Analyzingcollaborativeknowledgeconstruction: … · 2014. 3. 25. · tutorialgroups. Sociocultural theories of learning place a great emphasis on analyzing discourse in order to understand

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Analyzing collaborative knowledge construction:multiple methods for integrated understanding

Cindy E. Hmelo-Silver*

Department of Educational Psychology, Rutgers University, USA

Received 3 August 2002; received in revised form 15 April 2003; accepted 14 July 2003

Abstract

Documenting collaborative knowledge construction is critical for research in computer-supported colla-borative learning. Because this is a multifaceted phenomenon, mixed methods are necessary to construct agood understanding of collaborative interactions, otherwise there is a risk of being overly reductionistic. Inthis paper I use quantitative methods of verbal data analysis, qualitative analysis, and techniques of datarepresentation to characterize two successful knowledge building interactions from a sociocultural per-spective. In the first study, a computer simulation helped mediate the interaction and in the second, a stu-dent-constructed representation was an important mediator. A fine-grained turn-by-turn analysis of thegroup discussions was supplemented with qualitative analysis of larger units of dialogue. In addition,chronological representations of discourse features and tool-related activity were used in study 2 to gain anintegrated understanding of how a student-generated representation mediated collaborative knowledgeconstruction. It is only by mixing methods that collaborative knowledge construction can be well char-acterized.# 2003 Elsevier Ltd. All rights reserved.

Keywords: Collaborative learning; Research methodology; Post-secondary education; Problem-based learning;Simulation

Analyzing collaborative knowledge construction, central to sociocultural theories of learning,has much in common with the three blind men and the elephant from the Indian parable thatdescribes their observations, each from their own point of view:

0360-1315/$ - see front matter # 2003 Elsevier Ltd. All rights reserved.

doi:10.1016/j.compedu.2003.07.001

Computers & Education 41 (2003) 397–420

* Corresponding author at present address: 10 Seminary Place, New Brunswick NJ 08901-1183, USA. Tel.: +1-732-932-7496 ext. 8311.

E-mail address: [email protected] (C.E. Hmelo-Silver).

http://www.elsevier.com/locate/compedu/a4.3d

mailto:[email protected]

The First approached the Elephant,And happening to fallAgainst his broad and sturdy side,At once began to bawl:’’God bless me! but the ElephantIs very like a wall!’’

The Second, feeling of the tuskCried, ‘‘Ho! what have we here,So very round and smooth and sharp?To me ’tis mighty clearThis wonder of an ElephantIs very like a spear!’’

The Third approached the animal,And happening to takeThe squirming trunk within his hands,Thus boldly up he spake:’’I see,’’ quoth he, ‘‘the ElephantIs very like a snake!’’ (Saxe, n.d.)

Each of the men perceived only a small portion of the whole beast and thus could only providea limited description. This is much like analyzing collaborative knowledge construction—as withthe elephant, one needs to use multiple methods to understand the interaction. Documentingcollaborative knowledge construction is critical for research in computer supported collaborativelearning (CSCL). Because this is a multifaceted phenomena, mixed methods are needed to obtainan understanding of collaborative interactions and to avoid being overly reductionistic. In thispaper, I demonstrate how multiple methods were used to analyze collaborative discourse in twotutorial groups.Sociocultural theories of learning place a great emphasis on analyzing discourse in order to

understand learning as well as stressing the importance of tools in mediating knowledge con-struction (Cole, 1996; Engestrom, 1999; Palincsar, 1998; Pea, 1993). Discourse is an importantpractice that one must engage in to participate in a community of practice (Wenger, 1998). In thisview, knowledge is constructed through social interactions and activity (Vygotsky, 1978). Colla-borative discourse may be the primary mechanism for learning because learners’ ideas are exter-nalized and become objects for discussion, negotiation, and refinement and are only laterinternalized (Chinn & Anderson, 2000; Vygotsky, 1978). Instructional interventions developedfrom this perspective redistribute the responsibility for generating and evaluating questions andexplanations, placing a greater emphasis on student-centered discourse than in traditional class-rooms (Greeno, Collins, & Resnick, 1996).Both collaborative interactions and psychological tools mediate learning in specific contexts

and are a critical feature of sociocultural theories of learning (Cole, 1996; Kozulin, 1998). Psy-chological tools are the cultural artifacts that help people regulate their thinking and interactions

398 C.E. Hmelo-Silver / Computers & Education 41 (2003) 397–420

(Kozulin, 1998). These include material objects such as rulers, equations, drawings, and compu-ters as well as symbolic tools such as language. Understanding collaborative knowledge con-struction requires making sense of the conversations that students engage in and the tools thatmediate their learning. These sort of everyday learning practices have been studied using a varietyof techniques including discourse and conversation analysis, ethnography, and other qualitativemethods (e.g., Cazden, 1986; Cobb & Yackel, 1996; Koschmann, Glenn, & Conlee, 2000). Manyof these methods focus on social and linguistic processes. Although these are rigorous methods,they do not always address important cognitive issues. Borrowing from the verbal data analysistradition of Chi (1997), I extend this methodology to analyzing group interactions to quantifyqualitative information. In addition, the quantified information and qualitative data can be usedin complementary ways to understand mediated collaborative learning. The goals of the analysesreported in this paper are to be able to reliably understand the content and cognitive processesthat occur as students are trying to learn and the role that tools might play in mediating learning.Computer tools and other representations provide opportunities to study the role that both

