Tag Archives: students

CAN YOU PROVIDE SOME EXAMPLES OF PAST CAPSTONE PROJECTS COMPLETED BY SAIT CST STUDENTS

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Inventory Management System for Mid-Sized Retailer: A group of students developed a web-based inventory management system for a mid-sized retail store that sells clothing, accessories, and household items. The system allowed employees to track inventory levels in the warehouse and stores, place orders with suppliers, manage deliveries, and generate reports on best-selling products. It was built using PHP and MySQL and integrated with the retailer’s existing point-of-sale systems. This helped the retailer gain better visibility into inventory across locations and streamline the reorder process.

Customer Relationship Management Software for HVAC Company: Another team of CST students worked with a local HVAC installation and servicing company to build a Customer Relationship Management (CRM) system. The application allowed technicians to log service requests from customers, schedule appointments, track jobs, generate invoices and work orders. It helped office staff track communications with customers, manage billing and payments. The system provided insights into technicians’ performance, frequently serviced equipment types etc. which helped the company recognize opportunities and plan resource needs better. The students developed this system using Python, Django and PostgreSQL.

Online Booking System for a Salon Chain: For this project, students partnered with a salon chain that had five locations in Calgary. They developed a web-based booking system that let customers browse services offered, view stylist profiles and availability, and book appointments online. Stylists could log in to manage their schedules from any device. The administration module gave owners real-time visibility into bookings, revenue, top-selling services etc. The students built a robust, feature-rich system using PHP, JavaScript and MySQL which helped the salons reduce no-shows and improve customer experience.

Agricultural Equipment Monitoring Application: A group worked with a farm equipment manufacturer to create an IoT solution for monitoring inventory, usage and performance of farm vehicles and implements. Sensors were installed on equipment to track location, engine run-time, fuel levels etc. Real-time data was collected via edge gateways and synced to a central dashboard. Mechanics could now proactively service high-usage equipment before breakdowns. Owners got alerts for unauthorized usage, geo-fencing etc. The system utilized LoRaWAN, AWS IoT and other technologies to wirelessly connect diverse equipment across large areas.

Mobile App for Urban Horticulture Business: For their capstone, students developed a native iOS and Android app for an urban gardening company that designs and maintains green walls, rooftop gardens and other vertical garden setups in buildings. Key features included showing portfolio of projects, booking consultations, making payments, AR/VR guided tours of installations for clients. Employees could log maintenance tasks, receive work orders, upload before/after photos using the app. Integration with APIs for payments, GIS maps etc. provided a seamless experience. The app helped the company scale operations and engage more clients through a compelling digital presence.

As you can see from these examples, SAIT CST capstone projects are real-world, industry-driven solutions that address tangible business challenges. Students gain hands-on experience employing appropriate technologies and development methodologies to deliver functioning, production-ready applications. By collaborating directly with sponsor organizations, they comprehend user needs better and deliver solutions with tangible post-graduation impact. The in-depth projects help transition students smoothly into professional roles after graduation.

These were just a few high-level descriptions to illustrate the type, scope and impacts of capstone projects undertaken by SAIT’s Computer Systems Technology program. In reality, each project involves extensive planning, research, prototyping and iterations over 6-8 months before a polished product is delivered. More details on specific technical implementations, development workflows, testing processes, documentation practices etc. are usually not publicly disclosed or documented due to privacy agreements with sponsor partners. I hope this lengthy overview provides a good sense of how capstone projects help SAIT CST students gain real-world skills and foster industry connections through practical, client-focused application development experiences. Please let me know if you need any clarification or have additional questions.

WHAT ARE SOME OTHER TOPICS THAT STUDENTS HAVE EXPLORED FOR THEIR CAPSTONE PROJECTS

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Business/Management:

Developing a business plan for a start-up company
Conducting a market research study and analysis for a new product launch
Creating an employee training/development program for a local small business
Analyzing the strategic operations and performance of a public company
Proposing recommendations to improve business processes and operations

Engineering:

Designing and prototyping an automated assembly line for a manufacturing process
Developing architectural plans for a sustainable residential building
Researching and testing innovative materials and technologies for transportation applications
Conducting experiments on fluid dynamics properties to optimize machinery performance
Creating software programs and algorithms to solve complex computational problems

Health Sciences:

Investigating epidemiological trends and developing public health intervention plans
Conducting clinical research trials to test new medical treatments or devices
Designing rehabilitation protocols for patients with specific health conditions
Analyzing health policies and healthcare systems to address issues like access and affordability
Proposing and piloting nutritional and lifestyle programs to manage chronic diseases

Education:

Developing and evaluating new teaching methods, lesson plans, and curricula for different subjects
Researching education policies and reform initiatives to improve student outcomes
Designing e-learning modules and online courses for continuing education programs
Creating multimedia resources and interactive learning tools for the classroom
Conducting needs-assessments and proposing programs to support student populations

Social Sciences:

Studying demographic trends and their socio-economic impacts through surveys and interviews
Analyzing community development initiatives to promote sustainability and empowerment
Researching and reporting on social, political or economic issues through field work
Proposing new models, frameworks and theories based on critical analysis of literature
Conducting program evaluations of social services and interventions to address issues like poverty, inequality etc.

