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I predict that we will see a kind of semiotic turn in CSCL, with a focus on materiality; a rising interest in the kind of notional and representational systems that are used when people collaborate in particular practice fields. Semiotics is the study of sign systems, their symbolic as well as physical qualities (Eco, 1979).  While there was a certain interest in the first phase of CSCL--the discussions forums, online forums--in semiotic aspects of collaboration, those first generation semiotic devices were designed for the purpose of asynchronous communication and exchange (‘discussion forum’, ‘thread’). They were not so much informed by people's practices and activities. In more recent years, we've seen a continued interest in these systems, and a surging interest in talk, in synchronous communication. A particularly active area that yielded numerous ideas for representational notations as been research on computer-supported argumentation (Noroozi, Weinberger, Biemans, Mulder, & Chizari, 2012).

The new semiotic turn should focus on artifacts that are representative of people's practices, rather than artifacts designed specifically for the purposes of communication and learning. For instance, the blueprints that building engineers and architects use, the symbol system that musicians use, the specialized document types and codes medical practitioners use. There has been more interest on practice-related notations and artifacts in CSCW than in CSCL (e.g., Turner, Bowker, Gasser, & Zacklad, 2006), and still comparatively little work in CSCL that engages with authentic artifacts and their role in collaboration and learning. 

As an example for what CSCL research with a semiotic perspective could look like, think of Dan Suther's early work on the guidance function of specific notional systems (e.g., Suthers & Hundhausen, 2003), but now with a focus on notations and artifacts that have a more discipline/profession-specific grounding and are more practice-based. 

I can see a number of benefits of the ‘new semiotic turn’: For instance, content would become more important again; we are currently perhaps too much focused on the analysis of the collaboration process (Reimann & Yacef, 2013). But without a concern for content, process remains hard to understand.  Another benefit would be the development of stronger ties between CSCL and CSCW. Thirdly, CSCL would become more relevant for vocational and professional learning because we would now be studying and supporting collaborative learning around a range of artefacts much wider than dedicated ‘knowledge’ artifacts such as concept maps and math equations. Furthermore, a semiotic perspective on collaboration could contribute to HCI research (de Souza, 2005) and to the development of task-related applications that support learning in (collaborative) practice, in addition to getting a task done (solving a problem). 

A question I want to raise is what the reasons might be that practice-related artefacts play still such a little role in CSCL. Why are they left behind?  Maybe it is because they require specialized knowledge, and most of CSCL researchers are not at the same time engineers, doctors, musicians, accountants? Maybe it is because these kind of artefacts are difficult to analyze computationally? Maybe it is because we still make a strong distinction between learning and work, at least in K-12, arguably even in studies that take place in the tertiary sector? 

de Souza, C.S. (2005). The semiotic engineering of human-computer interaction. Cambridge, MA: MIT Press.Eco, U. (1979). A theory of semiotics. Bloomington, IN: Indiana University Press.

Noroozi, O., Weinberger, A., Biemans, H., Mulder, M., & Chizari, M. (2012). Argumentation-Based Computer Supported Collaborative Learning (ABCSCL): A synthesis of 15 years of research. Educational Research Review, 7, 79-106.

Reimann, P., & Yacef, K. (2013). Using process mining for understanding learning. In R. Luckin, S. Puntambekar, P. Goodyear, B. Grabowski, J. D. M. Underwood & N. Winters (Eds.), Handbook of design in educational technology (pp. 472-481). New York: Taylor & Francis.

Suthers, D.D., & Hundhausen, C.D. (2003). An experimental study of the effects of representational guidance on collaborative learning processes. The Journal of the Learnign Sciences, 12(2), 183-218.T

urner, William, Bowker, Geoffrey, Gasser, Les, & Zacklad, Manuel. (2006). Information Infrastructures for Distributed Collective Practices. Computer Supported Cooperative Work (CSCW), 15, 93-110.


