Namrata Srivastava
Biographic Data
| ID | 9494205 |
|---|---|
| NAME | Namrata Srivastava |
| GIVEN NAMES | Namrata |
| FAMILY NAME | Srivastava |
| SIGNATURE | SRIVASTAVA N |
| AFFILIATIONS | Centre for Learning Analytics Monash University Melbourne VIC Australia |
| ORCID | 0000-0003-4194-318X |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Can AI support human grading? Examining machine attention and confidence in short answer scoring
Large language models built upon artificial intelligence (AI) hold great promises to innovate automatic short answer scoring (ASAS) - significantly alleviating educators’ workload in assessing student answers. However, ASAS systems on such basis have seen limited adoption in authentic teaching environments due to the models’ inability to explain the predictions they generate. To address this, we recruited 32 human graders to comparatively analyse…
A learning analytics dashboard to support students' reflection on collaboration
When and how biases seep in
The use of predictive analytics powered by machine learning (ML) to model educational data has increasingly been identified to exhibit bias towards marginalized populations, prompting the need for more equitable applications of these techniques. To tackle bias that emerges in training data or models at different stages of the ML modelling pipeline, numerous debiasing approaches have been proposed. Yet, research into state‐of‐the‐art techniques fo…
No prominent works on this page.
Can AI support human grading? Examining machine attention and confidence in short answer scoring
Large language models built upon artificial intelligence (AI) hold great promises to innovate automatic short answer scoring (ASAS) - significantly alleviating educators’ workload in assessing student answers. However, ASAS systems on such basis have seen limited adoption in authentic teaching environments due to the models’ inability to explain the predictions they generate. To address this, we recruited 32 human graders to comparatively analyse…
A learning analytics dashboard to support students' reflection on collaboration
When and how biases seep in
The use of predictive analytics powered by machine learning (ML) to model educational data has increasingly been identified to exhibit bias towards marginalized populations, prompting the need for more equitable applications of these techniques. To tackle bias that emerges in training data or models at different stages of the ML modelling pipeline, numerous debiasing approaches have been proposed. Yet, research into state‐of‐the‐art techniques fo…
Computer Science (3 works) · Analytics (2 works) · Data science (2 works) · Learning analytics (2 works) · Psychology (2 works) · AI-based Problem Solving and Planning (1 work) · Artificial Intelligence (1 work) · Artificial Intelligence in Healthcare and Education (1 work) · Computer-Assisted Instruction (1 work) · Dashboard (1 work)