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The Learning Kit Project  Software Tools For     Collaborative Learning
 

The Learning Kit Project Software Tools For Collaborative Learning

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    The Learning Kit Project  Software Tools For     Collaborative Learning The Learning Kit Project Software Tools For Collaborative Learning Document Transcript

    • ARTICLE IN PRESS Computers in Human Behavior xxx (2008) xxx–xxx Contents lists available at ScienceDirect Computers in Human Behavior journal homepage: www.elsevier.com/locate/comphumbeh The learning kit project: Software tools for supporting and researching regulation of collaborative learning Philip H. Winne a,*, Allyson Fiona Hadwin b, Carmen Gress a a Faculty of Education, Simon Fraser University, Burnaby, British Columbia, Canada V5A 1S6 b University of Victoria, Canada a r t i c l e i n f o a b s t r a c t Available online xxxx Computer-supported collaborative learning (CSCL) is a dynamic and varied area of research. Ideally, tools for CSCL support and encourage solo and group learning processes and products. However, most CSCL Keywords: research does not focus on supporting and sustaining the co-construction of knowledge. We identify four Collaborative learning environment reasons for this situation and identify three critical resources every collaborator brings to collaborations gStudy that are underutilized in CSCL research: (a) prior knowledge, (b) information not yet transformed into Self-regulated learning knowledge that is judged relevant to the task(s) addressed in collaboration, and (c) cognitive processes Tracing used to construct these informational resources. Finally, we introduce gStudy, a software tool designed to advance research in the learning sciences. gStudy helps learners manage cognitive load so they can re-assign cognitive resources to self-, co-, and shared regulation; and it automatically and unobtrusively traces each user0 s engagement with content and the means chosen for cognitively processing content, thus generating real-time performance data about processes of collaborative learning. Ó 2007 Elsevier Ltd. All rights reserved. 1. Introduction interaction (Carroll, Neale, Isenhour, Rosson, & McCrickard, 2003) and negotiation tools designed to support group social skills and Computer-supported collaborative learning (CSCL) is a dynamic discussions (Beers, Boshuizen, Kirschner, & Gijselaers, 2005). De- area of research involving an assortment of methodologies, various spite much activity in the CSCL field, there is relatively little re- theoretical and operational definitions, and several technological search on how types of tools support and sustain productive tools for investigating multiple collaborative structures (Gress, collaboration (Gress, Hadwin, Page, & Church, this issue; Hadwin, Hadwin, Page, & Church, this issue). Overall, CSCL environments Gress, Page, & Ross, 2005). aim to advance research on models of collaborative learning and We identify four reasons for this situation. First, much research facilitate learners0 co-construction of knowledge (Koschmann, in CSCL focuses on developing and testing technologically-based 2001; Salovaara & Järvelä, 2003). More specifically, CSCL interactive tools (e.g., text chat tools, conferencing tools, email systems, and tools aim to encourage, support, and sustain solo and group regula- so forth) for sharing information (Gress, Fior, Hadwin, & Winne, tion of collaboration, learning processes and products by prompt- this issue). These tools provide a means for collaborating online, ing, coaching and providing interactive feedback (Kirschner, 2004). but facilitating research about how and why collaboration takes Collaboration typically is operationalized as student-centered the shape(s) it does and has the effect(s) it does is a much less small group activities in which learners are supposed to develop the goal of these projects. Second, a review of the CSCL literature skills for sharing the responsibility to be active, critical, creative (Gress, Hadwin, Page, & Church, this issue) uncovered multiple co-constructors of learning processes, and products. Conditions ‘‘modes” for collaboration. Some describe students working asyn- that facilitate effective collaborative processes include, for exam- chronously on individual contributions towards one document, ple, positive interdependence, positive social interaction, individ- others portray students working asynchronously on one document ual and group accountability, interpersonal and group social and reflecting on their collaborators’ contributions, and still others skills, and group processing (Johnson & Johnson, 1989; Johnson & describe students contributing to a shared document in a synchro- Johnson, 1999; Kreijns, Kirschner, & Jochems, 2003). Some CSCL nous environment. It may be that each model of collaboration is software tools attempt to support these kinds of engagements. best suited to a particular type of task and pedagogical approach, Examples are awareness tools designed to support positive social though this is not demonstrated. Notwithstanding, tools for collab- orating do (and probably should) differ dramatically depending * Corresponding author. Fax: +1 778 782 4203. upon instructional goals, tasks, and tools available to learners. E-mail address: winne@sfu.ca (P.H. Winne). Third, the extensive educational literature on cooperative and 0747-5632/$ - see front matter Ó 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.chb.2007.09.009 Please cite this article in press as: Winne, P. H. et al., The learning kit project: Software tools for supporting and researching ..., Computers in Human Behavior (2008), doi:10.1016/j.chb.2007.09.009
