Social, Cognitive, and Linguistic Markers of Collaborative Knowledge BuildingJianwei Zhang () State University of New York at Albanyhttp://tccl.rit.albany.edu

Acknowledgements of co-authors/collaboratorsMary LamonRichard MessinaRichard ReeveMarlene ScardamaliaYanqing Sun

A Driving Question Facing a knowledge-based society, how can schools engage students into knowledge-creating practices, with support of new technologies?

Knowledge BuildingKnowledge building: the creation of knowledge as a social product (Bereiter, 2002; Scardamalia & Bereiter, 2006).Knowledge and ideas have a social life out in the world (Bereiter, 2002; Brown & Duguid, 2000; Popper, 1972); Knowledge creation is a social and collective process (Csikszentmihalyi, 1999; Sawyer, 2003).

A Framing of Knowledge-CreatingWorld 3: Objective knowledge (e.g., in books)

(Popper, 1972)

World 2: The subjective/ mental worldWorld 1: The physical world

Augment Knowledge-Creating with Technologies Technologies as reorganizers of cognitive functioning (Pea, 1985, 1993) -- the relations/ distributions.

Challenges to ResearchersKnowledge building collaborative processes and outcomes; emergent goals; depth of understanding;diverse expertise. Traditional learningIndividual

Pre-designedContent coverageStandard content

This PresentationCollective responsibility, emergent knowledge building processesLiteracy growth through disciplinary knowledge buildingKnowledge Building Measures that Matter

Collective responsibility, emergent knowledge building processesSustained, creative knowledge work can be better supported through distributed, flexible, adaptive, social structures than centralized, rigid, or fixed structures (Amar, 2002; Chatzkel, 2003; Engestrm 2008; Gloor, 2006; Sawyer, 2003; Williams & Yang, 1999). Collaborative improvisation (Sawyer, 2003)Emergent goals (Valsiner & Veer, 2000)Collectively setting agenda (Barab et al., 1999)Moving between groups, leading to spread and contacts of ideas (Bielaczyc & Collins, 2005)

Collective Knowledge Work: An ExampleTotal engineers: 4925The Design of Boeing 787:

An Example: The Design of Boeing 787

Collective Cognitive Responsibility: A Key and Difficult PrincipleResponsibility for the success of a group effort is distributed across all the members;Tangible tasks + Staying cognitively on top of tasks and ideas as they evolve (e.g., whats happening, goals, agendas) (Scardamalia, 2002);Connecting ones own interests/expertise with those of the community (Amar, 2002).

Collaborative Learning DesignFixed small-group collaboration (Davis, 1993): The teacher designs and divides a project; Assigns different parts to different teams; Develops a time-line;Group presentation.

A Spectrum of DesignsSpecialized GroupsInteracting GroupsOpportunistic-CollaborationFixed Small-Groups(Zhang et al., in press)

A 3-Year Design ExperimentResearch design: A three-year design experiment (Collins, Joseph, & Bielaczyc, 2004)Participants: A teacher working with three different classes of fourth-graders (22 each year)Content domain: LightEnvironment: Knowledge Forum

Three DesignsYear 1: A specialized-group modelYear 2: An interacting-group modelYear 3: An opportunistic-collaboration model

Knowledge building in Year 3

Analyses of the online discourseSocial Network Analysis (SNA)Two types of interactions: Note reading, note linking (build-on, rise-above, reference)

Content analysis (Chi, 1997) of the teachers notesInquiry threads analysis (Zhang, 2004; Zhang et al., in press)

Assessing knowledge gains based on individual portfolio notesC2AC1C5C3C4Emailing

Measuring Collective Cognitive Responsibility

EffortsIndicatorsCommunity awarenessNote readingCollaborative contributionsNote linking; clique structuresDistributed engagementCentralization measures; Teacher-student exchange patterns

Community awareness: Networks of note-reading Students note reading contacts (i.e., who read whose notes): density 0.97, 0.95 and 0.99 (p > .10).

