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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

Baker, R., & Siemens, G. (2014a). Educational data mining and learning analytics. In R. K. Sawyer (Ed.), Cambridge Handbook of the Learning Sciences (2nd ed., pp. 253-274). New York: Cambridge University Press.

Baker, R., & Siemens, G. (2014b). Learning analytics and educational data mining. In R. K. Sawyer (Ed.), Cambridge Handbook of the Leaning Sciences (2nd ed., pp. 253-272). New York: Cambridge University Press.

Baker, R., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future vision. JEDM - Journal of Educational Data Mining, 1(1), 3-17.

Bryk, A. S. (2015). 2014 AERA Distinguished Lecture: Accelerating How We Learn to Improve. Educational Researcher.

Burt, Ronald S., Kilduff, Martin, & Tasselli, Stefano. (2013). Social network analysis: Foundations and frontiers on advantage. Annual Review of Psychology, 64, 527-547.

Clark, A. (2011). Supersizing the mind. Embodiment, action, and cognitive extension. Oxford, UK: Oxford University Press.

Council, National Research. (2013). Frontiers in Massive Data Analysis. Washington, D.C.: The National Academic Press.

diSessa, A.A. (2006). A history of conceptual change research: Threads and fault lines. In R. K. Sawyer (Ed.), The Cambridge Handbook of the Learning Sciences.

Elder-Vass, Dave. (2010). The causal power of social structures. Cambridge, UK: Cambridge University Press.

Ericsson, K. A., Charness, N., Feltovich, P., & Hoffman, R.B. (Eds.). (2006). The Cambridge Handbook of Expertise and Expert Performance. New York: Cambride University Press.

Ericsson, K. Anders, Krampe, Ralf Th., & Tesch-Römer, Clemens. (1993). The role of deliberate practice in the acquisition of expert performance. Psychological Review, 100, 363-406.

Fergusson-Hessler, M.G.M., & de Jong, T. (1987). On the quality of knowledge in the field of electricity and magnetism. American Journal of Physics, 55, 492-497.

Kapur, M., Hung, D., Jacobson, M.J., Voiklis, J., Kinzer, C. K., & Victor, Chen Der-Thang. (2007). Emergence of learning in computer-supported, large-scale collective dynamics: A research agenda Proceedings of the International Conference on Computer-supported Collaborative Learning (CSCL2007). New Brunswick, NJ.

Koedinger, K R, Baker, R S J D , Cunningham, K, Skogsholm, A., Leber, B., & Stamper, J. (2010). A data repository for the EDM community: The PSLC DataShop. In C. Robero, S. Ventura, M. Pechenizkiy & R. Baker (Eds.), Handbook of educational data mining (pp. 43-56). Boca Raton, FL.: Chapman&Hall/CRC.

Latour, B., & Woolgar, S. (1986). Laboratory life: The construction of scientific facts (2nd ed.). Princeton: Princeton University Press.

Mandinach, E. B. (2012). A perfect time for data use: Using data-driven decision  making to inform practice. Educational Psychologist, 47(2), 71-85.

Nathan, M.J., & Alibali, Martha Wagner. (2010). Learning Sciences. Wiley Interdisciplinary Reviews:Cognitive Science, 1(3), 329-345.

Prawat, R. S. (1999). Dewey, Peirce, and the Learning Paradox. American Educational Research Journal, 36, 47-76.

Reimann, P. (2009). Time is precious: Variable- and event-centred approaches to process analysis in CSCL research. International Journal of Computer-supported Collaborative Learning, 4, 239-257.

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. 

Sawyer, R. K. (2005). Social emergence. Societies as complex systems. Cambridge, UK: Cambridge University Press.

Sawyer, R. K. (Ed.). (2014). The Cambridge Handbook of the Learning Sciences (2nd ed.). New York: Cambride University Press.

Sawyer, R. K., & Greeno, J.G. (2009). Situativity and learning. In P. Robbins & M. Aydede (Eds.), The cambridge handbook of situated cognition (pp. 347-367). New York, NY: Cambridge University Press.

Siemens, G., & Baker, R.S.J. d. (2012). Learning analytics and educational data mining: Towards communication and collaboration. Paper presented at the Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK 2012). 

Stokes, D.E. (1997). Pasteur's quadrant: Basic science and technological innovation. Washington, DC: Brookings Institution Press.

van Geert, Paul. (1998). A dynamic systems model of basic developmental mechanisms: Piaget, Vygotsky, and beyond. Psychological Review, 105, 634-677.


The International Society of the Learning Sciences (ISLS) have just announced the upload of three more videos related to their NAPLeS webinar video series.

The three videos with Professor Sten Ludvigsen, University of Oslo discussing workplace learning with digital resources are now available online and can be accessed here:

The whole collection of the recently uploaded HD videos can be found here:

Each NAPLeS video involves a 5-minutes “teaser” and a 15-minutes HD video from a different lecturer.
Our various postgraduate study opportunities, including research degrees at the Masters and PhD levels and our coursework Masters in the Learning Sciences and Technology (MLS&T), at the CoCo centre are members of the NAPLES network.

