Exploring differences in how learners navigate in MOOCs based on self-regulated learning and learning styles: A process mining approach

Maldonado, Jorge; Palta, Rene; Vazquez, Jorge; Bermeo, Jorge; Pérez‐Sanagustín, Mar; Muñoz-Gama, Jorge

doi:10.1109/clei.2016.7833356

Cited by 22 publications

(21 citation statements)

References 29 publications

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“…In our program, a significant percentage showed interest in attending but never participated, while an equally significant percentage left the program during the first weeks, confirming a number of other studies (Dillahunt, Wang, & Teasley, 2014;Gütl et al, 2014;Heutte, Kaplan, Fenouillet, Caron, & Rosselle, 2014;Ho et al, 2014;Perna et al, 2014;Santos, Klerkx, Duval, Gago, & Rodríguez, 2014;Stein & Allione, 2014;Tucker, Dickens, & Divinsky, 2014;Whitmer et al, 2014;Wilkowski, Deutsch, Russell, 2014;Greene, Oswald, & Pomerantz, 2015;Kleiman, Wolf, & Frye, 2015;Koedinger, Kim, Jia, McLaughlin, & Bier, 2015;Lackner, Ebner, & Khalil, 2015;Skrypnyk et al, 2015;Allione & Stein, 2016;Davis, Chen, Jivet et al, 2016a;Evans, Baker, & Dee, 2016;Fidal-go-Blanco, Sein-Echaluce, & García-Peñalvo, 2016;Maldonado et al, 2016;Tseng, Tsao, Yu, Chan, & Lai, 2016;Crosslin, Dellinger, Joksimovic, Kovanovic, & Gaševic, 2017;Tawfik et al, 2017).…”

Section: Research Questionsupporting

confidence: 63%

The Effect of Self-Regulation in a MOOC Environment on the Completion and Performance of Learners

Giasiranis¹,

Sofos²

2020

JSS

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MOOCs were created to change the way universities provide education. If to some extent they have succeeded, since they enable many people to attend them without prerequisites and conditions, it is observed that a very small percentage of those who participate finally manage to complete them. In the present study, which is part of the first researcher's doctoral research, we examine the extent to which helping learners apply the Mental Contrasting with Implementation Intentions (MCII) self-regulatory strategy in conjunction with a number of other self-regulatory processes in Zimmerman's model, contributed to the increase of self-regulation, performance and completion rates of those who participated in the first MOOC program of the University of the Aegean (Greece) on "Violence and bullying in schools". 1309 people started the program and completed it, 1050. The two research groups into which they were divided, showed statistically significant differences in their self-regulation, but not in the completion rates of the program (control group: 80.1%, experimental group: 80.3%) and their performance (90%-100% scale: control group: 62.5%, experimental group: 66.5%). Nevertheless, a very high percentage managed to complete it (80.2%), achieving at the same time very high performance. This result shows that self-regulation is not the only factor that contributes to the successful completion of programs and high performance. The instructional design of the program, its organization, and the quality of the instructional material play also an important role. These results can be useful in the design of future MOOCs programs.

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Section: Research Questionsupporting

confidence: 63%

The Effect of Self-Regulation in a MOOC Environment on the Completion and Performance of Learners

Giasiranis¹,

Sofos²

2020

JSS

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“…In the set of selected papers there is a tendency of using Zimmerman's (Hood et al, 2015;Min & Jingyan, 2017;Littlejohn et al, 2016) or Pintrich's (Kizilcec et al, 2016;Alario-Hoyos et al, 2017;Kizilcec et al, 2017) self-regulation models, or a mix of both (Jansen et al, 2017;Maldonado et al, 2016), which is consistent with the findings of Panadero (2017). However, the base of most selected studies is unknown because they do not specify the chosen SRL model or consider several sub-processes which are not specific to one model.…”

Section: Rq4: What Are the Topics Of Analysis And Aspects Of Srl Takementioning

confidence: 99%

“…Task strategies refer to learners' ability to identify and use learning approaches or strategies to learn effectively, as well as adapting them throughout the performance phase. Among the selected documents, a variety of task strategies were identified such as note-taking , completing several consecutive lectures (Maldonado et al, 2016) or delving into the topic through external material (Littlejohn et al, 2016). Furthermore, low and high self-regulators differ in their approach towards learning and the adaptability of their strategies (Littlejohn et al, 2016).…”

Section: Performancementioning

confidence: 99%

“…Research works from that time on apply techniques and define instruments to understand both the SRL profile of MOOC participants and their behaviour. Main instruments which are being applied to study SRL in MOOCs are: log data (Min & Jingyan, 2017), questionnaires or surveys (Alario-Hoyos, Estévez-Ayres, Pérez-Sanagustín, Kloos & Fernández-Panadero, 2017), interviews (Littlejohn, Hood, Milligan & Mustain, 2016), or combinations thereof (Maldonado et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Self-regulated learning in MOOCs: lessons learned from a literature review

et al. 2019

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Self-regulated learning in MOOCs: Lessons learned from a literature reviewLearners in Massive Open Online Courses (MOOCs) are required to be autonomous during their learning process, and thus, they need to self-regulate their learning to achieve their goals. According to existing literature, Selfregulated learning (SRL) research in MOOCs is still scarce. More studies which build on past works regarding SRL in MOOCs are required, as well as literature reviews that help to identify the main challenges and future research directions in relation to this area. In this paper, we present the results of a systematic literature review on SRL in MOOCs, covering all the related papers published until the end of 2017. The papers considered in this review include real experiences with at least a MOOC (other learning scenarios sometimes claimed as MOOCs, such as blended courses, or online courses with access restrictions, are out of the scope of this analysis). Most studies on SRL in MOOCs share some common features: they are generally exploratory, based on one single MOOC and tend not to specify in which SRL model are grounded. The results reveal that high selfregulators engage in non-linear navigation and approach MOOCs as an informal learning opportunity. In general, they prefer setting specific goals based on knowledge development and control their learning through assignments.

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“…The main focus of the literature review reported in this document is the work that has been developed in supporting self-regulated learning as a process that must be endorsed in the context of MOOCs, so that participants can conclude satisfactorily their learning progress in these massive online courses [20], [21].…”

Section: Introductionmentioning

confidence: 99%

Self-Regulated Learning in Massive Online Open Courses: A State-of-the-Art Review

et al. 2021

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Exploring differences in how learners navigate in MOOCs based on self-regulated learning and learning styles: A process mining approach

Abstract: Este documento contiene información de prueba. Contáctese con el administrador del Centro para el acceso al documento originar del registro.

Cited by 22 publications

References 29 publications

The Effect of Self-Regulation in a MOOC Environment on the Completion and Performance of Learners

The Effect of Self-Regulation in a MOOC Environment on the Completion and Performance of Learners

Self-regulated learning in MOOCs: lessons learned from a literature review

Self-Regulated Learning in Massive Online Open Courses: A State-of-the-Art Review

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