While Part I  outlined some of what researchers take for being true about learning, and argued that learning analytics can make important contributions to the methodology of modern learning research, in this posting I describe how learning analytics might contribute to conducting design-based research (DBR), sometimes also referred to as design experiments. 

DBR has the goal “…to use the close study of learning as it unfolds within a naturalistic context that contains theoretically inspired innovations, usually that have passed through multiple iterations, to then develop new theories, artifacts, and practices that can be generalized to other schools and classrooms” (Barab, 2014, p. 151). Design-based research is a ‘natural’ fit between the learning sciences and learning analytics because DBR shares with learning analytics the goal to provide solutions to practical problems. At the same time, these solutions are expected to be grounded in a theory of learning, hence applying the solution can be seen as a (partial) test of the theory, and improving the solution incrementally over time can be seen as contributing to advancing theory over time. 

In design-based research, theory is essential for generalization because design experiments do mostly not use a control group logic, but are structured as within-subjects, repeated measurements designs: A baseline is observed, an intervention is performed (e.g., change in teaching style, a different curriculum, a new or different technology), and the effects of the intervention are gauged in terms of changes to the baseline. Design-based research makes often use of qualitative methods, frequently in combination with quantitative methods. This increases its value to inform the (re-) design of the intervention, and its value for theory building. The main difference between design experiments and standard control-group experiments is that in design experiments context is seen as part of the treatment, thus acknowledging the situated nature of learning; context variables are not seen as ‘interfering’, but as providing the resources through which theoretically expected learning processes become realized in a specific learning situation. This does not mean that DBR does not have a concept of interference, but it is not context ‘variables’ that are seen as potentially interfering; instead, other mechanisms that are active in the same context can interfere. The basis for generalization is provided by keeping the mechanisms that cause learning analytically separate from the context; this analytical distinction allows to formulate expectations how the mechanisms might play out in other contexts, and is hence the basis for the form of generalization most prevalent in design-based research: analytical generalizations  (Ercikan & Roth, 2014; Maxwell, 2004). The DBR methodology is in this respect similar to the methodology of case studies (Yin, 2003): Generalizing is performed by relating the specific case to theories with explanatory value. The specific case observations are not taken as applying in an identical manner to a “population”, but are related to similar processes, and/or more abstract types of processes. It is not the specific participants in the study who are seen as instances of a (in a statistical sense meaningful) ‘population’; instead, the specific observation is treated as “an instance of” something more abstract and, in this sense, more general (Reimann, 2013).  

In more concrete terms, theory enters into design-based research in form of conjectures that take mainly the form learning trajectories and design claims. A learning trajectory describes how learning develops in the absence of the intervention—humans, like any organism, cannot not learn—and how learning changes under the influence of the intervention, in particular the theory-informed aspects of the intervention. Learning trajectories specify expectations about the form of change, perhaps its extent (‘size’), and should say something about its temporal aspects: When will the effect materialize? For how long? Design claims are conjectures about how specific aspects of the intervention affect students’ learning and understanding. Like expectations about learning trajectories, design claims focus mainly on those aspects of the pedagogical and/or technical design that are are related to relevant theory. 

Cobb and Gravemeijer (2008) provide a good example for the role of theory in design-based research. Their study focuses on middle school statistics and describes a number of design cycles for creating computational representations that help teachers to introduce notions such as center, skewedness, spread, relative frequency coherently from the concept of a mathematical distribution. Based on statistics education literature and classroom observations, the authors identity as an important step in the learning trajectory that students will initially need to learn to appreciate the difference between numbers and data. Therefore tasks and computer-generated graphical representations that are intended to make students aware of the fact that they are analyzing data need to be developed. As a theoretical framing, the specific learning trajectory gets contextualized in the wider context of mathematical reasoning, in particular learning about data generation and about developing and critiquing data-based arguments. The authors developed three computational tools, with different, but synergistic representational notations, that in concert with capable teachers began to move students’ conceptions of distribution into a mathematically fruitful direction. 

