and post hoc comparisons with the Scheffé test of p < .05. As regards the 30 subsequent post hoc comparisons, only one is not significant: the one corresponding to the item "digital competences previously acquired in MOOCS", with respect to conglomerates 2 and 3.

  8. In the case of accreditation, no significant differences are observed if we analyze each indicator of obtaining certificates separately, either relative to the participation in the course (Chi-square = 1.621, p = .445) or the completion of the course (Chi-square = 1.621, p = .445).


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The data of this article were generated within the Moocknowledge project of the European Commission’s Joint Research Centre (JRC). The participation of the University of Seville was carried out through the project FIUS (Grant No. 3063/0227).

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Correspondence to Isidro Maya-Jariego.

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Appendix I

List of items to construct the three indicators used as criterion variables in the cluster analysis

Internal motivations (4 items)

 I participate in a MOOC because it is my preferred way to acquire knowledge and skills

 I participate in a MOOC because it suits my tendency to try new things out

 I participate in a MOOC because it suits my ambition to go with the times

 I participate in a MOOC because it aligns with how I want to learn

Extrinsic motivations (5 items)

 I participate in a MOOC because it is expected of me

 I participate in a MOOC because otherwise I will get a lot of troubles

 I participate in a MOOC because it will give me a certificate

 I participate in a MOOC because I can complete my study program

 I participate in a MOOC because it allows me to get good marks

Intention of persistence (5 items)

 I will make every effort to take and complete one or more MOOCs in the next 6 months

 I will try to take and complete one or more MOOCs in the next 6 months

 I will be persistent to take and complete one or more MOOCs in the next 6 months

 I do the best I can to take and complete one or more MOOCs in the next 6 months

 I will go to the extreme to take and complete one or more MOOCs in the next 6 months

  1. Each indicator is the average of the items that comprise it. Internal and extrinsic motivations are connected to self-determination theory, while intention of persistence is connected to the theory of reasoned action

Appendix II

Table of correlations of the variables of the theory of reasoned action and the theory of self-determination

















1. Beliefs—positive outcomes


2. Beliefs—negative outcomes



3. Evaluation positive outcomes




4. Evaluation negative outcomes





5. Descriptive normative behaviour






6. Descriptive normative beliefs







7. Descriptive normative control








8. Intrinsic motivation


− .066**







9. Integrated motivation










10. Identified motivation











11. Introjected motivation












12. Extrinsic motivation













13. Absence of motivation

− .068**


− .028





− .223**

− .131**

− .194**




14. Intention (readiness)


− .116**





− .021






− .234**


15. Intention (persistence)


− .036











− .221**


  1. *p < .05, **p < .01

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Maya-Jariego, I., Holgado, D., González-Tinoco, E. et al. Typology of motivation and learning intentions of users in MOOCs: the MOOCKNOWLEDGE study. Education Tech Research Dev 68, 203–224 (2020).

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