social factors and artifacts play in learning. This occurs because interfaces can be designed toguide collaboration (Hmelo & Guzdial, 1996; Roschelle, 1996). They can help structure thinkingby organizing and constraining activity. In addition, learners can construct representations thatthey use as tools in their thinking (Kozulin, 1998). These tools can enable learners to construct ajoint problem space (JPS) as they use collaborative turn-taking structures to negotiate meaningand production of visual representations that reflect their intermediate understandings. The JPSis a shared conceptual structure that supports learning and problem-solving activities (Roschelle,1996). Within this space, problem features, goals, operators, and methods are integrated. Com-puter tools afford unique opportunities for convergence upon shared meanings as learners use thetools to display, confirm, and repair their shared understanding. For example, Roschelle (1996)examined the conversation and action of two learners as they worked on a computer simulationusing conversation analysis. Convergence occurred as students used the simulation to display andnegotiate their shared understanding.Luckin and colleagues took a different approach to examining how alternative ways of struc-

turing hypermedia affected how students engaged in collaborative knowledge construction(Luckin et al., 2001). They coded all talk into task-oriented, non-task, and content categories withhigh reliability. Rather than looking at the summary frequencies of these categories, they plottedthe occurrence of these kinds of talk and the software features in a chronologically orderedrepresentation of discourse and features used (CORDFU) diagram. This analysis allowed them toexplore the relation between the software’s navigational features and collaborative knowledgeconstruction. The results demonstrated how different types of navigational features affectcontent-related talk.These are just two examples of studies that address the important question of how students

learn in a computer-based learning environment and the role of tools in collaborative knowledgeconstruction. There are many methodologies that can be used to analyze collaborative knowledgeconstruction. In this paper, I describe two studies of collaboration, one with and one withouttechnology and provide examples of the techniques used to analyze interactions. These examplesare important in understanding the characteristics of successful collaborative knowledge con-struction. For example, a fine-grained line-by-line coding allows the researcher to examine anentire corpus of discourse to identify important and representative cognitive and social processes

C.E. Hmelo-Silver / Computers & Education 41 (2003) 397–420 399

that can be reported as frequency counts. But this may be only one view of the elephant. Furtherqualitative analysis can be used to investigate larger phenomena that occur over greater units oftime. Finally, the fine-grained analysis can also be represented in ways that allow some of thechronological sequencing and tool use to become salient. Taken together, these three techniquespermit more comprehensive investigation than any single technique.

1. Study 1: the Oncology Thinking Cap: simulations as a collaborative context

This study focused on analyzing discourse to examine how students constructed a joint prob-lem-space (Roschelle, 1996) while using a simulation to learn about designing complex clinicaltrials (Hmelo, Nagarajan, & Day, 2000). The data collected for this study included transcripts ofthe videotaped sessions, computer-generated printouts of the students’ trial designs and finalresults, and pre- and post-tests. The students in the study were fourth-year medical students whoused the Oncology Thinking Cap, a simulation tool that allows investigators to model popula-tions of cancer cells. To help students use the software to learn to design clinical trials, a specialpurpose interface was developed, the Clinical Trial Wizard (shown in the Appendix). This inter-face organized the students’ input into categories that were relevant to the trial design process aswell as providing access to relevant data displays and graphs (see Hmelo et al., 2001 for detailsand the results of an evaluation study). This analysis used both quantitative and qualitativemethods to examine the role of prior knowledge on the construction of a JPS. We used verbaldata analysis methods in a comparative case study design. Six groups of four students each spentbetween two and three hours in one session designing a clinical trial to test a new cancer drugusing the Clinical Trial Wizard and Oncology Thinking Cap (OncoTCAP) software (Hmelo et al.,2001). The students were able to run their simulation to get feedback and then modify theirdesigns. The groups were formed randomly. Based on pre-test scores, the groups were dividedinto high and low knowledge groups. One high-knowledge (HK) group and one low-knowledge(LK) group were selected. The LK group had a pre-test score of 9.50 out of 24 whereas the HKgroup had a pre-test score of 17. Both groups did well at post-test (HK: 20.00; LK: 19.50). Thesetwo groups were studied in detail to examine how differences in knowledge affected collaborativeknowledge construction. Elsewhere, we have analyzed these discussions for the scientific reason-ing content (Hmelo, Nagarajan, & Day, 2002). The transcriptions were subjected to a fine-grainedanalysis of collaborative activities, coded on a turn-by-turn basis. This study uses both fine-grained coding and coarser qualitative analysis to capture both the general cognitive and socialcharacteristics of JPS construction, as well as illustrative examples that demonstrate phenomenathat go beyond the single turn. Together, they paint a rich picture of JPS construction.The number of conversational turns and trials conducted were counted. Because of the metho-

dological focus of this special issue, the coding scheme is described in detail in Table 1. Thecategories were designed to capture thinking processes involved in the construction of a JPS. Thecategorical variables were coded on a turn-by-turn basis in the following categories: knowledge,metacognition, interpretation, and collaboration. Collaboration included the coding categories ofconflict, questioning, and facilitator input.To understand construction of the JPS, the groups were initially compared on quantitative

measures followed by analyses of the fine-grained qualitative coding of the group discourse to


Table 1
Study 1 coding definitions and examples
Major category Subcategory Definition Example

Knowledge Conceptual

knowledge

Demonstration of knowledge gained prior

to starting the clinical trials design task.

That should, that NCI web page recommend

80% of the MTD.Prior Experience Actual experience in areas relevant to

designing clinical trials prior the simulation task.No, I just ah saw a lot of leukemia and ah. . .No, I have not seen anything like solid mass

tumors.Local analogies Comparisons made to other trials within the

same task or experimental situation.On our eight week leg. It’s not too muchdifferent on our eight-week leg.

Regional analogies Comparisons made to other trials outside the

current task using simulation.

The probabilities of getting a bad run much

higher in real life because the patients take off.Metacognition Monitoring Checking ongoing individual or group progress;

includes awareness of understanding.O.K. Now we know a little bit more aboutwhat’s going on here in a complete response.

Reflection Thinking about specific actions and their outcomesfrom previous trials to design current trial.

We thought that. . .you know if that was thecase we would stop running them, just cutthem down a dose. Is that right?