The potential for synergies between design-based research and learning analytics is obvious. DBR could greatly profit from data on students that are gathered unobtrusively, trace learning on multiple levels, and over longer stretches of time. It could further profit from making these data rapidly, if not continuously, available to teachers and students. Teachers are an essential part of most curricular activity systems (Roschelle, Knudsen, & Hegedus, 2010), and students have to learn how to monitor and steer their own learning (Bull, Johnson, Masci, & Biel, 2016). Learning analytics for its part would become more experimental, more interventionist. I see this as a good development to the extent that pedagogical and technical interventions have the goal to improve upon teaching, to innovate. This is preferable over the use of advanced analytical methods for reinforcing current practices, amongst them practices that might be pedagogically dubious. Along with becoming more experimental, learning analytics would also become more engaged in the advancement of theory via the testing of hypotheses (e.g., the testing of design claims and of conjectures of learning trajectories). This is not an alternative to learning analytics as an methodology for applied research (Pardo & Dawson, 2016), but adds a dimension that can benefit teaching and learning. 

Since learning analytics, in combination with educational data mining, is very comprehensive in terms of the method it encompasses, the shift I am suggesting is not a radical one. The two main ‘moves’ needed are, firstly, a closer alignment of learning analytics with interventionist types of educational research, such as design-based research, and with the emerging educational improvement science (Bryk, 2015). Secondly, learning analytics researchers and practitioners would need to engage more with the development and testing of learning theories, broadly conceived. I consider it particularly valuable if learning analytics would add to learning research--and to educational research in general--methods that go beyond the already well-established applications of the General Linear Model (mainly regression models and analysis of variance). Methods such as social network analysis, pattern learning, and others that allow to analyze the structures and properties that emerge from the relation between entities are potentially more interesting for theory building than linear modelling methods, which might be useful for practical purposes nevertheless. This would not only add incrementally to the method repertoire of learning research, but could transform to some extent how learning research is done: From  a discipline that mainly describes and orders phenomena and findings  with qualitative and statistical methods to a discipline that develops causal-explanatory accounts of learning-in-context. 

An additional transformative potential of learning analytics for educational research concerns the distribution of analytical work: At least in technical terms, it is a small step from gathering data comprehensively to making them available openly. Issues of data protection and privacy aside, there lies a huge innovation potential in making learning data available publicly, in usable formats, because educational challenges are truly too big for any single researcher or research team to solve (Weinberger, 2011). 


Barab, S. A. (2014). Design-based research: A methodological toolkit for engineering change. In R. K. Sawyer (Ed.), Cambridge Handbook of the Learning Sciences (2nd ed., pp. 151-170). New York: Cambridge University Press.Bryk, A. S. (2015). 2014 AERA Distinguished Lecture: Accelerating How We Learn to Improve. Educational Researcher, online first.
Bull, S., Johnson, M.D., Masci, D., & Biel, C. (2016). Integrating and visualising diagnostic information for the benefit of learning. In P. Reimann, S. Bull, M. Kickmeier-Rust, R. Vatrapu & B. Wasson (Eds.), Measuring and visualizing learning in the information-rich classroom (pp. 167-180). New York,NY: Routledge.
Cobb, P., & Gravemeijer, K. (2008). Experimenting to support and understand learning processes. In A. E. Kelly, R. A. Lesh & J. Y. Baek (Eds.), Handbook of design research methods in education (pp. 68-95). New York: Routledge.Ercikan, Kadriye, & Roth, Wolff Michael. (2014). Limits of generalizing in education research: Why criteria for research generalization should include population heterogeneity and uses of knowledge claims. Teachers College Record, 116(May), 1-28.
Maxwell, J.A. (2004). Using qualitative methods for causal explanations. Field Methods, 16, 243-264.
Pardo, A., & Dawson, S. (2016). Learning analytics: How can data be used to improve learning practice? In P. Reimann, S. Bull, M. Kickmeier-Rust, R. Vatrapu & B. Wasson (Eds.), Measuring and visualizing learning in the information-rich classroom (pp. 41-55). New York,NY: Routledge.
Reimann, P. (2013). Design-based research - designing as research. In R. Luckin, S. Puntambekar, P. Goodyear, B. Grabowski, J. D. M. Underwood & N. Winters (Eds.), Handbook of design in educational technology (pp. 44-52). New York: Taylor & Francis.
Roschelle, J., Knudsen, J., & Hegedus, S. (2010). From new technological infrastructures to curricular activity systems: Advanced designs for teaching and learning. In M. J. Jacobson & P. Reimann (Eds.), Designs for learning environments of the future (pp. 233-262). New York: Springer.
Weinberger, D. (2011). Too big to know: Rethinking knowledge now that the facts aren't the facts, experts are everywhere, and the smartest person in the room is the room. New York, NY.: Basic Books.
Yin, Robert K. (2003). Case study research : design and methods (3rd ed.). Thousand Oaks, CA: Sage.

