Supporting Self-Regulated Learning in Online Learning Environments and MOOCs: A Systematic Review

Figures & data

Table 1. Key terms used for the search that was conducted in April 2016.

Concept 1	Concept 2	Concept 3
1. Self-regulat* learning strategy* 2. Self-regulat* learning 3. Metacognition	1. Massive Open Online Course* 2. MOOC* 3. Massively Open Online Course 4. Online learning environment* 5. Online 6. Internet 7. Distance education 8. Personal learning environment 9. Web based 10. E-learning	1. human factor* 2. individual differences 3. culture 4. age 5. gender 6. expertise

Table 2. Summary of approaches and number of studies reviewed across approaches.

Approaches	Number of studies
Prompts	14
Feedback	2
Prompts and Feedback	4
Integrated Support Systems	10
Other approaches
Self-monitoring form	1
E-journal	1
Training	2
Conceptualization of learning outcomes	1

Table 3. Systematic review table of human factors addressed in studies (alphabetical order according to the first author).

No.	Author and Year	Findings on human factors
1.1	Bannert and Reimann (2012) Experiment 1	Significant effect of prior knowledge as a covariate (ŋ ^2 = .45) on transfer performance. No significant effects of compliance or other human factors (i.e., initial motivation, metacognitive knowledge and verbal intelligence) as covariates on transfer performance.
1.2	Bannert and Reimann (2012) Experiment 2	Significant effect of compliance (ŋ ^2 = .38) on transfer performance. No significant effect of prior knowledge or other human factors (i.e., initial motivation, metacognitive knowledge and verbal intelligence) as a covariates on transfer performance.
2	Biesinger and Crippen (2010)	Regardless of learning environment perceptions (i.e., mastery approach vs. performance approach), no significant effects on number of worked examples viewed, and changes in self-efficacy, goal orientation over time, and final course grades.
3	Chang (2007)	Regardless of English proficiency level, learners in the experimental group scored higher than the control group.
4	Chen and Huang, (2014)	Both females t = 5.949, p < .01) and males (t = 3.354, p = 0.001) in the experimental group had higher mean sustained attention than the control group. Female learners in the experimental group outperformed female learners in control group (t = 3.848, p < .001). No differences for males in both groups.
5	Chen et al. (2014)	Males in the experimental group had better reading annotation ability than males in the control group in all three reading units (t = −3.737, p = .001; t = −3.009, p = .007; t = −3.170, p = .003). Females in the experimental group had better reading annotation abilities only in the first reading unit (t = −2.425, p = .022). Females in the experimental group outperformed female learners in the first and third reading units (t = −2.708, p = .011; t = −1.648, p = .111; t = −2.636, p = .014). No differences in reading comprehension of the males in both experimental and control group.
6	Duffy and Azevedo (2015)	Significant effect of prior knowledge covariate, Pillai’s Trace = .52, F(2, 77) = 42.19, p < .05, ŋ^2 = .52. Significant condition x achievement goal interaction, Pillai’s Trace = .09, F(2, 77) = 3.82, p < .05, ŋ^2 = .09 Performance-approach learners in the prompt and feedback group > performance-approach learners in control group and master-approach learners in the prompt and feedback group. Mastery-approach learners did not significantly differ in achievement across conditions.
7.1	Lehmann et al. (2014) Experiment 1	Significant amount of variance in quality of written essay explained by human factors with domain specific knowledge and metacognitive regulation as a significant predictors, R^2 = .59, F(8, 55) = 9.94, p < .001. Significant amount of variance in quality of concept map explained by human factors with interest (subscale of motivation) as a significant predictor, R^2 = .47, F(8, 55) = 6.15, p < .001.
7.2	Lehmann et al. (2014) Experiment 2	Individual learning preferences did not significantly predict quality of written essays. Significant group differences on positive dimension of motivation at second measurement, F(2, 64) = 4.76, p = .012, ŋ^2 = .05. Directed preflection > Generic preflection > Control Significant group differences on negative dimension of motivation at third measurement, F(2, 64) = 4.48, p = .015, ŋ^2 = .12. Generic preflection > Directed preflection > Control
8	Manlove et al. (2007)	A significant condition × achievement interaction on model quality, F(1, 31) = 6.76, p < .05. Low achieving PC− dyads scored higher than low achieving PC+ dyads. No significant differences between the two high achieving groups.
9	Sitzmann et al. (2009) Experiment 2	Cognitive ability moderated the effect of prompting on basic performance (γ = .002, p < .05). Prompting had a stronger positive effect on basic performance over time for higher ability learners. Self-efficacy moderated the effect of prompting on strategic performance (γ = .096, p < .05). Prompting had a stronger positive effect on strategic performance over time for higher self-efficacy learners.
10	Wang (2011)	High SRL learners in PDA-WATA scored higher in LPI post-test than high SRL learners in control group, d = .53. Low SRL learners in PDA-WATA scored higher in LPI post-test than low SRL learners in control group, d = .93. In the PDA-WATA group, regardless of level of SRL, learners did not significantly differ their learning effectiveness (p > .05). In the control group, high SRL learners performed better than the low SRL learners.
11	Yeh et al. (2010)	Significant prior knowledge by prompt type interaction, F(2, 238) = 17.81, p < .01, ŋ^2 = .13 Lower prior knowledge learners, F(2, 119) = 66.10, p < .01, ŋ^2 = .53. Reasoning-based > predicting-based > no prompt Higher prior knowledge learners, F(2, 119) = 12.75, p < .01, ŋ^2 = .18. Predicting-based > reasoning-based and no prompt.
12	Zhang et al. (2015)	Significant effects of metacognition on analyzing practice, χ^2 = 6.74, p < .03 in the cognitive prompt only group. High and medium metacognition group > low metacognition group No significant differences between the high, medium and low metacognition groups in the combined metacognitive and cognitive prompt group.