Theory-drivenplanning

References to future actions that derive from priorknowledge, experience, or existing theories.

You know I mean? If we have to stop our trialbecause our toxicity is too high at three, we’llrun it again at two.

Data-driven

planning

References to future actions that derive from the

results of trials or data.

So by the way we did it, we killed people. Look

we had some dose modifications. And you mayhave been correct for dose, for two we shouldhave decreased it and three taken them off.

Unjustifiedplanning

No justifications are provided for plan or action. I kind of think that 20% of it was arbitrary

Interpretation Low-level Literal interpretations of particular screen displays. You can see the growth of liver and lung mets

in this patient.High-level Broader conclusions that are drawn on the basis of

the range of prior literal data interpretations.Well, if they’re not making it that far then andthey’re all dying of tumor that means that

they’re, they’re not getting it per cycle. They’renot getting enough drug.

Conflicts Conceptual Disagreement over a method of inquiry, or aboutbroader concepts involved in the task.

No, I thought that Phase II is efficacy response.If the drug works.

Task-specific Disagreement over software-use, specific values ofparameters, or low-level interpretations of the data.

Two weeks and four weeks. No. We don’t wantto repeat that.

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Table 1 (continued)

402


Questioning Plan-related Questions pertaining to the future course of action. So we do the next study twice as long?

Software-related Questions pertaining to software-use and literalinterpretation of the data.

What were the different lines? What’s the patientdisplay do?

Self-answered Students and immediately answers own question. Did he get all? Yeah.

General Other open-ended questions related to the task. Do we information about what a half-life is orCan you see toxicity at 10 if it’s really small?

Questioningfacilitator

Questions posed to the facilitator by students. So your model, your model, has its ras mutatedcells are more likely to be in another tissue, or if

it hasn’t spread?Responses Agreement with

facilitatorWhen students show agreement to the views of thefacilitator; coded in context of facilitator statement.

Facilitator: . . . I think they said that they try tominimize the number of different types of them.

Student: Yeah.

Agreement withgroup member

Student agrees with view of their group member. Student1: But you’d know more about the drug.

Student2: Yeah you ’ld know more about thekinetics of the drug

Seeking

clarification

Student seeks verification for their ideas, or specific

values chosen for parameters.

For pittamycin we’re worrying about neuro

and heme right?Brief answer Answers to general questions that do not include

an explanation of any kind.I’m opposed, I’m opposed to any Grade 4toxicities.

Explanation Answers that include a reason or justification. We picked the uh. We picked the P1. I think we

picked the P1, and then we, and then we pickedthe alpha,. . .the desired alpha and beta and thatthat basically then defines the, which row you’re

in and it also defines the P0.Elaborateexplanation

Answers that include a detailed explanation tojustify one’s beliefs or share one’s knowledge.

There’s probably a lot of reasons. I mean mostof it is probably resistance. I mean single drug

regimens usually are never very good becausesoon you’ll have, I mean you’re just going afterone mechanism of metastatic disease and, usuallythose cells are, are smart enough to probably get

around that mechanism and so usually multipledrug treatments usually attack different pointsin the cell cycle and make it a more efficient cell.

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Table 1 (continued)


Facilitatorinput

Monitoring Facilitator asks questions to monitor progress,and encourages collaboration.

But, what were you actually going to do to thepatients?O.K. So, so what did you guys learn from

doing this?Explainingsoftware

Answers to software-related questions and/orself-initiated orientation to software possibilities.

. . .if you click on ‘‘view this patient’’ on the. . .letme just give you some context for that. . .so just

say, ‘‘Ok’’. So it first starts tracking thepatient. . .here’s the breast primary here. It startstracking them when there’s a hundred cells. Andthen when there are 10 to the ninth cells. . .it’s

diagnosed. . . .. O.k. just, in fact. . .hit enter. Thislittle blip here. That’s where you gave yourpittamycin.

Explaining concepts Addresses higher-level concepts that might helpthe students in their task.

Well, meaning it’s real and you’ve got enoughpeople That it is sensitive enough to catch what’sgoing on. So you wanna make sure that you got

enough, so you know what it is you are lookingfor, how many patients you are going to see.To make sure your design is sensitive enough,

cause we are not looking for big effects here.

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explore how the groups’ activity changes as students converge on a solution. Finally, joint con-struction activity was illustrated with excerpts from the group transcripts, so three type of methodsare used in analyzing these data.

1.1. Quantitative results

A trial was defined as the planning and execution of an OncoTCAP simulation run. A trial wasconsidered terminated when the simulation finished running a given design. The LK group ranmore trials (14) and took more conversational turns (2973) to converge on a satisfactory trialdesign than the HK group (six trials and 1773 turns). The fine-grained coding illuminates whatthe students talked about in their conversational turns. These results are summarized in Table 2.

1.1.1. KnowledgeNeither group explicitly referred to a large amount of knowledge overall. Not surprisingly, the

HK group referred to conceptual knowledge more than the LK group (2.64% and 1.11% ofturns). The HK group made references to prior knowledge in all 6 trials, whereas the LK groupmade such references in only 4 of 14 trials.

1.1.2. MetacognitionAs shown in Table 2, the HK group demonstrated a higher percentage of metacognitive state-

ments than the LK group. The majority of metacognitive statements made by the LK group weremonitoring statements (54.71%). The HK group also had the majority of their statements clas-sified as monitoring, but they made more evaluative statements than the LK group. The HKgroup was better able to evaluate their progress than the LK group because their prior knowledgeprovided them with a basis for making evaluative judgments.The nature of planning activity differed between the two groups despite making similar num-

bers of statements in this category. When the LK group planned, it was generally in reaction tothe data they were faced with. In contrast, the HK group planned more and divided their plan-ning evenly between reactive (data-driven) and proactive (theory-driven) approaches. The HKgroup mediated some of their planning with data, but within the group they had sufficientknowledge resources to cycle between theory and data.