Learning analytics is a young field of research (Baker & Siemens, 2014a; Baker & Yacef, 2009), that along with educational data mining has rapidly grown, driven by the availability of (large) sets of data on students’ learning and the interest in analysing these data for the purpose of improving students’ learning and learning experience. I do not make much of the difference between learning analytics and educational data mining here, but it is worth keeping in mind that there are differences between the two fields, even though they are closely related and draw on an very much overlapping research communities. Siemens and Baker (2012) identify the following differences:

  • EDM researchers are more interested in automated methods for discovery, while LA is more interested in human-led, mixed-initiative methods for exploring educational data;•
  • EDM is more construct-oriented, while LA researchers emphasize a more holistic view of learning and learners;
  • Researchers in EDM develop methods for automatic adaptation of instruction, whereas LA researchers are developing applications that inform teachers, educators, and students. Hence the strong interest in LA on learning visualisations. 

The focus of this paper is on the relation between learning analytics (and EDM) and learning research, in particular the kind of learning research practiced in the Learning Sciences (Sawyer, 2014). My intention is thus similar to the one of Baker and Siemens in their contribution the second edition of the Cambridge Handbook of the Learning Sciences (Baker & Siemens, 2014b): To contribute to a stronger tie between learning analytics and learning (sciences) research. However, different from Baker and Siemens I believe that important contributions from learning analytics to learning research are still a matter of the future. I argue that that while there is the potential for that, it is far from realized, even from being realized. In terms of Pasteur’s Quadrant (Stokes, 1997), I see learning analytics as currently falling into the category of pure applied research, whereas learning sciences can be see as use-inspired basic research, in which the focus is on advancing “the frontiers of understanding but also inspired by considerations of use” (Stokes, 1997, p. 74).

 The main strategy I am following here is to develop some suggestions for how to make LA more relevant for foundational research on learning. I argue that the methods used in learning analytics (and EDM) have the potential to contribute to the applied as well as the foundational objectives of learning research. I further suggest that a more theory-oriented learning analytics can be more than an ‘addition’ to the ‘toolbox’  the learning researcher, that the ‘import’ could be more profound: It could change to a certain extent how we think about research methodology in the learning sciences. 

The potential of Learning Analytics in learning research

The potential of learning analytics for the advancement of the learning research can in my opinion unfold along the four dimensions: (i) data quantity, (ii) longitudinal data, (iii) data from multiple levels, and (iv) data from many locations.  In this section, I map these characteristics of data in learning analytics to modern conceptions of learning and main findings from learning research. 

Quantity of Data

The size of data sets is the primary argument for the value of LA: ”One of the factors leading to the recent emergence of learning analytics is the increasing quantity of analysable educational data (…) Papers have recently been published with data from tens of thousands of students.” write Baker and Siemens (2014a, p. 254). Size is not only measured in number of students; the number of data points per student (captured in log files of learning applications and platforms, for instance) is another quantitative dimension. The Pittsburgh Science of Learning Center DataShop (Koedinger et al., 2010), for instance, stores detailed recordings of students’ interactions with carefully designed tutor software that records step-by-step problem solving operations. 

