The term computational science, and its associated term computational thinking, came into wide use during the 1980s. In 1982, theoretical physicist Kenneth Wilson received a Nobel Prize in physics for developing computational models that produced startling new discoveries about phase changes in materials.

Nearly everybody had something to gain. Experimenters looked to computers for data analysis—sifting through large data sets for statistical patterns. Theoreticians looked to them for calculating the equations of mathematical models.

The desire for understanding is driven by something more human. It is peoples’ nature to seek connections - connections to others, to the earth, and to ideas. This sense of connectedness is not only at the level of individual cognition, it comes from a desire to know with one's heart and mind, emotions and cognitions, imagination and reason. People pursue understanding to feel connected in ways that tell them they are human. As Feynman suggests, people strive to understand for aesthetic reasons.

Around 2005, a new range of web tools began to find their way into general use, and increasingly into educational use. These can be loosely described as social media, as they reflect a different culture of web use from the former ‘centre-to-periphery’ push of institutional web sites.

When I see a paper that I find interesting, I make sure to send the author an e-mail or message them on Twitter. I say: “I just read your paper — it helped me with some concepts. I look forward to seeing your future work.” It lets people know that they have worth.

And the fourth concerns the idea of the adjacent possible. It just may be the case that biospheres on average keep expanding into the adjacent possible. By doing so they increase the diversity of what can happen next. It may be that biospheres, as a secular trend, maximize the rate of exploration of the adjacent possible.

I’m part of the policy working group for an international professional network called the Marie Curie Alumni Association. I’d like the working group to have a new mission: aligning the incentives and rewards of science with the type of work and productivity that we really want to see. We need to better reward non-traditional outcomes, such as data sets, research methods and code. And we need to better appreciate activities outside of the lab, such as public engagement, education and outreach. That’s the way towards achieving substantial and lasting change.

Everyone is publishing and publishing because that’s where the money in science comes from. But if everyone is publishing and nobody is reading, are we making a contribution?

There seems to be a center of gravity in the core of the learning sciences and we are lucky that this core is surrounded by multidisciplinary hubs and programs that are important motors of innovation within the learning sciences. We should refrain from excluding more peripheral research from learning sciences conferences and journals because it is not using certain concepts or methods. Reducing the learning sciences to its core (e.g., to DBR) will likely cause stagnation. Instead, we should embrace new methodological developments coming from other disciplines as currently can be seen in the context of big data, learning analytics, and educational data mining methods (Koedinger, D’Mello, McLaughlin, Pardos, & Rosé, 2015 Koedinger, K. R., D’Mello, S., McLaughlin, E. A., Pardos, Z. A., & Rosé, C. P. (2015). Data mining and education. Wiley Interdisciplinary Reviews: Cognitive Science, 6(4), 333–353. doi:10.1002/wcs.1350[Crossref], [PubMed], [Web of Science ®], [Google Scholar]; Wise & Shaffer, 2015 Wise, A. F., & Shaffer, D. W. (2015). Why theory matters more than ever in the age of big data. Journal of Learning Analytics, 2(2), 5–13. doi:10.18608/jla.2015.22.2[Crossref], [Google Scholar]). Learning sciences is a discipline and, if done well, a center of gravity for a powerful orbit of interdisciplinary collaborations on learning sciences themes.

But what about the majority of graduate learning sciences programs that are not part of this nucleus? Is it reasonable to say they are not part of the learning sciences community? Probably not, as merely 25% of the graduate programs that self-categorize as learning sciences programs explicitly address DBR. The question is rather how to represent those programs well who do not belong to the relatively small core. In what follows we explore the CoP metaphor and suggest a possible view on the specific relationship of the core and more peripheral programs in the learning sciences.

Comparing the results of the network analyses for the concepts and methods, it is striking that the average degree (relating to the number of connections within the network), as well as the average strength (relating to the magnitude of the connections), of the method network are considerably lower than the corresponding values of the concept network.

FIGURE 6 Network plot of all methods including three emerging clusters representing methods aimed at individuals (red), their interaction (blue) and methods at the intersection of both other clusters (green).