1.1.3. InterpretationAfter each trial, the learners spent a great deal of effort interpreting the data displays that were

available to them, using these as opportunities to test and repair their understandings. The groupsengaged in similar amounts of interpretation. Both groups did not stray far from the data withmany low-level interpretations. They rarely made high-level interpretations, though these wereoften important in helping them move forward.

1.1.4. CollaborationThe collaboration coding included three subcategories: conflict, questioning, and facilitator

input. Conflicts were rare and showed no difference across groups (1.5% of turns). The majorityof these were task-specific. The HK group generated more questions than the LK group. Themajority of these (48.85%) were clarification-seeking questions. These are important because they


Table 2Category frequencies and subcategory percentagesa

Coding categories
Low-knowledge group High-knowledge group
Frequency
% oftotal turns
Withincategory%

Frequency
% oftotal turns
Withincategory%

Number of turns
2973 – – 1773 – – Number of trials 14 – – 6 – –
Knowledge
33 1.11 47 2.64 Conceptual knowledge 11 33.33 23 48.94 Prior experience 1 3.03 3 6.38 Analogies 21 63.64 21 44.68
Metacognition
393 13.21 342 19.28 Monitoring 215 54.71 179 52.33 Evaluation 56 14.24 79 23.10 Reflection 53 13.49 37 10.82
Total Planning
69 17.50 47 13.74 Theory-driven Planning 16 4.07 19 5.50 Data-driven Planning 48 12.20 19 5.50
Unjustified
5 1.27 9 2.63
Interpretation
204 6.84 107 6.02 High-level 173 84.8 91 85.05 Low-level 31 15.2 16 14.95
Conflict
44 1.48 27 1.52 Conceptual 6 13.64 3 11.11 Task-specific 38 86.36 24 88.89
Questioning
266 8.95 219 12.35 Clarifications 116 43.60 107 48.85 Plan-related 56 21.05 51 23.28 Software-related 32 12.03 13 5.93 Self-answered 5 1.87 2 0.91 General 16 6.02 16 7.30 Facilitator 41 15.41 30 13.69
Responses
273 9.18 376 21.21 Agreement with facilitator 50 18.30 41 10.90 Agreement with partner 107 39.19 208 55.32
Brief answers
75 27.47 86 22.87 Simple explanations 35 12.82 13 3.45 Elaborate explanations 6 2.19 28 7.44
Facilitator’s input
449 15.05 352 19.80 Monitoring 242 53.89 233 66.19
Explaining concepts
53 11.80 23 6.53 Explaining Software 154 34.29 96 27.27
a For all major categories, the percentage of turns included in those categories was computed. For the subcategories,percentages were computed based on total number of turns within the major category.


reflect negotiation of shared meaning. The HK group displayed slightly more clarification seekingthan the LK group. The next most common type of questioning was plan-related questions,which was similar across the groups. The LK group asked more software-related questions thanthe HK group as shown in Table 2. There was no difference in facilitator questioning.The LK group was less likely to respond to facilitator questions than the HK group. The pat-

tern of responses was different for the two groups. The HK group engaged in a great deal ofconsensus seeking, as shown by the large number of turns spent in agreement with other groupmembers compared with the LK group. Consensus seeking is likely a part of the negotiating theJPS and converging on a shared understanding. The LK group was more engaged in constructingsimple explanations than the HK group, which was slightly more likely to generate elaborateexplanations than the LK group.The major action of the facilitator was monitoring. This accounted for 60% of the facilitator’s

input across both groups. The percentage of facilitator monitoring was lower for the LK groupbecause the facilitator needed to explain more about concepts and software than in the HKgroup. It is not surprising that the limited knowledge in the LK group required more content-specific facilitator support than in the HK group.

1.2. Qualitative results: construction of the joint problem space

The category frequencies are extremely informative regarding some aspects of the knowledgeconstruction process, but do not fully address how students constructed a JPS. We looked forexamples across multiple turns that illuminate how this space was co-constructed. In particular,instances of negotiating joint understanding of the task, planning, and collaborative explanationswere subjected to additional qualitative analysis.Initially, the students needed to construct a joint understanding of the task, software, and

relevant variables in the clinical trial design process. The HK group was able to do this morequickly than the LK group, getting the big picture of the clinical trial design process after thesecond trial. By the third trial, they tended to interpret the results of the previous trial, summarizewhat had happened, engage in high-level interpretation, and move on to planning their next trial.When they examined the results of a trial, they focused immediately on relevant information. Incontrast, the LK students did not get the big picture until after the third trial. Their pattern wasto start planning, realize that they needed more information, go to the data, and back to plan-ning. So for any given trial, they often cycled between planning and data interpretation. Thetranscripts indicated that their search through the data was often exhaustive. These studentsexamined the individual patient histories for most of the patients in the trial. Although qualita-tively different, both groups engaged in joint construction of the problem space as they con-structed interpretations, explanations, and plans (see Hmelo et al., 2000 for additional details).One example of collaborative knowledge construction occurred as the groups figured out how

to represent the problem (Hmelo et al., 2000). Understanding the problem space was difficult andrequired marshalling all the resources within the HK group. As they were planning their secondtrial, they were trying to understand how toxicity is graded in order to set up appropriate con-tingency rules. In testing new cancer drugs, investigators must strike a balance between ther-apeutic and toxic effects. They needed to plan for consistent changes to a treatment protocol inresponse to different toxic side effects. These contingency rules are if. . .then rules that state that


for a given level of a toxic side effect, some change to the patients’ treatment will be made. In theexample below, the HK students had already worked out an understanding of the need forrecovery time between trials, using knowledge about the role of bone marrow in producing bloodcells. While examining a handout with the standard toxicity grades for each organ system, theywere trying to understand the implications of this and set up contingency rules to reduce thedosages patients would receive at different toxicity grades. Students did not begin to consider theconsequences of imposing these rules until the tool provided a screen with this option, mediatingmany discussions of contingency rules. Lou started the dialog with a query to the group aboutwhat their plan would be. The computer tool helped guide their thinking about the dose modifi-cation toxicity rule. Helen responded by referring to the computer screen that provided anopportunity for them to set up one kind of toxicity rule. Other members of the group jumped inand worked to negotiate the meaning of the toxicity grades. They also tried to understand thedifference between off-treatment rules (which removed the patient from the trial) and contingencyrules.