There are a number of reasons why size is considered to matter. One is that the number of students is taken as useful for establishing the generalizability of findings—a statistical argument. Another is that the more data, the more ‘patterns’ can be found. The flip side to this is that the number of possible relations between variables increases exponentially with the number or variables included in the analysis (Council, 2013). More is needed than just data to ‘discover’ meaningful relations. 

A third argument for the value of large data sets is that they allow us to identify ‘rare’ events: events/patterns that occur in only small numbers of students or only sporadically (e.g., Sabourin, Rowe, Mott, & Lester, 2011).This is particularly interesting if the rare events are defined apriori: events that theory predicts, but that are seldom occurring spontaneously, or are seldom observable because of interactions with other processes (or because of measurement issues). The inverse is interesting as well: Theory might not allow certain events to happen; if they happen, their appearance is interesting because this might not only be just a measurement error, or due to ‘chance’, but indicate a limitation of the theory; it might even render the theory downright wrong. 

While all three aspects of data quantity are beneficial to learning research, the third aspect—rare event detection—deserves more attention. It is the one least often considered, but it can contribute to make learning sciences more theory-guided, and it can help to bridge the gap between qualitative and quantitative learning research. In qualitative research, the frequency with which an event occurs is not automatically identified with the importance of the event; in many cases, important events are rare. An example from learning research is conceptual change, which occurs rarely,  but when it occurs has profound effects on students’ understanding (diSessa, 2006).

Longitudinal Learning Data

Learning needs time.Learning in schools and universities requires often multiple skills—such as mathematical and writing skills—to master complex, hierarchically structured subject matter. In science education, for instance, the hierarchical nature of the subject knowledge also leads to the subject being an intricate association of concepts where deep learning of some basic concepts require comprehension of other basic concepts (Fergusson-Hessler & de Jong, 1987). Theoretical accounts for the depth and extend it takes to comprehend scientific concepts have been suggested from a cognitive psychology perspective and from a socio-cultural perspective. From the cognitive psychology perspective, one line of argument is that learning science can be seen as developing a form of expertise, and that any form of real expertise in cognitively demanding areas requires years of learning (the magic number is 10 years, plus/minus 2), as evidenced by novice-expert research, see (K. A. Ericsson, Charness, Feltovich, & Hoffman, 2006)  for a comprehensive overview. The currently best elaborated cognitive model of expertise development in the cognitive tradition is probably Ericsson’s Deliberate Practice theory (K. Anders Ericsson, Krampe, & Tesch-Römer, 1993). The reason why learning takes long in this model is the incremental nature of the underlying cognitive learning/change mechanisms (chunking, proceduralization). 

Another cognitive account, and one more specific to science education than general models of expertise development, is Chi’s and Slotta’s Ontology Shift theory (e.g., Chi, Slotta & de Leeuw, 1994). On this account, learning scientific concepts is hard and everyday concepts are resistant to change because scientific understanding requires in many cases a change in an ontological category. A classical example is the concept of heat, where students often see heat as a property of matter, whereas in physics it is seen in process terms, as the average velocity of particles. In this theory, the reason that learning stretches often over longer times is that while the ontology change itself can be fairly rapid, it needs often extended time (under current conditions of science learning) before students become sufficiently aware of the limitations of the initial ontology and are ready to accept an alternative one. 