Learning is embracing social networks, finds a new Open University report examining global trends in education

Education can be dramatically enhanced by social networks, according to a report released by The Open University on Nov 13th 2014. This 'network effect’ comes from many thousands of people learning from each other, but it needs careful management to reach its full potential.

Millions of people are now studying massive open online courses (MOOCs) for free. Massive open social learning exploits the ‘network effect’ where the value of a network increases as more people use it, bringing the benefits of social networks such as Facebook and Twitter to people taking online courses, by recommending, liking and following the best content created by other learners. This encourages online learners to connect to each other, join productive discussions, share ideas and create material that other learners can use.

“Social networks have transformed entertainment from delivering books, radio and television programmes into holding a global conversation. The same is about to happen with education through social learning. By its nature, we don’t know how this conversation will evolve. For instance, on an online course with 10,000 learners, there are 50 million ways that pairs of them could connect directly.

“That is a huge opportunity, but also a challenge to manage the discussion and file sharing. Learning on that scale can’t only be controlled centrally. It has to come through social network techniques that put learners in contact with others who share their interests, reward the best contributions and allow learners to report issues.”

Mike Sharples, Professor of Educational Technology at the OU and lead author of the Innovating Pedagogy report

The Innovating Pedagogy reports are published annually by The Open University to highlight new and future trends in education. The report identifies 10 methods of teaching, learning and assessment that are gaining influence but which have not yet had a major impact on education. Other key trends covered by the report include dynamic assessment, where learners are offered personalised tests to support their learning, learning through storytelling, threshold concepts that are difficult to teach, and bricolage or creative tinkering with resources.


The International Society of the Learning Sciences (ISLS) have just announced the upload of videos related to their NAPLeS webinar video series. The first three videos with Iris Tabak and Brian Reiser talking about Scaffolding are now available online and can be accessed here:

At least 15 topics will be covered in the coming weeks. With each lecturer we recorded a 5-minutes “teaser” and a 15-minutes HD video. In addition, we offer interviews about the role of the respective topic for the broader field of the Learning Sciences. The videos are on the same topic as the various webinars but are more condensed and of high video and audio quality to support local courses or joint online courses. More material is currently being edited and will be uploaded over the coming months.

Our various postgraduate study opportunities, including research degrees at the Masters and PhD levels and our coursework Masters in the Learning Sciences and Technology (MLS&T), at the CoCo centre are members of the NAPLES network.

Fotolia_44019078_XXS.jpgThe Reclaim Open Learning network has announced an open learning innovation contest inviting innovators whose work embodies the principles of connected learning to submit their stories and experiences for consideration.

This work might involve running online or offline courses, activities, learning programs, study groups, or hybrid classes or out-of-school (extra-institutional) activities having to do with independent learning and volunteer work. The contest focuses on independent learners and those who work with them mostly at the postsecondary level.

Winners will receive a $2000 honorarium and be invited to present at a summit on Reclaiming Open Learning at UC Irvine on September 26-27, 2013. Entries are due August 2, 2013.

To enter, see their website at

Reclaim Open Learning is a collaboration between the Digital Media and Learning Hub at UC Irvine and the MIT Media
Lab. More information can be found on their website.

Few weeks ago The National Academies Press has released pre-publication of the NRC Committee’s on the Mathematical Sciences in 2025 report called “Fueling Innovation and Discovery: The Mathematical Sciences in the 21st Century”. It introduces a nice set of recent advances in applied math domains. It is exactly the math that, I believe, could fascinate even those students who are scared of numbers and it is the math that could make the major difference in many practical fields. Educational research, decision-making, school management and learning are not exceptions. Learning analytics is one of classical examples of applied math on action, but I could predict that we will see much larger variety of social and behavioural research that draw heavily on much broader range of advanced data management, theoretical computer science and data visualisation techniques already in the nearest future. CoCo-Chai-Latte studies that use data mining to analyse collaborative writing, student use of computer models, etc are nice examples of applied math and data visualisation techniques for researching everyday learning. But “literacy in applied math” that underpins “visual learning science” might become one of the greatest learning challenges.

PS: A very simple yet insightful example of thinking broadly about new digital and visual methods in educational research could be Hogrebe and Tate’s paper on geospatial analysis of social dynamic published in the recent special issue on education and democracy of Educational Review of Research in Education. It is difficult to dismiss the value of such inquiry techniques in professional educational research and “citizen scientists” research.

ascilite just announced its 2012 Webinar series program. This year it has a range of presentations on various research approaches in eLearning. Peter Goodyear and I will be co-presenting the last Webinar "ICT-enhanced social and educational research methods", Thursday, 11 October 2012, 1pm NSW time.


A brief reminder about the call for papers for the BJET Special Issue on e-Research for Education that Peter Reimann and I are co-editing. Feel free to email us your questions.

Call for Papers for Special Issue "e-Research for Education: Applied, methodological and critical perspectives", The British Journal of Educational Technologies

This special issue aims to provide a comprehensive review of the emerging domain of ICT- enhanced research methods in educational research. It seeks contributions in the following broad categories: 1) methodological papers (e.g., learning analytics, collaborative video analysis, digital ethnography); 2) applied case studies of frontier e-research project; 3) conceptual explorations of eResearch implications. Guest editors:

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