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References

Citekey: @Littlejohn2016-um

Littlejohn, A., Hood, N., Milligan, C., & Mustain, P. (2016/4). Learning in MOOCs: Motivations and self-regulated learning in MOOCs. The Internet and Higher Education, 29, 40–48.

Notes

Nice research investigating SRL in MOOCs. Survey used for measuring SRL of learners; interviews were used to understand learners in greater details.

No use of clicklog data to triangulate these findings could be a future direction.

Highlights

Massive open online courses (MOOCs) require individual learners to be able to self-regulate their learning, determining when and how they engage. (p. 40)

This study investigates the self-regulated learning (SRL) learners apply in a MOOC, in particular focusing on how learners’ motivations for taking a MOOC influence their behaviour and employment of SRL strategies. Following a quantitative investigation of the learning behaviours of 788 MOOC participants, follow-up interviews were conducted with 32 learners. The study compares the narrative descriptions of behaviour between learners with self-reported high and low SRL scores. Substantial differences were detected between the self-described learning behaviours of these two groups in five of the sub-processes examined. Learners’ motivations and goals were found to shape how they conceptualised the purpose of the MOOC, which in turn affected their perception of the learning process. (p. 40)

The open nature of MOOCs, which allow anyone to enrol, leads to diversity in motivations and expectations among learners (Kizilcec et al., 2013). (p. 40)

However, there is growing concern that MOOCs have not had as profound or as immediate an impact on education as initially anticipated (Gillani & Eynon, 2014). Research has not adequately addressed the unique nature of learning and learners in MOOCs or examined the new methods of knowledge production and learning that MOOCs can support (Gillani & Eynon, 2014; Milligan, Littlejohn, & Margaryan, 2013). A number of studies have focused on what can easily be measured at scale, such as progression, retention and completion rates (Breslow et al., 2013; Guo & Reinecke, 2014; Kizilcec, Piech, & Schneider, 2013; Liyanagunawardena, Adams, & Williams, 2013). The employment of these measures as proxies of learning does not adequately take into account the unique structure of MOOCs, the new forms of learning opportunities they promote or the diversity of learners participating in MOOCs. More research is required, which focuses on the unique nature of learning and learners in MOOCs and examines the new methods of knowledge production and learning that they can support. (p. 40)

Studies suggest that learners who are better able to self-regulate their learning, in either formal or informal settings, employ more effective learning approaches in online settings (for a review of this research see Bernacki, Aguilar, & Byrnes, 2011). Initial research into the role that self-regulated learning (SRL) plays in supporting learning in MOOCs has identified a range of cognitive, affective and behavioural factors that impact learning in a MOOC (Hood, Littlejohn, & Milligan, 2015). (p. 40)

to read
check out this Hood 2015 paper (p. 40)

This study explores in detail how learners self-regulate their learning in the ‘Introduction to Data Science’ MOOC offered by the University of Washington through Coursera MOOC platform. The MOOC attracted 50,000 participants from 197 countries. The study is framed by the research question ‘What self-regulated learning strategies do learners apply in a MOOC?’ and explores how self-regulated learning (SRL) (p. 40)

strategies vary between learners who score low and high on a measure of SRL. (p. 41)

(Zimmerman, 2000a p. 14). Zimmerman identified three phases of self-regulated learning – forethought, performance and selfreflection – and a number of sub-processes associated with each phase. Self-regulation is not fixed. The ability to self-regulate one’s learning is mediated by both personal–psychological factors (cognitive and affective) and contextual–environmental factors (Pintrich, 2000). Self-regulation has been positively associated with academic outcomes in formal, offline learning contexts (Pintrich & de Groot, 1990; Zimmerman & Schunk, 2001) and an increasing number of studies have investigated the role that SRL plays in online learning environments (for a comprehensive overview, see Bernacki et al., 2011). (p. 41)

  1. Background (p. 41)

Another study, focused on self-regulated learning in a MOOC, identified four SRL sub-processes where differences were noted between people exhibiting high and low self-regulation — goal setting, self-efficacy, learning and task strategies, and help-seeking strategies (Milligan & Littlejohn, in press). Further research on self-regulated learning in a MOOC identified significant differences in self-regulated learning behaviour between learners from different contexts and professional roles (Hood et al., 2015). (p. 41)

The focus in these studies on progression, retention and MOOC completion rates as indicators of learning have enabled an understanding of the whole MOOC cohort but provide little insight into the behaviour and learning of the individual. Furthermore, the use of completion rates as a proxy for learning success is problematic in the MOOC context. (p. 41)

