Rapid changes occurring in our global world pose a challenge for higher education to advance online courses, such as Massive open online courses (MOOCs). MOOCs provide people from all over the world the opportunity to expand their education for free without any commitment or prior requirements (Colleagues & Author, 2016). Most MOOCs include short segments of video lectures arranged according to the course topics and the assessment method is based basically on closed-ended assignments. In this regard, research has focused on various aspects of learning via MOOC environments, such as: attrition and dropout rates (Ho et al., 2015), social engagement (Ferguson & Clow, 2015) and motivational patterns of MOOC enrollees (Kizilcec & Schneider, 2015). Most of these studies focused mainly on MOOC enrollees; however, little is known about cognitive and intrapersonal  characteristics  of MOOC completers, especially those who are registered university students (Colleagues & Author, 2019)and how these  characteristics affect their learning outcomes (e.g., Author &Colleague,  2021).

Using cognitive perspective (i.e. flexible thinking; Barak & Levenberg, 2016) and motivation theory (i.e. intrinsic motivation; Bandura, 2006), the current research sought to examine the effect of flexible thinking and intrinsic motivation on students' learning outcomes in a MOOC, taking into consideration a comparison with students who completed the course in a F2Fenvironment. More specifically, the current research examines relationships between flexible thinking and intrinsic motivation at the beginning of the course (Time1) and at the end of the course (Time 2) on learning outcomes  after completing a MOOC and a F2Fcourse. Further, the study examines the mediating effect of intrinsic motivation between flexible thinking at Time 1 and learning outcomes at Time 2.

Barak and Levenberg (2016, p.74) defined flexible thinking in education as “open-mindedness to others’ ideas—the ability to learn from others, manage teamwork, listen to multiple perspectives, and handle conflicts; 2. adapting to changes in learning situations—the ability to find multiple solutions, solve unfamiliar problems, and transfer knowledge to new situations; 3. accepting new or changing learning technologies—the ability to adjust to advanced technologies and effectively use them for meaningful learning”

We argue that completers demonstrating flexible thinking at the beginning of the MOOC will have flexible thinking at the end of the MOOC (Hypothesis 1a) and F2F course (Hypothesis 1b).

Intrinsic motivation refers to the inherent satisfaction to be engaged in activity for its own sake. Intrinsic motivation involves an inherent gratification prompted by the feeling that learning is interesting and enjoyable (Glynn et al., 2011). We argue that completers demonstrating intrinsic motivation at the beginning of the course will have intrinsic motivation at the end of the MOOC (Hypothesis 2a) and the F2F course (Hypothesis 2b).

Flexible thinkers are open to new experiences, adapt to new situations, and easily generate new ideas (Barak & Levenberg, 2016a). They adjust to varying circumstances and work well in a climate of uncertainty (Bransford et al., 2000). Further, completing a MOOC is a great challenge as it involves the understanding of complex contents; MOOCs support diverse populations, as each population can contribute to the knowledge and experience of the others (Colleagues & Author, 2018). Thus, we argue that completers’ flexible thinking (Hypothesis 3a) and intrinsic motivation (Hypothesis 3b) at Time 2 will affect their learning outcomesat Time 2 more in MOOC environments than in F2F environments.

Learning outcomes relate to students' achievement in the final course assignments.

We argue that completers with flexible thinking at the beginning of the course may affect their learning outcomes at the end of the course through their intrinsic motivation (Time 2) only in MOOC environments (Hypothesis 4).

The study included a sample of two groups of undergraduate students (N=204) taking the same course “Teaching Thinking,” a MOOC course (N=141) and a F2F classroom course (N=63). The MOOC and the F2F course were taught by the same lecturer and all learning materials and assignments were the same. The students were recruited by sending a message through an online mailing list at the beginning and at the end of the course, inviting them to participate in this study. Participation was voluntary with no extra credit or compensation. Measures Flexible thinking: We used a questionnaire developed by Barak and Levenberg (2016b) (19 items), ranked on a 6-point Likert type scale 1(strongly disagree) to 6(strongly agree)), with three dimensions: learning technology acceptance, open-mindedness in learning, and adapting to new learning situations. A sample item for learning technology acceptance: "I adjust quickly to new learning technologies." Intrinsic motivation: We used a questionnaire developed by Glynn and colleagues (2011) 5 items; 1(strongly disagree) to 5(strongly agree). A sample item: "I enjoy learning ‘Teaching Thinking.” Learning outcome: We analyzed students’ grades in the final exam at the end of the courses. Control variables: Students' prior knowledge was controlled in the current research. Prior knowledge was examined by one question at the beginning of the course: "How familiar were you with the subject area of the course? a. I am mostly new to this subject area, b. I am somewhat familiar with the subject area, c. I am very familiar with this subject area, d. I am an expert in this subject area.” All research measures received Reliability Cronbach Alpha more than 0.7, and fit indices more than 0.9 for construct validity. The proposed model was examined using AMOS program. To examine mediation, a bootstrap analysis was conducted, and confidence intervals were calculated as recommended by Preacher et al. (2010).

The MOOC environment model indicated a good fit between the model and the data (CFI = .99; NFI=.97; RMSEA = .049). A positive and significant relationship was found between flexible thinking in Time 1 and flexible thinking in Time 2 (β = .82; p <.001). The relationship between intrinsic motivation in Time 1 with intrinsic motivation in Time 2 was positive and significant (β = .21; p <.05), thus confirming hypotheses 1a, 2a. Fit indices were more than .90 between the data and the model in the F2F environment; however, RMSEA = .10, which should be lower than .10. A positive and significant relationship was found between flexible thinking in Time 1 and flexible thinking in Time 2 (β = .66; p <.001), thus confirming hypothesis 1b. The relationship between intrinsic motivation in Time 1 with intrinsic motivation in Time 2 was positive and significant (β = .37; p <.001), thus confirming hypothesis 2b. Regarding the MOOC model, intrinsic motivation was positively and significantly related to learning outcomes in Time 2 (β = .17; p <.05); however, flexible thinking was not related to learning outcomes in Time 2 (β = -.13). However, in F2F model, intrinsic motivation and flexible thinking were not significantly related to learning outcomes in Time 2 (β = .17; β = .06 respectively). Thus, hypothesis 3b was confirmed; hypothesis 3a was not confirmed. Finally, the indirect effects between skill flexibility (Time 1) to learning outcomes (time 2) through intrinsic motivation (time 2) in MOOC environments was found to be .03 (p < .01), with a 99.5% confidence interval ranging between .04 and .19. Mediation was not examined in F2F model because of lack of relations between flexible thinking and intrinsic motivation in Time 2 and learning outcomes in Time 2, thus, confirming hypothesis 4.

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