    • ARTICLE IN PRESS 2 P.H. Winne et al. / Computers in Human Behavior xxx (2008) xxx–xxx collaborative leaning (e.g., see Abrami, Lou, Chambers, Poulsen, & While our first claim is empirically justified, we acknowledge Spence, 2000; O0 Donnell & King, 1999; Slavin, 1999) is often ig- the second and third are speculations, though they have consider- nored in designing CSCL environments, tools, and research. As a re- able collateral support. What would be required to test these sult, research about CSCL and research about collaboration in propositions in the context of CSCL and lend empirical address general share too little ground, particularly regarding supports or to the possibility that models of collaborative learning are mis- instruction. Fourth, researching how individuals and groups learn specified? In this article, we offer a partial answer by describing to collaborate and support collaborators poses significant chal- an advanced software learning environment, called gStudy, that lenges for measurement and evaluation (Gress, Fior, Hadwin, & we and colleagues are developing (see Winne, Hadwin et al., Winne, this issue). 2006; and also Winne, Nesbit, et al., 2006; http://www.learning- kit.sfu.ca). gStudy software harnesses a platform for supporting solo SRL to support collaborative learning. Our explicit goal in 2. Supporting software tools for researching collaborative designing collaborative tools and structures has been to support learning students in learning to regulate collaborative learning activities and tasks. One goal of the Learning Kit Project is to research how students Since this paper overviews and introduces software tools dis- adopt and adapt strategies for solo and collaborative learning. Our cussed more deeply within other papers in this special issue, we work extends along a continuum from conventional models of self- provide illustrations of how each tool can be used to facilitate col- regulated learning (SRL) as (a) a solo activity to (b) learning in a laborative activity. In depth discussion of those collaborative group to (c) shared-collaboration of learning that emphasizes col- enterprises is found in the other papers in the special issue. lective negotiation and regulation of task understanding, goal set- ting, planning, and enacting strategies. The bulk of research on collaborative learning in classrooms and 3. gStudy as supported by software technologies has appropriately focused on features of collaboration per se; that is, the nature and patterns 3.1. Overview of exchanges of information among collaborators (see O0 Donnell & King, 1999). These efforts have revealed a great deal. Nonetheless, gStudy (Winne, Hadwin, et al., 2006) is a state-of-the-art, cross- we posit that models of collaboration are misspecified. Specifically, platform software system that puts into practice proposals Winne little and sometimes no attention has been focused on three critical (1992) made about using software to substantially extend research resources every collaborator brings to collaborations: (a) prior in the learning sciences. gStudy is a shell in which a learner or an knowledge, (b) information not yet transformed into knowledge instructional designer can create or import content about almost that is judged relevant to the task(s) addressed in collaboration, any topic. (Topics defined by enacting physical skills, such as play- and (c) cognitive processes used to construct these informational ing a piano or dissecting a frog, are excluded.) Information about resources. The logic upon which our conjecture rests is as follows. topics is rendered using the hypertext markup language (HTML) in forms including text, diagrams, photos, charts, tables, audio 1. In research on solo learning, measures of prior knowledge are and video clips—that is, the information formats common to hard- often the most potent variables affecting outcomes. While col- copy library resources and on the Internet. A unified collection of laboration may do much to fill gaps in an individual0 s knowl- these materials is called a learning kit. edge and to stimulate recall of an individual0 s knowledge that gStudy provides cognitive tools for learners to create, share, and otherwise would not be brought to bear, one collaborator0 s gain exchange information objects. Every information object is linked to in this respect often depends on another0 s knowledge. That is, a file, data the learner selects within a file, or data the learner se- collaborators0 knowledge as a group is almost