The Hack@LAK18 is a pre-conference workshop of the 8th International Learning Analytics and Knowledge Conference (LAK). It is the fourth in the series of Hackathons held at LAK, where we encourage a hands on approach to combining novel data sources in a realistic infrastructure for the benefit of Learning. The approach is multi-disciplinary, reviewed from all angles, self-organising and team building. Anyone is welcome to participate as long as they are motivated to be politely critical and work towards and expand common objectives.

Please feel free to bring along your research questions, datasets and methodologies to the workshop for incorporation in the multidisciplinary activities. If you would like to suggest open research questions that you think should be addressed at the event, then please see our Call for Proposals which is open until December 18. However, you don’t have to have submitted a proposal in order to participate!

The event itself will take place on March 5-9, 2018 in Sydney, Australia.

This workshop is called a data hackathon but it is not just for techies. If you are involved in education, whether as a teacher, instructor,  learning technologist or instructional designer, this is the chance to gain better insight into what is kinds of data are already available and also to help system developers gather the information you really need to better support your vision for user-centred learning analytics.

Topics, objectives and outcomes

The principal aim of Hack@LAK18 is to enable multi-disciplinary thinking over key open challenges in Learning Analytics based on a problem-oriented, pragmatic approach. In line with the traditional definition of a hackathon, the expected outcomes of the event are the identification and initial (concrete, technical) pilot implementations of prototype tools/systems/data/studies which arise from the synthesis of educational technology, software development, and data science perspectives. As for previous events, the hackathon will generate a repository of code, sample data, screenshots, slides etc., from the activity of participants. An important intangible outcome will be an improved understanding of the different kinds of expert present about what is both desirable and technically-feasible.

While we welcome research questions, challenges, tools and data from all participants, we expect to emphasise the following topics which the organisers feel focus particularly on user-centred learning analytics:

Personal analytics supporting self-directed learning: Much of the data supporting the analysis of learning experiences are generated by the learners, and can be used to their benefit. Here, we are looking to understand what “learning analytics for the learner” can mean, especially in the context of self-directed learning using web platforms. The AFEL project has developed tools to collect such data from the learner’s browser and social media accounts, creating data spaces of online activities around the learner. Through the Hackathon, we hope to enable a greater understanding and initial practical solutions on the way those data spaces could be used to support learners in improving their own experience of using the Web for learning.

Goal setting and analytics: Goal setting is a potent tool to enhance the performance of individuals. However, we rarely use in education. With the help of learning analytics now, it is possible to set a goal and monitor those goals over time using data. This field is a novel and a vaguely explored area that gives a lot of room for creativity and development. We will build on the LAK16 Goal setting workshop [10] and available open source applications.

Playgrounds for data literacy: An emerging challenge for LA solutions concerns the lack of data literacy in both the academic and student populations. As we create more data and analytic models, can the people using it understand what it means? In alignment with the emerging field of critical data studies, there is an increasing need to develop an awareness among our students of the potential uses of their data and the possible consequences, including the development of tools that support this work.  We first proposed the concept of a data playground in the 2017 Hackathon; we will return to this idea in the 2018 Hackathon to try to translate some of the initial ideas into demonstrable outputs.

Furthermore, we will continue to emphasise and develop the following enabling objectives:

Student facing Open APIs: While we are increasingly providing LA solutions for students, a significant opportunity arises to investigate the way in which universities and other open government services can enrich and expose their data stores and analytics, thus fostering the rapid development of student facing solutions. Institutions are increasingly moving towards an API based architecture which will add flexibility to their core IT infrastructure, a situation that offers many opportunities for rapid innovation and development of solutions by the people who will use them (rather than by external providers). At the same time, many universities host a pool of highly motivated students with fundamental ideas about how to improve the student experience by offering innovative new IT services that are beyond core business. This challenge is to facilitate such access pathways for systems built using the core infrastructure provided by official university data warehousing situations. We will investigate Security, data format, mapping and other core properties.

Infrastructure-integrated approaches for the joint exploitation of distributed data sources, including synthetic data: While previous events have focused on the exploitation of data from single sources, including the synthetic generation of data from those sources and their exploitation in visual analytics methods, a key aspect here regards the integration of the increasing number of data sources. From which we can assess the learning activities, learning environment and learners’ experiences. Here, we will be looking at profiles and technical approaches to enable the joined-up use of data coming as much from institutional systems, as from the learners’ other platforms (e.g. social media). A specific emphasis here will include the ability to generate synthetic, homogeneous and exploitable data from the kind of sources manipulated within the AFEL and Jisc projects.

Analytics beyond user-computer interaction data. We will try to move from the datasets of user-to-computer interactions (the actor-verb-object paradigm) to focus more on the user-to-world interactions which we can elicit with motoric and physiological sensors (the sensor-sample-value paradigm). Multimodal datasets collected from practical and workplace learning experiences will be made available for analysis. These learning experiences are composed of atomic actions defined by an Experience API statement (e.g. the “expert pulls lever”), which will point to a list of multimodal sensor recordings. Some research questions associated with this type arise including: what are the essential features that we can extract from sensor data streams? Which data analysis and techniques are suitable? At the input level, how can we compare two or more action executions  What is the most efficient way to integrate sensor data with xAPI? What is a meaningful visualisation?

Format of the Event

Hack@LAK18 will be an inclusive, open workshop over two days. For the first half day, there will be a period of orientation and an introduction to the core themes and mundane details such as how to interact with the data and the infrastructure.  Team building from the very start, we place the participants in the centre of activities, evolving the schedule based on their feedback and expressed objectives. We then divide into teams of 6-8 to fulfill specific missions. At the end of each day we discuss progress, lessons learned and next steps. At the end of the second day, we summarise and plan future follow on actions.

This year, we are asking on a voluntary basis for short submissions detailing research questions and associated datasets. We will use these as opportunities to seed the set of problems teams can work and collect as part of a final report. Furthermore, Hack@LAK18 will innovate upon previous organisational structures by hosting pre-hackathon events at the University of Technology Sydney’s (UTS) Connected Intelligence Centre (CIC). UTS runs data and analytics programs.  The pre-events will expose the audience to the student facing analytics, helping them to think about how they would like the area to evolve. Building their student facing LA solutions as a response to many different challenges set by the hackathon organisers (e.g. how to improve graduate employability, pathways to expertise, social connectedness). We will investigate sponsorship so that the winners of these pre-events can attend the main LAK hackathon and continue with their work on the global stage.