Tracking learning that stretches over months and years—another example for this would be the development of second language skills—is very rarely done in learning research. One reason are the costs, and the logistics, of performing such research. But the costs are being substantially lowered as learning analytics methods find their place in schools and universities. It would be of tremendous benefit  if such data could be made available to researchers, and their acquisition planned in coordination with research projects. Methods for process mining are particularly relevant in this context (Reimann, 2009). Not only would this help to conduct specific projects that study long-term learning, it would also change the way we think about the nature of projects in learning research: From short-term interventions with immediate effects assessment to longer-duration interventions with continuous, long-durations effects (and side-effects!) monitoring. A variant of this kind of research we see developing with improvement research (Bryk, 2015), and the continuous use of data for decision making (Mandinach, 2012). 

Data from Learning on Multiple Levels - Learning is complex

Learning does not only place over long durations, but on other levels of analysis is happening within seconds and even milliseconds. Nathan and Alibali (2010) distinguish between learning in milliseconds and below (biological), seconds (cognitive), minutes to hours (rational), days to months (sociocultural), and years and beyond (organizational). This can be seen as an expression of strictly different kinds of learning, but more productively it may be seen as an expression of the fact that learning takes place at multiple levels at the same time. We can see learning ‘events’ as being produced by a complex, multi-layered system, with minimally three levels: A biological stratum with neurophysiological processes, a cognitive stratum (rational thinking, knowledge) , and a socio-cultural stratum (tools, practices). These strata, or levels, are set in relation to each other by processes of emergence (Sawyer, 2005). 

The concept of emergence as used here is relational: It refers to the phenomenon that wholes (entities, agents, organisms, organisations) have properties that cannot be found in any of their parts.  An emergent property “is one that is not possessed by any of the parts individually and that would not be possessed by the full set of parts in the absence of a structuring set of relations between them.” (Elder-Vass, 2010, p. 17). A key aspect of (relational) emergence is therefore the organization of the parts, how the parts are set in relation to each other, how the whole is structured. Not all properties of an object are emergent; some will be resultant properties. For instance, most objects have mass, which is an resultant property: the mass of the whole is the sum of parts’ masses. Some objects have colour, which is an emergent property; it is dependent on the organization of the objects’ parts. 

If we conceive of learning as a complexity phenomenon (Kapur et al., 2007), then learning needs not only be studied at multiple levels, but the analysis of the relation between the levels—the nature of the emergence—must take center stage. This requires not only to ask what affects learning over time, but also how learning is constituted at each moment in time: Which configurations of neural, cognitive, motivational, emotional, social and contextual processes/elements give rise to a ‘learning event’? Answering the latter question requires  appropriate instrumentation, and appropriate analytical methods. The methods cannot be (only) variants of the General Linear Model (e.g., regression models, including so-called ‘structural’ or ‘causal’ variants), amongst other reasons because these are not appropriate for non-linear complex systems, for systems that transform themselves or get transformed. Instead, methods for the analysis of non-linear systems will be needed (e.g., van Geert, 1998), and methods that can be used to describe relations between parts, in particular graph-theoretical methods such as Social Network Analysis (Burt, Kilduff, & Tasselli, 2013). Learning analytics and educational data mining can play a key role in advancing the learning sciences by bringing about such methodological advances and by making them usable for learning researchers. These includes, but should not be confined to, methods for recording bio-signals, learning behavior and the cognitive-motivational processes causing them,  as well as the social dimension of learning in great detail, with high precision, repeatedly and frequently, if not continuously. 

Data from Learning in Many Contexts - Learning is Distributed 

The methods being developed in learning analytics and educational data mining to capture aspects of students’ behaviour—and physiological and emotional parameters that go along with behaviour—not only over time, but also across locations is tremendously valuable for research. This because learning is situated: It is highly dependent on the resources available to the learner in specific contexts. Not only is learning happening (quasi-)synchronously across multiple levels, it is also distributed over the socio-physical environment—the situation—the learner finds herself in (Sawyer & Greeno, 2009). As Greeno and others have argued, any analysis of learning will be incomplete if it does not (also) conceptualise learning as a socio-cultural practice, as an activity system that stretches far beyond the somato-physical boundaries of the cranium and the body. 