Clique: a sub-set of actors who are more closely tied to each other than they are to actors who are not part of the group (Hanneman, 2001, p. 77).Higher collective responsibility pervasive collaboration a larger number of overlapping cliques, instead of a few isolated sub-groups. Collaborative and Complementary Contributions: Clique Structures

Year 1: Specialized-groupCliques (sub-communities)

Year 1: Specialized-groupCliques (sub-communities)

Year 1: Specialized-groupCliques (sub-communities)

Year 1: Specialized-groupCliques (sub-communities)

Year 1: Specialized-groupCliques (sub-communities)

Year 1: Specialized-groupCliques (sub-communities)

Year 1: Specialized-groupCliques (sub-communities)

Year 2: Interacting-group

Year 3: opportunistic-collaboration

Specialized-groupInteracting-groupOpportunistic-collaboration

Centralized vs. Distributed Framework: Freemans Graph Centralization Measures A star network: the most centralized networkC2AC1C5C3C4

Questions for ideasTeacher-Student Exchange Patterns Questions on ideas (I thought worms do not have eyes, so then how do they sense light? )

Questions for ideas (X2 = 21.78, df = 2, p < .001)Questions on ideas (X2 = 8.87, df =2, p < .05) Categories of the teachers notes in the three years

Knowledge diffusion (Brown et al., 1993). Identified 25 common inquiry themes (e.g., eclipse, rainbow)Coded each students portfolio note, e.g.,

There are two kinds of eclipses[,] one is a lunar eclipse which happens when the earth gets between the sun and the moon and a solar eclipse is when the moon gets in between the sun and earth. (by RI, about eclipses)Students Knowledge Gains

Depth of understanding: epistemic complexity X scientific sophisticationEpistemic complexity: 1 - unelaborated facts, 2 elaborated facts, 3 unelaborated explanations, and 4 - elaborated explanations;Scientific sophistication: 1 - pre-scientific, 2 - hybrid, 3 - basically scientific, and 4 - scientific. Students Knowledge Gains

Knowledge DiffusionNumber of inquiry themes about which a student reported knowledge advances in his/her portfolio note (F(2, 63) = 64.14, p < .001, 2 = 0.88).Specialized-groupInteracting-groupopportunistic-collaboration

Depth of UnderstandingStudent ideas were rated based on scientific sophistication and epistemic complexity (F(2, 63) = 5.69, p < .01, 2 = 0.15).

The Evolution of the Community Knowledge Space in the Third Year

Conversation threads: conversation turns, build-on trees. No content!Inquiry threads: An inquiry thread is a conceptual line of conversation consisting of a series of notes that address a shared principal problem (Zhang, 2004; Zhang et al., 2007).

An Example of Inquiry ThreadsThe inquiry thread of shadows, lasting from early February to mid-April, included 11 notes authored by 11 students seeking a deeper understanding of the nature of shadows, with all 22 students as readers.

Diverse ParticipationOn average, each inquiry thread engaged students as writers 7.52 (SD=4.92) and readers 18.07 (SD=4.48) (all writers were also readers). Every student contributed to multiple inquiry threads as an author (M=9.91, SD=2.52). A strong relationship between the number of cliques a student belonged to and the number of inquiry threads s/he participated in as a writer (Pearson r = 0.58, p = .001).

Progressive Questioning in a ThreadIdentify what they need to know, and address increasingly complex problems. The thread on rainbows (#5):How are rainbows made? Rain droplets split sunlight... How can a big thing like a rainbow be activated by mere raindrops? (by SL) There are lots of colors of the rainbows, why are they always in the same order? (by KT)

Contributing New Information and Data to a ThreadIntroducing resources vs. Going beyond resourcesReporting observations/experiences vs. explanatory use of evidence

Idea Improvement in a Thread Code ideas on a four-point scale (1 - pre-scientific, 2 - hybrid, 3 - basically scientific, and 4 - scientific)

(F(2, 159) = 13.51, p < .001, 2= 0.15).

Stage 1

Stage 2

Stage 3

Mean

1.93

2.46

2.86

SD

.90

.90

.99

n

57

55

50

Map the Threads to the CurriculumThese inquiries covered all the required topics listed in The Ontario Curriculum of Science and Technology for Grade 4, as well as many topics expected for Grade 8, for instance, light waves, color vision, colors of opaque objects, concave and convex lenses.

This PresentationCollective responsibility, emergent knowledge building processesLiteracy growth through disciplinary knowledge buildingKnowledge Building Measures that Matter

RationaleTwo challenges facing education:To raise literacy of all students, close gaps;To develop creative capacityLiteracy as a complex social practice is best learned through dialogic communication and apprenticeship into literate discourse communities (Applebee, Langer, Nystrand, & Gamoran, 2003). Vocabulary learning in the knowledge building contexts.Lexical Frequency Profiles of student authentic written discourse (Laufer, 1994; Nation, 2001).

ContextsA class of 22 students in Grade 3 and then 4;KB/KF over two school years.

e.g., theory, evidence, hypothesis, approach, challenge, clarify, identify, expand, adjust, category, conclude(Sun, Zhang, & Scardamalia, in press)

Figure 2. The percentage of the 1st 1000 words in each students writing.

