The outline of the paper is as follows. In section 2 we present our main results obtained for the model defined by equations 1 and 2. We present here explicit expressions for the probability density function pdf in the steady-state, the steady-state current and an ensemble-averaged order parameter. In section 3 we discuss the behavior of the ensemble-averaged quantities and of their counterparts defined for a single realization of noises, and outline some perspectives for future research. Details of calculations are relegated to the appendix.
Here, we provide the Fokker—Planck equation associated with the minimal Langevin model in equations 1 and 2 , and present its solution in the limit. We also describe our numerical approach, which is based on the discretization of the Langevin equations in equation 1. This normalization constant can be calculated exactly and is given by , where I 0 x is a modified Bessel function of the first kind.
We note that equation 3 here represents a particular case of a more general result derived in [ 28 ]. Figure 2. Ensemble-versus time-averaged properties. The curves correspond to the analytical prediction made in equation 3. The curves correspond to the analytical prediction made in equation 5.
The symbols represent the results of numerical simulations for a single-realization time-averaged current in equation 13 with s. The curves correspond to the analytical prediction made in equation 7 , which defines the ensemble-averaged order parameter Q. The symbols represent the results of the numerical simulations for the time-averaged order parameter equation 14 , based on a single realization of the noises with s i. Note that for Hz, i. A remarkable feature of the minimal model with two different temperatures is, that in the non-equilibrium steady-state a nonzero current J occurs.
This is a well-known aspect for stochastic dynamics of coupled components, each evolving at its own temperature see, e. However, in the case at hand this nonzero current has a peculiar form due to the fact that the coupling term in equation 1 is a periodic function of the phase difference. The components J 1 and J 2 of this current can be inferred directly from the Fokker—Planck equation A1 see appendix , equation A2. They obey. One can straightforwardly check that. In figure 2 b , for a particular example with , we present a 'phase chart' for the sign i.
These drifts are correlated in that they have the same sign i. In order to quantify this novel synchronization mechanism, and also in order to render it observable either experimentally or in numerical simulations, one has to introduce a meaningful order parameter. As in general, there is, however, some liberty in choosing this parameter. This gives the following dimensionless order parameter see equations 3 and 5. It is rewarding to determine the asymptotic behavior of Q in several particular limits.
For instance, in the high-temperature limit one has. In these cases the system is close to the realm of the standard noiseless Kuramoto model and one finds directly from equation 7 that, in leading order in the parameter , the order parameter Q varies as. It is not always possible to generate a statistical sample of large enough size, either in experiments or in numerical simulations, which allows one to average over an ensemble of trajectories.
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We introduce the current as an average over the observation time :. Note that the expressions in the first and the second line in equation 13 correspond to the components of the current J and differ with respect to the time derivative of the phases, i. As we have shown above, the components of the ensemble-averaged current are exactly equal to each other. We thus expect and verify via numerical simulations that the same holds for their introduced time-averaged counterparts. Dividing the result by the mean effective temperature see the definition of Q in equation 7 , we obtain.
We expect that, similarly to the time-averaged quantity equation 12 and to the time-averaged current in equation 13 , for large observation times , converges to Q given in equation 7. The case , i. In contrast, the trajectories in panels a and d of figure 1 correspond, within the present choices, to the extreme case of maximal disparity between the temperatures. Interestingly, the stochastic phase locking described in [ 14 ] is seemingly strongest in the case of equal temperatures panels c and f , is less pronounced for the combination and panels b and e and it is weakest for and panels a and d for which the periods of synchrony are hardly visible.
Therefore, the synchronization observed in [ 14 ] degrades if the effective temperatures become unequal. Such an agreement is, however, achieved for trajectories which are substantially longer than the ones shown in figure 1. In figure 2 d we show Q obtained from equation 7 together with following from equation We observe full agreement between our theoretical prediction in equation 7 , which is defined for an ensemble of trajectories, and as introduced in equation 14 , which is defined for a single realization of noises.
This implies that the latter can be conveniently used for a single-trajectory analysis of corresponding experimental and numerical data. Finally, we note that a rather long observation time s has been used in figure 2 d in order to demonstrate convergence of the time-averaged order parameter to the ensemble-averaged one. The observation that the order parameter deviates from zero in out-of-equilibrium conditions can be made already for more moderate values of n , although the data will look more noisy.
