Elusive Learning—Using Learning Analytics to Support Reflective Sensemaking of Ill-Structured Ethical Problems: A Learner-Managed Dashboard Solution

Abstract

Since the turn of the 21st century, we have seen a surge of studies on the state of U.S. education addressing issues such as cost, graduation rates, retention, achievement, engagement, and curricular outcomes. There is an expectation that graduates should be able to enter the workplace equipped to take on complex and "messy" or ill-structured problems as part of their professional and everyday life. In the context of online learning, we have identified two key issues that are elusive (hard to capture and make visible): learning with ill-structured problems and the interaction of social and individual learning. We believe that the intersection between learning and analytics has the potential, in the long-term, to minimize the elusiveness of deep learning. A proposed analytics model is described in this article that is meant to capture and also support further development of a learner's reflective sensemaking.

Staying on target: A systematic literature review on learner‐facing learning analytics dashboards

“…While colour‐coded messages (Baneres et al, 2019; Broos, Verbert, et al, 2017) and traffic light metaphors (Cha & Park, 2019; Raza et al, 2019; Ullmann et al, 2019) represented the most frequent types of designs; other types of visualisation elements such as social network representations (Vovides & Inman, 2016), plant images (Muldner et al, 2015), timeline (Sedrakyan et al, 2017), avatars (Charleer et al, 2016) and speedometer metaphor (Michel et al, 2017) were also used.…”

“…These results indicate that most LAD target outcomes refer to monitoring processes such as awareness of learning performance (Park & Jo, 2015), emotional states (Sedrakyan et al, 2017) or general awareness related to learning progress, patterns and strategies (Aljohani et al, 2019; Aljohani & Davis, 2013; Baneres et al, 2019; Bodily, 2018; Park & Jo, 2015; Seanosky et al, 2017; Sedrakyan et al, 2017). Other papers described reflection‐related target outcomes that focused on learners’ consideration of their performance (Broos, Peeters, et al, 2017; Broos, Verbert, et al, 2017; Muldner et al, 2015; Van Horne et al, 2018), behaviour and collaboration (Clayphan et al, 2017; Hill, 2018; Lkhagvasuren et al, 2016; Michel et al, 2012), learning (Cha & Park, 2019; Mejia et al, 2016; Michel et al, 2017) and reflective sense‐making (Vovides & Inman, 2016).…”

“…This result may be due to the exploratory nature of the field where much of the research is still based on finding user‐friendly LAD design approaches. Furthermore, this result reflects the sizeable proportion of usability studies ( N = 11, 39%), which focused on learners’ perceptions and satisfaction in using the LADs (Bodily, 2018; Broos, Peeters, et al, 2017; Broos, Verbert, et al, 2017; Cha & Park, 2019; Michel et al, 2012, 2017; Seanosky et al, 2017; Sedrakyan et al, 2017; Solis, 2016; Ullmann et al, 2019; Vovides & Inman, 2016).…”

Learning Analytics Dashboard for Teaching with Twitter

“…Unlike Backstage, systems such as Cohere [35], Wikiglass [16], and Social Networks Adapting Pedagogical Practice (SNAPP) [6] are dashboards designed to visualize and support decision making processes by students and teachers involved in online asynchronous discussions. Cohere visualizes topics in discussions through Concept Mapping, a network-type representation showing relatedness between terms mined from discussion threads.…”

“…For example, previous researchers have used social networks to draw insights about relationships [24], and support real-time representations of threaded discussions and collaborative work [1]. We sought to avoid those previous designs which added visual complexity, negatively impacting usability and interaction in the discourse [35]. At the same time, we wanted to leverage the expressed support for a tool that enables connectivity and discourse in a non-linear manner.…”