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AbstractFeedback has long been considered a significant lever to enhance learning experience and success in higher education. However, students have shown much discontent with the current feedback practice. Feedback experienced as a relational process in which students feel recognized and valued is perceived as paramount for helping support the uptake of feedback and promote positive learning dispositions. However, little attention has been paid to suggesting how instructors in higher educational institutions can facilitate relational feedback. In response, we conducted a rapid literature review with a particular focus on the creation of feedback content. In total, 17 peer-reviewed publications on relational feedback are included. Based on a qualitative analysis of these papers, we developed a framework of 12 characteristics for offering relational feedback. These 12 characteristics were further categorised into four types of feedback conductive to a relational process, including (i) clarifying performance, (ii) suggesting improvement, (iii) inviting further communication, and (iv) evoking positive emotions. We examined how each of these characteristics supports the relational aspects of feedback. Based on the qualitative analysis results, we offered practical recommendations for instructors to apply relational feedback in their pedagogical practices. [ABSTRACT FROM AUTHOR]

A common recommendation in critiques of datafication in education is for greater conversation between the two sides of the (critical) divide – what might be characterised as sceptical social scientists and (supposedly) more technically-minded and enthusiastic data scientists. This article takes the form of a dialogue between two academics representing these different perspectives – Dragan Gasevic (a data scientist who is one of the founders of the 'learning analytics' field) and Neil Selwyn (a social scientist and long-time critic of educational technology). Over the course of a six-month email exchange, we explore various arguments for and against the datafication of higher education. Thus, we are able to add a computational dimension to well-worn social criticisms of data representativeness, reductionism and injustice, as well as exploring social tensions inherent in technical claims to data-based precision, clarity and predictability. Crucially, these exchanges allow us to explore ways in which our two viewpoints converge, and therefore highlight opportunities for productive inter-disciplinary exchange and collaboration. [ABSTRACT FROM AUTHOR]

Feedback has a powerful influence on learning. However, feedback practices in higher education often fail to produce the expected impact on learning. This is mainly because of its implementation as a one-way transmission of diagnostic information where students play a passive role as the information receivers. Dialogue around feedback can enhance students' sense making from feedback and capacities to act on it. Yet, dialogic feedback has been mostly implemented as an instructor-led activity, which is hardly affordable in large classrooms. Dialogic peer feedback can offer a scalable solution; however, current practices lack a systematic design, resulting in low learning gains. Attending to this gap, this paper presents a theoretical framework that structures dialogic feedback as a three-phase collaborative activity, involving different levels of regulation: first, planning and coordination of feedback activities (involving socially shared regulation), second, feedback discussion to support its uptake (involving co-regulation), and last, translation of feedback into task engagement and progress (involving self-regulation). Based on the framework, design guidelines are provided to help practitioners shape their feedback practices. The application of the principles is illustrated through an example scenario. The framework holds great potential to promote student-centred approaches to feedback practices in higher education. [ABSTRACT FROM AUTHOR]

Research and development in learning analytics has established viable solutions for scaling personalised feedback to all students. However, questions remain regarding how such feedback is perceived, interpreted and acted upon by stakeholders. The present study reports on the analysis of focus group data from four courses to understand students' sensemaking of personalised, learning analytics-based feedback. Deductive analysis and epistemic network analysis were carried out to identify students' perceptions and associated emotional responses to the feedback. The results demonstrate that students mostly held positive perceptions of their feedback – especially in regard to quality, task-focus and instructor care. While emotions to feedback were often negative, students also noted this increased their motivation to invest more effort in the learning tasks. The results of the study provide further evidence of the interpersonal communication variables influencing students' proactive recipience of feedback. Importantly, the findings show that students were mostly actively engaged with their personalised feedback, and that the instructor-mediated approach to feedback based on learning analytics is aligned to the principles of effective feedback. [ABSTRACT FROM AUTHOR]

Although dialogue can augment the impact of feedback on student learning, dialogic feedback is unaffordable by instructors teaching large classes. In this regard, peer feedback can offer a scalable and effective solution. However, the existing practices optimistically rely on students' discussion about feedback and lack a systematic design approach. In this paper, we propose a theoretical framework of collaborative peer feedback which structures feedback dialogue into three distinct phases and outlines the learning processes involved in each of them. Then, we present a web-based platform, called Synergy, which is designed to facilitate collaborative peer feedback as conceptualised in the theoretical framework. To enable instructor support and facilitation during the feedback practice, we propose a learning analytics support integrated into Synergy. The consolidated model of learning analytics, which concerns three critical pieces for creating impactful learning analytics practices, theory, design and data science, was employed to build the analytics support. The learning analytics support aims to guide instructors' class-wide actions toward improving students' learning experiences during the three phases of peer feedback. The actionable insights that the learning analytics support offers are discussed with examples. [ABSTRACT FROM AUTHOR]