Lou: Ok, So now what?Helen: Should I pick some of these? (referring to options on the computer screen)Carl: Dose reduction criteria to prevent irreversible (?).Lou: So if we have a Grade 3 or above and we want to do something, we’ll probably, if we havea Grade 3 or above we’re gonna have to stop anyway so. . .Maddy: Stopping the trial except for two’s.Helen: So we only have, this means that we can only pick two?Facilitator: Right.Helen: Unless it has toxicity.Lou: Yeah, unless we move up our, to four on our overall and then you’re three here, right?

This example demonstrates how students posed questions and sought clarification from eachother as they addressed issues and negotiated their understanding.The LK group began negotiating the meaning of toxicity when Sean said ‘‘O.K., so this is

heme, neuro stuff so we had to in order to. It says neutropenia, thrombocytopenia. So we have tolook ah at the first box, white blood cells, for instance and platelets are going to be both.’’ Thisindicates that they were focusing on effects on blood cell production. This statement was closelyconnected to the OncoTCAP display that the students were viewing. They went on to discusslevels of platelets associated with different toxicity grades to arrive at a consensus that grade 4would be unacceptable (it is, in fact, life threatening). They began talking about the number ofthe platelet cells, necessary for blood clotting, as Sean and John tried to reach consensus:

John: I’d be worried like at ah, plateletsSean: You really don’t get, needs to be at less than 20.John: 25Chuck: RightJohn: 20.Sean: Right?John: 17


Sean: So for severe, 4, will Grade 4 toxicities stop the platelets? We’re going to press on at theplatelets of 30. I don’t know, I mean.

In this example, students used their understanding of platelets to negotiate what would be anacceptable level and then realized that going to the lower limit would have ‘‘severe’’ con-sequences. In the remainder of the discussion, they reconsidered the consequences of differentlevels of toxicity. John offered a plan to instead take patients off treatment at Grade 3. Seandemonstrated that he accepted this idea by putting that information into the computer, simulta-neously reading from the screen as Chuck finished the statement. The LK group started theirdiscussion very concretely, from the absolute level of one indicator of one toxicity. Moreover,they were treating off-treatment criteria as the only possible way to deal with adverse toxicity.This contrasts with the HK group, which began by trying to distinguish different types of rulesand to negotiate, generally, what level of toxicity would be acceptable. For both groups, the toolhelped mediate the discussions, as students completed various screen-based forms to set up theirtrials and interpreted different data displays and graphical representations. Although this is abrief snapshot of the analytic technique, it demonstrates how the fine-grained coding and coarseranalyses complement each other and provide a more complete picture of collaborative knowledgeconstruction than either technique would alone. The fine-grained analysis provides a view of thedata that summarizes the cognitive and social processes involved in constructing the JPS. It doesnot convey all the richness of the sequence of events or social interaction. The second analysiscomplements the summary analysis by demonstrating bigger units of activity and how these aremediated by the tools that are available.

2. Study 2: studying tool-mediated collaboration in a problem-based learning group

In this study, a single student group was analyzed as they spent five hours in a problem-basedlearning (PBL) tutorial (Hmelo-Silver, 2002a, 2002b). Problem-based learning is a student-cen-tered instructional method in which students work in small, facilitated groups to learn throughproblem-solving (Barrows & Tamblyn, 1980). One goal of this study was to examine how thestudents collaboratively constructed knowledge. A second goal was to examine how use of arepresentational tool helped mediate learning. Thus, this study focused on how content, process,and tools interact during social knowledge construction. Three different analyses were conductedto address these goals. As in Study 1, verbal data analysis (Chi, 1997) was used to conduct a fine-grained analysis of the discourse and additional qualitative analysis was used to capture colla-borative explanations. In addition, the CORDFU technique developed by Luckin and colleagues(2001) was adapted to address the second goal.The participants in this study were a group of five second-year medical students and an expert

facilitator. The discussion was videotaped and transcribed as the students tried to understand acase of pernicious anemia, a blood disorder that causes nervous system problems. The entiretranscript was coded for the types of questions and statements in the discourse.All the questions asked were identified and coded on a turn-by-turn basis. The turn was gen-

erally the unit of analysis, however, turns were parsed when the topic changed, or additionalquestions or statements were included in a single turn. Question-asking, especially by students,


can indicate that learners are actively thinking. It helps learners organize and reformulate theirideas and connect new information to their prior knowledge (King, 1999). Three major categoriesof questions were coded as shown in Table 3 (Graesser & Person, 1994). Short answer questionsrequired simple answers of five types: verification, disjunction, concept completion, feature spe-cification, and quantification. Long answer questions required more elaborated relationalresponses of nine types: definitions, examples, comparisons, interpretations, causal antecedent,causal consequences, expectational, judgmental, and enablement. The meta category referred togroup dynamics, monitoring, self-directed learning, clarification-seeking questions, and requestsfor action. To examine collaborative knowledge building, statements were coded as to whetherthey were new ideas, modifications of ideas, agreements, disagreements, or metacognitive state-ments. Each of these statements was coded as to its depth. Statements were coded as simple ifthey were assertions without any justification or elaboration. These corresponded to responses tothe short answer questions. These included verifications, concept completions, and quantities.Elaborated statements went beyond simple assertions by including definitions, examples, com-parisons, judgments, and predictions. Statements were coded as causal if they described the pro-cesses that lead to a particular state or resulted from a particular event. These last two types ofstatements are indicative of deep cognitive processing.In addition to these fine-grained analyses, an additional episode was selected for further