In summary, we have presented a generalization of a minimal model introduced in [ 14 ] to the case in which the phases in equation 1 are subject to noises with different amplitudes. This can be thought of as a noisy Kuramoto or Sakaguchi model of two coupled oscillators with distinct effective temperatures. From a physical point of view, the original model in [ 14 ] has been introduced in order to describe the noisy synchronization of two identical flagella of a biflagellate alga.
Our generalized model is expected to be appropriate for the description of a noisy synchronization of two flagella having different lengths. Indeed, the analysis in [ 20 ] has revealed that the noise amplitudes depend on the length of the flagella. Viewed from a different perspective, our study provides an, apparently first, solvable example for the synchronization of coupled oscillators under out-of-equilibrium conditions.
Hence, it opens new perspectives for a similar analysis of more complicated models, such as a FitzHugh—Nagumo model see, e. Note that in the example studied here the difference between the effective temperatures is not artificially imposed but emerges naturally. We have shown, both analytically and numerically, that in such a system a very peculiar form of a synchronization of two coupled oscillators takes place. It is mediated by an emerging, current-carrying steady-state. More specifically, we have shown that, on top of the synchronization of the phases as observed in [ 14 ], i.
In order to quantify the degree of such a synchronization, we have introduced a characteristic order parameter, which vanishes if the effective temperatures become equal to each other. This order parameter has been determined as the average over an ensemble of realizations of the stochastic evolution of phases, as well as on the level of an individual realization. We applied the same procedures as in the first iteration, with the exception that only one interviewer was present.
The bus firm treated the initiative as a business development program. Here are the key findings of research and interviews of three different firms. Moreover, they increasingly utilize sensor data, such as access control logs and working time recordings. In the second case the provider of system integration the data is internally mostly applied to product development prioritization. Based on the big data, internal analyzes have also been carried out to compare customer-specific data volumes and other business customers trends. In this case, customer data is enriched with other external sources.
Based on the above, the purpose is to develop a reporting service that provides benchmarking data to customers. For example, how does our business compare to industry in general in terms of transactions of subscription volumes. In the case of the bus company they set the business goal as follows: to educate and motivate the drivers to change their driving habits, which will in turn lead to reduction in fuel consumption. They appointed the CTO to lead the program. They consulted potential software and hardware vendors and selected the technologies. The implementation started with a pilot project, which integrated the technologies using data from ten buses.
After an assessment of the pilot, a full-scale implementation project followed. During the following months, the project team implemented a production-ready system and solved several data quality-related issues. At the same time, the firm created a change management plan for the release and rollout. After two years of use, the system has proved to be a success. It has met the original business goal. The system and its usage have costs, but the net result is a significant cost reduction due to permanently lower fuel consumption. Moreover, the firm has recorded remarkable improvement with regard to the traveling experience in customer satisfaction surveys.
The passengers have noticed that the drivers drive smoothly. A third achieved benefit is, of course, the reduction of carbon-di-oxide emissions. In this section we first analyze our findings from the demonstration. Next, we offer building blocks for practitioners by describing an exemplary process and discussion on how to apply the framework in practical situations. The first iteration of the demonstration phase confirmed that the framework provides a mental model for evaluating the effects of big data in the business context. The interviewees found the framework useful for this purpose.
The HR solution provider emphasized that the framework helps to create a systematic approach that will save time. Managing a growing business is hectic; they could not tie key resources to long consulting projects. The integration services provider stated that the framework offers guidance and provides understanding of big data adoption. They saw big data as a strategic issue.
Applying the framework would help identify the required changes, as the business model aspect of the framework emphasizes the strategic importance of big data. In both firms, we noted that the framework helped keep the discussion focused. It helped in keeping concentrated on one topic at a time. Moreover, the discussion regarding new ideas was lively. Although this may be related to personal characteristics, the framework facilitated the ideation.
Both firms considered the framework useful in their feedback. After the first iteration, we reflected the discussions in our framework. From the theory point of view, no new aspects were recognized. By looking at the practical side, the interviewees appreciated our orientation material, as it helped them to perceive the framework.
