Category Archives: Uncategorized

Note: Website has moved; this will remain an irregular blog

This is just to briefly document, that I’ve re-arranged my web-presence, such that

What can computer technologies do for learning? – An overview on the roles and functions of technologies in learning.

Designing technologies for workplace learning is the absolutely major part of my research. But what can technologies do for learning?

I don’t know of authoritative literature that categorises the different roles and functions of technologies in learning activities – so over time I’ve created a brief overview for myself.

The below listed categories of technologies are not completely mutually exclusive. For instance, technologies that support the consumption of digital learning materials might as well be game technologies; and simulations may also support communication, etc.

Additionally, while categories are intended to be exhaustive, in detail one could argue about additions: For instance, open learner modeling is now subsumed in both data analytics and intelligent systems, as open learner modeling in the end has been decided to be a more fine-granular term than the others in the list. Another example is e-Assessment, which has been left out as simple question-based assessment is technologically not research-intensive, although it may be very much from a learning sciences perspective!; and task-based or implicit assessment fits into multiple categories such as game technologies or virtual simulations or data analytics.

Finally, infrastructure and device technology necessary to actually access content and run software for learning, such as network infrastructures or mobile devices have not been listed at all. Nonetheless, these of course play a key role in enabling technology-enhanced learning.

References, where included, have been selected to be relatively modern and introductory to the category.

  • Technologies support the documentation of learning activities and results
  • Technologies support the distribution and consumption of digital learning materials (including learning management systems, MOOCs)
  • Communication technologies and social software featuressupport discussions between learners as well as between learners and teachers (cp. Stahl et al., 2014).
  • Virtual simulations support experimentation that is safer, sometimes cheaper, or even impossible in reality (cp. de Jong, 1991).
  • Data analytics are used to derive insights about learning activities and results from all kinds of sources for a wide variety of stakeholders. This includes learners, who shall be supported in learning; teachers, who shall be supported in teaching; and institutions, who shall be supported in institutional decision making thereby impacting learning (cp. Siemens, 2013).
  • Intelligent technologies proactively recommend learning materials and relevant people and guide learning activities in complement or as substitute to human teachers (recommender systems – cp. Manouselis et al., 2010; intelligent tutoring – see Baker, 2016 for a critical discussion that includes an overview of intelligent tutoring literature).
  • Gaming technologies finally design for learning in a playful environment (serious games or learning games – cp. van Eck , 2006 for an overview).

(Baker, 2016) Baker, R. Stupid Tutoring Systems, Intelligent Humans. International Journal of Artificial Intelligence in Education, 2016, Vol. 26/2, p.600-614.

(de Jong, 1991) De Jong, T. Learning and instruction with computer simulations. Education and Computing, 1991, Vol. 6/3, p. 217-229.

(Manouselis et al., 2010) Manouselis, N.; Drachsler, H.; Vuroikari, R.; Hummel, H. & Koper, R. Recommender Systems in Technology Enhanced Learning. Recommender Systems Handbook, Springer, 2010, p. 387-415.

(Siemens, 2013) Siemens, G. Learning Analytics: The Emergence of a Discipline. American Behavioral Scientist, 2013, Vol. 57, No. 10, p. 1380-1400.

(Stahl et al., 2014) Stahl, G., Koschmann, T. & Suthers, D. Computer-Supported Collaborative Learning. The Cambridge Handbook of the Learning Sciences, Cambridge University Press, Ch.24, p. 479-500, 2014.

(van Eck, 2006) Van Eck, R. Digital Game-Based Learning: It’s Not Just the digital Natives Who Are Restless. Educause Review, 2006, Vol 41/2, p.16-30.

We are Designers – We shape our future with and without Artificial Intelligence

Yesterday I held a keynote in the Digital Business Trends Event Series.

My talk drew strongly from activity theory and communities of practice as theoretical background of understanding human activity and the role of tools, and of human development, and embedding in social contexts.

Key points of my talk are described (in German) in the DBT Blog:

In short, I think that we need to frame the discussion around artificial intelligence as starting from the question: What are our goals and values – as individuals, as organisations, and as society. Secondly, we need to create – at all these levels – an understanding of how artificial intelligence can contribute to these goals. This necessitates both a technical understanding (without everyone of us needing to become artificial intelligence experts, data analysts or data scientists), a business understanding of what these technologies could mean for an organisation, and a societal understanding of wider implications. Finally, we need to understand technologies in their social context – so we need to consider and keep an eye open on impact on the whole sociotechnical system.

At all levels, we therefore need to develop on the one hand self-efficacy in relationship to artificial intelligence technologies; and leading towards this, we need to develop the competence to understand artificial intelligence in relationship to our goals and values.

We will not be able to solve everything at either level: individual, organisation or society – but we are designers of our life with artificial intelligence (or without it) at all these levels; not only as software developers and developing organisations, but also as users and user organisations.

Becoming a computer scientist

I was asked by Prof. Wolfgang Slany (As initiator of Catrobat also present in Twitter) to motivate and inspire freshly started computer science students – especially female students as a sort of role model – for the field they have decided to dive into.

This post is the written, permanent, persistent-on-the-web-version of the talk I will give on Thursday, Oct. 2, 2014, in the lecture “Programmieren 0” at TUG.

I decided to study “Telematik” (a mixture of computer science and electrical engineering) because I thought it would be challenging, and a little bit less abstract than mathematics. In my master studies, I focused on artificial intelligence and telecommunication engineering. I found computer science in the end the more interesting field, and did my PhD in computer science. By now I am assistant professor at the Knowledge Technologies Institute, and lead a team of 13 researchers and developers at the competence center “Know-Center” (that carries out applied research). My research and teaching is now on the topics computer-supported cooperative working and learning, human-computer-interaction, and ubiquitous and mobile technologies.

What interests me in computer science is that it is, in some ways, free from nature’s law.  In physics you study how to understand the material world, in psychology how to understand humans, and in electrical and civil engineering, the laws of physics govern what can be constructed. In computer science it is much more one’s own creativity that decides the end results. Computer science also pervades our modern life: Nearly every domain now relies on computer science, and as computer scientist you can make an impact in nearly every domain – if you wish to.

In computer science – as probably in every field that you would study – there will be some subjects that already while studying fascinate you, some that you come to find interesting only later, some that you will find horribly difficult, and some that you find honestly boring as well. I think it is important, in order to get through even the hard and the boring stuff, to realise that

  • I cannot and need not be perfect in all subjects
  • Not everyone who gives the impression of being really brilliant really IS brilliant
  • I am responsible for finding something that interests me in the subjects that are taught.

Actually I find the last to grow more and more important as I progress in my career.