Semester

%

_1215525576.xls

Figure 3. The percentage of the 2nd 1000 words in each students writing.

Semester

%

_1208960162.xls

Figure 4. The percentage of academic words in each students writing.

Semester

%

_1209196189.xls

Domain-Specific Vocabulary

(Sun, Zhang, & Scardamalia, in press)

Of or below Grade 4 Beyond Grade 4 Total # of domain words identified464389# of domain words used by students413071

(Sun, Zhang, & Scardamalia, in press)

Correlations (Pearson r and p) between Students Literacy Scores on CTBS (Grade 4) and Their Participation in Online Knowledge Building Discourse over the Two School Years.

% of notes read

# of notes written

# of words written

Spelling score

.39

(.074)

.38

(.081)

.49*

(.021)

Vocabulary score

.41

(.056)

.39

(.070)

.53*

(.012)

Reading score

.41

(.058)

.36

(.097)

.45*

(.036)

* P < .05 (two-tailed).

This PresentationCollective responsibility, emergent knowledge building processesLiteracy growth through disciplinary knowledge buildingKnowledge Building Measures that Matter

Social interactions Correlations (Pearson r) between indicators of social interactions and depth of understanding achieved

Notes written% of notes readCliques belonging toNote linking in-degreeDepth of understanding.437 (.042).398 (.067).469 (.028).431 (.045)

Contributions to Inquiry Threads

Table 6. Correlations (Pearson r and p) between the KB indicators in the inquiry threads and the evaluation of the portfolio notes

portfolio notes

KB indicators in inquiry threads

# of writers

# of readers

# of notes

# of notes addressing factual problems

# of notes addressing explanatory problems

# of notes introducing resources

# of notes beyond resources

# of notes using evidences

# of students

.402**

(.034)

.488***

(.008)

.31

(.109)

-.14

(.471)

.34*

(.078)

.50***

(.007)

.19

(.345)

.20

(.309)

# of ideas

.42**

(.025)

.43**

(.021)

.26

(.174)

.21

(.296)

.23

(.231)

.39**

(.038)

.02

(.916)

.23

(.247)

Correctness of ideas

.31

(.106)

.18

(.374)

.20

(.310)

-.18

(.356)

.24

(.214)

.41**

(.030)

.17

(.395)

.20

(.309)

Epistemic level

.388**

(.042)

.196

(.317)

.36*

(.059)

-.425**

(.024)

.446**

(.017)

.21

(.262)

.34*

(.090)

.45**

(.016)

Note: * p

Lexical IndicatorsCorrelations between lexical indicators and depth of understanding

Total words writtenTotal domain wordsUnique domain words% of the academic words% of the 1st 1,000 words Depth of understanding.646 (.001).660 (.001).458 (.032).506 (.016)-.646 (.001)

Teacher Reflection on Knowledge Building Discourse

QuestionsFeedback data to collect and interpret Are there active participations and substantive contributions? Note writing; Total words Are there intensive and distributed interactions?Note reading and linking networks (distributed, dense) Is the discourse idea-centered? Use resources and go beyond; justify ideas; dwell on key concepts (domain words, key terms); elaborated and sophisticated representation of ideas (connecting words, academic words).Is there sustained, progressive inquiry?Extended conversations about important issues (inquiry threads); progressive questioningAre students taking on more control?Student-initiated inquiries, as opposed to responding to teacher-assigned tasks.Are students achieving deep understanding?Explanatory questions; scientific sophistication and epistemic complexity of ideas

ThanksDownload this presentation and related papers at:http://tccl.rit.albany.edu

A thumb question that drives my research work has been the following: This big question is attacting attention not only from researchers, but also from the public. For example, the most recent issue of the Time magazine published a cover article titled how to build a student for the 21st century. It calls for a shift from focusing on achievement gaps to developing students 21 century skills. Im glad that this education story has been given a better position than the spy story. My research of technology-supported innovative learning generally draws on research in knowledge creation and innovation. Particularly noteworthy is the conceptual work of philosopher Karl Popper. Three decades ago, to explain how scientific knowledge is created and advanced, Popper distinct three worlds: The rapid advances of knowledge in our society is largely enabled by the creation of World 3, which enables the critical evaluation and continual improvement of knowledge, leading to its historical evolution. When discussing the role of technology in cognition and learning, Roy Pea argued that technologies are not merely amplifiers of cognition, but reorganizers of cognitive functioning. It can be used to reshape/change the relations in a distributed cognitive system. Putting this idea into Poppers framework of 3 worlds, we can see how technologies can reshape the relations and interactions in the knowledge-creating process. For example, simulations and data logging tools can mediate our cognitive manipulation and observation of the physical world; CSCL enhances the interaction and cooperation of individuals; Internet-based communal knowledge databases drastically increase the opportunities for people to access public knowledge and contribute their ideas into it. Contributing to World 3 has traditionally been a privilege of a small population, e.g., scholars. Technology is democratizing this opportunity. A major focus of my research has been on how use technologies like simulations, CSCL environments, and communal knowledge databases to enable the kinds of learning interactions essential to knowledge-creating processes.