certainly greater lects within a remote web site outside the learning kit. Each tool than any one collaborator0 s knowledge. Those with less knowl- has been designed, as much as possible, to instantiate research that edge about a particular sector of the collaborative task benefit demonstrates using the tool will positively influence solo and col- from group mates0 prior knowledge. That knowledge may be laborative learning and problem solving. In this article, we high- about the task, the content, or the collaborative process itself. light features designed to promote collaborative learning in 2. Information that a collaborator can access but which is not yet particular but these features are seamlessly interwoven with fea- anyone0 s knowledge—information that can be contributed to tures designed to promote solo learning. Hadwin, Oshige, Gress, the group but is only partly understood by the contributor— and Winne (this issue) describe some specific models of collabora- may be a powerful resource in collaboration. This is because tion that can be supported in the gStudy software, based on three each member0 s partial knowledge may, in concert with contri- different views of regulation of learning. butions of such information made by others in the group, gen- Fig. 1 shows one of gStudy0 s views, the browser view, onto a erate a synergy that boosts the group0 s productivity past a learning kit. The kit may belong to a learner, to a collection of critical threshold. Through collaboration, information that is learners, or to a learner and instructor. In Fig. 1, all of gStudy0 s pan- initially no one0 s knowledge may become knowledge forged els are exposed to show search, concept maps, the catalog of kits within the group. (including the one being viewed), the selected kit0 s table of con- 3. How learners learn—the tactics and strategies learners know tents, and a panel that identifies information objects that are and apply to transform information into knowledge—tends to linked to data in the section of the kit that is in view. In practice, be stable. The simplest demonstration of this is the considerable it would be rare that a student exposed all of gStudy’s panels effort that must be spent to teach learners new tactics and simultaneously. strategies for learning and, once these are learned, the addi- tional effort that must be spent to coax learners to use those 3.2. Information objects newly acquired tactics and strategies. Consequently, the pro- cesses that each learner typically uses to learn very likely are In gStudy, each information object is characterized by metadata carried over to the collaborative setting. Furthermore, there that specify: author of the object, date created, and date modified. presumably are tactics and strategies that learners collabora- This allows identifying each information object by one or several of tively develop and refine to engage in collaboration itself. these characteristics. It means a student can share objects and Please cite this article in press as: Winne, P. H. et al., The learning kit project: Software tools for supporting and researching ..., Computers in Human Behavior (2008), doi:10.1016/j.chb.2007.09.009
    • ARTICLE IN PRESS P.H. Winne et al. / Computers in Human Behavior xxx (2008) xxx–xxx 3 Fig. 1. gStudy main window several panels displayed. information about those objects with one another. It also means Notes record information in a template that is a schema for var- that students who are co-constructing a collaborative learning kit ious forms of information (see Fig. 2). At a minimum, a template can review information about objects added to the kit, who added consists of a text field that ‘‘titles” the note. Beyond this, a template the object and when it was added. Types of information objects can include (a) fields for recording text, (b) sliders for rating fea- implemented in gStudy and methods for creating them are de- tures of interest, (c) checkboxes that allow the learner to enumer- scribed in the following section. ate one or more items in a list, (d) radio buttons that provide for selecting one and only one attribute within a set, (e) a field where 3.3. Notes the learner can attach files, such as a text file or a picture file, and (f) instructions or labels, (e.g., ‘‘Rate the importance of this infor- Learners can elaborate on any source information by creating a mation by dragging the slider”). note. The primary method for creating a note is to select (click and Templates can be designed by someone else—the author of a drag the cursor across) text, a region within a graphical display, or learning kit, a teacher, or a collaborator. As well, a learner can con- a frame in an audio or video clip; then right-click (in the Windows struct new templates to suit a particular need. For example, collab- environment or control-click under Apple OS X) to display a con- orators may develop a template for sharing information and textual menu and choose ‘‘Link to new note”. understandings about a task (task analysis note) or a template Fig. 2. A note with template. Please cite this article in press as: Winne, P. H. et al., The learning kit project: Software tools for supporting and researching ..., Computers in Human Behavior (2008), doi:10.1016/j.chb.2007.09.009
