Information about my web presence, this is an irregular blog.

This is to document that I’ve 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.

Activity Theory 101 – A Brief Introduction

What is Activity Theory?

Activity Theory was originally developed by Russian psychologists; first and foremost Vygotsky and Leontjew.

Activity Theory is a descriptive, theoretical framework; it provides a terminology and structure for thinking about human activities, taking some particular philosophical stances.

Activity Theory explores human activities; it explores “practice”, how things are done.

As computer technology pervades humans´ lives; having come out of the laboratories and backrooms since quite some time now, human-computer interaction becomes relevant – with the actual usage, usefulness, and integration of techology into the practice of humans taking an important place in HCI discourse.

What is an activity?

Activities are the central unit of analysis and thought in activity theory. An activity is directed towards an object or objective.

subject-objectThe object is what is created or manipulated in the activity; it is also what motivates the activity. The object describes, or constitutes, the problem space. An activity is carried out by a subject; and the subject is aware of the activity and the object. If a subject is not aware of the object(ive), it is only a participant; but every activity has a subject.

Activities are differentiated by their subject, and their object: If two activities look the same but have different object(ive)s, then they are not the same. If I am walking fast along the street in order to get fitter; and you are walking fast along the street in order to reach the train in time; these are two essentially different activities.

Activity theory principles

  • Human agency: Activity theory takes a very strong stance in seeing a conscious agent, a subject, as responsible for an activity; as driving it. It is not systems that interact, but humans that act and interact with each other. This contrasts for instance with theories of distributed cognition but also Luhmann´s theory of cognitive and social systems.
  • The environment exists on its own and is meaningful; it is not only our brains who somehow construct reality. This is a different approach than taken by constructivism, where meaning is constructed by and within the human mind.
  • Unity of activity and consciousness: Consciousness evolved to help humans survive in a real environment. Consciousness may drive actions in the real world; but it is the real world and the actions in it that count; not the consciousness itself. Remember – the theory was created by psychologists; but they do take the stance that it is not interesting or relevant to think in terms of what happens inside humans´ brains, except for when it is realised as actions.
  • Tools mediate activities, i.e. change essential qualities of the activity; tools shape activities. Tools are sometimes also called “artefacts” in activity theory literature. Tools can be both internal tools, such as concepts, factual or procedural knowledge (methods!) or external tools such as physical objects (e.g., a hammer, a book) or digital resources (e.g., software, documents).
  • Development processes are inherently included in analysis: Tools contain a historical development process; they are condensed human experience and knowledge. We stand on the shoulders of giants in our activitie by using tools and artefacts that not we ourselves have created.
  • Activities > actions >  operations: Activities are broad patterns directed to an overarching object(ive). In order to realise, create, manipulate an object(ive), the subject carries out a sequence of actions. Every action has a particular goal, which contributes to reaching the overall objective. Actions are operationalised as operations – operations heavily depend on a given situation.
  • Internal and external are mixed: Activity theory consistently combines rather than separates the internal and the external aspects. Tools can be both internal, conceptual as well as external, physical. Objects and objectives, too, can be both internal, conceptual (writing my thesis) as well as external, physical (sculpting a statue).

Activity Theory and HCI

To understand human computer interaction means to understand activity: What is the object(ive)? What is the level of interaction; i.e. does a tool support/accompany an activitiy, an action, or only a specific operation?

Computers are tools that humans use to achieve objectives that have meaning outside the human-computer system; and this “environment”, this setting of human-computer interaction is the context given when emphasizing the human activity rather than the human computer interaction.

HCI, then, is concerned with integrating computers as tools into human activities, with appropriating computers as tools to serve human object(ive)s.

Computers serve as functional organs – resources (tools) that enhance humans´ capabilities by providing / externalising functions. Books (the traditional ones) do the same by providing “external storage”; so we should think about what assistance computers can offer us, instead of what tasks computers can “take over” in order to “maximise” human experience of life.

Extending activity theory to collaborative practice

Collaborative practice is conceptualised in activity theory by relating the subject of an activity to a community. This community again consists of conscious subjects; and a community is defined as subjects who have the same object(ive)s.

The relationship between the subject and the community is mediated by rules; as tools and artefcts, also rules inherently contain the result of a historical development.

The relationship between the community and the object(ive) is mediated by a division of labour. Also the division of labour contains the result of a historical development.



I found the following references really informative and inspiring:

  • Kaptelinin, V. Computer Mediated Activity: Functional Organs in Social and Developmental Contexts, Ch 3 in Context and Consciousness – Activity Theory and Human-Computer Interaction, The MIT Press, 1996
  • Kaptelinin, V.: Activity Theory: Implications for Human-Computer Interaction, Ch 5 of Context and Consciousness – Activity Theory and Human-Computer Interaction, The MIT Press, 1996
  • Kuutti, K.: The Concept of Activity As a Basic Unit of Analysis for CSCW Research, Proceedings of the Second Conference on European Conference on Computer-Supported Cooperative Work, Kluwer Academic Publishers, 1991, 249-264
  • Nardi, B. A.: Activity Theory and Human-Computer Interaction, Ch 1 of Context and Consciousness – Activity Theory and Human-Computer Interaction, The MIT Press, 1996
  • Nardi, B. A.: Studying Context: A Comparison of Activity Theory, Situated Action Models, and Distributed Cognition, Ch 4 in Context and Consciousness – Activity Theory and Human-Computer Interaction, The MIT Press, 1996

Mobile MoodMap for iOS and Android Published

My Know-Center team “Ubiquitous Personal Computing ” just published the mobile MoodMap App for iOS and Android:

The Apps were developed by Peter Marton initially as a technical study of the the cross-platform development Titanium. Since this worked out fine overall – voilà two Apps!

