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Purpose: The purpose of this paper is to explore and establish the contours of trust in learning analytics and to establish steps that institutions might take to address the “trust deficit” in learning analytics. Design/methodology/approach: “Trust” has always been part and parcel of learning analytics research and practice, but concerns around privacy, bias, the increasing reach of learning analytics, the “black box” of artificial intelligence and the commercialization of teaching and learning suggest that we should not take stakeholder trust for granted. While there have been attempts to explore and map students’ and staff perceptions of trust, there is no agreement on the contours of trust. Thirty-one experts in learning analytics research participated in a qualitative Delphi study. Findings: This study achieved agreement on a working definition of trust in learning analytics, and on factors that impact on trusting data, trusting institutional understandings of student success and the design and implementation of learning analytics. In addition, it identifies those factors that might increase levels of trust in learning analytics for students, faculty and broader. Research limitations/implications: The study is based on expert opinions as such there is a limitation of how much it is of a true consensus. Originality/value: Trust cannot be assumed is taken for granted. This study is original because it establishes a number of concerns around the trustworthiness of learning analytics in respect of how data and student learning journeys are understood, and how institutions can address the “trust deficit” in learning analytics.

AbstractThis article seeks to explore different combinations of human and Artificial Intelligence (AI) decision-making in the context of distributed learning. Distributed learning institutions face specific challenges such as high levels of student attrition and ensuring quality, cost-effective student support at scale using a range of technologies, such as AI. While there is an expanding body of research on AI in education (AIEd), this conceptual article proposes that combinations of human-algorithmic decision-making systems need careful and critical consideration, not only for their potential, but also for their appropriateness and ethical considerations. We operationalize a framework designed to consider robot autonomy at four key events in students’ learning journeys, namely (1) admission and registration; (2) student advising and support; (3) augmenting pedagogy; and (4) formative and summative assessment. We conclude the article by providing pointers for operationalizing options in human-algorithmic decision-making in distributed learning contexts.

Higher education institutions are moving to design and implement teacher-facing learning analytics (LA) dashboards with the hope that instructors can extract deep insights about student learning and make informed decisions to improve their teaching. While much attention has been paid to developing teacher-facing dashboards, less is known about how they are designed, implemented and evaluated. This paper presents a systematic literature review of existing studies reporting on teacher-facing LA dashboards. Out of the 1968 articles retrieved from several databases, 50 articles were included in the final analysis. Guided by several frameworks, articles were coded based on the following dimensions: purpose, theoretical grounding, stakeholder involvement, ethics and privacy, design, implementation, and evaluation criteria. The findings show that most dashboards are designed to increase teachers’ awareness but with limited actionable insights to allow intervention. Moreover, while teachers are involved in the design process, this is mainly at the exploratory/problem definition stage, with little input beyond this stage. Most dashboards were prescriptive, less customisable, and implicit about the theoretical constructs behind their designs. In addition, dashboards are deployed at prototype and pilot stages, and the evaluation is dominated by self-reports and users’ reactions with limited focus on changes to teaching and learning. Besides, only one study considered privacy as a design requirement. Based on the findings of the study and synthesis of existing literature, we propose a four-dimensional checklist for planning, designing, implementing and evaluating LA dashboards.

In the socio-technical imaginary of higher education, algorithmic decision-making offers huge potential, but we also cannot deny the risks and ethical concerns. In fleeing from Frankenstein’s monster, there is a real possibility that we will meet Kafka on our path, and not find our way out of the maze of ethical considerations in the nexus between human and nonhuman agencies.In this conceptual article, I map seven dimensions of student surveillance on an experimental matrix of human-algorithmic interaction to consider some of the ethical implications of algorithmic decision-making in higher education. The experimental matrix of human-algorithmic decision-making uses the four tasks of ‘sensing’, ‘processing’, ‘acting’ and ‘learning’ to open up algorithmic-human agency as comprising a number of possibilities such as (1) where only humans perform the task; (2) where the task is shared between humans and algorithms; (3) where algorithms perform the task but with humans supervising; and (4) where algorithms perform the tasks with no human oversight.I use this matrix to engage with seven dimensions of how higher education institutions collect, analyse and use student data namely (1) automation; (2) visibility;(3) directionality; (4) assemblage; (5) temporality; (6) sorting; and (7) structuring. The article concludes by proposing a number of pointers to be taken into consideration when implementing algorithms in a higher education context from a position of an ethics of care.

The objective of this paper is to map the role of capital in the process of learning to become a postgraduate supervisor. Economic, technological and geopolitical changes in higher education call into question previous assumptions about supervision. Supervision is no longer primarily seen as an intellectual and social enterprise but is increasingly seen as professional work, where one's capital (or lack of) shapes the process of being and becoming a supervisor. It is frequently assumed that the students are the only ones learning in a supervisory relationship. Novice supervisors are, however, often left to their own devices to discover or learn the inherent rules, epistemologies and ontologies in becoming and being supervisors through a process of compromise and negotiation. In this article, we specifically focus on the need for novice supervisorsto understand and navigate the field, and plot their career trajectories, as a constant exchange of different aspects of capital. This article is conceptual, rather than empirical. We suggest that it may be helpful to understand the field of supervision in a Bourdieusian sense, with specific reference to the role of (academic) capital in the formative processes of becoming and being a supervisor. We discuss social capital, gender and race, age, values, beliefs and experiences, as well as linguistic abilities, as factors influencing the individual habitusof the supervisor in relation to the doxaof the field.

In the social imaginary of higher education, there are many mutually constitutive forces shaping academic identities, such as academics’ habitus, dispositions, race, gender and student expectations. Our queer academic identities are furthermore robustly intertwined with, and emerging within, cultural, political and economic histories and realities. In post-apartheid South Africa, our academic identities are constituted in the nexus of historical white privilege on the one hand, and on the other hand, the prevailing heteronormative and homophobic public sentiment on the African continent. In the intersection of race and gender as technologies of power, we perform our identities as academics as processamidst often incommensurable and multiple claims and counterclaims. In the context of being and becoming academics, our identities as nomadsprovide a particular coherence, continuity and stability, albeit a continually ‘changing core but the sense of a core nonetheless – that at any given moment is enunciated as the “I”’ (Brah, A. 1996. Cartographies of diaspora: Contesting identities. London, UK: Routledge, pp. 123–124). In this collaborative autoethnographic narrative, we explore becoming and being nomad as one particular lens among many others to describe our personal journeys and map a selection of issues of (dis)location and transitions that constitute our subjective and material experiences and our own trajectories of becoming. Three analytics constructs are explored: namely spaces of becoming, cycles of becoming and negotiating and performing becoming.