Such an understanding of learning practices is necessary for theoretical as well as pedagogical purposes. For the purpose of theory development, an understanding of the socio-material practices around knowledge objects contributes to de-mystifying the process of learning—how is it possible to learn something genuinely new?— and of idea and knowledge creation more generally (Prawat, 1999). As the entanglement of cognitive work with physical, symbolic and social resources becomes ever better documented and understood—in general (e.g., Clark, 2011) and for specific areas such as scientific research (e.g., Latour & Woolgar, 1986)—it becomes clear that a theory of learning, creativity and idea generation will need to be grounded not only in psychology, but also in sociology, organization science, and semiotics. Any specific study will need to capture knowledge practices in a comprehensive sense. 

The fact that with learning analytics methods behavioural, interactional, and increasingly even some physiological parameters of students’ ‘learning’ activities can be captured across locales and contexts constitutes an essential prerequisite for researching learning-in-context at scale. Learning analytics methods will need to become substantially more sophisticated to become really useful for studying learning-in-context, though. It is not sufficient to keep track of students’ activities (and related parameters) alone; in addition, the context needs to be described and logged as well. This is easier said than done; just think of the many artefacts and tools that students use on average on every day of a semester: at school/uni, at home, while commuting. Along with technical advancements for capturing aspects of students’ behaviour and experience, a main focus of research in learning analytics should therefore be to develop languages, and standards, for describing the context within which behaviour and experience arise, and for describing the relation between the learners and the social, physical and symbolic aspects of learning context. 


In summary, I argue that there lies a huge potential in learning analytics to advance learning research, and that in order to realize this potential learning analytics researchers should devote more attention to (finding) rare learning events, to focus more on long-term learning, to make more of the fact that learning can be recorded on multiple levels of a complex system (the human learner), and to develop methods for capturing the context in which learning activities occur. None of this can be done without building on theory, on conceptualizations of learning and cognition. Theory is essential, and it is important to repeat what two of the key researchers write: ”The theory-oriented perspective marks a departure of EDM and LA from technical approaches that use data as their sole guiding point…” (Baker & Siemens, 2014b, p. 256/257). Suggestions such as made by Anderson (2008) that big data will render the scientific method obsolete not only express a deep misunderstanding of what the method is about, they are also committing the logical (and ethical) error of using descriptions of the past as prescriptions for the future.  



Anderson, C. . (2008). The end of theory: The data deluge makes the scientific method obsolete. Wired Magazin.   Retrieved 14 December, 2015, from

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Sabourin, J., Rowe, J., Mott, B., & Lester, J. (2011). When off-task is on-task: The affective role of off-task behavior in narrative-centered learning environments. . Paper presented at the Proceedings of the 15th International Conference on Artificial Intelligence in Educatoin. 

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In 1973, when the doors were first opened to the Sydney Opera House, most people had never heard of a “digital experience”. Since then we’ve watched digital devices make their way into every detail of our lives from business to exercise, health, politics, friendships, and romance. They are now continuous players in our moment-by-moment lived experience and the future “Internet of Things” promises little separation between digital and non-digital experience at all. But one critical question remains…

Are we any happier?


The STL network would like to offer our congratulations to the following students on their graduation:

Karen Margaret Scott
Thesis: Change in university teachers’ elearning beliefs and practices

Mary-Helen Ward
Thesis: Living in Liminal Space: The PhD as accidental pedagogy

Thomas Finlay Whiston Kerr

Pamela Raquel Branas
Alison Margaret Marshall
Erin Catherine Redfern
Christina Wilkinson
Zhe Hong
Kanae Miyajima
Harry Thandiza Mwanza
Judith Oa Nukuitu


Rafael Calvo recently attended the Microsoft Research Faculty Summit in Redmond, Washington, where he had a chance to ask Bill Gates about using technology to teach emotional intelligence and "mindset" skills. Here's Bill's response.

