Tag Archives: student

WHAT ARE SOME EXAMPLES OF CREATIVE STUDENT ENTREPRENEURSHIP PROGRAMS THAT ADDRESS FOOD INSECURITY ON COLLEGE CAMPUSES

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One innovative program that addresses the issue of food insecurity among college students is FarmHouse Delivery at the University of Missouri. The program was started in 2018 by a group of students as a social entrepreneurship project. It functions as a grocery delivery service that provides healthy, affordable food options to students on campus. Students can order groceries through an app and have them delivered directly to their dorm or campus apartment within a few hours.

FarmHouse sources its products from local farms and producers to keep costs low. This gives students access to fresh fruits and vegetables as well as pantry staples. It aims to fill the gaps between dining hall meals at an affordable price point. Pricing and partnership with the university’s food bank helps make healthy groceries accessible to low-income students as well. The student-run operation models sustainable business practices and food systems education. It has grown steadily since inception and continues to address campus hunger through an entrepreneurial solution.

Another notable program is Student Emergency Services (SES) at the University of California, Berkeley. Founded in 2010 by three students, SES operates a food pantry and meal delivery service for students experiencing food and housing insecurity. Like FarmHouse Delivery, it relies on a student-run cooperative business model. SES collects food donations from campus dining halls and local supermarkets which it redistributes free of charge to students in need via the on-campus pantry.

Through its Bear Necessities program, SES delivers free emergency food bags to students unable to physically access the pantry due to issues like illness, disability or lack of transportation. This service helps address barriers to accessing campus food resources. Students sign up online and receive groceries hand-delivered to their dorm within a few hours. SES is a non-profit that raises operating funds through campus fundraising and donations. It exemplifies how entrepreneurial problem-solving by students can directly help peers facing financial hardship.

Another standout program is the Locker Project at Seattle University. Launched in 2016 through a student initiative and now run in partnership with the campus dining department, it provides free food storage lockers across campus. The lockers are stocked daily with non-perishable foods, toiletries and menstrual products donated by the university community. Students can anonymously take what they need from the lockers at any time without stigma or paperwork. This innovative approach eliminates obstacles to discreetly accessing resources on demand.

The founders designed the Locker Project specifically with food-insecure students’ experiences, needs and perspectives in mind. Maintained through student and university staff volunteers, it fills an important gap, as many college students grapple with intermittent or unpredictable access to food. By normalizing the lockers as convenient additions to campus rather than solutions only for those facing hardship, it helps further reduce stigma. The program has effectively addressed institutional knowledge gaps around student hunger through grassroots, empathetic entrepreneurship.

A program with broader institutional support is the Grocery On-the-Go Market at Iowa State University. Launched in 2016, it is a partnership between Dining Services, the Dean of Students office and student groups. The market operates out of a custom-built food truck that parks in alternating high-traffic campus locations for set weekly hours. Students can purchase pre-packaged fresh, canned and dry goods at discounted prices using dining dollars, cash or credit. Partnerships with local anti-hunger organizations allow the market to offer select culturally appropriate frozen meal options as well.

Unlike most food banks or pantries, the market avoids stigma by being open to all students, not just those facing need. Its entrepreneurial approach of meeting students where they are has proven popular—serving hundreds per week and freeing up resources for other initiatives to coordinate with. The on-campus employer hires work-study eligible students, promoting leadership and skills development too. By bridging various student and campus partners campus-wide through an innovative model, Grocery On-the-Go Market effected positive change on multiple levels.

These programs demonstrate some creative ways that students themselves are developing solutions to food insecurity on campuses through social entrepreneurship. By directly addressing gaps, reducing stigma and empowering peers in need, they are making a tangible difference. Partnering with various campus and community stakeholders allows these initiatives to operate sustainably while continually improving services. Their innovative, action-oriented models inform how future programs and university policies could better serve students facing basic needs barriers to academic success. Student entrepreneurship shows great potential to address this pressing issue in impactful yet pragmatic ways.

HOW DID YOU ENSURE THE SECURITY OF THE STUDENT DATA IN THE SIS CAPSTONE PROJECT

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We understood the importance of properly securing sensitive student data in the SIS project. Data security was prioritized from the initial planning and design phases of the project. Several measures were implemented to help protect student information and ensure compliance with relevant data privacy regulations.

First, a thorough data security assessment was conducted to identify and address any vulnerabilities. This involved analyzing the entire software development lifecycle and identifying key risks at each stage – from data collection and storage to transmission and access. The OWASP Top 10 security risks were also referenced to help uncover common issues.

Second, we carefully designed the system architecture with security in mind. The database was isolated on its own private subnet behind a firewall, and not directly accessible from external networks. Communication with backend services occurred only over encrypted channels. Application code was developed following secure coding best practices to prevent vulnerabilities. Authentication and authorization mechanisms restricted all access to authorized users and specific systems only.

Third, during implementation strong identity and access management controls were put in place. Multi-factor authentication was enforced for any account with access to sensitive data. Comprehensive password policies and account lockout rules were applied. Granular role-based access control (RBAC) models restricted what actions users could perform based on their organization role and need-to-know basis. Detailed auditing of all user activities was configured for security monitoring purposes.