  1. Methodology (p. 41)

Quantitative data was collected through a survey posted on the course message board. The survey was a slightly modified version of a published, validated instrument designed to measure SRL and SRL sub-processes of adult learners in informal learning contexts (Fontana, Milligan, Littlejohn, & Margaryan, 2015). The survey instrument is available at http://dx.doi.org/10.6084/m9.figshare.866774. (p. 41)

Investigations of learning in MOOCs must also take into account the non-formal nature of MOOCs, which allows learners to engage in nonlinear learning trajectories that do not follow a pre-established, sequential progression (Guo & Reinecke, 2014). (p. 41)

Participants who completed the survey and identified as data professionals (n = 362) were invited to participate in a semi-structured interview. A semi-structured interview instrument was designed to probe the full range of SRL sub-processes identified by Zimmerman (2000a) with questions developed iteratively over a number of studies (Fontana et al., 2015; Milligan & Littlejohn, in press). The questions were adapted to fit the context of this study and to ensure that they were directly relevant to participants’ experience of the MOOC. Relevant questions are included in the results section below, with the whole interview script available at http://dx.doi.org/10.6084/m9.figshare.1300050. (p. 41)

to read
check out Guo 2014 as well (p. 41)

Analysis of learner behaviour in four MOOCs determined that certificate earners viewed on average only 78% of learning sequences, completely skipping 22%, and navigation backjumps from assessments to lectures were more common than lecture-to-lecture backjumps (Guo & Reinecke, 2014). (p. 41)

Data were analysed in three successive rounds, with the eight SRL sub-processes operating as the initial coding framework. First, each of the 32 transcripts was coded independently by two researchers. (p. 42)

3.1. Construct definitions (p. 42)

Second, participants were each assigned a rank (1 to 32) corresponding to their overall SRL score, which was calculated by adding the responses for each of the 39 items, with a minimum possible score of 39 and a maximum score of 195. In order to develop a more robust understanding of self-regulated learning behaviour, alongside their overall rank position, participants were also ranked for each of the eight SRL subprocesses reported in Table 1. (p. 42)

Motivation and goal setting refers to learners’ motivations and reasons for taking the course as well as the aims for learning and performance outcomes they established at the start of the MOOC. (p. 42)

In the third round of analysis, transcripts were analysed in relation to participants’ SRL scores, in order to see whether there were any discernible differences in the interviews of participants with higher and lower SRL scores. (p. 42)

Self-efficacy refers to the extent to which an individual feels confident in their ability to engage with and complete the learning activities offered on the MOOC and their ability to persevere when learning becomes challenging. (p. 42)

Self-satisfaction and evaluation combine two of Zimmerman’s SRL sub-processes, from the self-reflection phase. Evaluation refers to an individual’s awareness of their learning behaviour and the manner in which they compare their self-observed performance against some standard, such as their prior performance, another person’s performance or an absolute standard of performance (Zimmerman, 2002). Self-satisfaction encompasses how satisfied learners are with their performance and progression towards their objectives. (p. 43)

Task strategies encompass the ability of the learner to plan their learning and to identify and employ learning approaches that will enable them to learn. It also incorporates the ability of learners to adjust their strategies and plans throughout their learning journey. (p. 43)

  1. Findings and discussion (p. 43)

Task interest value refers to learners’ perceptions of how valuable the MOOC is to them. (p. 43)

This section presents analysis of data from interviews with participants in the Introduction to Data Science MOOC. (p. 43)

4.1. Motivation and goal setting (p. 43)

In this study, twelve participants with higher SRL scores discussed their motivation in relation to their professional development and how the MOOC would contribute to their professional roles and work context. Their goals were focused primarily around improving their skillset and gaining general content knowledge (p. 43)

Learners who believed that they had a good basic understanding of data science were more confident in their ability to learn and engage in the MOOC. (p. 44)

The specificity of learning objectives and goals highlighted in this quote was a common feature among high self-regulators. (p. 44)

The second factor affecting self-efficacy was whether participants had previously participated in a MOOC (p. 44)

In contrast, low self-regulators tended to discuss their learning in more abstract terms. (p. 44)

Being able to connect their learning experiences in a MOOC either to their existing knowledge or to previous learning experiences appears to increase learners’ self-efficacy. (p. 44)