The emerging networks show close relations, especially for the various concepts, represented by thick gray lines.

In particular, peripheral participation can take place without a trajectory towards the core because, for example the peripheral members are deeply rooted in one or more other communities and just share certain interests with the community under consideration (“peripheral experts”).

Here, the question of whether learning sciences is a discipline on its own with a clear common core and a learning sciences “brand” or whether it instead represents a tent (Nathan et al., 2016 Nathan, M. J., Rummel, N., & Hay, K. E. (2016). Growing the learning sciences: Brand or big tent? Implications for graduate education. In M. A. Evans, M. J. Packer, & R. K. Sawyer (Eds.), Reflections on the learning sciences (pp. 191–209). Cambridge, UK: Cambridge University Press.[Crossref], [Google Scholar]) for various research from different disciplines related to learning, is repeatedly brought up.

learning sciences

Results reveal that the concepts addressed most frequently were real-world learning in formal and informal contexts, designing learning environments, cognition and metacognition, and using technology to support learning. Among research methods, design-based research, discourse and dialog analyses, and basic statistics stand out.

./run_and_watch_all.sh

corporate

Find the Hotspot

This engagement may encompass experts from areas such as human-computer interaction; data streaming, assimilation, visualization, and analytics; machine learning and deep learning; multi-modal analytics; social network analyses; and adaptive rapid experimental design.

rich and highly adaptable environments for learners that may: (a) serve as a forum for active research and development studies by researchers; (b) serve as a testbed for analytics that support the environment's adaptability; and (c) in the spirit of design-based research, serve as a collaborative space for teachers, mentors, and learners to work with researchers as co-developers of the learning environment.

Most extraterrestrial creatures are likely deep inside their home planets, in subsurface oceans crusted over in frozen water ice

units of analysis in the social sciences can usually be divided into various subunits: Communities may be divided into families or households, geographic neighborhoods, or individual members.

Units of analysis may be different from the units of observation.

generalize

Reading HistoryWe draw several conclusions from this brief history, noting that it is, like all histories, somewhat arbitrary. First, each of the earlier historical moments is still operating in the present, either as legacy or as a set of practices that researchers continue to follow or argue against. The multiple and fractured histories of qualitative research now make it possible for any given researcher to attach a project to a canonical text from any of these historical moments. Multiple criteria of evaluation compete for attention in this field. Second, an embarrassment of choices now characterizes the field of qualitative research. There have never been so many paradigms, strategies of inquiry, or methods of analysis to draw on and use. Third, we are in a moment of discovery and rediscovery as new ways of looking, interpreting, arguing, and writing are debated and discussed. Fourth, the qualitative research act can no longer be viewed from within a neutral or objective positivist perspective. Class, race, gender, and ethnicity shape the process of inquiry, making research a multicultural process. Fifth, we are clearly not implying a progress narrative with our history. We are not saying that the cutting edge is located in the present. Rather, we are saying that the present is a politically charged space. Complex pressures inside and outside of the qualitative community are working to erase the positive developments of the past 30 years or so.

A triple crisis of representation, legitimation, and praxis confronts qualitative researchers in the human disciplines. Embedded in the discourses of poststructuralism and postmodernism, these three crises are coded in multiple terms, variously called and associated with the critical, interpretive, linguistic, feminist, and rhetorical turns in social theory. These new turns make problematic two key assumptions of qualitative research. The first assumption presumes that qualitative researchers can no longer directly capture lived experience. Such experience, it is argued, is created in the social text written by the researcher. This is the representational crisis. It confronts the inescapable problem of representation but does so within a framework that makes problematic the direct link between experience and text.The second assumption makes problematic the traditional criteria for evaluating and interpreting qualitative research. This is the legitimation crisis. It involves a serious rethinking of terms such as validity, generalizability, and reliability—terms already retheorized in postpositivist constructionist–naturalistic, feminist, interpretive and performative, poststructural, and critical discourses. This crisis asks the question: How are qualitative studies to be evaluated in the contemporary poststructural moment? The first two crises shape the third crisis, which asks the question: Is it possible to effect change in the world if society is only and always a text? Clearly, these crises intersect and blur, as do the answers to the questions they generate.