The emergence of personalised data technologies such as learning analytics is framed as a solution to manage the needs of higher education student populations that are growing ever more diverse and larger in size. However, the current approach to learning analytics presents tensions between increasing student agency in making learning-related decisions and 'datafying' students in the process of collecting, analysing and interpreting data. This article presents a study that explores staff and student experience of agency, equity and transparency in existing data practices and expectations towards learning analytics in a UK university. The results show a number of intertwined factors that have contributed to the tensions between enhancing a learner's control of their studies and, at the same time, diminishing their autonomy as an active agent in the process of learning analytics. This article argues that learner empowerment should not be automatically assumed to have taken place as part of the adoption of learning analytics. Instead, the interwoven power relationships in a complex educational system and the interactions between humans and machines need to be taken into consideration when presenting learning analytics as an equitable process to enhance student agency and educational equity. [ABSTRACT FROM AUTHOR]

The emergence of personalised data technologies such as learning analytics is framed as a solution to manage the needs of higher education student populations that are growing ever more diverse and larger in size. However, the current approach to learning analytics presents tensions between increasing student agency in making learning-related decisions and 'datafying' students in the process of collecting, analysing and interpreting data. This article presents a study that explores staff and student experience of agency, equity and transparency in existing data practices and expectations towards learning analytics in a UK university. The results show a number of intertwined factors that have contributed to the tensions between enhancing a learner's control of their studies and, at the same time, diminishing their autonomy as an active agent in the process of learning analytics. This article argues that learner empowerment should not be automatically assumed to have taken place as part of the adoption of learning analytics. Instead, the interwoven power relationships in a complex educational system and the interactions between humans and machines need to be taken into consideration when presenting learning analytics as an equitable process to enhance student agency and educational equity. [ABSTRACT FROM AUTHOR]

Learning analytics (LA) employs educational data to improve the timeliness of support for learners. Apart from technical aspects, there is a need to understand social complexities brought about by different stakeholders, so as to systematise the adoption of LA in Higher Education (HE). We present an analysis of the situation, needs and challenges of LA in the context of Spanish HE, considering managers', teachers' and students' perspectives. Qualitative research is employed using recursive abstraction. Results reveal that the level of institutional adoption is low and none of the analysed institutions had an LA policy. Furthermore, only two of these institutions had an initial LA strategy. While the institutions shared some commonalities in their objectives for LA, chosen tools and adoption challenges, the distinct differences in the political contexts and institutional practices among the institutions reaffirmed that LA solutions and services cannot be implemented in the same manner. Moreover, different needs for LA and concerns are identified about its adoption among managers, students and teachers. These observations lead to our conclusion that the main challenges to implement LA in Spain are not related to technological issues but to the social and cultural issues rooted in institutions and those associated with different stakeholders. [ABSTRACT FROM AUTHOR]

Predictors of student academic success do not always replicate well across different learning designs, subject areas, or educational institutions. This suggests that characteristics of a particular discipline and learning design have to be carefully considered when creating predictive models in order to scale up learning analytics. This study aimed to examine if and to what extent frequently used predictors of study success are portable across a homogenous set of courses. The research was conducted in an integrated blended problem-based curriculum with trace data (n = 2,385 students) from 50 different course offerings across four academic years. We applied the statistical method of single paper meta-analysis to combine correlations of several indicators with students' success. Total activity and the forum indicators exhibited the highest prediction intervals, where the former represented proxies of the overall engagement with online tasks, and the latter with online collaborative learning activities. Indicators of lecture reading (frequency of lecture view) showed statistically insignificant prediction intervals and, therefore, are less likely to be portable across course offerings. The findings show moderate amounts of variability both within iterations of the same course and across courses. The results suggest that the use of the meta-analytic statistical method for the examination of study success indicators across courses with similar learning design and subject area can offer valuable quantitative means for the identification of predictors that reasonably well replicate and consequently can be reliably portable in the future. [ABSTRACT FROM AUTHOR]

Student evaluation of teaching (SET) is now common practice across higher education, with the results used for both course improvement and quality assurance purposes. While much research has examined the validity of SETs for measuring teaching quality, few studies have investigated the factors that influence student participation in the SET process. This study aimed to address this deficit through the analysis of an SET respondent pool at a large Canadian research-intensive university. The findings were largely consistent with available research (showing influence of student gender, age, specialisation area and final grade on SET completion). However, the study also identified additional influential course-specific factors such as term of study, course year level and course type as statistically significant. Collectively, such findings point to substantively significant patterns of bias in the characteristics of the respondent pool. Further research is needed to specify and quantify the impact (if any) on SET scores. We conclude, however, by recommending that such bias does not invalidate SET implementation, but instead should be embraced and reported within standard institutional practice, allowing better understanding of feedback received, and driving future efforts at recruiting student respondents. [ABSTRACT FROM AUTHOR]