    • ARTICLE IN PRESS 4 P.H. Winne et al. / Computers in Human Behavior xxx (2008) xxx–xxx for providing feedback on each other0 s writing (peer feedback objects they create solo and review strategies peers have tried and note). In a shared kit, students can add to and revise each other0 s then choose whether to add that strategy to their individual strat- note templates as well as the content of notes. Students using indi- egy library. vidual learning kits can exchange note templates and instantiated note objects with one another. For example, in a Women0 s studies 3.6. Labels course, students might develop a template for critiquing current media representations from a Feminist perspective. That template Learners often categorize information according to various can be shared amongst a group working on a class project to iden- attributes of a personal or task-related nature, e.g., ‘‘confusing”, tify themes across a range of media presentations. (See Hadwin, ‘‘surprising”, ‘‘needs more study!” and the like. gStudy provides a et al., this issue, for further discussions on collaborative works.) label tool to serve this function. Labels are created in the same way as notes (drag over content and right/control click) and orga- 3.4. Glossary entries nized in a tree (outline). When students collaborate on a task, labeling can become a primary tool for organizing and assigning Glossary entries are information objects that record data about specific roles and tasks amongst members in the group. For exam- fundamental elements of the information in a domain of knowl- ple, after completing a text-based chat discussion about a collabo- edge. Each glossary entry is recorded according to a template (sim- rative project, students can review the chat log, highlight specific ilar to a note template). For example, a glossary entry template tasks and goals that were discussed, and label them with the name might be designed to record essential data about an element in of the group member who will follow up on that item. the periodic table of the elements such as its symbol, atomic num- ber, atomic mass, density, group, series, and so on. Another glos- 3.7. Search sary entry template might be designed to record data about the discovery of each element: who discovered it, the date of discov- gStudy provides a sophisticated search tool for locating infor- ery, method of discovery, etc. The method for creating a glossary mation in one or multiple learning kits (see Fig. 4). Searches can entry is the same as for a note. Similar to notes and other objects, be simple, such as identifying where every occurrence of a term students can co-construct a glossary in a shared kit, or exchange appears in one learning kit. Searches also can be complex Boolean their interpretations of terms and concepts by sending each other queries that seek data within specific kits and examine a particular specific glossary notes. kind of information object, such as notes. To search for information, the learner clicks a button in gStudy0 s 3.5. Strategy library toolbar. This opens a window in which the learner titles the search query and designs the search to be carried out. On clicking The strategy library is a set of pre-stocked notes and note tem- ‘‘Search”, gStudy constructs a table to display every occurrence of plates providing information about a range of learning strategies information that satisfies the search query. Various metadata that (see Fig. 3). Each template describes the strategy, explains when describe each ‘‘hit” are identified in the rows of this table. Clicking to use it, why it helps, and provides examples. Students can edit on a row displays the result in context. Fig. 4 shows a search query each strategy note, add new strategy notes, and delete ones they and a table of results. In collaborative work, searches can, for deem ineffective. Collectively, students can co-construct a library example, identify objects authored by each group member and of strategy notes they judge to work really well for them in the area thereby help the group monitor and review contributions to the where they are working. Alternatively, students can share strategy collective project. Fig. 3. Strategy Library. Please cite this article in press as: Winne, P. H. et al., The learning kit project: Software tools for supporting and researching ..., Computers in Human Behavior (2008), doi:10.1016/j.chb.2007.09.009
