The use of “big data” is part of what is known as the “quantified self”, namely “the phenomenon of consumers being able to closely track data that is relevant to their daily activities through the use of technology” (Johnson et al., 2014, p.44). The application of “quantified self” to education is known as “Learning Analytics”, whereby data about learner and teacher activities can be analysed, in order to identify patterns of behaviour and provide actionable information to improve learning and learning-related activities (Harmelen & Workman, 2012). An example of this is the “Course Signals” system developed at Purdue University which uses data from multiple sources (including grades, demographics, academic history and current effort) to predict students who are at risk of failing their course (Arnold & Pistilli, 2012). Students receive a personalized email, along with a “traffic light” indication of their progress, and crucially, this “early warning” system also alerts tutors to the need to develop an intervention. By adopting approaches such as this, there are great expectations that the use of learning analytics will “provide educational institutions with opportunities to monitor, support and engage learners’ attitudes (e.g., emotions, motivation, engagement), behaviour (e.g., contributions to discussion forums, clicks, likes) and cognition” (Rienties & Rivers, 2014). However, these authors highlight the challenges to be faced in attempting to measure emotions for learning analytics, including the influence of researchers’ assumptions about emotions, the different theoretical views on the nature of emotions, and the difficulty of deciding at which level to evaluate them. It is uncertain how learning analytics and the quantified self will impact upon education – the 2014 Horizon report suggested that the Time-to-Adoption Horizon for Quantified Self was 4-5 years, and stated, “Educators at the moment can only hypothesize about a new era of the academic quantified self, but interest is strong and growing” (Johnson et al., 2014, p.45). There may be a role for learning analytics in moving assessment methods from summative towards formative approaches, although to achieve this, educators will need to be able to visualize personalized data for students in ways that are easy to understand and have beneficial effects on learning (Ferguson, 2012). There are also privacy and security concerns which will need to be addressed, given the vast amounts of personal data which could be vulnerable to hackers and thereafter available for unauthorized and unforeseen uses, including financial gain. There may also be some philosophical reservations - is the quantification of the self a reduction, and one which makes the self more open to
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commercial interests and more amenable to being monetized? Is this a step away from education for the sake of self-development, emancipation and transformation? There are concerns that these technologies might promote “techno-utopian, enhancement and healthist discourses, and the privileging of the visual and metric in representing the body via these devices” (Lupton, 2013, p.393).
Although not fully implemented in this programme, the current research points to the possibility of using an “emotional learning analytics” approach to enhance students’ experience of emotions during their learning. Learning analytic data could be made available to e-learning students and tutors on their own levels of engagement, collaborative activity and emotional state during learning; this could be extended to include the engagement, collaborative activity and emotions of others, if that was conducive to the cohesion of the learning community and the achievement of learning outcomes. So for example, students could be given feedback on their emotional trajectories during the course of a week’s learning on a given topic, and invited to discuss them with others. As well as signalling those students who are struggling to engage with their study, such a system could be configured to flag up students who are likely to be experiencing a mental health problem; early detection of this group of students would be very valuable, enabling tutors to provide early support, and if needs be refer the student for psychological assistance.
E-learning is, of course, a developing field, and new technologies bring new possibilities, and challenges. For example, a range of other tools could in time be used to measure, understand and feed back information on learners’ emotions, and these include intelligent tutoring systems and emotion detection via facial gesture, voice expression or other physiological measures; successful implementation of these technologies would bring the possibility of monitoring and feeding this information back to learners in real-time (Rienties & Rivers, 2014). And the scale of e-learning has undergone some recent changes with the development of MOOCs, which I have been involved in as a learner and a developer (ScHARR, 2013). The implications of these new ways of delivering and engaging in online learning for learners’ emotions have begun to be addressed, e.g. given the high attrition rates in MOOCs, Cheng notes the impact of non-achievement emotions which are not directly linked to achievement activities or outcomes (Cheng, 2014). Different models of MOOC instruction are likely to engender different emotional flavours to learning.
But before we embrace a technological solution, we should pause to consider whether there really is a problem, and whether a technologically driven solution is really required - this “solutionism” is criticised by Morozov (2013), amongst others. We might reasonably conclude that maximising the student experience is an important responsibility of educators, and given the increase on e-learning, which looks set to continue, finding ways of providing
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emotionally engaging learning is well worth pursuing. That is not to say that the solutions need to be technologically focused, and there are also dangers of technological determinism whereby “research on the educational uses of technology frequently overemphasizes the influence of technology” (Oliver, 2011, p.373). We should be cognisant of the skills and experiences of the learners and the learned; it is the learners who are the experts in their own experience, and the perceptions and judgements of students and tutors remain of paramount importance. The added value of a learning analytic approach would be in augmenting information already available to learners and teachers or in capturing information not available to them, and enabling reflection on this.