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The notion of self-regulated learning (SRL) has been defined as “the process through which an individual actively and consciously controls his/her own learning in terms of cognition, motivation and affect, and behaviour” (Persico, Milligan, & Littlejohn, 2015, p. 2481). This notion is believed by many authors to be of paramount importance, as it is interwoven with academic success (Pintrich, 1995; Pintrich & De Groot, 1990; Zimmerman, 1998). SRL is especially relevant in Technology Enhanced Learning (TEL) environments because these environments both facilitate and challenge learners’ SRL abilities (Azevedo, Behnagh, Duffy, Harley, & Trevors, 2012; Dabbagh & Kitsantas, 2004; Persico & Steffens, 2017).
Research in the field of SRL and TEL has investigated how to support the development of learners’ SRL skills in TEL environments (Bartolomé, Bergamin, Persico, Steffens, & Underwood, 2011). Assuming that SRL competence can be developed through feedback on practice (Van den Boom, Paas, Van Merrienboer, & Van Gog, 2004), a promising research strand looks into ways to provide feedback that supports both SRL practice and its development. Among other methods to provide such feedback, Learning Analytics (LAs) (Lang, Siemens, Wise, & Gasevic, 2017) combined with tracked data visualizations in the form of dashboards (Verbert, Duval, Klerkx, Govaerts, & Santos, 2013; Verbert et al., 2014) are being adopted to support SRL in online learning environments. The underlying principle is to keep track of the students’ actions and feed them back with progress insights (Boulanger, Seanosky, Guillot, & Kumar, 2017; Panadero, Klug, & Järvelä, 2016). More specifically, these tools are able to incorporate different types of data, displaying aspects such as, for example, students’ effort (Arnold and Pistilli, 2012) and learning progress (Nussbaumer, Hillemann, Gütl, & Albert, 2015), with individual and/or group views (Davis, Chen, Jivet, Hauff, & Houben, 2016).
Given the (presumed) affordances of such dashboards, it is worthwhile examining the practical implications of using such tools in real settings, with a focus on those enabling simultaneous measurement and promotion of SRL behaviors (Panadero, Klug, & Järvelä, 2016). Furthermore, it would be useful to examine whether and to what extent any impact on learning performance is attained that can be directly attributed in some way to the usage of these tools, especially as little such investigation has been conducted until now (Beheshitha, Hatala, Gašević, & Joksimović, 2016; Kim, Jo, & Park, 2016; Santos, Verbert, Govaerts, & Duval, 2013).
Although some of the most well known SRL models (Puustinen & Pulkkinen, 2001) concern learning in formal education, SRL takes place in all learning contexts, including continuous professional development. Indeed, learning does not stop when an individual leaves academic education; rather, it continues at the workplace, where SRL behaviours often intertwine with professional activities. Some researchers (Edwards, Ranson, & Strain, 2002; Littlejohn, Milligan, & Margaryan, 2012; Milligan, Littlejohn, & Margaryan, 2014) have studied SRL at work, providing insights into SRL behaviours in these contexts and the differences compared with SRL in academic settings.
Specifically, the way students self-regulate in academic learning contexts is rather different from the way knowledge workers self-regulate their learning in the workplace, as the latter case is almost exclusively based on informal networking and is mostly unplanned. To describe the SRL dynamics of knowledge workers in the workplace, Milligan et al. (2014) proposed a framework outlining four basic behaviours that would allow professionals to control their learning.
It should be noted that a number of recent studies have focused on Learning Analytics (LA) based approaches in the workplace and professional development contexts (Berendt, Vuorikari, Littlejohn, & Margaryan, 2014; Ley, Klamma, Lindstaedt, & Wild, 2016; Ruiz-Calleja, Prieto, Ley, Rodríguez-Triana, & Dennerlein, 2017). Conversely, dashboard visualizations have received comparatively much less research attention. It is therefore important to further explore this aspect and collect more research findings about it.
Starting from the above mentioned considerations, the study presented in this paper aimed to advance research in the two above mentioned research areas, i.e. a) exploration of Learning Analytics and dashboards as means to simultaneously measure and promote SRL behaviours, with special focus on the impact on learning performance; b) exploration of the dashboard’s particular impact within professional development contexts.