    • ARTICLE IN PRESS P.H. Winne et al. / Computers in Human Behavior xxx (2008) xxx–xxx 5 Fig. 4. Search panel. 3.8. My documents collaborative work (see Morris, Church, Hadwin, Gress, & Winne, this issue). For example, chat roles, prompts and scripts might tar- This tool is for learners to write essays, lab reports, and other get kinds of text processing activities found in reciprocal teaching compositions. It is a basic HTML editor disguised as a word proces- (summarizer, questioner, clarifier, predictor). Alternatively, roles, sor called ‘‘My Documents”. Once a document is finalized, the lear- scripts and prompts might emphasize different aspects of the ner can save these information objects in the learning kit. Like self-regulatory cycle such as task understanding, goal setting/plan- other gStudy objects, students can share documents and use them ning, enacting the task, and reviewing, adapting, and revising pro- to build a collaborative studying kit or project kit. cesses. Chat can also be used to guide students in applying peer review collaboration strategies by prompting students to construc- 3.9. Chat tively comment on different aspects of a writing project and prompting the author to direct responses toward clarification Using a near synchronous or true synchronous chat tool, learn- questions and elaborations rather than reactive responses. It also ers collaborate online. As learners chat, they construct a record that provides a channel for learners to share information objects, such supports later review and reflection about their conversation and as notes, glossaries, and essays, with collaborators. The learner information objects they shared. Our chat tool (see Fig. 5) can be drags an information object to the text entry field and drops it to configured to provide prompts and roles to guide learners in their distribute it to all participants in the chat. Fig. 5. Chat window. Please cite this article in press as: Winne, P. H. et al., The learning kit project: Software tools for supporting and researching ..., Computers in Human Behavior (2008), doi:10.1016/j.chb.2007.09.009
    • ARTICLE IN PRESS 6 P.H. Winne et al. / Computers in Human Behavior xxx (2008) xxx–xxx 3.10. Concept map ing the strategy library or the chat tool), specific tools selected, scrolling actions, editing actions (e.g., adding new content to a Every information object created in gStudy is linked to some- note) and links a learner creates between content and other infor- thing, either the kit as a whole, another information object, or a mation objects. In addition, gStudy logs, for example, options cho- subset of information within an information object. When the lear- sen in menus, buttons clicked, kits selected, windows opened and ner wishes, information objects and links among them can be dis- closed, and chat discussion logs viewed. Every logged event is played as a concept map, i.e., a node-link graph (see Fig. 6). A time-stamped. concept map can represent all the information objects in a learning The data are formatted according to an XML (extensible markup kit or be filtered to display a subset of them. As well as showing al- language) schema that supports detailed analyses of occurrence, ready existing information objects, learners can construct informa- frequency, sequence, pattern, and other qualities of how learners tion objects in the concept map tool by identifying the type of study (Winne, Gupta, & Nesbit, 1994). Targets of analyses can be, information object in a palette, then drawing in the concept map for example, (a) processes an individual uses to learn new skills where they want to place that object in the concept map space. or information, (b) content learners select for further operation Links between information objects also can be created in the con- or ignore, (c) information they choose when asked or assigned to cept map. Concept mapping offers a platform for students to share pick a side and debate, and (d) learners0 beliefs about the credibil- with one another complex representations and perceived relation- ity of information presented in various ways. When students col- ships amongst concepts (glossary notes) or strategies (strategy laborate, gStudy collects data about which objects were shared notes), for example. Furthermore, concept maps can be harnessed and added by individuals and how information objects are inte- by collaborative groups as a tool for reflecting upon collaborative grated in the kit. These kinds of data about which content learners progress and individual contributions to collective projects. study and how they study it is key to corroborating models about what goes on when learners learn solo and collaboratively. To our 3.11. Kits knowledge, research has not attempted to examine the weave of individual and collaborative activity in collaborative learning pro- Kits may be pre-configured by an author (instructional de- jects. Data collected in gStudy affords opportunities to examine signer, teacher, commercial author) to conform to a particular this interplay of SRL and social activity across time and a range model of information presentation. When students engage in solo of academic tasks. learning activities, they use a kit and supplement it with their own To analyze traces, our team designed Log Analyzer (Hadwin, information objects. gStudy also allows learners to create their Winne, Nesbit, & Joulovian, 2005) which inspects gStudy0 s XML own kits and, through the Kit Management System, students can logs of a learner0 s or multiple learners0 studying session(s). Log share entire kits with one another by checking them into a repos- Analyzer computes statistics such as (a) frequencies of events itory and taking turns to check out collaborators0 kits. Sharing (e.g., how many labels were made), (b) properties of event se- might constitute (1) taking turns with a single shared kit that in- quences (e.g., length, duration, density of information), and (c) cludes contributions from each collaborator or (2) making an indi- properties of patterns of learning events (strategies) by analyzing vidual0 s kit accessible to other collaborators (see Hadwin, et al., this transition matrices of traces in terms of graph theoretic methods issue). (e.g., cohesion within a pattern, structural equivalence of two pat- terns) (Winne et al., 1994). 