I study how AI can help teachers give feedback that students act on and learn from, combining large language models, AI for analysing text, and learning analytics on real student assignments and teacher feedback.
I am a PhD candidate in Information Technology (Learning Analytics) at Monash University's Centre for Learning Analytics, submitting my thesis in December 2026, and a Teaching Associate in Monash's Faculty of Information Technology. I am also a Lecturer (Assistant Professor) at Universitas Teknokrat Indonesia. I hold a Bachelor's and a Master's in Mathematics from Universitas Lampung.
Turning an established feedback framework into measurable categories: does feedback guide students’ next tasks (future impact), help them understand their current work (sensemaking), and encourage them to take charge of their learning (agency)?
Designing and evaluating LLM feedback workflows across prompting conditions, and comparing AI-generated with teacher feedback on how useful it is for learning and how it is written.
Building datasets of real teacher feedback and training AI models (such as BERT) that recognise several feedback qualities in one comment, checked carefully against human judgement.
Linking feedback, educator–AI interaction, and student revision to subsequent academic performance.
Methods & tools: Python · R · SQL · BERT · scikit-learn · PyTorch · LLM APIs & prompt design · statistical modelling · qualitative thematic analysis · dataset annotation
Five connected studies, from mapping feedback to automating its analysis to how teachers and students use it.
Analysed authentic higher-education feedback, its alignment with learner-centred components, and patterns across student groups.
Published · Computers & Education, 2025Machine learning and NLP to classify learner-centred feedback; the AI models agreed almost perfectly with trained human coders (Cohen's κ up to 0.956). Implemented in PolyFeed, a research tool from the Centre for Learning Analytics that helps students act on feedback and teachers write better feedback.
Published · Computers and Education: AI, 2024Comparing human and generative AI feedback: 5,156 human- and LLM-generated feedback texts on 1,289 student proposals, across prompting strategies, judged on how useful they are for learning and how they are written.
Under reviewHow educators use, revise, and trust AI-supported feedback in PolyFeed, including ethics and usability.
Published · Assessment & Evaluation in Higher Education, 2026Linked learner-centred feedback components to revision actions and subsequent performance for 1,929 students across three semesters.
In preparationPolyFeed, an AI-supported feedback analytics system, includes the classification model I built in Study 2. Meet the PolyFeed team.
Full list on Google Scholar.
From better feedback to better learning: AI feedback that students act on, and that carries over to their next piece of work.
Test, in randomised experiments across courses and semesters, whether AI feedback reviewed and approved by teachers improves students’ performance on later assignments, not only their revisions.
Evaluation frameworks that combine pedagogical quality, accuracy, and fairness across student groups and languages, including Indonesian, so institutions can adopt AI feedback tools with evidence.
Structured action planning that keeps planning and monitoring with students (metacognitive onloading), and whether strategic rather than surface uptake explains when feedback leads to learning.
Postgraduate data science in the Faculty of Information Technology, including FIT5145: tutorials, consultations, assessment, and feedback.
Undergraduate data mining, statistics, data visualisation, data warehousing, and data science; curriculum design and thesis supervision.
Supported undergraduate and graduate teaching in information technology and programming.
PhD, Information Technology (Learning Analytics)
Monash University · 2023–2026 · BPI scholarship
Master of Mathematics
Universitas Lampung · 2019
Bachelor of Mathematics
Universitas Lampung · 2015
Peer reviewer, 2024–2026
Computers & Education: AI · Teaching and Teacher Education · Education and Information Technologies · Assessment & Evaluation in Higher Education · Heliyon · Cogent Business & Management
Member, Society for Learning Analytics Research (SoLAR), 2024–present
I am open to postdoctoral positions from 2027 in AI in education, learning analytics, and educational NLP, and welcome conversations with researchers and educators.