This difference in motivation and goal-orientation between high and low self-regulators is supported by the literature. Zimmerman (2000a) suggests that high self-regulators are more likely to adopt a ‘mastery goal orientation’, structuring their learning around the development of content knowledge and expertise. Pintrich and de Groot (1990) further suggest that intrinsic motivation is linked to self-regulation. (p. 44)

Five participants who had high overall SRL scores scored in the bottom half of learners for self-efficacy. These participants were not as confident in their existing content knowledge; however, they considered themselves to be effective learners and had confidence in their ability to engage with the course material. (p. 44)

The difference in motivation and goals between the two groups of learners in this study aligns with Pintrich and de Groot’s (1990) distinction between learning versus performance goals. While many learners with low SRL scores did mention they were motivated by a love of learning, their goals were focused around traditional measures of performance (i.e. passing assignments, and course completion) whereas high self-regulators were less concerned about outward measures of performance than developing knowledge and expertise that was relevant to their professional needs. (p. 44)

4.2. Self-efficacy (p. 44)

4.3. Task interest value (p. 44)

The majority of participants, both high and low self-regulators, exhibited high self-efficacy scores. (p. 44)

High SRL participants placed greater value on acquiring skills and content knowledge than those with low SRL scores. (p. 44)

progression through the MOOC, instead determining their own trajectory based on their individual needs. They also were more likely to adapt their approach to learning over time in order to best suit their needs. (p. 45)

Learners with high SRL scores were more likely to evaluate their learning in relation their professional roles, where the value of the learning process is inseparable from its application in their workplace context. (p. 45)

Zimmerman (2000a) suggests that higher levels of self-regulation are related both to a wide range of strategies and the ability to rethink the strategies employed throughout the learning process. (p. 45)

In contrast, learners with low SRL scores tended to be more structured and linear in their approach to learning. They were more likely to follow the course in a structured way, scheduling specific times each week to engage with the MOOC and pre-determining the activities and tasks they wanted to complete. (p. 45)

High SRL participants not only evaluated their learning in relation to their current professional context and needs but also were more likely to connect their learning to their future needs (p. 45)

As many of the low SRL learners had the goal of completing all of the assessments and earning the certificate of completion, they not only engaged with more of the material but were also more likely to dedicate greater amounts of time to the MOOC. (p. 45)

In contrast, while five low SRL participants mentioned the importance of real-world applicability, task interest value was more readily associated with performative outcomes by these learners. (p. 45)

Value was measured extrinsically, with the certificate functioning as a signal of their learning. Seven low SRL learners, compared with only one high SRL learner, discussed the importance of the certificate for demonstrating to their employers what they had learned. (p. 45)

This learner employed a structured approach to their learning, establishing the strategies that they will utilise prior to engaging. (p. 45)

These learners approached the MOOC as a more formal learning opportunity, similar to a traditional HE course. In contrast, learners (generally those with higher self-regulated learning scores) who conceptualised the MOOC as a non-formal learning opportunity that supported their professional learning and development were less structured in how they approached the MOOC. (p. 45)

4.4. Task strategies (p. 45)

A wide range of task strategies were identified with little observable pattern between participants overall SRL score and the specific task strategies they employed. (p. 45)

interesting
this section about task strategies is the most interesting so far. it challenges some of the most used proxies of MOOC success (e.g., earning a certificate) by distinguishing low vs. high SRL learners within groups (e.g., learners earning certificates) that are otherwise treated as the same. (p. 45)

4.5. Self-satisfaction and evaluation (p. 45)

Both high SRL and low SRL participants used the exercises and assignments in the MOOC as a benchmark to evaluate their performance. (p. 45)

While the specific task strategies being employed were evenly distributed among participants, differences were detected in the overall learning approaches between learners with high and low self-regulated learning scores. (p. 45)

High SRL participants used the activities as an opportunity to monitor their progression. (p. 45)

Highly self-regulated learners tended to be more flexible in their approach to learning. They were less likely to follow a linear (p. 45)

This connection between the forethought phase (motivation and goal setting) and the self-reflection phase (evaluation and self-satisfaction) aligns with Zimmerman’s (2008) suggestion that learners who set specific goals were more likely to adopt mastery criteria to self-evaluate rather than normative criteria. (p. 46)

Importantly, however, these types of learners are not attempting to measure their performance against others but rather are using other people to question how they can improve their own approach. (p. 46)

  1. Conclusions (p. 46)

In contrast, the low SRL participants were more likely to conceptualise the assignments as being summative, functioning as the end point of their learning. (p. 46)

