In the context of the hype, promise and perils of Big Data and the currently dominant paradigm of data-driven decision-making, it is important to critically engage with the potential of Big Data for higher education. We do not question the potential of Big Data, but we do raise a number of issues, and present a number of theses to be seriously considered in realising this potential.The University of South Africa (Unisa) is one of the mega ODL institutions in the world with more than 360,000 students and a range of courses and programmes. Unisa already has access to a staggering amount of student data, hosted in disparate sources, and governed by different processes. As the university moves to mainstreaming online learning, the amount of and need for analyses of data are increasing, raising important questions regarding our assumptions, understanding, data sources, systems and processes.This article presents a descriptive case study of the current state of student data at Unisa, as well as explores the impact of existing data sources and analytic approaches. From the analysis it is clear that in order for big(ger) data to be better data, a number of issues need to be addressed. The article concludes by presenting a number of theses that should form the basis for the imperative to optimise the harvesting, analysis and use of student data.

This article introduces the concept of the Assessment Purposes Triangle in order to illustrate the balancing of purposes that needs to be considered in assessment for educational quality. The triangle is a visual aid for stakeholders making value judgements about equitable resource allocation in an environment of scarcity. The article argues that each of the three basic purposes of assessment, Assessment to support learning; Assessment for accountability; Assessment for certification, progress and transfer need to enjoy appropriate attention to support quality education. Accountability and certification, progress and transfer are inherently high stakes due to systemic pressures such as institutional funding, accreditation with various bodies, legislative requirements, national and international competition, and public and media pressure. It is thus likely that resources will be diverted to these purposes from the lower stakes Assessment to support learning. The article illustrates the inherent dangers of over-emphasising certain purposes to the detriment of others in any educational system. It also discusses the power inherent in assessment and argues for a harnessing of this power for quality education through catering for the three basic assessment purposes in an equitable manner. All actors in an educational system should be cognizant of the goals of the education system as a whole when developing and engaging with assessment. The article provides a conceptual tool for stakeholders in educational assessment to engage with these goals when considering the allocation of appropriate resources to each of the assessment purposes.

Assessment and learning analytics both collect, analyse and use student data, albeit different types of data and to some extent, for various purposes. Based on the data collected and analysed, learning analytics allow for decisions to be made not only with regard to evaluating progress in achieving learning outcomes but also evaluative judgments about the quality of learning. Learning analytics fall in the nexus between assessment of and for learning. As such it has the potential to deliver value in the form of (1) understanding student learning, (2) analysing learning behaviour (looking to identify not only factors that may indicate risk of failing, but for opportunities to deepen learning), (3) predicting students-at-risk (or identifying where students have specific learning needs), and (4) prescribing elements to be included to ensure not only the effectiveness of teaching, but also of learning. Learning analytics have underlying default positions that may not only skew their impact but also impact negatively on students in realising their potential. We examine a selection of default positions and point to how these positions/assumptions may adversely affect students’ chances of success, deepening the understanding of learning.

Open Educational Resources (OER) emerged within the context of open education which is typically characterized by the sharing of knowledge and resources and the exchange of ideas. Unisa as a mega open distance learning (ODL) university has publicly communicated its intention to take part in the use and creation of OER. As global and local university research on OER is limited, this prompted an investigation to gauge the uptake of OER at Unisa, by staff, with the purpose of institutional information gathering for decision making and planning in this area. During 2014, a survey was undertaken for this reason. The survey examined knowledge of OER, Intellectual Property (IP) Rights and Licensing, participation in OER, barriers to OER and OER in the Unisa context with a view to determining the stage at which the institution is in terms of adopting and engaging with the OER initiative. The results indicated that although there is knowledge and understanding of OER, this has not been converted into active participation. It further highlighted the barriers that are prohibiting the operationalization of OER and resulted in recommendations for planning and activities in respect of OER. The constructs investigated and the results thereof might not be generalizable to other contexts, although commonalities are likely. The insights should prove useful to a variety of contexts. The paper illustrates the need for institutions, irrespective of context, to take stock of the impact of initiatives and in this case evaluate how the institution and staff mature through various phases in the uptake of OER in order to guide effective planning, decision making and implementation.

Student success and retention is a primary goal of higher education institutions across the world. The cost of student failure and dropout in higher education is multifaceted including, amongst other things, the loss of revenue, prestige, and stakeholder trust for both institutions and students. Interventions to address this are complex and varied. While the dominant thrust has been to investigate academic and non-academic risk factors thus applying a “risk” lens, equal attention should be given to exploring the characteristics of successful students which expands the focus to include “requirements for success”.Based on a socio-critical model for understanding of student success and retention, the University of South Africa (Unisa) initiated a pilot project to benchmark successful students’ habits and behaviours using a tool employed in business settings, namely Shadowmatch®.The original focus was on finding a theoretically valid measured for habits and behaviours to examine the critical aspect of student agency in the social critical model. Although this was not the focus of the pilot, concerns regarding using a commercial tool in an academic setting overshadowed the process. This paper provides insights into how academic-business collaboration could allow an institution to be more dynamic and flexible in supporting its student population.