examination. This episode occurred late in the second session as the students drew a flowchartand a diagram that helped them integrate their understanding. The representation constructionactivity lasted for approximately one half hour and was coded at a very coarse level as to whetherthe drawing actions focused on anatomy and physiology, biochemistry, or clinical signs andsymptoms. To examine how the representation mediated the students’ collaborative knowledgeconstruction, a chronologically-ordered representation of discourse and tool-related activity(CORDTRA) was constructed in order to gain an integrated understanding of how students usedthe representation as a tool for collaborative knowledge construction (Luckin et al., 2001).

2.1. Quantitative results: questions and explanations

Students were expected to ask a substantial number of questions. The meta questions wereexpected to be the major category for the facilitator. The distribution of questions is shown inFig. 1. Because these were experienced PBL students, they were also expected to pose many metaquestions. A total of 809 questions were asked. The students asked 226 short answer questions, 51long answer questions, and 189 meta questions. Of the short answer questions, the modal ques-tion type was to elicit the features of the patient’s illness from the medical record, suggesting thatthe students were building rich problem representations. The facilitator asked 39 short answerquestions, 48 long answer questions, and 256 meta questions. Short answer questions were usedto focus students’ attention. Long answer questions often asked the students to define what theyhad said or interpret information as, for example, when the facilitator asked a student ‘‘But Imean what produces the numbness at the bottom of the feet?’’ Meta questions were the dominantmode for the facilitator for example, as he asked the students to evaluate one of their hypotheses‘‘Well yeah, multiple sclerosis. How about that? How do you feel about that?’’ These statementsalso included monitoring the group dynamics. The facilitator asked few content-focusedquestions.


Table 3Categories of questions

Question type
Description Example
Short answer
1. Verification For yes/no responses to
factual questions.
Are headaches associated with highblood pressure?
2. Disjunctive
Questions that require a simpledecision between two alternatives.
Is it all the toes? Or just the great toe?

3. Concept completion
Filling in the blank or the detailsof a definition.
What supplies the bottom of the feet?Where does that come from??

4. Feature specification
Determines qualitative attributes ofan object or situation.
Could we get a general appearanceand vital signs?

5. Quantification
Determines quantitative attributes ofan object or situation.
How many lymphocytes does she have?

Long answer
6. Definition Determine meaning of a concept. What do you guys know about
pernicious anemia as a disease?
7. Example Request for instance of a particular
concept or event type.
When have we seen this kind ofpatient before?
8. Comparison
Identify similarities and differencesbetween two or more objects.
Are there any more proximal lesionsthat could cause this? I mean I knowit’s bilateral.

9. Interpretation
A description of what can be inferredfrom a pattern of data.
You guys want to tell me what yousaw in the peripheral smear?

10. Causal antecedent
Asks for an explanation of what stateor event causally led to the current stateand why.
What do you guys know aboutcompression leading to numbnessand tingling? How that happens?

11. Causal consequence
Asks for explanation of consequences ofevent/ state.
What happens when it’s, when the,when the neuron’s demyelinated?

12. Enablement
Asks for an explanation of the object,agent, or processes allows some actionto be performed.
How does uhm involvement of veinsproduce numbness in the foot?

13. Expectational
Asks about expectations or predictions(including violation of expectation).
How much, how much better is her,are her neural signs expected to get?

14. Judgmental
Asks about value placed on an idea,advice, or plan.
Should we put her to that trouble,do you feel, on the basis of whatyour thinking is?

Task oriented and meta
15. Group dynamics Lead to discussions of consensus or
negotiation.of how group should proceed

So Mary, do you know what theyare talking about?

16. Monitoring
Help check on progress, requestsfor planning.
Um, so what did you want to do next?

17. Self-directed learning
Relate to defining learning issues,who found what information.
So might that be a learning issue wecan, we can take a look at?

18. Need clarification
The speaker does not understandsomethingand needs further explanation orconfirmation of previous statement.
Are you, are you, Jeff are you talkingabout micro vascular damage that then,which then causes the neuropathy?

19. Request/Directive
Request for action related toPBL process.
Why don’t you give, why don’t yougive Jeff a chance to get the board up.


If knowledge were being collaboratively constructed, the students’ statements should be inresponse to previously introduced ideas. This was indeed the case. The facilitator made a total of243 statements and the students made a total of 3763 statements. Eighty percent of these state-ments were directly related to concepts that were important for the problem. The distribution ofstatement types is shown in Fig. 2. This demonstrates that the students were doing most of thetalking and they were engaged with curriculum-relevant content. The facilitator made few state-ments, rarely offering new ideas or modifying existing ideas. The facilitator was most likely tooffer a comment monitoring the group’s progress or encouraging students to consider that apoorly elaborated idea might become a learning issue. Both the metacognitive questioning andstatements helped support the students’ collaborative knowledge construction as they built on thenew ideas offered by others, expressing agreement, disagreement, and modifying the ideas beingdiscussed. Of the first four categories of statements, the majority were simple statements (1641),but the students also made elaborated statements (464) and causal explanations (211). Whilemany of the statements taken individually were simple statements, taken as a collaborativeexplanation, they were elaborated, over several speakers and conversational turns. Variousexcerpts can be used to illustrate the collaborative explanations (see example below).

Fig. 1. Distribution of question types.


