Open Universiteit, The Netherlands
Learning is more than an outcome, a final grade, or a degree; learning is an ongoing process leading to an outcome. Similarly, the assessment of learning also requires considering the process and not just the outcome. To better understand and improve learning or assessment it is thus, important to look at the ‘how' and ‘why' on the route to these outcomes. However, learners often lack the ability - or capability - to critically introspect on their way of learning or taking an exam. And the self-report measures that we have such as learning style inventories and cognitive load scales also have many shortcomings and biases (e.g., it is questionable what they measure and whatever it is, they measure processes after learning (or its assessment) has taken place). With the development of new technology, it is possible to observe, measure, and understand learning and assessment processes more objectively while they are happening, that is, online and determined in real time. For instance, analysis of logging data unravels the order in which learners visit pages of a learning or testing environment or surf the world wide web, the time they spend on each page, and the actions they execute there. From that we can understand how they digest information (i.e., linear or nonlinear). In more detail, eye tracking shows which information learners focus on per page (i.e., actually read or skip). It can also online yield insight in how much mental effort they experience, without intruding the learning or testing process. While logging data delivers information on actions that learners made, eye tracking provides insight into perceptual, cognitive, and even affective/emotional processes. In other words, these techniques provide a unique look behind the curtain of learning and assessment itself. Still, both techniques face the challenge of relating their data to actual learning or assessment processes. Furthermore, large amounts of data and the detection of relevant events in them are challenges both techniques continuously face and that require additional insight from a computer science perspective (cf. data mining).
This SIG aims to unite researchers working in diverse fields of both education and information technology in developing and using objective, online methodologies to make learning and assessment processes visible and capture them and to discuss and exchange insights on the use of the different technologies. The technologies can include, but are not limited to: eye tracking, logging data, data mining, technological analyses of observational videos.
SIG 27 Biennial Conference 2022