HOW CAN STUDENTS ENSURE THAT THEIR CAPSTONE PROJECT DEMONSTRATES MASTERY OF THE FIELD

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Thoroughly research and narrow down their topic. Students should choose a topic that truly interests them and allows them to deeply explore an important area or issue within their field. Researching existing literature and identifying gaps or new perspectives that could contribute meaningful knowledge is crucial. Speaking to faculty advisors can help ensure the topic is robust and lends itself well to demonstrating high levels of learning.

Develop clear learning objectives and a project plan. Once a topic is chosen, students need to articulate very specific learning goals and intended outcomes of the project. These objectives should be ambitious and cover multiple dimensions of mastery, going beyond simply producing a final product. Students should also map out the major milestones and tasks required to accomplish the objectives, including timelines. This planning stage ensures the project scope and goals are appropriately rigorous for a capstone experience.

Engage in comprehensive analysis of the topic. To show expertise, students must analyze their topic from multiple perspectives through in-depth research. This involves collecting and critically examining all relevant prior works, data, theories, models, case studies, and more based on the methodologies of their field. Students should synthesize the most important theories, findings and implications to demonstrate comprehensive command of the background literature.

Apply higher-level cognitive skills. Mastery requires taking research and analysis to a higher level through application skills like evaluation, design/creation, and problemsolving. Students may apply their research through various approaches like developing an original model, conducting new research to address limitations, translating theories into practice through a program/intervention, solving a real-world problem situation, etc. This stage separates capstone projects from typical research papers by providing an opportunity for students to independently utilize their skills and produce new work.

Incorporate feedback into ongoing refinements. Continuous improvement is important for mastery-level work. Students should seek input from their advisor, peers, and other experts throughout the process. Minor course corrections are expected based on feedback, but students should also reevaluate larger elements of their work in light of insights. Project drafts need to thoughtfully integrate constructive feedback to strengthen the final product. Students should maintain ongoing reflections on their learning process as well.

Present findings in a clear, polished manner. The final deliverable matters greatly for conveying mastery. Strong written and oral communication skills are required to summarize the project journey and synthesize key findings/takeaways for various audiences, both expert and non-expert. Multimedia presentation formats may be appropriate depending on the topic and methodology. Students should professionally present their work and be prepared to thoughtfully discuss and defend all aspects, recognizing limitations.

Reflect on growth and future applications of learning. In a culminating reflection, students need to evaluate their development, including strengths/weaknesses and specific skills/knowledge gained through the process. Reflection involves tying the project back to broader learning objectives and discussing how interests/perspectives evolved. Students should also connect their new expertise to potential future studies or career applications. This self-assessment demonstrates the lifelong learning process.

Crafting a capstone project that truly exhibits mastery requires far more than simply completing required elements or producing a final report. Students must approach their topic rigorously with ambition to independently apply higher-level cognitive skills and contribute specialized knowledge. Incorporating ongoing feedback and meticulous attention to clear communication allows the work to reach its full potential and stand out as an exemplar of what students have gained from their entire program of study. Following this framework leads to an experience that transforms students and showcases their readiness to excel professionally within their chosen field.

HOW DO NURSING STUDENTS CHOOSE THEIR CAPSTONE PROJECTS

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Nursing students have several options and factors to consider when choosing their capstone project for graduation. The capstone project is intended to be a culmination of the student’s nursing education where they can apply their knowledge and skills to a real-world health care issue or problem. It allows students to gain experience in areas of interest and to explore potential career paths.

Usually during their final semester or year of the nursing program, students will meet with their capstone project coordinator or faculty advisor to begin discussing ideas. Many programs provide examples of past successful capstone projects or have specialty areas they encourage exploration in such as community health, leadership, research, or education. Browsing these examples can spark interest in particular topics.

Students may also draw from clinical rotations they’ve had where they witnessed an issue firsthand that they want to further investigate. For example, if a student noticed a gap in patient education materials on a specialty unit, they may propose creating new materials as their project. Practicum experiences are a great place to get real world exposure to potential projects.

Personal interests are another driver for many students. If they have a passion for women’s health or pediatrics for example, they will likely gravitate towards a project involving that population. Career goals after graduation also factor in, as certain projects can help students gain experience and skills directly applicable to their desired nursing path. Projects related to their goal specialties strengthen resumes and applications for post-grad roles.

Faculty advisors provide guidance on balancing project ideas with feasibility and available resources. They ensure the scope is appropriate for a semester-long endeavor and that necessary approvals, materials, and partnerships can be reasonably obtained if needed. Advisors also screen ideas against established learning outcomes to confirm the project meets curriculum requirements for skills like leadership, research, or community engagement.

