Ver o conteúdo principal

SUCCESS@NOVA

Projetos T

About the Project

SUCCESS@NOVA - Strategies to Underpin College Course Engagement and Student Success at NOVA

The project addresses the critical challenges of academic dropout and student success in higher education. These issues have become more pronounced due to the disruptions caused by the COVID-19 pandemic, which highlighted the need for innovative interventions to support students. The project builds upon the pilot initiative STEPS@NOVA, previously implemented at NOVA SBE and NOVA IMS, and expands its scope to all units of Universidade NOVA de Lisboa (UNL).

The project is organized into two main action lines. The first involves establishing the Observatory for Retention and Academic Success to monitor dropout rates, analyze key contributing factors, and develop predictive models to identify students at risk. These models, powered by advanced machine learning techniques and data integration from Learning Management Systems (LMS), aim to provide actionable insights for targeted interventions.

The second action line focuses on implementing a comprehensive intervention program across all UNL units offering first-cycle degrees. This program includes activities designed to enhance social and academic integration, provide tailored support, and promote career planning. Key initiatives, such as "Discovery Week," mentoring programs, proactive advising, and student support services, aim to create an inclusive academic environment that fosters student engagement and success.

By combining predictive analytics with personalized interventions, SUCCESS@NOVA seeks to minimize dropout rates, improve academic performance, and equip students with the skills needed to thrive in their academic and professional journeys.

Our Contribution

NOVA IMS plays a central role in the SUCCESS@NOVA project through its expertise in data science, analytics, and predictive modeling. Specific contributions include:

  1. Development of Predictive Models: NOVA IMS leads the design and implementation of machine learning algorithms to forecast dropout risks and predict academic success. These models integrate data from LMS platforms, academic records, and socio-demographic factors, enabling early identification of at-risk students.

  2. Analytical Infrastructure: The institution contributes to developing an analytical framework that supports data-driven decision-making across UNL. This infrastructure facilitates extracting, processing, and analysing diverse datasets to uncover actionable insights.

  3. Support for Intervention Design: NOVA IMS collaborates with other units to design and implement tailored intervention strategies, leveraging data insights to address specific student needs.

  4. Capacity Building: The institution provides training and technical support to stakeholders involved in the project, ensuring the effective use of predictive tools and analytics.

Through these efforts, NOVA IMS enhances the project's ability to reduce dropout rates and promote academic success across NOVA.

Funding

Website: under construction

Funding programme: Directorate-General for Higher Education (DGES)

Funding to NOVA IMS: € 254 796,38

Duration: 2023-2026

Contribution to the SDGs