Collective knowledge creating is becoming pervasive in our society. An interesting example is the design of the Boeing 787 aircraft. This creative design work was accomplished by a large team of nearly 5,000 engineers distributed around the whole world. That number does not include production workers.The engineering work took place simultaneously in different countries. And eventually, all the different parts were put together.Whats evident in this sort of collective work is that members of an organization/team share collective cognitive responsibility for idea advancement. The responsibility for the success of a group effort is distributed across all the members instead of being concentrated in the leader/manager. Members are not only responsible for finishing tangible tasks, but also for staying cognitively on top of tasks, for example to understand why these tasks, whats happening in ones own work as well as in the community, and what are the emerging agendas. In this context, members need to spontaneously connect their own interests and expertise with those of the community, so that they can realize their own development in collective endeavors.With regards to collective cognitive responsibility, current inquiry learning practice in online and offline environments involves certain designs for student collaboration. Interestingly, almost all the inquiry learning programs rely on a fixed small-group approach. The teacher designs a project and divides it into different parts, then assigns those parts to different teams, and develops and time-line in terms of who will do what by when. These small groups only interact with each other through the final session for group presentation. Small-group design dominates the literature. But is small-group the only approach to collaboration?So what we see in the literature is a spectrum of different designs for collective cognitive responsibility, with the fixed small groups approach on one end, and an opportunistic-collaboration approach on the other. The fixed small group approach may have two variation: Stand-alone specialized groups, and an enhanced version that encourages cross-group interaction. The study presented here examines these different designs in the context of knowledge building communities.Basically, this is a three-year design experiment focusing on formative evaluation and improvement of instructional designs in a real context. In three years, the teacher in this study worked with three different classes of fourth-graders to building knowledge about the same topic--light, using the technology environment of KF.In Year 1, the teacher adopted a specialized-group design. Specifically, the teacher and students identified areas of interests. According to their interests, students were divided into six groups. Each group created a view in KF, and directed their inquiry within the area of their specialization. In Year 2, the teacher implemented an interacting-group design, which represents an enhanced version of the small-group approach. Students still worked in six groups based their interests. Each group created a view in KF. But the teacher particularly encouraged cross-group interaction. For example, students were encouraged to read other groups notes, and to contribute notes to other groups views when they had helpful information and ideas. As well, students were encouraged to conduct collaborative experiments to address issues of common interests.In Year 3, the teacher abandoned the fixed small group design in favor of a more fluid collaboration structure. He experimented with an opportunistic-collaboration design. Students and the teacher collectively identified their top-level goal, which was to build knowledge about light. Then progressively elaborated their sub-goals, and created views in accordance with these sub-goals. However, students were not assigned to any particular view. But rather, on a daily basis, they had the freedom of work on any problem from any view. They took responsibility for the growth of the whole database.The knowledge building in this year began with a classroom talk about students notes created in G 3 discussing how worms sense light. They realized that light was a really interesting area, and decided to study light. They created a Light view in KF and investigated various issues about light. After 2 weeks or so, students said that their Light view was getting too messy, and needed more views to cluster their notes. They collectively identified major focuses in the Light view, and created four new views and clustered their notes into the new views. Later, the Other Light further evolved into four new views. They pursued deep conversations in each view. For example, this is the colours of light view. As they dug deeper, they saw deeper issues, like They framed this view into different areas to highlight these important issues. They used features of KF to manage an emergent process of deepening inquiry.To evaluate collective cognitive responsibility under the three models, we analyzed students discourse in Knowledge Forum. An important method we used was SNA. So what is a social network? As an example, we can think about the email communications during this faculty recruitment process. We have several actors, including the candidates and Albany, each of which is represented as a node. The email contact between two actors is represented as a line between them. So we have a social network like this. From this network, can you tell who played the most important role? Now youve got it. In the present study, we particularly looked at two types of interactions

As well, to understand the teachers role under different models, we made content analysis of the his notes. To understand the process and participatory structure of opportunistic collaboration, I used a new method called ITA.

