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jordankelvin700
Jan 06, 2023
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Improving Student Engagement
A big part of STEM education is getting students to apply science, math, engineering, and technology concepts to actual problem-solving activities. STEM projects, like robot building and mini hackathons, can foster an engaged classroom through the more active and collaborative nature of these exercises.
But activities and experiments can be time and resource intensive. They also don’t work for every lesson and subject, whether within or apart from STEM fields. In these cases, getting students to engage and pay attention in class can be a constant challenge for any educator.
Why is lack of attention in class such a common problem? Is there anything that can be done to get more students paying attention and engaging with their lessons?
What Prevents Students From Paying Attention?
Statistics for the average attention span by age usually show that the older one gets, the more their capacity for paying attention increases. For example, a preschooler will be able to focus for 6 to 12 minutes, which gradually increases to 30 to 50 minutes by the time they reach high school.
However, attention spans can be difficult to accurately gauge and track for a lot of reasons. Not only do attention spans vary greatly from one person to the next, in one situation to the next, but digital technology has been a very recent and major disruptor to how humans focus. https://www.magoda.com/stem/improving-student-engagement/
A big part of STEM education is getting students to apply science, math, engineering, and technology concepts to actual problem-solving activities. STEM projects, like robot building and mini hackathons, can foster an engaged classroom through the more active and collaborative nature of these exercises.
But activities and experiments can be time and resource intensive. They also don’t work for every lesson and subject, whether within or apart from STEM fields. In these cases, getting students to engage and pay attention in class can be a constant challenge for any educator.
Why is lack of attention in class such a common problem? Is there anything that can be done to get more students paying attention and engaging with their lessons?
What Prevents Students From Paying Attention?
Statistics for the average attention span by age usually show that the older one gets, the more their capacity for paying attention increases. For example, a preschooler will be able to focus for 6 to 12 minutes, which gradually increases to 30 to 50 minutes by the time they reach high school.
However, attention spans can be difficult to accurately gauge and track for a lot of reasons. Not only do attention spans vary greatly from one person to the next, in one situation to the next, but digital technology has been a very recent and major disruptor to how humans focus. https://www.magoda.com/stem/improving-student-engagement/
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Abhijit BhupendrasinghVirsen
Aug 04, 2025
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In an age where data is everywhere and decisions are more complex than ever, the ability to make smart, ethical, and evidence-based choices is one of the most valued skills across industries. Whether it's a government deciding how to distribute vaccines or a company planning its expansion strategy, Decision Science is the engine driving modern problem-solving. At RV University, the Decision Science programme is designed to train future-ready professionals who combine technical skill with real-world judgement.
Offered through the School of Computer Science and Engineering, this innovative programme is inherently interdisciplinary. It combines elements of statistics, behavioural economics, machine learning, and data visualisation, equipping students to translate raw data into actionable insights. What makes RVU’s Decision Science curriculum unique is its balance between quantitative tools and human-centred thinking—ensuring graduates are not just data-savvy, but also decision-savvy.
Students begin their academic journey with foundational courses in mathematics, statistics, and coding. These form the bedrock for more advanced study in subjects like predictive modelling, data mining, Bayesian inference, and optimisation techniques. Simultaneously, they are introduced to behavioural science—learning how psychological biases, group dynamics, and social norms influence the choices people make.
One of the programme’s strengths is its focus on ethical and responsible decision-making. In today’s world, where data can be used to manipulate opinions or reinforce inequalities, it’s not enough to understand what a decision will do—you also need to understand what it means. RVU’s courses on AI ethics, stakeholder mapping, and policy analysis teach students to factor in the social and ethical consequences of the recommendations they generate.
Students don’t just learn theories in the classroom—they apply them in real-world simulations and projects. Through labs and case studies, they work on problems like: How should a city plan its public transport system using passenger flow data? How can a hospital reduce patient wait times without compromising on care? How do we optimise marketing campaigns for a product launch across multiple cities? These exercises allow students to test models in dynamic, often unpredictable conditions.
To support this hands-on learning, RVU provides access to state-of-the-art technology tools such as Python, R, SQL, Excel, Tableau, and Power BI. Students use these platforms to clean data, build dashboards, conduct regressions, and build forecasting models.