2.2. An integrated view of the ‘‘drawing episode’’: combining qualitative and quantitative results

To examine how students collaboratively constructed knowledge, I zoomed in on an episodenear the end of the activity in which students were drawing a representation of their under-standing of the case, adapting the cordfu methodology (Luckin et al., 2001; Luckin et al., 1998) tocreate a chronologically-ordered representation of discourse and tool-related activity (cordtra)diagram, shown in Fig. 3.Late in the second session, the facilitator suggested ‘‘Um, probably the best way to pull this all

together I suppose is to uh, uh tell me what you think is involved in her nervous system. Can youuh, can you draw a diagram of where you think the problem is?’’ This prompt led to a rich 29 mindiscussion in which the group members worked at pulling together their understanding. Thisepisode had three phases: a brief phase in which the group planned the drawing, the majority ofthe drawing phase with an important segment in which the students make the connectionsbetween the signs and symptoms and different levels of functioning, and finally, a wrap up that ischaracterized by references to the drawing and tying up loose ends. The group’s final drawing isshown in Fig. 4. To understand these episodes in greater detail, the CORDTRA diagramsallowed simultaneous examination of talk and tool-related activity. To illustrate this methodol-ogy, this paper will discuss the second phase of this activity.

Fig. 2. Distribution of statement types.


2.2.1. Interpreting CORDTRAIn Fig. 3, the numbers along the x-axis refer to the line number of each conversational turn.

Along the y-axis are line numbers that represent categories. The entries in the graph refer tospeakers, drawing activity, and instances of discourse that are coded in the categories indicatedby its position on the y-axis at the turn indicated by the position along the x-axis. Lines 1–6identify the speakers. Line 1 is the facilitator. Lines 2–6 are the students in the group (the legendidentifies the students in order). Lines 7–9 refer to short, long, and meta questions, respectively.Lines 10–19 refer to statements. Recall that many of the statement types—new idea (New),modification (Mod), conceptual agreement (CA), task-related agreement (TRA), conceptual dis-agreement (CD), and task-related disagreement (TRD)—could also be coded as to whether theywere simple assertions, elaborations, or causal statements. Lines 20–22 refer to the actual activityof constructing the representation. The first of these lines refers to representing the phenomenonat the level of structures and functions (anatomy and physiology, D-AP), the next refers to thebiochemical level of explanation (D-Bchem), and the final level refers to the level of signs andsymptoms (D-SS). The last three lines, 24–26, are references to the drawing. Line 23 refers togestures directed at the drawing, Line 24 refers to talk related to drawing conventions and plan-ning, and Line 25 is for other spoken references. The phase of the activity presented here is when

Fig. 3. CORDTRA diagram of students mapping between different levels of analysis in middle phase of activity.


students begin making connections between their hypotheses about the patient’s disease and theobserved effects (signs and symptoms).

2.2.2. Mapping between causes and effectsAfter a fairly detailed discussion of the biochemistry, Jeff and Jim have a brief discussion about

representational conventions in lines 265–269.

Jim: One of, one of the last things about that besides the, which you’re going to write the,you should write that up about the megaloblastic cells, just as another arrow.Jeff: Yeah we could have like symptoms here.Sheila: Uh hmm. Yes.Denise: Yeah. Yeah.Jeff: I’ll draw the symptoms in black.

Fig. 4. Student-constructed representation.


That triggers the next phase as the students began connecting their hypotheses about causalmechanisms (i.e., anatomy, physiology, biochemistry) to the evidence (signs and symptoms),shown in Fig. 3. This is important because this discussion of how to represent processes and signsand symptoms moves the students’ thinking forward. Thus, the representation serves as a tool intheir collaborative knowledge construction and a focus for negotiation.The CORDTRA diagram shows the relation of the discourse to the drawing activity. This

makes salient the nature of student talk as they switch between different levels of representation.At the junctures where student drawing activity switches from representations of basic scienceprocesses to signs and symptoms, or between levels of science, the students engage in causal ela-borations. In the discussion preceding this next excerpt, the students were focused on basicscience mechanisms without connecting their ideas to the patients’ signs and symptoms. Thefacilitator jumped in and asked: ‘‘Okay. Now you’re going to bring it into the nervous system.’’Students respond to this by first completing their biochemical explanation, but then connecting itto the clinical signs (in bold) in lines 312–322.

Jeff: Where exactly isJim: You should, you should start offJeff: off hereJim: Methylmalanil to succinylJeff: Right hereJim: YeahSheila: Yeah, there, yeahJim: We, you start with odd number fatty, odd number of carbons for the fatty acids.Mary: Fatty acidsSheila: RightMary: And then you incorporate it a, a carbon dioxide that it’s a carboxylation reaction for the

propianol Co-A to the methylmalanil Co-A. So you convert it from an odd chain with three to afour chain and then you do, it’s actually a mutase reaction for the methyl.

This discussion continues until they get to how the membranes for the neurons are formed,which is directly relevant to the patient’s problem, and they continue in lines 334–344:

Jeff: So these get incorporated into theMary: MembranesJim: In the handout that I gave you, the last sheet gives the um pathogenesis of this vitamin B12

deficiency.Jeff: So incorporated into the membranes and then you get. . . neuron loss, demyelination.Jim: Specifically dorsal column. Yeah. Specifically dorsal columnMary: RightJim: And it, it’s called like the, the term, the category is a, is a metabolic demyelinization.Mary: And you get neuronal also um, various things that happen. I believe you get neuronal

cell swelling within the membrane and then you can get neuronal death. And that’s when you getthe paralysis and once it progresses to that stage, as we know, neurons will regenerate.