Institutional requirements also shape project decisions. Some nursing programs may designate preferred project types like original research studies involving data collection and analysis. Others promote evidence-based practice projects where students systematically review literature and develop policies or programs. Understanding the rubrics used to evaluate projects helps students design proposals with those grading criteria in mind.

A thorough literature review is an important part of the process to demonstrate the rationale and need for the chosen topic. Finding gaps in existing research or best practices validates that the proposed project would make an original contribution. Students may discuss ideas with librarians, connect with subject experts, or interview healthcare professionals informed their discussions with advisors.

Narrowing the focus also requires refinement. Some programs have minimum or maximum page counts set for final written reports that influence topics that can reasonably be covered at that length. Gaining necessary approvals from places like an ethics review board takes time which factors into timeline feasibility. Narrowing from broad interests to specific populations,locations, interventions or comparisons comes with advising support.

Budget requirements are another consideration. While many projects involve no direct costs, others may need funding for material development, event hosting, statistical software licensing, travel for data collection or dissemination activities. Students vet cost estimates early on and consider backup plans if full budgets cannot be obtained. Sustainability of any proposed solutions or programs initiated also factors into project design discussions with mentors.

Nursing capstone projects offer valuable opportunities for hands-on learning at the end of degree programs. By considering factors like personal interests, career goals, required competencies and skills demonstrated, and feasibility within timelines and available resources, students can thoughtfully select topics that are engaging as well as appropriate culminating experiences for their nursing education. Advisor guidance plays an important role in navigating options and designing strong project proposals to maximize the learning experience.

HOW WOULD THE STUDENTS EVALUATE THE ACCURACY OF THE DIFFERENT FORECASTING MODELS

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The students would need to obtain historical data on the variable they are trying to forecast. This could be things like past monthly or quarterly sales figures, stock prices, weather data, or other time series data. They would split the historical data into two parts – a training set and a testing set.

The training set would contain the earliest data and would be used to develop and train each of the forecasting models. Common models students may consider include simple exponential smoothing, Holt’s linear trend method, Brown’s exponential smoothing approach, ARIMA (autoregressive integrated moving average) models, and regression models with lagged predictor variables. For each model, the students would select the optimal parameters like the alpha level in simple exponential smoothing or the p, d, q parameters in ARIMA.

Once the models have been developed on the training set, the students would then forecast future periods using each model but only using the information available up to the end of the training set. These forecasts would be compared to the actual data in the testing set to evaluate accuracy. Some common metrics that could be used include:

Mean Absolute Percentage Error (MAPE) – This calculates the average of the percentage errors between each forecast and the actual value. It provides an easy to understand measure of accuracy with a lower score indicating better forecasts.

Mean Absolute Deviation (MAD) – Similar to MAPE but without calculating the percentage, instead just looking at the average of the absolute errors.

Mean Squared Error (MSE) – Errors are squared before averaging so larger errors are weighted more heavily than small errors. This focuses evaluation on avoiding large forecast misses even if some smaller errors occur. MSE needs to be interpreted carefully as the scale is not as intuitive as MAPE or MAD.

Mean Absolute Scaled Error (MASE) – Accounts for the difficulty of the time series by comparing forecast errors to a naive “random walk” forecast. A MASE below 1 indicates the model is better than the naive forecast.

The students would calculate accuracy metrics like MAPE, MAD, MSE, and MASE for each model over the test period forecasts. They may also produce graphs to visually compare the actual values to each model’s forecasts to assess accuracy over time. Performance could also be evaluated at different forecast horizons like 1-period ahead, 3-period ahead, 6-period ahead forecasts to see if accuracy degrades smoothly or if some models hold up better farther into the future.

Additional analysis may include conducting Diebold-Mariano tests to statistically compare model accuracy and determine if differences in the error metrics between pairs of models are statistically significant or could be due to chance. They could also perform residual diagnostics on the forecast errors to check if any patterns remain that could be exploited to potentially develop an even more accurate model.

After comprehensively evaluating accuracy over the test set using multiple error metrics and statistical comparisons, the students would identify which forecasting model or models provided the most accurate and reliable forecasts based on the historical data available. No single metric alone would determine the best model, but rather the preponderance of evidence across the board in terms of MAPE, MAD, MSE, MASE, visual forecasts, statistical tests, and residual analysis.

The students would report their analysis, including details on developing each model type, describing the accuracy metrics calculated, presenting the results visually through tables and graphs, discussing their statistical findings, and making a conclusion on the most accurate model indicated by this thorough ex-post evaluation process. This would provide them significant insight into forecasting, model selection, and evaluation that they could apply in practice when working with real time-series data challenges.

While accuracy alone cannot guarantee a model’s future performance, this process allows the students to rigorously benchmark the performance of alternative techniques on historical data. It not only identifies the empirical ex-post leader, but also highlights how much more accurate or less accurate other methods were so they can better understand the practical value and predictive limitations of different approaches. This in-depth workflow conveys the types of analysis real-world data scientists and business analysts would carry out to select the optimal forecasting technique.