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The initiative started when the firm noticed that the current technology enabled them to collect detailed data from the buses. This idea led ultimately to the big data initiative. The management of the company considered that the big data initiative would strengthen their competitive situation due to increased operational efficiency. The firm set a clear objective for the program, although they did not define an explicit value for it. They also set a member of the executive board the CTO to lead the program. They focused on meeting the business goal, i. The firm also recognized real-time monitoring needs on the horizon.
However, as the fuel consumption analysis did not require real-time processing, they decided to postpone the implementation to later phases. The project team, consisting of members of the firm and a software vendor, identified the required datasets and drew up a cost-effective architecture without paying attention to additional features. In the pilot phase, neither the firm nor the software vendor considered real-time needs.
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The project members may have discussed possible future scenarios in their coffee breaks, but the project did not consider those scenarios officially. This ultimately led to a situation where further development to meet the real-time needs is difficult. Our framework would have helped the bus firm to understand the situation better at the beginning of the program. The owner of the big data initiative shared this view and saw that the framework would have been helpful.
A better understanding of the data might have helped them to earlier identify certain data-related issues that reflected on the attitudes of middle management. Moreover, the framework would have helped to develop capabilities to tackle the data issues. Another capability-related matter was the shortage of analytical capabilities, which has been a hindrance to making use of the gathered data.
In addition, using the framework in the early phases would probably have stressed the future needs. This, in turn, would probably have led to a different architecture or technology selection in the project phase. The two companies that had less experience with big data paid only a little attention to developing capabilities, whereas the postmortem indicated that more attention should have been paid to capability development during the project.
Another observation, although an expected one, was that the usage of the framework benefits from facilitation. Preparing supporting material for the interviews and group work adds a middle layer between the framework and the daily operations. This helps the participants connect the framework concretely with their own context. The demonstration phase confirmed our initial assumption that simplicity is a virtue when providing building blocks to practitioners.
This assumption in mind we carefully considered in the first place the balance between theoretical completeness and practical viewpoints in the presentation of the framework. Companies can choose different practical approaches to the framework according their situation and objectives. Increasing the role of data in current business model, e. Based on the evaluation phase of our framework, we synthesized an exemplary usage scenario as follows.
Assess current situation and big data impact: This can be done by looking at the business model components see Figure 2 one by one and creating scenarios of potential big data effects. Effects can be either risks or opportunities; some of the effects are such that they cannot be influenced, some of them are within the reach of the company.
Current-mediated synchronization of a pair of beating non-identical flagella
Define business objectives. Clear goals and ownership are best practices for any business development initiative. Especially in the case of new things solid leadership is important to achieve the set goals. This phase might be iterative, innovating back and forth between the business model and big data. What internal or external data could affect our business? What data do we need in order to best run our business? Novel ideas or long-term, strategic goals often require experimenting for verification.
Many innovations are human-driven, but increasingly, data are a source of innovations. Humans ideate things that require gathering and combining new and existing data in novel ways. Accordingly, experimenting with data may reveal insights that spark ideas. Human-driven and data-driven innovations are not mutually exclusive.
Instead, they can, and should, support each other, effectively creating an innovation loop. Our framework provides support for these activities by explaining the theoretical background and enablers for an effective innovation process. Identify required datasets and capabilities. Identifying data that is beneficial to the business is a two-way operation. This, in turn, reveals possible gaps in ICT capabilities. Adding categorization elements to the dimensions see Figure 2 and classifying any available or required data source internal or external accordingly helps to identify potential development needs of the current IT platform.
For example, a business need may need to harvest and analyze social media data. The framework helps to translate the business need into an IT-related requirement: Do we have the hardware and software that is required to gather and process relatively low volumes of unstructured data in near real-time? It should, however, be noted that there are technical and data-related challenges Benabdellah et al. Big data for supply chain management: opportunities and challenges. Conceptualizing big data: Analysis of case studies. Intelligent Systems in Accounting, Finance and Management, 23 4 , — Technical challenges are hardware- and software -related, such as managing vast volumes of data with a Hadoop cluster, or using a not only SQL NoSQL database to store unstructured data.
Analytical capabilities are human-centric, like building a predictive analytics model or interpreting a business need into an algorithm. A firm can develop technical and analytical capabilities in-house, or it can leverage service providers. The paradigm shift towards more data-intensive business landscape is inevitable. Companies must consider the combination of big data, innovations and potential value against the required transformation when they plan their big data initiatives.