Here, the students went through a causal explanation in which they clarified their ideas andintegrated different levels of analysis, though they only just begun to get to the clinical level, andin fact, they brought their explanation to the level of a hypothetical symptoms. This explanationis highly collaborative as students monitor each other’s statements and complete each other’ssentences. The students got more specific and started to identify structural and functionalabnormalities that account for the patients’ symptoms in response to the facilitator’s question:

Facilitator: Okay now you want to, would you please summarize those structures that areinvolved in the nervous system. What, where is that happening? This swelling of the neuronsand loss of myelin.Mary: Centrally and peripherallyFacilitator: NiceFacilitator: Now narrow it down just a little tadJim: Dorsal columnMary: Dorsal column, specifically dorsal columnSheila: YeahDenise: JustFacilitator: Is that it? Just the dorsal columnsSheila: That’s the main place right? It doesn’t happen in..Jim: That’s what causing her symptomsJeff: What are her symptoms?Denise: And then, then Mary eventually do you get um..Jim: Paresis, paresthesia

Jeff: Paresthesia

Jim: Which is numbness and tingling and hyperexcitability

Jeff: OkaySheila: Um. . .. and then the loss of. . .yeahJim: And then gait

Sheila: Then the loss of, yeah. The proprioception and vibratory loss

Mary: Ataxia, sensory ataxia is what it’s called for the gait abnormalityFacilitator: You want to describe what sensory ataxia means?Mary: Sensory ataxia um, is specific when, is it, it’s a problem when you actually lose sensation.For example, if you lose your um, position sense, you then are not able to walk properly oryou’re not able to do movements that you would normally do because you don’t have a sense ofwhere your fingers or toes or your feet are. So, for someone who has a gait disturbance as she

has, you’d classify that as a sensory ataxic.Sheila: Although actually the description of hers doesn’t quite fit.

This excerpt corresponds to lines 347–374 on the CORDTRA diagram. Here the students weregetting closer to bringing the problem of demyelination to specific structures (the dorsal column)and then mapping it onto the signs and symptoms that the patient is actually exhibiting. More-over, they were monitoring the fit between the symptoms that she is exhibiting and their theore-tical descriptions. All the students were involved in this collaborative sense-making. The drawingwas an important tool in this discussion. It served as a concrete referent that students can point


towards and negotiate as they are elaborating and monitoring their joint understanding (whichthey did in the final phase of this episode). This analysis provides considerable information aboutthe relationship among variables and representation construction that the frequency counts donot provide. The fine-grained analysis summarizes the cognitive and social activity, but does notcapture the richness of the collaborative explanations that students construct. The analysis of thelarger units of discourse help shed light on this phenomenon, as well as providing some infor-mation about how the representation served as a tool for the students’ collaborative thinking.The CORDTRA diagram makes salient the relation of metacognitive talk and causal explanationto the conceptual space covered in the drawing activity and supports making complex inferencesthat might otherwise be difficult. This allows exploration of the relationship between tool use (inthis case, a drawing) and collaborative knowledge construction. The different methods providethe opportunity to see more of the elephant than any one method does by itself.

3. Discussion

In these two studies, several analytic techniques were used. In study 1, the focus was on codingfeatures of collaborative discourse that were related to joint knowledge construction such asquestioning and explaining. A combination of quantitative methods (frequency counts) andillustrative qualitative analyses were used to help answer the research questions. In study 2, anattempt was made to integrate the different aspects of the analysis to gain a bigger picture of howa representation mediated collaborative knowledge construction. Although this latter study doesnot look at technology, this technique has a great deal of potential to support analysis of colla-borative knowledge construction in a computer-based learning environment. The CORDTRAtechnique would have been extremely helpful in analyzing study 1 but there was not sufficientdata for such an analysis.Activity theory is a descriptive theory of human thought and behavior in context. This theory

suggests that learning needs to be considered as an activity system that involves subjects andmediating artifacts (be they representations, computers, or other tools) that act to transformparticular objects of activity to achieve an outcome (Engestrom, 1999). The activity system is alsoaffected by other social and historical factors as well. Understanding such a complex system is asubstantial undertaking and the use of multiple methods are often required to understand howknowledge is constructed (Salomon, 1991). Rather than rigid methodological orthodoxy, thecombination of methods used must be tailored to one’s research questions—which aspects ofinteraction, in these instances, collaborative knowledge construction, one seeks to understand.For example, in study 2, the fine-grained coding answered questions about the students’ cognitiveactivity by providing information about how they asked questions, monitored, and elaboratedtheir understanding. The excerpts answered questions about the interactive processes involved ingenerating a collaborative explanation. The CORDTRA analysis was directed at questions abouthow a representation mediates learning. These methodological techniques have great potential toinform analysis of data in CSCL systems as investigator seek to answer cognitive, social, andtool-related questions in an integrated way.In these studies, a mixture of quantitative methods and qualitative methods were used as a

means of analyzing interaction. Recall the story of the three blind men looking at an elephant.


One man said oh, this animal is like a wall. Another said that it was like a spear. The thirddescribed a snake. But in their reductiveness none of them had the complete picture. The argumentthat I make here is that to see the whole elephant, we need to mix our methods to get the big picture.

Acknowledgements

This research was partially funded by a National Academy of Education/ Spencer FoundationPostdoctoral Fellowship.

Appendix. Screenshots from the Oncology Thinking Cap

Step 1 of the Clinical Trial Design Wizard:Defining the dose and schedule.

taken off-treatment.

Step 2 of the Clinical Trial Design Wizard:Modifying the dose due occurrence o toxicity.

Setting the statistical parameters.
Step 3 of the Clinical Trial Design Wizard:Deciding when individual patients will be
Step 4 of the Clinical Trial Design Wizard:


Multiple patient simulation result screen showing the number of patients in the trial, the num-ber of complete responses (CR), partial responses (PR) and recurrences. In addition it showspatient deaths to to tumor, toxicity (Tox), and those that reached the end of the trial either withno evidence of disease (NED) or with a tumor. The bottom half of the screen allows the user toview the history of an individual patient.

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Analyzingcollaborativeknowledgeconstruction: … · 2014. 3. 25. · tutorialgroups. Sociocultural theories of learning place a great emphasis on analyzing discourse in order to understand