Understanding the Web
WebSciX 2025
- Date: October 14-15, 2025
- Venue: J N Tata Auditorium, IISc Bangalore
About the conference
WebSciX India is a proposed satellite event of the ACM Web Science Conference, dedicated to advancing the interdisciplinary study of the Internet, World Wide Web (WWW), and Artificial Intelligence (AI) and their impact on human lives in India and surrounding regions. WebSciX builds upon the Web Science for Development (WS4D) series of events conducted in India since 2019.
WebSciX 2025 is a community-building workshop intended to build a strong core technical committee that can organize the annual WebSciX conferences. WebSciX 2025 is hosted as a co-located track at the Symposium on Data for Public Good, organized at IISc Bangalore, between October 14-15 2025.
Venue
Schools which teach class 6 – 8 in Urban Uttar Pradesh were considered
Based on the analysis, the following are deduced:
| Lower Dropouts | Higher Dropouts |
| Higher percentage of Private Schools | Higher percentage of Minority Schools |
| Hindi as medium of instruction | Urdu as medium on instruction |
| Good Classrooms | Teachers who are not graduates |
In some of these factors, the heatmaps of dropout and factors are very similar, in some the visual difference in not striking, hence we have also included a scatter plot which displays the effects of the factor
Schools which teach class 6 – 8 in Rural Uttar Pradesh were considered
Based on the analysis, the following are deduced:
| Better Learning Outcomes | Lower Learning Outcomes |
| Good Classrooms | Hindi medium |
| Schools with Electricity | Schools with lower internet connectivity |
| Any female above yrs of age having attended school | More Government Schools |
In some of these factors, the heatmaps of dropout and factors are very similar, in some the visual difference in not striking, hence we have also included a scatter plot which displays the effects of the factor
Schools which teach class 6 – 8 in Rural Uttar Pradesh were considered
Based on the analysis, the following are deduced:
| Lower Dropout | Higher Dropout |
| Classrooms in Good condition | From districts with lower GDDP/ GSDP |
| Higher percentage of Female teachers | Teachers Qualification Below Graduate |
| English medium as first language | Hindi medium as First Language |
In some of these factors, the heatmaps of dropout and factors are very similar, in some the visual difference in not striking, hence we have also included a scatter plot which displays the effects of the factor
We also have a district wise sensitivity – so as to enable bird’s eye view of the major contributing factors in each district
Schools which teach class 3 – 5 in Rural Uttar Pradesh were considered
Based on the analysis, the following are deduced:
| Better Learning Outcome | Lower Learning Outcome |
| Regular Government Teachers | Children who are under weight |
| Any Female above 6 years ever attended schools | Children who are stunted |
| Classrooms in good condition | More enrollment in Government Schools |
In some of these factors, the heatmaps of dropout and factors are very similar, in some the visual difference in not striking, hence we have also included a scatter plot which displays the effects of the factor
शिक्षण की गुणवत्ता और ड्रॉपआउट दोनों को संबोधित करके शैक्षिक परिणामों का रूपांतरण
डेटा विश्लेषण का दायरा
2 मिलियन से अधिक डेटा पॉइंट्स का विश्लेषण, जिनमें शामिल:
• 75 ज़िले
• 2.55 लाख विद्यालय
• 5.76 करोड़ छात्र
• अनेक डेटा सेट, जैसे UDISE, NFHS, SECC, ASER, यूपी सांख्यिकी
भाषाई परिदृश्य
उत्तर प्रदेश की प्रमुख बोलियाँ हैं — अवधी, बघेली, भोजपुरी, ब्रज भाषा, बुंदेली, खड़ी बोली, और कन्नौजी।
जनसांख्यिकीय और आर्थिक मुख्य बिंदु
माध्यमिक शिक्षा में कम ड्रॉपआउट दर के शीर्ष 3 कारण
ग्रामीण क्षेत्र:
शहरी क्षेत्र:
माध्यमिक शिक्षा में अधिक ड्रॉपआउट दर के शीर्ष 3 कारण
ग्रामीण क्षेत्र:
शहरी क्षेत्र:
बोली के अनुसार ग्रामीण प्रगति के पैटर्न
खड़ी बोली–भाषी ज़िले (पश्चिमी यूपी): ग्रामीण क्षेत्रों में मजबूत सीखने के परिणाम लेकिन शहरी क्षेत्रों में ड्रॉपआउट का अधिक जोखिम, संभवतः औद्योगिकीकरण और नौकरी पलायन के कारण।
भोजपुरी–भाषी ज़िले (पूर्वी यूपी): ग्रामीण क्षेत्रों में मजबूत सीखने के परिणाम लेकिन शहरी क्षेत्रों में ड्रॉपआउट अधिक, संभवतः शिक्षा या रोजगार के लिए शहरी क्षेत्रों या अन्य राज्यों में पलायन के कारण।
अवधी – भाषी ज़िले : राज्य के लगभग मध्य भाग में स्थित, जिसमें कानपुर, लखनऊ, अयोध्या, प्रयागराज जैसे शहरी मिश्रण वाले क्षेत्र शामिल हैं। ग्रामीण क्षेत्रों में औसत सीखने के परिणाम दिखाई देते हैं, लेकिन शहरी क्षेत्रों में यह प्रवृत्ति काफी सुरक्षात्मक है, जहाँ अनेक निजी शैक्षणिक संस्थान और सरकार द्वारा प्रदत्त मजबूत शैक्षिक अवसंरचना मौजूद है।
Schools which teach class 3 – 5 in Rural Uttar Pradesh were considered
Based on the analysis, the following are deduced:
Lesser Dropout rates were absorbed in the following:
| Less Dropouts | More Dropouts |
| Schools with good Classrooms | Children who are stunted |
| More Female teachers | Districts which have low GDDP / GSDP |
| English as medium on instruction | More Government Schools |
In some of these factors, the heatmaps of dropout and factors are very similar, in some the visual difference in not striking, hence we have also included a scatter plot which displays the effects of the factor
This dashboard visualizes the relationship between student dropout rate and influencing factors using two key visual tools. The scatter plot illustrates the correlations between dropout rates and specific variables such as infrastructure, or teacher-student ratio, etc. The heatmap highlights how contributing factors vary across various regions in Karnataka. Together, these visuals help identify critical patterns and areas needing intervention.
You can select the variable for which you want to see correlation with wheat yield, from the dropdown menu.
If the p-value for a variable is less than 0.05, then that variable has a significant correlation with wheat yield.
It is found that the following factors have significant correlation with Wheat Yield
1. KCC(Kisan Credit Card) Distributed (+ve correlation)
2. Net Irrigated Area (+ve correlation)
3. NPK(Nitrogen Phosphorus Potassium fertilizer) Distributed (+ve correlation)
4. Regional Rural Bank Loans (+ve correlation)