For incumbents, the transformation may be more revolutionary than evolutionary, which implies a complicated process. Our practitioner oriented framework helps in understanding the role of innovations and big data in the digital transformation. Understanding these factors forms the basis for informed management decisions regarding business transformation and the capabilities the transformation requires.
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In this paper, we have presented a multi-disciplinary framework that contributes to research by pinpointing the role of human and data-driven innovation capabilities as a mediator between big data and the business model. The framework combines elements from strategic management, innovations research and resource-based theory. It helps to understand the role of human and data-driven innovations, and innovation capabilities in an organizational context.
Understanding the theoretical background of the innovation process in the big data context will help practitioners to focus on developing the capabilities and methods that best support the transformation towards data driven business models. Skip to Main Content. Search in: This Journal Anywhere. Advanced search. Journal homepage. Published online: 05 Feb In this article Abstract 1.
Introduction 2. Research method 3. Building the framework 4. Evaluating the framework 5. Conclusion References. Innovation capabilities as a mediator between big data and business model. Abstract The digital transformation is forcing organizations to change towards more data-driven business models. Introduction Linking the opportunities of big data and the business transformation imperative resulting from digitization leads to a situation where incumbent firms must re-think and innovate in their business models and create new capabilities in order to stay competitive in their business ecosystem.
Research method As big data is an emerging area for both scholars and practitioners, the results of the research should benefit both theoretical and practical viewpoints. Innovation capabilities as a mediator between big data and business model All authors. Published online: 05 February Display full size. Building the framework Linking the opportunities of big data as a consequence of datafication and the business transformation imperative resulting from the digital transformation leads to a situation where incumbent firms must re-think innovate their business models and create new capabilities in order to stay competitive in their business ecosystem.
Theoretical background The framework treats innovation capabilities as a mediating factor between the business model and big data.
Business model The concept of business model has been vividly discussed in the strategic management discipline. Innovation In order to innovate effectively and develop an innovative organization culture, the firm needs to understand the nature of innovation. Innovation capabilities as mediator framework The value proposition of big data is that companies can gain benefits by making use of it. Evaluating the framework The purpose of demonstration and evaluation phases was to assess the applicability of the framework in practical situations in business.
Demonstration The following sections describe the demonstration phase. Demonstration process First, we presented the framework to two big data intensive firms. Demonstration contexts The arrangements for the demonstrations of the framework were as follows: we prepared materials for the interviews beforehand. Demonstration findings Here are the key findings of research and interviews of three different firms.
Evaluation In this section we first analyze our findings from the demonstration. Evaluation of the demonstration The first iteration of the demonstration phase confirmed that the framework provides a mental model for evaluating the effects of big data in the business context. Suggestions for making use of the framework Companies can choose different practical approaches to the framework according their situation and objectives.
Conclusion The paradigm shift towards more data-intensive business landscape is inevitable. Article Metrics Views. Article metrics information Disclaimer for citing articles. I was thinking of a thought and typing it but, as I was typing it someone else said it first. Or when you are trying to make a contribution to a question and someone else posts a new question the same time you are still answering. It is like you are competing for time and carrying on two or three conversations at the same time.
Group M ultimately returned to the asynchronous modality for the third case analysis task. Ann reflected:. With the first and final case study, we used the discussion board, which I preferred to use. It allowed me to reflect what everybody else had written about the questions. I did not have to quickly answer questions or respond to questions like in the chat. I could look up information in the book that I thought backed up my answers and theirs. We used the discussion board instead of the chat tool because it was easier to communicate through … Since we had already done the first case study with the discussion board and it worked fine we decided to do it that way again.
Thus the technical difficulties, need to be logged in at the same time, and incoherent conversations were drawbacks that Group M felt could be overcome by returning to the discussion forum. Unlike Group M, Group L members described a process of creating and circulating a first draft of the synthesis of their chat, with each member making changes and additions.
Lori have done better? Group L also referred to the textbook, although not as often as Group M. Group L also stayed on task. This may account for that fact the Group M chatted for 18 minutes longer than Group L. Group L had some technical difficulties, but these did not seem as serious as in Group M. It was extremely frustrating because I would try to type a comment about what a group member had said and everything was delayed … I was always one step behind on my remarks.
