Jisc is leading the field in learner analytics. It’s never been easier to track and record every detail of a student’s journey, their successes and challenges, their interest and engagement. In this blog post Julia Taylor argues that we must not forget that the aim of learner analytics is to continually improve the student experience – for everyone.
Of course there are potential problems too. Data can be interpreted and used in a myriad of ways. That’s why the Jisc Code of Practice for Learning Analytics makes clear that the data we collect must be used for the benefit of all students including those with disabilities and disadvantages. This poses two specific challenges. Providing equality of access via student apps, dashboards and websites and ensuring that the right data is collected and used effectively and intelligently to support everyone’s needs.
However comprehensive it may be, data is only useful to those who can access it. Is the data presented in a format that is accessible? Does everyone know how to access it? Do ALL students, even those using assistive technology or with sensory impairments have the same quality of experience. Equally important, does everyone contribute to the data? Are all disadvantaged students properly represented? Are they enabled to participate in suggesting appropriate data – data that will identify their individual progress and needs and promote their best interests accurately?
The potential of this data for those who are disadvantaged is enormous. Students would have the wherewithal to monitor their own progress against their individual objectives and the possibility of summoning timely targeted interventions that will deal directly with the barriers they are experiencing – as they happen. Teachers could actively use this data to differentiate responses, enable more personalised learning and improve inclusive teaching practice. In an Effective Learning Analytics blog post on the accessible potential of learner analytics, my colleague Niall Sclater has reflected on the Jisc code of practice and identified the 8 ways in which institutions can support an inclusive approach to learner analytics.
All data is open to misinterpretation and misuse. Being clear about the purpose is key. Wherever data contributes to assessment the implications for each students needs to be considered. We should remind ourselves that the main purpose of learner analytics is to inform and improve the decision making process. For example, simply counting attendance is not evidence of engagement. It will actually disadvantage students with mobility issues or long term illness unless it results in interventions designed to address the barriers they face. At the very least it should facilitate further analyses of the reasons that they are not attending. A better alternative would be analytics that are designed to monitor levels of real engagement across curricula. This could be used to identify accessibility issues in teaching modules or activities and lead to more accessible course design.
Clearly, there will be issues of consent, confidentiality and discretion but the potential benefits for anyone prepared to disclose a disability are huge – if the institutional response is focused on supporting their success. As Widening Participation becomes a priority across the sector, the office of fair access (OFFA) are not alone in suggesting institutions take an evidence based collaborative approach to develop deeper ways of demonstrating value for money and impact based on student data. So it is increasingly important to manage the consistency, relevance and quality of the data analysis. In order to make best use of this information institutions will need to ensure they collect and interpret data without re-enforcing stereotypes and discriminatory practice and that it is used proactively to support more accessible delivery and wider participation.
We are not short of data sources but we are at the beginning of a journey in data driven decision making. Jisc co-design challenges are asking for your feedback about the use of data in quality assurance , teaching delivery and business intelligence. Considering how much it could contribute to improving the student experience for all, it must be used as an opportunity to shape learner analytics with inclusive practice in mind.
Jisc Effective Learner Analytics Project
Jisc Business intelligence project
Student experience: Data, Quality Assurance and improving the student experience.