Conference schedule overview

A tentative conference schedule (small changes are still possible)

Monday Tuesday Wednesday Thursday Friday
Time Pre conference Main conference
07:30 AM Registration Registration Registration Registration Registration
08:30 AM Full day events and half day (morning) events Full day events and half day (morning) events Welcome and opening remarks Opening remarks Opening remarks
08:45 AM Keynote introduction Keynote introduction Keynote introduction
09:00 AM Keynote: Keynote: Keynote:
10:00 AM Morning tea Morning tea Morning tea Morning tea Morning tea
10:30 AM Full day events continue and half day (morning) events continue Full day events continue and half day (morning) events continue Concurrent sessions: Concurrent sessions: Concurrent sessions:
12:00 PM Lunch Lunch Lunch Lunch Lunch
01:00 PM Full day events continue and half day (afternoon) events Full day events continue and half day (afternoon) events Concurrent sessions: Concurrent sessions: Concurrent sessions:
02:30 PM Afternoon tea Afternoon tea Afternoon tea Afternoon tea Afternoon tea
03:00 PM Full day events continue and half day (afternoon) events continue Full day events continue and half day (afternoon) events continue Concurrent sessions: SoLAR annual general meeting Community Building Panel
03:45 PM Closing remarks
04:00 PM Departure
04:30 PM Learning@Scale closing keynote (open also to LAK participants) Transfer to theatre
05:15 PM Firehose
05:45 PM 15 min break
06:00 PM Joint reception with L@S Posters, technology showcase, and welcome reception Conference dinner
07:30 PM
09:00 PM
‘Learning as a machine’ can be ‘read’ in three ways. First, it can refer to the learning process itself, as a kind of machinery, as a mechanistic or deterministic process. I will call this the Pavlov approach and inquire whether Pentland’s and Helbing’s social physics continues this approach in the era of data-driven exploration. Read more...
Second, it can refer to the learning process of machines, notably to ‘machine learning’ as one of the most promising techniques of artificial intelligence. I will call this the Herbert Simon approach, even though Simon was quite sceptical of machine learning. Third, ‘learning as a machine’ can refer to the fact that human beings increasingly live in a world saturated with data-driven applications that are more or less capable of machine learning. Since this will require human beings to anticipate how their intelligent environment learns, I will argue that – to some extent – humans will engage in ‘learning as if a machine’. In my keynote I will investigate what this could mean in terms of human liberty and human dignity – two key terms in the privacy debate – and explain how it relates to the employment of learning analytics to aid human learning processes. This will include a discussion of legal protection by design in the context of learning analytics, notably providing students with profile transparency while protecting their fundamental right to data protection. For an example of profile transparency in another context, see Read less…
Learning Analytics (LA) is being greeted with the same adoration, praise, and/or joy (hosanna!) as we saw with many other information-technological innovations of the past few decades. There is, however, both a positive and a negative difference between LA and earlier innovations. Read more...
First the good news. LA is probably the first in the long line of innovations and promises based upon them that can possibly achieve many of the educational futures that people are hoping for such as adaptivity, differentiation, tailor-made instruction, and so forth. And now the bad news. While most of the others failed and in their failure were innocuous (if you consider wasting time and money to be innocuous), LA has the potential to also do harm in a number of ways. This keynote will try to put both the possible utopian futures and their dystopian counterparts on the research and praxis agendas. Read less…
Psychometrics has a long history of creating methods to reason from students’ behavior to their proficiencies more broadly conceived and how they might be improved. This talk discusses concepts from psychometrics I believe hold value for learning analytics. Read more...
Some are familiar, but others are quite new to the field itself. Key insights that have evolved over the years have been (1) viewing the problem as one of statistical inference, (2) building models that suited the inferential problem as cast in then-current psychological theory, informed by then-current forms of data, and (3) developing methods to define and operationalize properties such as reliability, validity, comparability, generalizability, and fairness – not just measurement principles, Messick (1994) reminds us, but “social values that have meaning and force outside of measurement wherever evaluative judgments and decisions are made” (p. 13). Rapid advances in technology and sociocognitive branches of psychology have sparked a renaissance in the field, as psychometricians work across disciplines to disentangle key insights from the particulars of historical models, forms of data, and psychological perspectives. Current work seeks to synthesize hard-won psychometric understandings with computational data analysis methods; results from the learning sciences, domain-based research, and cognitive and sociocultural psychology; and simulation, game, and immersive environments. Several of the challenges we are tackling connect with those faced in learning analytics. I will note connections where I see them, with an anticipation of mutual benefit. Read less…