It left some dead time on the computer when all four participants were typing and you did not know if you should wait to continue talking about the concept at hand or move on to the next. Ultimately Julie and Angie in Group L enjoyed the chat more than the forum. While it can be difficult to find time when each group member can meet, I would like for the third case study to be conducted in the same fashion.
Regardless, Group L chose to use the chat for the third case analysis. Deciding on a time is not easy when there are four people all of whom are full time student, part time workers, and have families. Comparing the two methods I would prefer the first case study method because while it was tedious to check the discussion board everyday, it was easier then trying to get all the group members together at the same time. Both groups identified similar drawbacks to the chat environment, yet Group L returned to chat for the third case analysis.
One factor could have been that they received a higher grade in the synchronous environment. During the asynchronous discussion Group M exchanged 93 functional moves and functional moves in the synchronous chat. Otherwise individuals participated at similar levels in both modes. Jean and Mary also contributed roughly one third of the moves in the synchronous discussion 84 and 76 moves respectively. Group L exchanged 31 moves in the asynchronous environment and functional moves in the synchronous environment.
In both cases, Julie contributed about a third of the moves 11 asynchronous and 60 synchronous. Figure 1 illustrates the percentage of each type of functional move exchanged by the groups in the asynchronous and synchronous environments. Both groups exchanged mostly participatory contributions in the asynchronous environment, with over half of the moves being participatory.
Flagellar synchronization through direct hydrodynamic interactions
Figure 2 compares how the groups used specific functional moves to make participatory contributions to the conversations in each CMC mode. Names were used more by both groups in the asynchronous environment, perhaps because a sense of presence needs to be established in the absence of real-time interaction. However, using names could lend coherence to chat conversations. Perhaps the delayed time in these environments encourages participants to be explicit in encouraging response since immediate feedback is not possible. Phatic exchanges such as greetings and closings occurred in both modes, but slightly more in asynchronous than synchronous.
Transitional and temporal moves were used by both groups mostly if not exclusively in the chat environment. These were used, for example, when the group was referring to the textbook or their class notes. In chat, functional moves were often real-time negotiation of meaning such as temporal and transitional moves. Figure 3 compares how the groups used specific functional moves to make factual contributions to the conversations in each CMC mode.
Ask and answer moves were only present in chat. Often more answers were provided than questions asked. Table 4 provides an example from Group L:. In time-delayed environments it may be easier provide opinions rather than interactively ask and answer questions. Figure 4 compares how the groups used specific functional moves to make reflective contributions to the conversations in each CMC mode. Agreeing was by far the most common move in both modes. Group members responded to challenges only in the chat environment, but not in the asynchronous environment.
Table 5 provides an example from Group M:. Group M contributed three in the asynchronous mode, and Group L contributed one in the synchronous mode — too few to warrant further breakdown. While learning contributions did not occur often in either mode, Table 6 illustrates the one that did occur in Group L:.
In summary, participation patterns were asymmetrical in both modes. Groups contributed more participatory moves to establish presence in the asynchronous mode and more factual moves in the synchronous mode. More asking, answering, challenging and responding occurred in the synchronous environment, and more claims were made in the asynchronous mode.
Reflective moves in both modes were mainly to agree, and learning moves did not occur frequently in either mode. The purpose of the study was to see how students approached case analysis in the two CMC environments and to help educators better design such online discussion tasks. Our goal was for students to engage in meaningful dialogue, utilizing the Booth and Hulten framework to better understand their conversations. Participants engaged in negotiation of meaning in the chat in a way that did not occur in the asynchronous forum.
More technical difficulties occurred in the chat, and conversations moved rapidly, pointing to the need for participants to be familiar with this environment before embarking on the task. As in the case of Robert and Deborah, students who were inexperienced or encountered technical difficulties were not able to contribute as much to the conversations. While previous studies have reported more equal participation in synchronous environments e.
Pena-Shaef et al. Yet chat was perceived as having more equal participation. Ultimately the groups returned to the environment in which they had had initial success and had contributed the most learning moves: Group M to the discussion forum and Group L to the chat, even though two members of Group L did not like the chat. Inconvenience, technical difficulties and incoherent conversations were the greatest drawbacks to the chat environment. Problems with incoherent conversations in chat were also reported by Davidson-Shivers et al.