The summit, held on July 15, was Microsoft Research’s fourteenth annual Faculty Summit. More than 400 academic researchers from 200 institutions and 29 countries joined Microsoft Research to assess and explore today’s computing opportunities with Bill Gates setting the topic of “Innovation and Opportunity—the Contribution of Computing to Improving Our World". Some of the talks and keynotes from the summit are available for viewing online.

On Monday 8th July, the University of Sydney is hosting a meeting on MOOCs and the student experience of blended learning. This blog entry is the starting point for an online discussion to supplement the presentations and debate in the meeting.

You can participate in the discussion by adding a Comment to this entry. Comments will be treated like 'letters to the editor' - they will be moderated, may be edited, should be expressed in concise and temperate language and will only be published if - in the view of the editor - they make a contribution to advancing the discussion. Be relevant and interesting. Anonymous comments will not be published. Please conclude your comment with your name and brief affiliation.

Peter Goodyear, Faculty of Education & Social Work, STL Network

16 comments |

Peter_Sloep.jpgLast year we had the pleasure of meeting another Peter, Peter B. Sloep from the Open University in the Netherlands. This week I came across his blog which lead me to his, via my twitter feed. I’m always on the lookout for interesting commentators and have worked hard to train my Zite feed to keep me in good reading. Whilst this has been informative it leaves me feeling like a consumer of information of varying degrees of quality.

In an attempt to find a way into conversations I have tentatively begun blogging and signed up with twitter. Together they provide me with a space in which to ‘say’ something, a soap box of sorts (WordPress) and a very useful message board (twitter) – but I have yet to learn how to harness them as tools for conversation. I thought this was my shortcoming. That was until I read 'A year of content curation' in which Peter describes the missing link, the ability to go beyond recycling, to add value:

“As a content curator I want to go beyond mere filtering and collecting, I want to explain why something is striking to me, to put it in the context of the topic on networked learning as a whole, and even to take an explicit stance on some issue or other. For academic topics such as mine voicing such an opinion probably adds much value.”

I highly recommend both his blog and his Scoop, not just for their content but for what we can learn from his example.

Harbour_Fireworkssml.jpgThings will be quiet here for the next two weeks, as the University of Sydney closes from December 19th to January 1st (inclusive) for the holidays.

We'll be taking a bit of a break but will return in the new year with more events, news and discussion on the sciences and technologies of learning. It's been a big and busy year for the network. We'd like to say thank you to everyone who joined us, whether we saw you at conferences or seminars, or worked with you on projects and papers, or chatted with you in person or online.

Wishing you all a very happy holidays, and looking forward to seeing you in 2013!

Learning space design in higher education is important because it influences the way students and teachers enact learning and teaching. Advances in technologies enable new designs that support our current understanding of learning and pedagogy - the award winning learning studio in the University's PNR building is a great example of technology-enhanced learning space design on a large scale. Other examples of innovative design include common learning spaces (e.g. Carslaw Learning Hub) which are found at universities around the world. Such environments enhance learning and teaching by integrating the physical environment with digital technologies.


At a recent research symposium at Hong Kong University the opening panel discussion was targeted higher education next generation virtual and physical learning environments. The panel discussed the changing habits of students today and considered the future challenges for institutions. Mobile computer devices and Internet connectivity was thoroughly discussed.

In a paper presented at this symposium I highlight a growing trend to provide access and support for students outside of regular classroom activity. The paper draws attention to the importance of autonomy in new learning spaces and discusses some of the attributes that promote autonomy.

I wish to thank the ongoing support of my research supervisors and colleagues at the CoCo Research Centre and the University through the Postgraduate Research Support Scheme.

Stellan Ohlsson is Professor of Psychology at the University of Illinois, Chicago and is a Visiting Researcher in the Sydney Sciences and Technologies of Learning (STL) network.

In the following piece, Stellan reflects on the genius of George Miller, who died recently.


About the Blog

Research by the University's Centre for Research on Learning and Innovation (CRLI).