To evaluate students knowledge gains, we analyzed their portfolio notes that summarized each students personal knowledge advances about light.This table shows how I used these analyses to measure three types of efforts regarding collective cognitive responsibility. At a basic level, CCR entails community awareness, which means that members need to go beyond the work of their own and understand knowledge advances and problems of their whole community. A simple measure of this is the note reading relationship.In further, CCR requires members to make collaborative and complementary contributions. They need to build on each others competence and contributions, and rise above diverse ideas to advance the knowledge of the community. A basic indicator for this dimension is note linking interaction, particularly, clique structures in the note linking networks. Lying at the heart of CCR is a distributed framework for decision making and community coordination. This dimension was addressed using the centralization measures of SNA and also through the content analysis of the teachers role.

The teacher is here, belonging to five of the six cliques, playing a quite central role, coordinating and connect the work of different groups. While almost all the students belonged to only one clique each. There is no much overlap between the cliques. This represents a more teacher-centered, small-group based collaboration framework.The next part of the analyses was to look at the framework of the networks in terms centralized vs. distributed, using Freemans Graph Centralization measures. The most centralized network is a star network, in which one actor has connections to all the other actors, and any of the other actors has and only has a tie with the central member. Freemans Graph Centralization measures indicate to what extent a network resembles a star network of the same size. In the case of this study, we looked at two directions of note linking interactions: receiving and sending out note links. From this graph we can see that the networks in year 1 and 2 are more than 50% or even 60% of a star network. These measures drop to a bit more 30% in year 3 under the opportunistic-collaboration approach.To understand the roles played by the teacher under different designs, we made content analysis of his notes. A large proportion of his notes raised questions to students. Those questions fell into two categories. First, questions for ideas, which were raised by the teacher to bring new inquiry issues to students, inviting understanding of new concepts, explanations of new phenomena. This type of questions often led to a pattern of teacher-initiated discourse, which is typical of teacher-student interactions in in traditional classrooms. For example, in the investigation of colors, the teacher wrote: So in this type of discourse, the teacher tends to play a more directive role. The second type of questions are questions on ideas, which were posted in response to students ideas concerning their clarity, plausibility, and coherence. For example, in the inquiry of how worms sense light, one student reported an experiment showing that worms that sense light. In response to that idea, the teacher wrote Through this type of questions, the teacher tried to push forward the lines of inquiry initiated by students, instead of directing them to new tasks.

As this graph shows, the proportion of questions for ideas dropped from around 60% in year 1 to about 10% in year 3, and there is a significant increase in the proportion of questions on ideas, showing that under the opportunistic-collaboration design, the teacher turned more responsibilities over to his students. Instead of waiting for the teacher to assign tasks of inquiry, students actively identified deeper questions and pursuit deeper understanding.The final part of the analyses examines students knowledge gains based on their portfolio notes, focusing on idea spread or knowledge diffusion, which has become an interesting issue in a context where student haveSpecifically, we first identified 25 inquiry themes that had been commonly addressed in the three years. Then we coded each students portfolio note to see whether he/she summarized knowledge gains about each of the themes. For example, The final part of the analyses examines students knowledge gains based on their portfolio notes, focusing on idea spread or knowledge diffusion, which has become an interesting issue in a context where student haveSpecifically, we first identified 25 inquiry themes that had been commonly addressed in the three years. Then we coded each students portfolio note to see whether he/she summarized knowledge gains about each of the themes. For example, This graph shows the mean number of inquiry themes about which a student reported knowledge advances. There were significant differences in favor of opportunistic collaboration, followed by the interacting-group design. Similar patterns were observed in the analyses of the quality of students ideas. Through a more opportunistic collaboration process, students were able to identified deeper issues at the crossing points of different lines of inquiry, and achieve more scientific understanding. 28 inquiry threads, Student inquiries covered all the required topics listed in The Ontario Curriculum of Science and Technology for Grade 4, as well as many topics expected for Grade 8, for instance, light waves (thread #19), color vision (thread #9), colors of opaque objects (thread #7), concave and convex lenses (thread #15). This was true despite the fact that the students were not led by the teacher through pre-decided tasks or assignments to these concepts; rather, the process they collectively engaged in led them deeper into the conceptual domain, and new and more demanding concepts came to the fore as they conducted their research.This presentation will focus on the analyses of the first principle.ANOVA across the three stages