Capstone projects are a critical component of the Decision Science programme. In their final year, students collaborate with organisations—ranging from startups and corporates to government departments and NGOs—to solve high-stakes problems. Past projects have included predicting student dropout risk in public schools, modelling loan repayment behaviour for microfinance institutions, and forecasting energy consumption patterns in urban neighbourhoods.
In addition to technical coursework, students engage with subjects like systems thinking, operations research, behavioural strategy, and design thinking. These help them approach decisions from multiple perspectives—strategic, operational, human-centred, and ethical.
RVU's programme also offers strong industry alignment. Students benefit from guest lectures and mentorship sessions with decision scientists, public policy experts, and data leaders from global firms. Internships are facilitated with consulting companies, analytics firms, policy labs, and even government think tanks. These experiences help students understand how decision science functions in professional settings.
Offered through the School of Computer Science and Engineering, this innovative programme is inherently interdisciplinary. It combines elements of statistics, behavioural economics, machine learning, and data visualisation, equipping students to translate raw data into actionable insights. What makes RVU’s Decision Science curriculum unique is its balance between quantitative tools and human-centred thinking—ensuring graduates are not just data-savvy, but also decision-savvy.
Students begin their academic journey with foundational courses in mathematics, statistics, and coding. These form the bedrock for more advanced study in subjects like predictive modelling, data mining, Bayesian inference, and optimisation techniques. Simultaneously, they are introduced to behavioural science—learning how psychological biases, group dynamics, and social norms influence the choices people make.
One of the programme’s strengths is its focus on ethical and responsible decision-making. In today’s world, where data can be used to manipulate opinions or reinforce inequalities, it’s not enough to understand what a decision will do—you also need to understand what it means. RVU’s courses on AI ethics, stakeholder mapping, and policy analysis teach students to factor in the social and ethical consequences of the recommendations they generate.
Students don’t just learn theories in the classroom—they apply them in real-world simulations and projects. Through labs and case studies, they work on problems like: How should a city plan its public transport system using passenger flow data? How can a hospital reduce patient wait times without compromising on care? How do we optimise marketing campaigns for a product launch across multiple cities? These exercises allow students to test models in dynamic, often unpredictable conditions.
To support this hands-on learning, RVU provides access to state-of-the-art technology tools such as Python, R, SQL, Excel, Tableau, and Power BI. Students use these platforms to clean data, build dashboards, conduct regressions, and build forecasting models.
Capstone projects are a critical component of the Decision Science programme. In their final year, students collaborate with organisations—ranging from startups and corporates to government departments and NGOs—to solve high-stakes problems. Past projects have included predicting student dropout risk in public schools, modelling loan repayment behaviour for microfinance institutions, and forecasting energy consumption patterns in urban neighbourhoods.
In addition to technical coursework, students engage with subjects like systems thinking, operations research, behavioural strategy, and design thinking. These help them approach decisions from multiple perspectives—strategic, operational, human-centred, and ethical.
RVU's programme also offers strong industry alignment. Students benefit from guest lectures and mentorship sessions with decision scientists, public policy experts, and data leaders from global firms. Internships are facilitated with consulting companies, analytics firms, policy labs, and even government think tanks. These experiences help students understand how decision science functions in professional settings.
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Abhijit BhupendrasinghVirsen
Aug 04, 2025
|
In an age where data is everywhere and decisions are more complex than ever, the ability to make smart, ethical, and evidence-based choices is one of the most valued skills across industries. Whether it's a government deciding how to distribute vaccines or a company planning its expansion strategy, Decision Science is the engine driving modern problem-solving. At RV University, the Decision Science programme is designed to train future-ready professionals who combine technical skill with real-world judgement.
Offered through the School of Computer Science and Engineering, this innovative programme is inherently interdisciplinary. It combines elements of statistics, behavioural economics, machine learning, and data visualisation, equipping students to translate raw data into actionable insights. What makes RVU’s Decision Science curriculum unique is its balance between quantitative tools and human-centred thinking—ensuring graduates are not just data-savvy, but also decision-savvy.
Students begin their academic journey with foundational courses in mathematics, statistics, and coding. These form the bedrock for more advanced study in subjects like predictive modelling, data mining, Bayesian inference, and optimisation techniques. Simultaneously, they are introduced to behavioural science—learning how psychological biases, group dynamics, and social norms influence the choices people make.