3.12. Analysis Trace data are proximal indicators of particular information processing and can be counted to generate ratio scales of cognitive An additional benefit gStudy offers learners and researchers is activities. Trace-based methodology, now emerging in some its ability to automatically and unobtrusively trace each user0 s empirical studies, does not interrupt cognitive processing like a engagement with the content as well as the methods learners think-aloud can and does not rely on learners0 fallible memories choose for cognitively processing content. gStudy records log data of how they studied or summations of how they study in general. about every selection and modification made in information ob- Thus, trace data can provide a powerful complement to other jects (e.g., notes, strategies, glossary notes, labels), changes in a forms of data that characterize how learners learn and self-regu- learner0 s ‘‘view” onto the information in a learning kit (e.g., select- late collaborative learning. Real-time analysis of traces (now being Fig. 6. Concept map. Please cite this article in press as: Winne, P. H. et al., The learning kit project: Software tools for supporting and researching ..., Computers in Human Behavior (2008), doi:10.1016/j.chb.2007.09.009
    • ARTICLE IN PRESS P.H. Winne et al. / Computers in Human Behavior xxx (2008) xxx–xxx 7 implemented) would provide a basis for characterizing individual Acknowledgement tactics, collaborative activity, and patterns of individual versus col- laborative engagement. Feedback could be provided immediately Support for this work was provided by grants to Philip H. Winne and in context about whether tactics are applied, how long it takes from the Social Sciences and Humanities Research Council of Can- to apply each, and conditional probabilities that the learner ada (410-2002-1787; 512-2003-1012, R. Azevedo, A. F. Hadwin, S. chooses some follow-on tactics over others conditional on a prior Lajoie, J. Nesbit, & V. Kumar, -Co-Investigator), the Canada Research tactic having been used. Scores can be presented in terms of fre- Chair program, and Simon Fraser University; and to Allyson Had- quency of use of tactics, the conditional probability that a tactic win from the Social Sciences and Humanities Research Council of is used when a cue signals it should be applied, whether tactics Canada (410-2001-1263). are applied randomly or according to a pattern, and the degree to which the pattern of study tactics is similar (or appropriately dis- References similar) across tasks and studying episodes (Winne et al., 1994). In addition to logging events within individual or collaborative Abrami, P. C., Lou, Y., Chambers, B., Poulsen, C., & Spence, J. C. (2000). Why should we group students within-class for learning? Educational Research & Evaluation, kits, gStudy also records logs of students0 text-based chat discus- 6, 158. sions (chat logs). Chat logs provide information regarding idea ex- Beers, P. J., Boshuizen, H. P. A. E., Kirschner, P. A., & Gijselaers, W. H. (2005). change and formulation, use of collaborative scaffolds, such as Computer support for knowledge construction in collaborative learning environments. Computers in Human Behavior, 21, 623–643. roles and prompts, and a record of objects that were shared Carroll, J. M., Neale, D. C., Isenhour, P. L., Rosson, M. B., & McCrickard, D. S. (2003). through chat. Analyzing chat logs provides opportunities to trace Notification and awareness: Synchronizing task-oriented collaborative activity. patterns of discourse and transitions in idea as collaborators at- International Journal of Human–Computer Studies, 58, 605. Gress, C. L. Z., Fior, M., Hadwin, A. F., & Winne, P. H. (this issue). Measurement and tempt to co-construct shared meanings, plans, and reflections with assessment in computer supported collaborative learning. Computers & Human respect to a given task. Behavior. Gress, C. L. Z., Hadwin, A. F., Page, J., & Church, H. (this issue). A review of computer- supported collaborative learning: Informing standards for reporting CSCL 4. How gStudy contributes to research on collaboration research. Computers in Human Behavior, doi:10.1016/j.chb.2007.05.012. Hadwin, A. F., Gress, C. L. Z., Page, J., & Ross, S. P. (2005). Computer supported A challenge in researching collaborative learning has been gath- collaborative work: A review of the research 1999–2004. Paper presented at the annual meeting of the Canadian society for the study of education, London, ON. ering data essential to modeling cognitive and motivational vari- Hadwin, A.F., Oshige, M., Gress, C.L.Z., & Winne, P.H. (this issue). Innovative ways for ables that generate collaborative processes and characterize how using gStudy to orchestrate and research social aspects of self-regulated these processes support collaborators as they construct products learning. Computers in Human Behaviour, doi:10.1016/j.chb.2007.06.007. Hadwin, A. F., Winne, P. H., Nesbit, J. C., & Joulovian, T. (2005). Log analyzer: A achieved by collaboration. Unless collaborators are physically adja- toolkit for analyzing gStudy log data and computing transition metrics (version cent, all their work—solo and collaborative—is rarely captured. 