Figure 1. Impact of human factors on approaches to support SRL, SRL strategies, and learning performance.

*Bannert, M., & Reimann, P. (2012). Supporting self-regulated hypermedia learning through prompts. Instructional Science, 40(1), 193–211. doi:10.1007/s11251-011-9167-4

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*Biesinger, K., & Crippen, K. (2010). The effects of feedback protocol on self-regulated learning in a web-based worked example learning environment. Computers & Education, 55(4), 1470–1482. doi:10.1016/j.compedu.2010.06.013

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*Chang, M. M. (2007). Enhancing web-based language learning through self-monitoring. Journal of Computer Assisted Learning, 23(3), 187–196. doi:10.1111/j.1365-2729.2006.00203.x

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*Chen, C. M., & Huang, S. H. (2014). Web‐based reading annotation system with an attention‐based self‐regulated learning mechanism for promoting reading performance. British Journal of Educational Technology, 45(5), 959–980. doi:10.1111/bjet.12119

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*Chen, C. M., Wang, J. Y., & Chen, Y.-C. (2014). Facilitating English-language reading performance by a digital reading annotation system with self-regulated learning mechanisms. Educational Technology & Society, 17(1), 102–114.

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*Duffy, M. C., & Azevedo, R. (2015). Motivation matters: Interactions between achievement goals and agent scaffolding for self-regulated learning within an intelligent tutoring system. Computers in Human Behavior, 52, 338–348. doi:10.1016/j.chb.2015.05.041

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*Lehmann, T., Hähnlein, I., & Ifenthaler, D. (2014). Cognitive, metacognitive and motivational perspectives on preflection in self-regulated online learning. Computers in Human Behavior, 32, 313–323. doi:10.1016/j.chb.2013.07.051

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*Manlove, S., Lazonder, A. W., & de Jong, T. (2007). Software scaffolds to promote regulation during scientific inquiry learning. Metacognition and Learning, 2(2–3), 141–155. doi:10.1007/s11409-007-9012-y

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*Sitzmann, T., Bell, B. S., Kraiger, K., & Kanar, A. M. (2009). A multilevel analysis of the effect of prompting self-regulation in technology-delivered instruction. Personnel Psychology, 62(4), 697–734. doi:10.1111/peps.2009.62.issue-4

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*Wang, T. H. (2011). Developing Web-based assessment strategies for facilitating junior high school learners to perform self-regulated learning in an e-Learning environment. Computers & Education, 57(2), 1801–1812. doi:10.1016/j.compedu.2011.01.003

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*Yeh, Y. F., Chen, M. C., Hung, P. H., & Hwang, G. J. (2010). Optimal self-explanation prompt design in dynamic multi-representational learning environments. Computers & Education, 54(4), 1089–1100. doi:10.1016/j.compedu.2009.10.013

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*Zhang, W. X., Hsu, Y. S., Wang, C. Y., & Ho, Y. T. (2015). Exploring the Impacts of Cognitive and Metacognitive Prompting on Students’ Scientific Inquiry Practices Within an E-Learning Environment. International Journal of Science Education, 37(3), 529–553. doi:10.1080/09500693.2014.996796

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