Some students would have preferred completing the analysis face to face. I do not know if it would be better because of scheduling conflicts but I feel it would be better than the chat. In fact both groups chose to start their dialogue by planning face to face. Benbunan-Fich and Hiltz found their participants to be more satisfied with the face to face interactions to discuss cases, but produced better solutions in the asynchronous context.
Heckman and Annabi also found that the asynchronous discussions generated as much and even more high level cognitive processes than did the face to face discussions. Davidson-Shivers, et al. Levin et al. This is consistent with findings by Rourke and Anderson in which their small groups of instructional technology graduate students strategically chose which technologies to use to complete their tasks, going so far as to not use the tools they were asked to use for purposes of the study.
Experience seemed to help both groups complete the second case studies more smoothly. Once we got comfortable with the technology and the requirements of the case studies the entire process went very well. Despite the instructor attempts to cultivate a new epistemological stance after the first case analysis, few of the pre-service teachers seemed to alter their stances.
Excerpts from the reflections make this clear. I simply prepared for the case study before the chat and I learned what I needed to then. We plan to next examine the third case analysis of the course, where students were able to choose the CMC mode. However, previous studies have shown that students do not necessarily perform the best in their preferred communication mode. Over time students may become more comfortable in modes they do not initially prefer. Instructors should be patient, giving students time to get used to each mode, understand its affordances, and then select what works best for them.
Initial face to face conversations may help build trust and facilitate a focus on the task more quickly. Participation is not necessarily more equitable in any mode, so expectations in this area should be clearly outlined for the students. Changes in epistemological stance do not happen quickly. We believe, though, that the more students are exposed to this type of learning activity, the more they may come to value the contributions of their peers. Tsai and Chung emphasize the relationship between epistemological beliefs and preference for open-ended group tasks in Internet learning environments.
Epistemological changes, like changes in preference of CMC mode, occur over a longer period of time, perhaps beyond the semester confines of our time with them. Our understanding of what constitutes evidence of learning in a conversation has changed as a result of this study. We note the difficulty of adapting existing analytic frameworks to understand learning contributions to online discussions.
Our assumption entering the study was that learning contributions were preferred to the other types; we understand now that all types of contributions are needed and have value. Participatory contributions, for example, are essential in asynchronous discussions. The prevalence of reflective rather than learning contributions in both modes by both groups are likely connected to the type of task. While the case analysis was intended to be viewed as open-ended, the students viewed it as a chance to display their mastery of the textbook material.
Thus they did not view themselves as having something original to offer to the task at hand. Critical reflection, rather than problem solving, is a more apt description of what was intended by the case analysis. For these pre-service teachers, making meaning consisted of supporting each other to understand the textbook and the case. Rather than creating something entirely new, they engaged to reflect upon and master the theories of the course.
Thus the participatory, factual and reflective moves are logically more relevant to the particular conditions of this task. We conclude from our findings that adopting a blended approach to online case study analyses may be ideal. Both synchronous and asynchronous modes can be viable for meaningful conversations e. Each mode has particular affordances. Asynchronous environments may be more convenient and linear, but participants may spend more time establishing their presence with participatory contributions.
Synchronous environments may support interactive negotiation of meaning, but participants initially may find conversations difficult to follow and more prone to technical difficulties. Integrating the various modes will be our next iteration of the design of this task. We will give students the opportunity to talk and build trust face to face, post initial claims and reflections in the asynchronous forum, and then follow up with a synchronous chat where they engage in dialogue about what they had posted.
As with this iteration of the task, we will provide clear guidance as to what type of participation and dialogue is expected. Further research needs to connect what is happening in such online conversations with external measures of learning of course objectives. Student preferences, epistemological beliefs, characteristics of the conversations and learner achievement should be investigated over time to better understand longitudinal change and how these processes intersect.
Case study 2 covers chapters 10, 11 and It will last for one week and is worth 5 points of your overall course grade. The second case study officially begins on Thursday, March 18 and ends on Thursday, March 25 at midnight. You will be in the same groups as for case study 1. You will want to schedule at least one chat session of approximately hours in length to talk about the case studies. Decide with your group when to hold these sessions and which tool you will use.
It is very important that you save an archive of your chat transcripts to submit along with a summary analysis. Your group can use whatever instant chat tool you like, as long as you are able to save the transcripts! Our graduate assistant will come to class on Thursday.