The findings of this study have identified differences in behaviour associated with five SRL sub-processes – motivation and goals setting, self-efficacy, task interest value, task strategies, and self-satisfaction and evaluation – between MOOC participants classified as having high overall SRL scores and those with low overall SRL scores. (p. 46)

Five low SRL participants acknowledged that it was often difficult to perceive and measure their own learning ‘Yeah that’s a difficult question because I don’t perceive my own learning’ (Number 396, ranked 17 for SRL, 18 for self-satisfaction and evaluation). (p. 46)

Individuals with high SRL scores engaged with the MOOC primarily as a professional learning opportunity. Their motivation and goals for participation centred around the development of knowledge and expertise that was tied specifically to their workplace context rather than more extrinsic motivations such as passing the assignments and receiving the certificate of completion. This led to their conceptualisation of the MOOC as a non-formal learning opportunity, enabling each learner to independently determine activities and material they would engage with based on their individual needs, rather than the more formal course requirements. By centring their engagement around their specific needs, they adopted more flexible task strategies, which enabled them to readily adapt their approach as and when they needed to. The close connection between their participation in the MOOC and their workplace needs (both present and future) led these learners to score highly for task interest value, as they could apply their learning directly to their professional roles and workplace tasks. This focus on expertise development for their professional roles rather than formal achievement translated into higher overall satisfaction. (p. 46)

Self-satisfaction was strongly connected to learners’ initial expectations and goals for the MOOC. Those participants (six low SRL and two high SRL) who wanted to complete all the assignments and receive a certificate of completion and approached the MOOC as a formal learning opportunity tended to be less satisfied with their learning. (p. 46)

Among the high SRL participants, there was a consistent pattern of being highly satisfied with their participation and learning. These learners were more motivated by a desire to enhance their expertise than to achieve certification. (p. 46)

Because they were not conceptualising the MOOC as a traditional HE course, with a linear progression towards pre-established performative goals (assessments and completion), these learners had to determine independently with what content and how often they would engage. These participants evaluated their learning in relation to how well it related to their professional roles and continually adjusted their engagement in order to ensure that they were gaining maximum learning outcomes (as determined by themselves, rather than in relation to formal assessments or external markers). (p. 46)

The binary division between completers and non-completers is not an adequate measure of quality or of learning in MOOCs. It fails to take into account the varied goals of learners or the ability of individual learners to determine personal markers of success. (p. 47)

In contrast, those learners with low overall SRL scores tended to be more concerned with gaining a certificate of completion and consequently were more focused on completing all of the activities and assessments. While also engaging in the course to support their professional development, these learners were more likely to be driven by extrinsic motivation factors, and used external markers as evidence of their learning. These learners were less likely to connect their learning to specific workplace tasks or to discuss how they were actively applying the knowledge and expertise they were developing in their work context. They tended to conceptualise the MOOC as a formal learning activity, adopting a more uniform and linear approach to their engagement. Their learning was centred primarily on the MOOC itself, rather than being distributed between the MOOC and the offline setting of their workplace. This led to lower levels of task interest value, as learners were less consciously connecting their learning to their wider settings. This influenced the ways in which these learners assessed their learning, with most learners using extrinsic measures, such as achievement in the assessments or completion of the course, to evaluate their learning. It is possible that the higher levels of extrinsic motivation and the conceptualisation of the MOOC as a formal learning activity among these low SRL learners meant that they had less need to self-regulate their learning compared with participants who adopted a less linear and more flexible approach to their learning. (p. 47)

In doing so, they question traditional pathways, purposes and outcomes in education. Research must engage deeply with these differences in order to enhance the learning opportunities that MOOCs, and future forms of open learning, provide for all learners. (p. 47)

While a connection between a learner’s motivations and goals and their conceptualisation of learning in MOOCs is apparent in this study, it is not possible to determine which is the driving factor. Does a learner’s motivation and goals for a MOOC influence the learning approach they adopt and consequently their overall self-regulated learning? Or, does a learner’s self-regulated learning influence the approach that they adopt and shape their motivations and goals for the MOOC? (p. 47)

Guo, P., & Reinecke, K. (2014). Demographic differences in how students navigate through MOOCs. L@S ‘14 proceedings of the first ACM conference on learning @ scale conference (pp. 21–30). New York: ACM. (p. 48)

Hood, N., Littlejohn, A., & Milligan, C. (2015). Context counts: How learners’ contexts influence learning in a MOOC. Computers & Education, 91, 83–91. (p. 48)

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