The preceding arguments have been developed viewing writing as a method of inquiry that moves through successive stages of self-reflection.

New models of truth, method, and representation were sought. The erosion of classic norms in anthropology (e.g., objectivism, complicity with colonialism, social life structured by fixed rituals and customs, ethnographies as monuments to a culture) was complete. Critical epistemology, feminist epistemology, and epistemologies of color now competed for attention in this arena. Issues such as validity, reliability, and objectivity, believed to be settled in earlier phases, were once again problematic. Pattern and interpretive theories, as opposed to causal linear theories, were now more common as writers continued to challenge older models of truth and meaning.

These works made research and writing more reflexive and called into question the issues of gender, class, and race. They articulated the consequences of Geertz's “blurred genres” interpretation of the field in the early 1980s.

Geertz argued that the old functional, positivist, behavioral, and totalizing approaches to the human disciplines were giving way to a more pluralistic, interpretive, and open-ended perspective. This [Page 315]new perspective took cultural representations and their meanings as its point of departure. Calling for “thick descriptions” of particular events, rituals, and customs, Geertz suggested that all anthropological writings were interpretations of interpretations.

Computers entered the situation, to be fully developed as aids in the analysis of qualitative data in the next decade, along with narrative, content, and semiotic methods of reading interviews and cultural texts.

By the beginning of the third stage (1970–1986), or blurred genres, qualitative researchers had a full complement of paradigms, methods, and strategies to employ in their research. Theories included symbolic interactionism, constructivism, naturalistic inquiry, positivism and postpositivism, phenomenology, ethnomethodology, critical theory, neo-Marxism, semiotics, structuralism, feminism, and various racial/ethnic paradigms.

In this way, work in the modernist period clothed itself in the language and rhetoric of positivist and postpositivist discourse.

Modernist ethnographers and sociological participant observers attempted rigorous qualitative studies of important social processes, including deviance and social control in the classroom and society. This was a moment of creative ferment.A new generation of graduate students across the human disciplines encountered new interpretive theories (e.g., ethnomethodology, phenomenology, critical [Page 314]theory, feminism). They were drawn to qualitative research practices that would let them give a voice to society's underclass.

the Chicago School, with its emphasis on the life story and the “slice-of-life” approach to ethnographic materials, sought to develop an interpretive methodology that maintained the centrality of the narrated life history approach. This led to the production of the texts that gave the “researcher as author” the power to represent the subject's story.

Old standards no longer hold. Ethnographies do not produce timeless truths. The commitment to objectivism is now in doubt. The complicity with imperialism is openly challenged today, and the belief in monumentalism is a thing of the past.

The works of the classic ethnographers are seen by many as relics from the colonial past.

Now at the dawn of this new century, we struggle to connect qualitative research to the hopes, needs, goals, and promises of a free democratic society.

The postmodern and postexperimental moments were defined in part by a concern for literary expression and the narrative turn—a concern for storytelling, for composing ethnographies in new ways.

Arthur Vidich and Stanford Lyman's history covers the following (somewhat) overlapping stages: early ethnography (to the 17th century); colonial ethnography (17th-, 18th-, and 19th-century explorers); ethnography of the “other,” the American Indian (late 19th- and early 20th-century anthropology); community studies; ethnographies of American immigrants (early 20th century through the 1960s); and studies of ethnicity and assimilation (mid-20th century through the 1980s).

Qualitative research is a situated activity that locates the observer in the world. Qualitative research consists of a set of interpretive material practices that make the world visible. These practices transform the world. They turn the world into a series of representations, including fieldnotes, interviews, [Page 312]conversations, photographs, recordings, and memos to the self. At this level, qualitative research involves an interpretive naturalistic approach to the world. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them.

In the postmodern/experimental moment, researchers continued to move away from foundational and quasifoundational criteria. Alternative evaluative criteria were sought—those that might prove to be evocative, moral, critical, and rooted in local understandings.