Use the Apps to:

  • Express your mood in colours
  • Tag your mood: What happened in that instant? What are your (private) comments?
  • Visualise: Have a look at your own mood over time, and at the mood of the team around you.
  • React: It is up to you to react to mood changes. If things are going well: Enjoy! If things are not: Re-think the way you are communicating and working.

The mobile MoodMap App was developed in the context of the MIRROR EU project on reflective learning at work, based on the web-based, collaborative MoodMap App developed within MIRROR. We experimented with mood-tracking at work in order to identify things that go well and things that do not – with the goal to improve work practice of course.

Related publications (based on the collaborative MoodMap App) are:

Mood in the City – Urban Location-Based Mood Tracking

I am currently brainstorming around the topic of urban location-based mood tracking.

The starting point for this brainstorming was our work on collaborative mood tracking for the purpose of workplace learning in the context of the EU IP MIRROR (FP7). I followed up on this idea together with Anna Weber in a discussion paper that got accepted at the Smart City Learning WS @ ECTEL 2014 in Graz. Both our paper and the slides (presented by Jörg Simon as both Anna and myself were finally unavailable – thanks!) are available from the WS. site.

I repeat the abstract here, in the hope that it represents well the core idea and leads interested people to read the whole paper (only 4 pages after all…)

In the paper we discuss urban location-related mood self-tracking with respect to interaction design and benefits of use. The design of the interaction workflow in conjunction with software architecture needs to consider in which way mood data will be gathered, stored, shared and represented. Interaction and collected information could serve for single citizens to become aware of one’s own and others’ mood in relation to public spaces. From this viewpoint, the proposed system could serve citizens to learn about themselves in relation to a smart, in the sense of ‘technologically enhanced’, city. Additionally, collected information could be useful to trigger reflection on city-level in terms of viewing the city as socio-technical system. In this sense the proposed system could serve city government to learn about city design by collecting data from its most central constituent: the people visiting, living or working in a city.

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.

“Home” and “Work” Semantic Place Detection in MyPlacesDiary App

Today we published the MyPlacesDiary App in the PlayStore: – Download and enjoy!

There are loads of cool things about this App, from a functionality point of view that it automatically detects your “home” and “work” place. In addition, it is the result of a lot of really nice collaborations:

  • Elisabeth, Oliver, Jörg and I worked on and published a paper on semantic place detection: “Lex, E., Pimas, O., Simon, J., Pammer, V – Where am I? Using mobile sensor data to predict a user’s semantic place with a random forest algorithm
  • Anna Weber did a cool master project in which she used the winning features for the categories “home” and “work” to create a heuristics-based semantic place detection algorithm
  • My team “Ubiquitous Personal Computing” at the Know-Center collaborated with Joanneum Research in a joint R&D project – very nice collaboration: Thanks Alfred, Anna, Stefan, Patrick!

Ubiquitous Personal Computing for Learning and Creativity

I am interested in encouraging and enabling learning and creative thinking in ubiquitous personal computing environments. My research is very often set in the context of work, i.e. my goal is to foster work-related learning and creativity.

  1. Why learning?
    Learning is – nearly per definition – part of knowledge work (Kelloway & Barlin 2001). As routine tasks become more and more automated, the percentage of knowledge work in our jobs rises – and this demands (lifelong) learning.
  2. Why creativity?
    Creative thinking plays a role in knowledge work when new approaches to complex situations need to be “imagined” and tried out. The outcomes need to be reflected upon and the initial approach refined for the future (Resnick 2007). Such “little-c creativity […] – creativity within one’s personal life” (Resnick 2007) is also increasingly expected from educated workers in the 21st century Europe (OECD 2008). Creative thinking plays a role both in action, in order to solve an immediate problem (see Isaksen & Treffinger 2004 for creative problem solving), and after action[1] (Resnick 2007).

    [1] Note that from another viewpoint, “after action” means “before the next action” – if no similar action is expected to follow in the future, than learning after action from the work experience does not make sense with a view on the future.

  3. Why ubiquitous personal computing?
    Ubiquitous personal computing technologies stand in the centre of the interaction concepts I investigate as gateway for people who want, need, have to learn. The reason is simply their prevalence as personal computing devices. What changes with ubiquitous personal computing when compared to personal computing? The obvious: People can (work), learn, and be creative anywhere and anytime. They can – in the reverse direction – also be distracted anywhere and anytime.
  • Isaksen S. G. and Treffinger D. J. (2004), Celebrating 50 years of Reflective Practice: Versions of Creative Problem Solving. The Journal of Creative Behavior, 38: 75–101.
  • Kelloway, E. K. & Barling, J. Knowledge Work as Organizational Behaviour International Journal of Management Reviews, 2000, 2, 287-30.
  • OECD (2008). 21st Century Learning: Research, Innovation and Policy Directions from recent OECD analyses. In: OECD/CERI International Conference on Learning in the 21st Century: Research, Innovation and Policy, Paris, May 2008.
  • Resnick, M. (2007). All I Really Need to Know (About Creative Thinking) I Learned (By Studying How Children Learn) in Kindergarten. ACM Creativity & Cognition Conference, Washington DC, June 2007.