Proposal Information

Under the MOOC context, where a high dropout rate usually happens (e.g., Reich & Ruipérez-Valiente, 2019),  a rising attention has been paid to how to support and facilitate learning engagement (e.g., Joksimović et al., 2018; Kuo, Tsai, & Wang, 2021). Learners actively engage in learning, which results in better or higher-level learning outcomes, such as course completion, academic outcomes (e.g., Hone & El Said, 2016). Getting learners involved in MOOCs is not enough, it is crucial to take advantage of teaching and learning in MOOCs to deepen learning engagement (Johnson et al., 2016). Scholars have argued that learning engagement is a multi-faceted construct comprising cognitive, behavioral, and affective engagement, which manifests the learning process from multiple perspectives (Fredricks, Blumenfeld, & Paris, 2004). Grounded on recent research work, evidence in a multifaceted engagement still requires further research on the psychometrical mechanism of learning engagement to gain insights into the learning processes (e.g., Deng, Benckendorff, & Gannaway, 2020; Joksimović et al., 2018). To this end, drawing attention to learning engagement with multiple facets can help precisely expound on how learners think, behave, and feel within the learning processes.

As the funnel of participation is a prominent characteristic of MOOCs (Clow, 2013), and diverse learners are more self-determined for learning to maintain participation or drop out of courses (e.g., Wang & Baker, 2018), only a small proportion of learners have completed course learning (e.g., Cagiltay, Cagiltay, & Celik, 2020). As learners declines within the course progress, it could be problematic to capture the learning outcomes what if only solely anchored on different types of graded assessments like campus courses do. In that case, we argue that there is a missing voice of how learners perceive their learning outcomes obtained from MOOCs. Further research on MOOC learners’ perceived learning outcomes is essentially needed, which could mirror to what extent learners have achieved personally desirable learning outcomes.

Some studies have found that motivational factors broadly concern the reasons or goals for making choices, persisting, and performing in learning activities (e.g., Kuo et al., 2021; Moore & Wang, 2021). Benefiting from prior review work grounded on the MOOC studies (e.g., Alemayehu & Chen, 2021; Deng, Benckendorff, & Gannaway, 2019), until recently, scholars have claimed that how motivational factors relate to learning engagement and learning outcomes still need to be further clarified. Motivation is a complicated construct, the expectancy-value theory is one of the frameworks to explain motivational belief processes (i.e., self-efficacy, task value, and perceived cost), which concern students’ achievement-related choices, persistence, and performance for doing learning activities (Eccles, 1983; Wigfield & Eccles, 2000). In the current study, therefore, we aim to probe to what extent self-efficacy, task value, and perceived cost can predict learning engagement and perceived learning outcomes. 

Aiming at acquiring precise knowledge, research questions were proposed to be addressed as follows: 

         RQ1: How does self-efficacy relate to task value, perceived cost, learning engagement, and perceived learning outcomes in MOOCs?

         RQ2: How do task value and perceived cost relate to learning engagement in MOOCs?

         RQ3: How do task value and perceived cost relate to perceived learning outcomes in MOOCs?

         RQ4: How do task value and perceived cost mediate the relationship between self-efficacy and learning engagement?

         RQ5: How do task value and perceived cost mediate the relationship between self-efficacy and perceived learning outcomes?

Methods: 1. Participants Ultimately, the final sample of this study comprises 232 Chinese learners who attended MOOCs during the academic year 2020-2021. 2. Measuring instruments 2.1. Attitude Attitude toward learning in MOOCs was assessed with six items, which were adapted from Admiraal et al. (2017). Attitude refers to the general belief of favorability and benefits of learning in MOOCs. A five-point Likert scale, anchoring from 1 (Does not apply at all) to 5 (Does apply to a great deal). 2.2. Self-efficacy The scale Self-efficacy of Pintrich, Smith, Garcia, and McKeachie (1991) was adapted to match the MOOC learning context. Self-efficacy states learners’ competence beliefs to be well-performed and expectancy of success in MOOC tasks. A five-point Likert scale, ranging from 1 (Very untrue for me) to 5 (Very true for me). 2.3. Task value The scale Task value with 15 items was based on Perez et al. (2019) and was adapted to assess the task value for the MOOC that learners attended. The scale comprises three subscales including intrinsic value (7 items), attainment value (4 items), and utility value (4 items). A five-point Likert scale anchored with 1 (Strongly disagree) to 5 (Strongly agree). 2.4. Perceived cost Perceived cost toward learning in a MOOC was measured with the scale of Perceived cost from Flake, Barron, Hulleman, McCoach, and Welsh (2015). The scale examines three aspects of costs in terms of task effort cost (5 items), loss of valued alternatives (5 items), and emotional cost (6 items). A five-point Likert scale, scoring from 1 (Strongly disagree) to 5 (Strongly agree). 2.5. Learning engagement Three aspects of learning engagement (18 items) were measured by adapting the scales of Cognitive Engagement and Behavioral Engagement from Reeve and Tseng (2011), and the scale of Emotional Engagement from Skinner et al. (2008). A five-point Likert scale ranges from 1 (Strongly disagree) to 5 (Strongly agree). 2.6. Perceived learning outcomes We assessed perceived learning outcomes (9 items) using the adapted version of the scale Course Outcomes from Paechter, Maier, and Macher (2010). A five-point Likert scale ranges from 1 (Strongly disagree) to 5 (Strongly agree). 3. Data analysis IBM SPSS 25 and Mplus 8.3 were utilized as the statistical tool for data analysis. Firstly, structural equation modeling was implemented to address RQ1 to RQ3. Second, mediation analyses were carried out through bias-corrected bootstrapping of 5000 samples with a 95% confidence interval to address RQ4 and RQ5.

Main findings: To address RQ1 to RQ5, we implemented SEM to examine the relationships among latent variables and performed mediation analyses through bias-corrected bootstrapping of 5000 samples with a 95% confidence interval. Concerning RQ1, we found that self-efficacy was to be found positively and significantly related to intrinsic value (p < 0.001), learning engagement (p < 0.01), and perceived learning outcomes (p < 0.01), but showed a non-significant association with task effort (p = 0.171). Regarding RQ2 and RQ3, the results entailed that intrinsic value had a positive and significant effect on learning engagement (p < 0.001) and perceived learning outcomes (p < 0.001). However, task effort non-significantly predicted learning engagement (p = 0.165) and perceived learning outcomes (p = 0.054). Regarding RQ4, the results indicated self-efficacy had both a significant direct effect (p < 0.01) and a significant indirect effect (p < 0.001) on learning engagement. To be specific of the mediation paths, intrinsic value significantly mediated the relationship between self-efficacy and learning engagement (p < 0.001). However, task effort failed to play the mediating role in the relationship between self-efficacy and learning engagement (p = 0.856). Regarding RQ5, it was found that self-efficacy had both a significant direct (p < 0.001) and a significant indirect effect (p < 0.001) on perceived learning outcomes. When looking at the specific mediation paths, the results documented that the indirect effect of intrinsic value between self-efficacy and perceived learning outcomes showed statistical significance (p < 0.001). Nevertheless, the indirect effect of self-efficacy on perceived learning outcomes through task effort was non-significant (p = 0.840).

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