Successive waves of epistemological theorizing move across these eight moments. The traditional period is associated with the positivist foundational paradigm. The modernist or golden age and blurred genres moments are connected to the appearance of postpositivist arguments. At the same time, a variety of new interpretive qualitative perspectives were taken up, including hermeneutics, structuralism, semiotics, phenomenology, cultural studies, and feminism. In the blurred genres phase, the humanities became central resources for critical interpretive theory and the qualitative research project broadly conceived. The researcher became a bricoleur, learning how to borrow from many different disciplines.

NASA researcher checking hydroponic onions with Bibb lettuce to his left and radishes to the right

growing plants without soil

For details from a physicist’s perspective see Pierre-Andre’s talk

2. Staying with a closed, proprietary system & not moving to the adoption of open standards.

A major change will be our movement away from ‘social’ aspects in Diigo. While we may have been swayed by the ‘social media movement’, at the end of the day Diigo has always been more of a personal platform.

Now I can read updates in that private channel, I can receive desktop notifications, and I can also push notifications to Slack on my mobile devices.

a dyadic format

Part II

the difference between the mathematical and statistical approaches to social network analysis

Welcome

Quality of Relational Data

In the node-list format, the first node in each row is ego, and the remaining nodes in that row are the nodes to which ego is connected (alters).

Working in public is exciting and enriching, and I have seen my students thrilled by the connections they have made and engaged by the ability to produce work for a larger academic commons.  That being said, working in public, and asking students to work in public, is fraught with dangers and challenges.  Students need to understand privacy and safety issues (and so do we; in case you haven’t had FERPA waved in your face recently let me do that for you now). They may not know about trolling or how to respond to it (seriously, we can’t even say there is a universally agreed-upon best practice for handling trolling). They may (will) face vicious harassment, racism, sexism, homophobia, and all of the other things that we do a reasonably good job at regulating in our classrooms (maybe?), depending on the kind of work they do or the kind of digital profiles they put forward, purposefully or otherwise.  They will put crappy work online sometimes (sometimes they will know it’s crappy and sometimes they won’t); is that ok? Will it come back to haunt them when they look for a job (we need to take this concern seriously, given the debt they incur to study with us)? What professional risks do I assume when my pedagogy is so fully exposed? And who in the academy can afford to take those risks…and who cannot?

“Our MIT online course data already suggests students perform better when they have help and the social connection to support their learning,” Siemens said. “This connection contributes to their willingness to persevere through the course and could come in the form of interaction on the social network platform, experience in leveraging online social capital and personal motivation.”

understanding the antecedents and consequences of network phenomena

After all, characteristics such as one's academic history or educational aspirations influence who one knows and spends time with.

you can highlight the error and include two tags – snaEd and issues – in your annotation.

one of three axes: URL, tag, or user

Air pollution is the introduction of particulates, biological molecules, and many harmful substances into Earth's atmosphere, causing diseases, allergies, death to humans, damage to other living organisms such as animals and food crops, or the natural or built environment. Air pollution may come from anthropogenic or natural sources.

The Social Network Perspective

Inductive reasoning should be used to develop statements (hypotheses) to be tested during the research process.

Writing Science: How to Write Papers That Get Cited and Proposals That Get Funded, Joshua Schimel

Gather student feedback in the first three weeks of the semester to improve teaching and learning.

Be redundant. Students should hear, read, or see key material at least three times.

Distribute a list of the unsolved problems, dilemmas, or great questions in your discipline and invite students to claim one as their own to investigate.

“low-“

Both call for a comparison between the observed rate of growth against a goal line (also known as the aim line), which depicts a desired rate of growth. The goal line is a straight line that connects a student's initial level of performance to the desired level of performance at the end of the intervention period. For the data point decision rule, decisions are based upon whether a student's CBM-R data (i.e., WRCM) across time are above or below the established goal line. A commonly used guideline is that 3 to 5 consecutive data points below the goal line are indicative of an ineffective intervention

WordWhipsers is a new feature added to Knowledge Forum 6 to support collaborative bootstrapping of writing vocabulary in a knowledge-building community.

and