Fanjie Li

Fanjie Li

PhD student

New York University

Biography

Hello πŸ‘‹ I am a PhD student at NYU Steinhardt’s ECT Program, and a doctoral researcher at the NYU Learning Analytics Research Network (NYU-LEARN).

My work encompasses human-centered design and implementation of learning analytics and the research and development of actionable, conversational analytics interfaces.

Before joining NYU-LEARN, I worked as a research assistant at the HKU CCMIR Lab, with a focus on the R&D of user-centered, context-aware music recommenders through physiological sensing, music (audio) processing, and UX research.

Interests

  • Learning Analytics
  • Ecudational Data Science
  • Human-Centered Informatics
  • Design Thinking
  • Affective Computing

Education

  • PhD, 2022 - 2027

    New York University

  • M.Sc, 2018 - 2020

    University of Hong Kong

  • B.Mgt | B.Eng, 2014 - 2018

    Sichuan University

Experience

 
 
 
 
 

Conference Reviewer

LAK 2023

September 2022 – October 2022
The International Learning Analytics and Knowledge Conference
 
 
 
 
 

Conference Reviewer

ISMIR 2020, 2021, 2022

May 2020 – June 2022
The International Society for Music Information Retrieval Conference
 
 
 
 
 

Research Assistant

HKU SIRI, University of Hong Kong

March 2020 – June 2021 Hong Kong S.A.R.
Culture Computing and Multimodal Information Research (CCMIR) Lab
 
 
 
 
 

Summer Intern

University of Notre Dame: iSURE Program

July 2016 – August 2016 Indiana, USA
Social Sensing Lab, Department of Computer Science and Engineering

Projects

*
All Learning with media Curriculum analytics Multimodal analytics

Data Science Education

Developing and Evaluating Interdisciplinarity and Internationalization in the Curriculum of Bachelor of Arts and Sciences in Social Data Science.

Background Music for Studying: A Naturalistic Study

Field experiments that probe how students study with music in the background, in light of cognitive-affective theory of learning with media.

Multimodal Analysis of User-music Interactions in the Lab

Leveraging multimodal data to probe the effects of five different types of background audio (four types of instrumental music and one environmental sound) on reading comprehension.

Music Recommender Systems Based on Physiological Signals

This project aims to enhance the emotion-aware music recommendation via physiological sensing.

Deep Reading in the Omnimedia Era

This study discussed the mechanisms underlying deep reading in terms of (a) the cognitive-affective process inside the reading brain and (b) reading as a social process.

Recent Publications

Background Music for Studying: A Naturalistic Experiment on Music Characteristics and User Perception

IEEE MultiMedia
Project DOI

Detecting Music-Induced Emotion Based on Acoustic Analysis and Physiological Sensing: A Multimodal Approach

Applied Sciences, 12(18), 9354.
Project DOI

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Hybrid Reading - A New Form of Deep Reading Behavior in the Omni-Media Era [Translated title]
Project

Studying with Learners’ Own Music: Preliminary Findings on Concentration and Task Load

In Proceedings of the 11th International Conference on Learning Analytics & Knowledge (LAK 2021).
Project DOI Full Article Slide

Exploring Interdisciplinary Data Science Education for Undergraduates: Preliminary Results

In Diversity, Divergence, Dialogue - The 16th International Conference on Information (iConference 2021).
PDF Project DOI Slide iConference

Learning with Background Music: A Field Experiment

In Proceedings of the 10th International Conference on Learning Analytics & Knowledge (LAK 2020).
PDF Project DOI

Can Background Music Facilitate Learning? Preliminary Results on Reading Comprehension

In Proceedings of the 9th International Conference on Learning Analytics & Knowledge (LAK 2019).
PDF Project DOI

On the Relationships between Music-Induced Emotion and Physiological Signals

In Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR 2018).
PDF Project Poster ISMIR

Deep Reading: Controversy and Reconsideration

In Journal of the National Library of China. πŸ“‘ Reprinted by the Information Center for Social Science of RUC and Chinese Social Sciences Net.
PDF Project DOI Abstract Cited By