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Coastline Community College: Change Process Examination Essay


The process of change is known for its complexity and the multitude of barriers it faces along the way. One of the reasons that may slow down is implementation is the seeming lack of urgency (Hall & Hord, 2015). The following paper examines the introduction of the predictive analytics service program at the Coastline Community College in 2015 by reviewing the chosen model and the assessment techniques used by the team.

Reasons for change

The core issue that led to change was the lack of predictability in certain areas. The area that suffered the most was in direct connection with the innovations taking place in the organization. Coastline Community College has a long record of successful innovative strategies and ideas that increase the effectiveness of the learning process. While the fact of the constant innovation is by no means negative in itself, it introduces uncertainty and the need to analyze the possible outcomes of the process and barriers to its implementation (Zentner, 2015).

While certain techniques were utilized previously, they were not standardized enough and thus did not provide an opportunity for the systemic and unified analysis of the situation. Besides, the limited amount of staff members and part-time employees further limited the capability of assessing and addressing all the possible adverse issues of each innovation. Finally, the process is usually costly, so constant monitoring is required to evaluate its financial side. The monitoring should consider the validity of any given change and prevent the losses resulting from unnecessary investments.

While the issue was not immediate, some negative consequences would inevitably arise in the future. First, the limited possibilities of predicting the financial side will likely result in budget shortages and possible debt, the issues which are known to persist among the high schools (Mitchell, Palacios, & Leachman, 2014). Second, the absence of a dedicated predictive analytics service platform will require the allocation of human resources, which, given the already limited staff amount, would sooner or later result in either under-staffing of other areas or the excessive pressure and stress among employees. Finally, the student performance would likely lag behind considering the constantly changing learning environment and gradually rising demand for a competent and skillful workforce.

Change model

To implement a predictive analytics service platform the organization decided to use Lewin’s change management model. The model consists of three basic steps – unfreezing, change, and refreezing (Cummings & Worley, 2014). In the case of Coastline Community College, the unfreezing included a range of surveys among both the faculty staff members and students. The results helped to evaluate readiness to change and to some degree raise awareness of the current state of events.

The latter was also achieved by crafting an approachable and concise report on the matter and outlining the benefits of the change implementation. Finally, the available tools were reviewed and compared to the statistical data produced by the college to find the best match. Simultaneously, the team of competent faculty staff and administration members was created. At the unfreezing stage, the team was tasked with developing a plan for change and determining the means of assessing its success.

At the second stage, the predictive analytics software was implemented. Certain predetermined individuals and departments went through training that ensured the effective use of the software. The first benchmarks were reached at this stage, so the preliminary assessment was already possible.

Finally, once the process was fully launched, the refreezing stage commenced. The use of the software was encouraged to produce meaningful results (as opposed to the mastering and testing activities of the second stage) and the element of the challenge was introduced by launching competitive projects. Besides, the majority of assessment was done at this stage, mostly by highlighting the pre-planned benchmarks. The proper evaluation process also demands the use of a thorough review of the recent results and comparison to the previous ones. This stage, however, requires more time to be conducted (usually one to four years), and will likely be fully implemented in the subsequent years.

Lewin’s model is appropriate primarily because of the nature of the change in question. The implementation of the predictive analytics service platform is largely a technical process. While its usage benefits both the students and the faculty members, it requires mostly the effort of the latter. Besides, it does not directly alter the education process – instead, it introduces the augmentation of one aspect of the monitoring process. This leads to the relatively low reliance on the attitude to change and, by extension, to the weaker dependence on leadership. While Lewin’s model does not downplay the role of leaders, it obviously emphasizes the technical side of the question, securing precision, timely assessment, and transparency. These qualities make it appropriate for the described process.

Outcome evaluation

Alternatively, Fullan’s framework for change (Fullan, 2001) could be considered. Three of five of its central components – understanding change, knowledge creation, and sharing, and coherence making – are visibly relevant to the implemented technology. To a certain degree, they coincide with the steps made by the Coastline Community College administration. Besides, as one of the main purposes of the change was improving the student-teacher interaction, relationship building is also applicable, but only indirectly.

However, the role of moral purpose is negligible in this particular case. Besides, three key characteristics – enthusiasm, energy, and hope, are only marginally relevant to the problem. As a result, the Fullan’s framework is only partially appropriate for the change in question, while Lewin’s model addresses the process more precisely and does not require adjustments.

The implementation of the change was consistent with the suggested model. All of the steps (conducting and analyzing the surveys, selecting tools, assembling a responsible team, and assigning the milestones) was made in time and without delay, with the exception of the last one (the concise assessment), which requires more time and will be available in subsequent years.

As the change did not target the educational process directly, the only mid-course adjustment consisted of additional training for students and staff members directly involved with operating the new software. The assessment of the process was done mostly by reaching milestone benchmarks, which allowed for controlling the progress and at the same time boosted the motivation of the involved parties (Mento, Jones, & Dirndorfer, 2002). However, no specific assessment was conducted prior to the change. This fact, along with the organization’s history of innovations, suggests that the change was preventive in character rather than triggered by a specific issue.


Thus, the current results indicate the stable progress in mastering new analytical tools and reaching the marked milestones, but can not be conclusively correlated to the students’ success and do not yet allow them to assess the financial outcomes. The change conducted in the Coastline Community College clearly shows the benefits of the implementation according to the Lewin’s model in terms of precision and levels of control, but additional research is needed to fully assess all the positive outcomes.


Cummings, T., & Worley, C. (2014). Organization development and change. Stamford, CT: Cengage Learning.

Fullan, M. (2001). Leading in a culture of change. San Francisco, CA: John Wiley & Sons.

Hall, G. E., & Hord, S. M. (2015). Implementing change: Patterns, principles, and potholes. Upper Saddle River, NJ: Pearson.

Mento, A., Jones, R., & Dirndorfer, W. (2002). A change management process: Grounded in both theory and practice. Journal of Change Management, 3(1), 45-59.

Mitchell, M., Palacios, V., & Leachman, M. (2014). States are still funding higher education below pre-recession levels. Web.

Zentner, A. (2015). . Web.

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