Fourth, we implemented robust data protection mechanisms. All student data stored in the database and transmitted over networks was encrypted using strong industry-standard algorithms like AES-256. Cryptographic keys and secrets were properly secured outside of the codebase. Backup and disaster recovery procedures incorporated data encryption capabilities. When designing APIs and interfaces, input validation and output encoding was performed to prevent data tampering and vulnerabilities.

Fifth, the principle of least privilege was followed assiduously. Systems, services and accounts were configured with minimal permissions required to perform their specific function. Application functions were segregated based on their access levels to student information. Unused or unnecessary services were disabled or removed from systems altogether. Operating system weak points were hardened through configuration of services, file permissions, and host-based firewall rules.

Sixth, ongoing security monitoring and logging facilities were established. A web application firewall was deployed to monitor and block malicious traffic and attacks. Extensive logging of user and system activities was enabled to generate audit trails. Monitoring dashboards and alerts notified on any anomalous behavior or policy violations detected through heuristics and machine learning techniques. Vulnerability assessments were conducted regularly by independent assessors to identify new weaknesses.

Seventh, a comprehensive information security policy and awareness program were implemented. Data privacy and protection guidelines along with acceptable usage policies were drafted and all team members had to acknowledge compliance. Regular security training ensured the staff were aware of their roles and responsibilities. An incident response plan prepared the organization to quickly detect, contain and remediate security breaches. Business continuity plans helped maintain operations and safeguard student records even during disaster situations.

We conducted privacy impact assessments and third party audits by legal and compliance experts to ensure all technical and process controls met statutory and regulatory compliance requirements including GDPR, FERPA and PCI standards. Any non-compliances or gaps identified were urgently remediated. The system and organization were certified to be compliant with the stringent security protocols required to safely manage sensitive student information.

The exhaustive security measures implemented through a defense-in-depth approach successfully secured student data in the SIS from both external and internal threats. A culture of security best practices was ingrained in development and operations. Comprehensive policies and controls continue to effectively protect student privacy and maintain the project’s compliance with data protection mandates.

CAN YOU PROVIDE AN EXAMPLE OF A MACHINE LEARNING PIPELINE FOR STUDENT MODELING

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A common machine learning pipeline for student modeling would involve gathering student data from various sources, pre-processing and exploring the data, building machine learning models, evaluating the models, and deploying the predictive models into a learning management system or student information system.

The first step in the pipeline would be to gather student data from different sources in the educational institution. This would likely include demographic data like age, gender, socioeconomic background stored in the student information system. It would also include academic performance data like grades, test scores, assignments from the learning management system. Other sources of data could be student engagement metrics from online learning platforms recording how students are interacting with course content and tools. Survey data from end of course evaluations providing insight into student experiences and perceptions may also be collected.

Once the raw student data is gathered from these different systems, the next step is to perform extensive data pre-processing and feature engineering. This involves cleaning missing or inconsistent data, converting categorical variables into numeric format, dealing with outliers, and generating new meaningful features from the existing ones. For example, student age could be converted to a binary freshmen/non-freshmen variable. Assignment submission timestamps could be used to calculate time spent on different assignments. Prior academic performance could be used to assess preparedness for current courses. During this phase, exploratory data analysis would also be performed to gain insights into relationships between different variables and identify important predictors that could impact student outcomes.

With the cleaned and engineered student dataset, the next phase involves splitting the data into training and test sets for building machine learning models. Since the goal is to predict student outcomes like course grades, retention, or graduation, these would serve as the target variables. Common machine learning algorithms that could be applied include logistic regression for predicting binary outcomes, linear regression for continuous variables, decision trees, random forests for feature selection and prediction, and neural networks. These models would be trained on the training dataset to learn patterns between the predictor variables and target variables.

The trained models then need to be evaluated on the hold-out test set to analyze their predictive capabilities without overfitting to the training data. Various performance metrics like accuracy, precision, recall, F1 score depending on the problem would be calculated and compared across different algorithms. Hyperparameter optimization may also be performed at this stage to tune the models for best performance. Model interpretation techniques could help understand the most influential features driving the model predictions. This evaluation process helps select the final model with the best predictive ability for the given student data and problem.

Once satisfied with a model, the final step is to deploy it into the student systems for real-time predictive use. The model would need to be integrated into either the learning management system or student information system using an application programming interface. As new student data is collected on an ongoing basis, it can be directly fed to the deployed model to generate predictive insights. For example, it could flag at-risk students for early intervention. Or it could provide progression likelihoods to help with academic advising and course planning. Periodic retraining would also be required to keep the model updated as more historic student data becomes available over time.

An effective machine learning pipeline for student modeling includes data collection from multiple sources, cleaning and exploration, algorithm selection and training, model evaluation, integration and deployment into appropriate student systems, and periodic retraining. By leveraging diverse sources of student data, machine learning offers promising approaches to gain predictive understanding of student behaviors, needs and outcomes which can ultimately aid in improving student success, retention and learning experiences. Proper planning and execution of each step in the pipeline is important to build actionable models that can proactively support students throughout their academic journey.