The introduction of massive open online courses (MOOCs) has changed the way learners access educational resources of higher education. The features of MOOCs in terms of openness, a massive number of participants, and in most cases of courses for free, offering multiple opportunities to fulfill what learners are willing to learn from. Indeed, previous studies indicated that learners expressed diverse motivation for signing up for MOOCs,  for example, professional development, general interests, a supplement for campus-course, and earning course certificate, which could predict course grades and MOOC completion (Brooker, Corrin, De Barba, Lodge, & Kennedy, 2018; Semenova, 2020). During the process of learning in MOOCs, learners experienced and engaged with learning activities, such as video lectures watched, peer learning, and discussion forums, and employed self-regulated learning (SRL) strategies in learning. To some degree, the aspects of learning experiences, the extent of engagement, and SRL strategies predicted learners’ academic performance, dropout, and course satisfaction (Bonafini, Chae, Park, & Jablokow, 2017; Chiu & Hew, 2018; Hew, Hu, Qiao, & Tang, 2020; Lan & Hew, 2020; Moreno-Marcos et al., 2020). The existing studies have widely examined multiple aspects of learners learning in MOOCs. However, there is no study that integrates how learners are motivated, what they experience from and how they engage with course learning, and what they actually do learn from courses. The current study aims to bridge learners’ motivation, learning processes, and perceived learning, which identifies the influence of learners’ motivation, learning experiences, learning engagement, and self-regulated learning strategies on perceived learning in MOOCs. Firstly, based on the self-determination theory, we will connect learners’ motivational profile and their learning outcomes to explain the differences in their perceived cognitive, behavioral, and affective outcomes. Secondly, further exploration will focus on the influence of learning experiences, learning strategies, and learning engagement on learners’ perceived learning in MOOCs.

To this end, specific research questions are proposed to address:

RQ1: How are the motivations of learners related to their perceived learning in MOOCs?

RQ2: How are learners’ motivations, learning experiences, learning engagement, and self-regulated learning strategies related to their perceived learning in MOOCs?

RQ3: How does learning engagement affect the relationship between learners’ motivations, learning experiences, self-regulated learning strategies, and perceived learning?

Methods 1. Participants This study will conduct an online survey from the learners on the Chinese University MOOC (https://www.icourse163.org/). 2. Measuring instruments Quantitative data will be gathering from the online survey. Concerning all the items of scales of learning experience, learning strategies, and perceived learning, participants scored on a six-point Likert scale, with anchors ranging from 1 (Strongly disagree) to 6 (Strongly agree). All items of learning engagement are scored on a five-point Likert scale ranging from 1 (Never) to 5 (Very often). Qualitative data will be collected from open questions concerning the reasons that learners attend in MOOCs. 2.1 Learning experience Learning experiences in MOOCs evaluated by adapting the scale of the Students’ Experiences employed in e-learning courses (Paechter, Maier, & Macher, 2010). The subscales of Learning experiences included in the scale: (1) Course design (3 items), (2) Interaction with the instructor (4 items), (3) Interaction with peer students (4 items), and (4) Individual learning processes (4 items). 2.2 Learning strategies Learners’ strategies toward learning in MOOCs is measured by adapting the Learning Strategies scale, which belongs to the Motivated Strategies for Learning Questionnaire (MSLQ) (Pintrich, Smith, Garcia, & McKeachie, 1991). The scale comprising four indicators: (1) Elaboration (6 items), (2) Critical thinking (5 items), (3) Metacognitive self-regulation (8 items), and (4) Time and study environment management (7 items). 2.3 Learning engagement Ten items are designed to measure learners’ engagement with learning activities in MOOCs. Based on the previous explorations of (Bonafini et al., 2017; Chiu & Hew, 2018; Lan & Hew, 2020), ten-items measurement is proposed aims to assess the frequency of learners engage in learning activities. 2.4 Perceived Learning Perceived learning is assessed by adapting the Course Outcomes scale (Paechter et al., 2010). Three subscales: (1) Cognitive outcomes, (2) Behavioural outcomes, and (3) Affective outcomes, each of which has three items. 3. Data analysis To answer the research questions, IBM SPSS 25 is utilized as the statistical tool for data analysis. The one-way ANOVA and stepwise regression analysis will be employed to examine how motivations, learning experiences, self-regulated learning strategies, and learning engagement are related to perceived learning in MOOCs.

Expected outcomes Based on the self-determination theory, learners’ motivations of participation in MOOCs are categorized by five groups, namely (1) Intrinsic motivation, (2) identified regulation, (3) external regulation, (4) Intrinsic motivation & identified regulation, and (5) nothing reported. Participants’ perceived learning differs among groups could be enclosed, and learner motivation profiles can explain the significant differences in their perceived learning. The behind mechanism of motivations of each group will be distinguished and discussed, which contribute to understand the connection of motivation and perceived learning. We will try to come up with several practical implications for maintaining and stimulating learners’ motivation for learning in MOOC settings. Moreover, the significant factors that affect learners’ perceived learning in MOOCs will be extracted from learning experiences, learning strategies, and learning engagement. Upon the results, the relationships between these factors and perceived learning are able to be determined and discussed, and implications also will be proposed.

Bonafini, F., Chae, C., Park, E., & Jablokow, K. (2017). How much does student engagement with videos and forums in a MOOC affect their achievement? Online Learning Journal, 21(4). Retrieved from https://www.learntechlib.org/p/183772/. Brooker, A., Corrin, L., De Barba, P., Lodge, J., & Kennedy, G. (2018). A tale of two MOOCs: How student motivation and participation predict learning outcomes in different MOOCs. Australasian Journal of Educational Technology, 34(1). https://doi.org/10.14742/ajet.3237 Chiu, T. K. F., & Hew, T. K. F. (2018). Factors Influencing Peer Learning and Performance in MOOC Asynchronous Online Discussion Forum. Australasian Journal of Educational Technology, 34(4), 16. https://doi.org/10.14742/ajet.3240 Hew, K. F., Hu, X., Qiao, C., & Tang, Y. (2020). What predicts student satisfaction with MOOCs: A gradient boosting trees supervised machine learning and sentiment analysis approach. Computers & Education, 145, 103724. https://doi.org/10.1016/j.compedu.2019.103724 Lan, M., & Hew, K. F. (2020). Examining learning engagement in MOOCs: a self-determination theoretical perspective using mixed method. International Journal of Educational Technology in Higher Education, 17(1), 1-24. https://doi.org/10.1186/s41239-020-0179-5 Moreno-Marcos, P. M., Munoz-Merino, P. J., Maldonado-Mahauad, J., Perez-Sanagustin, M., Alario-Hoyos, C., & Kloos, C. D. (2020). Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCs. Computers & Education, 145, 103728. https://doi.org/10.1016/j.compedu.2019.103728 Paechter, M., Maier, B., & Macher, D. (2010). Students’ expectations of, and experiences in e-learning: Their relation to learning achievements and course satisfaction. Computers & Education, 54(1), 222-229. https://doi.org/10.1016/j.compedu.2009.08.005 Pintrich, P. R., Smith, D. A. F., Garcia, T. a., & McKeachie, W. J. (1991). A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ). https://files.eric.ed.gov/fulltext/ED338122.pdf Semenova, T. (2020). The role of learners’ motivation in MOOC completion. Open Learning: The Journal of Open, Distance and e-Learning, 1-15. https://doi.org/10.1080/02680513.2020.1766434