One of the programme’s strengths is its focus on ethical and responsible decision-making. In today’s world, where data can be used to manipulate opinions or reinforce inequalities, it’s not enough to understand what a decision will do—you also need to understand what it means. RVU’s courses on AI ethics, stakeholder mapping, and policy analysis teach students to factor in the social and ethical consequences of the recommendations they generate.
Students don’t just learn theories in the classroom—they apply them in real-world simulations and projects. Through labs and case studies, they work on problems like: How should a city plan its public transport system using passenger flow data? How can a hospital reduce patient wait times without compromising on care? How do we optimise marketing campaigns for a product launch across multiple cities? These exercises allow students to test models in dynamic, often unpredictable conditions.
To support this hands-on learning, RVU provides access to state-of-the-art technology tools such as Python, R, SQL, Excel, Tableau, and Power BI. Students use these platforms to clean data, build dashboards, conduct regressions, and build forecasting models.
Capstone projects are a critical component of the Decision Science programme. In their final year, students collaborate with organisations—ranging from startups and corporates to government departments and NGOs—to solve high-stakes problems. Past projects have included predicting student dropout risk in public schools, modelling loan repayment behaviour for microfinance institutions, and forecasting energy consumption patterns in urban neighbourhoods.
In addition to technical coursework, students engage with subjects like systems thinking, operations research, behavioural strategy, and design thinking. These help them approach decisions from multiple perspectives—strategic, operational, human-centred, and ethical.
RVU's programme also offers strong industry alignment. Students benefit from guest lectures and mentorship sessions with decision scientists, public policy experts, and data leaders from global firms. Internships are facilitated with consulting companies, analytics firms, policy labs, and even government think tanks. These experiences help students understand how decision science functions in professional settings.
Offered through the School of Computer Science and Engineering, this innovative programme is inherently interdisciplinary. It combines elements of statistics, behavioural economics, machine learning, and data visualisation, equipping students to translate raw data into actionable insights. What makes RVU’s Decision Science curriculum unique is its balance between quantitative tools and human-centred thinking—ensuring graduates are not just data-savvy, but also decision-savvy.
Students begin their academic journey with foundational courses in mathematics, statistics, and coding. These form the bedrock for more advanced study in subjects like predictive modelling, data mining, Bayesian inference, and optimisation techniques. Simultaneously, they are introduced to behavioural science—learning how psychological biases, group dynamics, and social norms influence the choices people make.
One of the programme’s strengths is its focus on ethical and responsible decision-making. In today’s world, where data can be used to manipulate opinions or reinforce inequalities, it’s not enough to understand what a decision will do—you also need to understand what it means. RVU’s courses on AI ethics, stakeholder mapping, and policy analysis teach students to factor in the social and ethical consequences of the recommendations they generate.
Students don’t just learn theories in the classroom—they apply them in real-world simulations and projects. Through labs and case studies, they work on problems like: How should a city plan its public transport system using passenger flow data? How can a hospital reduce patient wait times without compromising on care? How do we optimise marketing campaigns for a product launch across multiple cities? These exercises allow students to test models in dynamic, often unpredictable conditions.
To support this hands-on learning, RVU provides access to state-of-the-art technology tools such as Python, R, SQL, Excel, Tableau, and Power BI. Students use these platforms to clean data, build dashboards, conduct regressions, and build forecasting models.
Capstone projects are a critical component of the Decision Science programme. In their final year, students collaborate with organisations—ranging from startups and corporates to government departments and NGOs—to solve high-stakes problems. Past projects have included predicting student dropout risk in public schools, modelling loan repayment behaviour for microfinance institutions, and forecasting energy consumption patterns in urban neighbourhoods.
In addition to technical coursework, students engage with subjects like systems thinking, operations research, behavioural strategy, and design thinking. These help them approach decisions from multiple perspectives—strategic, operational, human-centred, and ethical.
RVU's programme also offers strong industry alignment. Students benefit from guest lectures and mentorship sessions with decision scientists, public policy experts, and data leaders from global firms. Internships are facilitated with consulting companies, analytics firms, policy labs, and even government think tanks. These experiences help students understand how decision science functions in professional settings.