2.0). University of Victoria, Victoria, British Columbia, Canada. gStudy can fill this gap. When data that gStudy gathers are merged Johnson, D. W., & Johnson, R. T. (1989). Cooperation and learning: Theory and research. Edina, MN: Interaction Book Company. with data collected using other instrumentation, such as video Johnson, D. W., & Johnson, R. T. (1999). Learning together and alone: Cooperative recordings and self-reports gathered at various times (before, dur- competitive and individualistic learning (5th ed.). Boston, MA: Allyn & Bacon. ing and after collaboration), gStudy fine-grained, time-stamped Kirschner, P. A. (2004). Design, development, and implementation of electronic indicators of these variables offers researchers opportunities to tri- learning environments for collaborative learning. Educational Technology Research & Development, 52, 39–46. angulate their interpretations of collaboration. Koschmann, T. (2001). Revisiting the paradigms of instructional technology. Paper Other opportunities are afforded because gStudy0 s records of presented at the annual conference of the Australasian society for computers in chats are made available as resources. These chat records can be learning in tertiary education (ASCILITE 2001), Melbourne, Australia. Kreijns, K., Kirschner, P. A., & Jochems, W. (2003). Identifying the pitfalls for social mined by researchers and collaborators alike. This adds opportuni- interaction in computer-supported collaborative learning environments: A ties to investigate issues such as whether previously generated col- review of the research. Computers in Human Behavior, 19, 335–353. laborative products overly anchor future collaborations, and Morris, R., Church, H., Hadwin, A. F., Gress, C. L. Z., & Winne, P. H. (this issue). The use of roles, scripts, and prompts to support CSCL in gStudy. Computers in whether groups can augment collaborative effectiveness by re-vis- Human Behavior. iting previous collaborative episodes. 0 O Donnell, A. M., & King, A. (1999). Cognitive perspectives on peer learning. Mahwah, Because collaborators can share an information objects with NJ: Lawrence Erlbaum. Salovaara, H., & Järvelä, S. (2003). Students0 strategic actions in computer-supported members of their group, gStudy also affords opportunities to inves- collaborative learning. Learning Environments Research, 6, 267–284. tigate the roles of information resources in shaping collaboration Slavin, R. E. (1999). Comprehensive approaches to cooperative learning. Theory into and the effects of sharing different types of information resources Practice, 38, 74–79. Winne, P. H. (1992). State-of-the-art instructional computing systems that afford on the flow and effectiveness of collaborative activities. Marrying instruction and bootstrap research. In M. Jones & P. H. Winne (Eds.), Adaptive data that trace key features of collaboration with, for example, col- learning environments: Foundations and frontiers (pp. 349–380). Berlin, laborators0 perceptions of flow and effectiveness can better reflect Germany: Springer-Verlag. rhythms in collaboration and the ways collaborators orchestrate Winne, P. H., Gupta, L., & Nesbit, J. C. (1994). Exploring individual differences in studying strategies using graph theoretic statistics. The Alberta Journal of their process. Educational Research, XL, 177–193. In sum, the kinds of and extent of data gStudy provides about Winne, P. H., Hadwin, A. F., Nesbit, J. C., Leacock, T., Kumar, V., & Beaudoin, L. (2006). how learners collaborate, how they interleave solo work within gStudy: A toolkit for developing computer-supported tutorials and researching learning strategies and instruction (Version 3.1). Simon Fraser University, collaborative episodes, and the very information that is the focus Burnaby, British Columbia, Canada. of collaboration will help researchers observe variance in collabo- Winne, P. H., Nesbit, J. C., Kumar, V., Hadwin, A. F., Lajoie, S. P., Azevedo, R. A., et al. ration and identify factors that influence that variance. Subsequent (2006). Supporting self-regulated learning with gStudy software: The learning kit project. Technology, Instruction, Cognition and Learning, 3, 105–113. articles in this special issue elaborate on these themes. Please cite this article in press as: Winne, P. H. et al., The learning kit project: Software tools for supporting and researching ..., Computers in Human Behavior (2008), doi:10.1016/j.chb.2007.09.009